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• 101.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Learning rule-based models of biological process from gene expression time profiles using gene ontology2003In: Bioinformatics, ISSN 1367-4803, Vol. 19, no 9, p. 1116-1123Article in journal (Refereed)
• 102. Illergard, Kristoffer
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Structure is three to ten times more conserved than sequence-A study of structural response in protein cores2009In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 77, no 3, p. 499-508Article in journal (Refereed)

Protein structures change during evolution in response to mutations. Here, we analyze the mapping between sequence and structure in a set of structurally aligned protein domains. To avoid artifacts, we restricted our attention only to the core components of these structures. We found that on average, using different measures of structural change, protein cores evolve linearly with evolutionary distance (amino acid substitutions per site). This is true irrespective of which measure of structural change we used, whether RMSD or discrete structural descriptors for secondary structure, accessibility, or contacts. This linear response allows us to quantify the claim that structure is more conserved than sequence. Using structural alphabets of similar cardinality to the sequence alphabet, structural cores evolve three to ten times slower than sequences. Although we observed an average linear response, we found a wide variance. Different domain families varied fivefold in structural response to evolution. An attempt to categorically analyze this variance among subgroups by structural and functional category revealed only one statistically significant trend. This trend can be explained by the fact that beta-sheets change faster than alpha-helices, most likely due to that they are shorter and that change occurs at the ends of the secondary structure elements. Proteins 2009; 77:499-508. (C) 2009 Wiley-Liss, Inc.

• 103. Jeon, J T
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Comparative analysis of a BAC contig of the porcine RN region and the human: implications for the cloning of trait loci.2001In: Genomics, ISSN 0888-7543, Vol. 72, no 3, p. 297-303Article in journal (Other scientific)
• 104.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Toxicology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Center for Food Science, Department of Food Toxicology, Veterinary University, Hannover. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Toxicology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Toxicology.
Short-Time Gene Expression Response to Valproic Acid and Valproic Acid analogs in Mouse Embryonic Stem Cells2011In: Toxicological Sciences, ISSN 1096-6080, E-ISSN 1096-0929, Vol. 121, no 2, p. 328-342Article in journal (Refereed)

The prediction of potential developmental toxicity in vitro could be based ontoxicogenomic endpoints a short time after exposure in cultured embryo-derived celllines. Our previous microarray studies in P19 mouse embryonal carcinoma cells andmouse embryos have indicated that the teratogen valproic acid (VPA), an inducerof neural tube defects, deregulates the expression of a large number of genes, manyof which have critical roles in neural tube formation and closure. In this study weexposed undifferentiated R1 mouse embryonic stem (ES) cells to VPA and VPA analogto define genes whose expression responses may be related to teratogenic potential.After 6 h of exposure, RNA samples were subjected to microarray analysis usingCodeLinkTM Mouse Whole Genome Bioarrays. VPA (1 mM) and the teratogenic VPAanalog (S)-2-pentyl-4-pentynoic (0.25 mM or 0.5 mM) deregulate a large numberof genes, whereas for the non-teratogenic (and potentially pharmacologically active)analog 2-ethyl-4-methyl-pentanoic acid (1 mM) the expression of only a few geneswas affected. Biological process ontology groups related to embryonic development,morphogenesis, and cell behavior were overrepresented among the affected teratogentarget genes. Multivariate analysis indicated that as few as five genes (out of ~2500array probes correlating with the separation) could separate the data set accordingto teratogenicity. Genes deregulated by the two teratogens showed a substantialoverlap with genes previously found to be deregulated by VPA in P19 cells and mouseembryos. A panel of candidate genes was defined as potential markers predictiveof teratogenicity and evaluated through TaqMan low density array analysis. Theteratogens butyrate and trichostatin A, which like VPA and (S)-2-pentyl-4-pentynoicacid are known histone deacetylase (HDAC) inhibitors, induced similar responsesas these two teratogens for a large subset of markers. This indicates that HDACinhibition may be a major mechanism by which VPA induces gene deregulation andpossibly teratogenicity. Other teratogenic compounds tested had no effect on thepanel of selected markers, indicating that they may not be predicitive of teratogenicityfor compounds acting through other mechanisms than VPA.

• 105.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Toxicology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Toxicology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Toxicology.
Exploring Transcriptional Response toValproic Acid and Valproic Acid Analogs in Human Embryonic Stem CellsManuscript (preprint) (Other (popular science, discussion, etc.))

Developmental toxicity is a major concern for manufacturers of new pharmaceuticals,and current testing requires many laboratory animals. Human embryonic stem (hES)cells, potentially being close in function to cells in the developing embryo, mayprovide a technology for classification of candidate drugs in the early phase of toxicityevaluation. Altered gene expression in such system may be predictive of teratogenicproperties of a substance if important gene regulatory pathways are affected, and mayhence be used as appropriate endpoint. In the present study we used the pluripotenthES cell line SA002 (Cellartis AB), and microarrays to profile the response tovalproic acid (VPA), a known human teratogen causing increased risk of e.g. spinabifida and cognitive disorders in exposed embryos We also investigated three closelyrelated VPA analogs with differing in vivo teratogenicity in mice as well as histonedeacetylase (HDAC) inhibition, a proposed teratogenic mechanism of VPA. hEScells in an undifferentiated state were exposed for 24 h to either 1 mM VPA, 0.25mM or 0.5 mM (S)-2-pentyl-4-pentynoic acid a more potent teratogen and HDACinhibitor than VPA, 1 mM 3-propyl-heptanoic acid, a potent teratogen but not anHDAC inhibitor, 1 mM 2-ethyl-4-methyl-pentanoic acid, a non-teratogen and non-HDAC inhibitor, or 0.1% DMSO. Gene expression was subsequently profiled usingCodelink Human Whole Genome BioArrays. We found the HDAC inhibitors tostrongly deregulate largely the same genes. Further, a concordance of altered geneontology groups, predominantly neurogenic processes, was evident between all theteratogenic substances. Also, comparison with mouse ES cells showed an overlap ofderegulated genes as well as species specific gene to be deregulated.

• 106. Jin, Haining
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Influences on gene expression in vivo by a Shine-Dalgarno sequence.2006In: Mol Microbiol, ISSN 0950-382X, Vol. 60, no 2, p. 480-92Article in journal (Other scientific)

The Shine-Dalgarno (SD+: 5'-AAGGAGG-3') sequence anchors the mRNA by base

pairing to the 16S rRNA in the small ribosomal subunit during translation initiation. We have here compared

how an SD+ sequence influences gene expression, if located upstream or downstream of an initiation codon.

The positive effect of an upstream SD+ is confirmed. A downstream SD+ gives decreased gene expression.

This effect is also valid for appropriately modified natural Escherichia coli genes. If an SD+ is placed

between two potential initiation codons, initiation takes place predominantly at the second start site.

The first start site is activated if the distance between this site and the downstream SD+ is enlarged

and/or if the second start site is weakened. Upstream initiation is eliminated if a stable stem-loop

structure is placed between this SD+ and the upstream start site. The results suggest that the two start

sites compete for ribosomes that bind to an SD+ located between them. A minor positive contribution to

upstream initiation resulting from 3' to 5' ribosomal diffusion along the mRNA is suggested. Analysis

of the E. coli K12 genome suggests that the SD+ or SD-like sequences are systematically avoided in the

early coding region suggesting an evolutionary significance.

• 107. Johansson, Anna M.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Genome-Wide Effects of Long-Term Divergent Selection2010In: PLoS Genetics, ISSN 1553-7390, Vol. 6, no 11, p. e1001188-Article in journal (Refereed)

To understand the genetic mechanisms leading to phenotypic differentiation, it is important to identify genomic regions under selection. We scanned the genome of two chicken lines from a single trait selection experiment, where 50 generations of selection have resulted in a 9-fold difference in body weight. Analyses of nearly 60,000 SNP markers showed that the effects of selection on the genome are dramatic. The lines were fixed for alternative alleles in more than 50 regions as a result of selection. Another 10 regions displayed strong evidence for ongoing differentiation during the last 10 generations. Many more regions across the genome showed large differences in allele frequency between the lines, indicating that the phenotypic evolution in the lines in 50 generations is the result of an exploitation of standing genetic variation at 100s of loci across the genome.

• 108. Keeling, L
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Farmakologi 3. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Chicken genomics: Feather-pecking and victim pigmentation2004In: Nature, Vol. 431, p. 645-6Article in journal (Refereed)
• 109.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
From Physicochemical Features to Interdependency Networks: A Monte Carlo Approach to Modeling HIV-1 Resistome and Post-translational Modifications2009Doctoral thesis, comprehensive summary (Other academic)

The availability of new technologies supplied life scientists with large amounts of experimental data. The data sets are large not only in terms of the number of observations, but also in terms of the number of recorded features. One of the aims of modeling is to explain a given phenomenon in possibly the simplest way, hence the need for selection of suitable features.

We extended a Monte Carlo-based approach to selecting statistically significant features with discovery of feature interdependencies and used it in modeling sequence-function relationships in proteins. Our approach led to compact and easy-to-interpret predictive models.

First, we represented protein sequences in terms of their physicochemical properties. This was followed by our feature selection and discovery of feature interdependencies. Finally, predictive models based on e.g., decision trees or rough sets were constructed.

We applied the method to model two important biological problems: 1) HIV-1 resistance to reverse transcriptase-targeted drugs and 2) post-translational modifications of proteins.

In the case of HIV resistance, we were not only able to predict whether the mutated protein is resistant to a drug or not, but we also suggested some new, previously neglected, mutations that possibly contribute to drug resistance. For all these mutations we proposed probable molecular mechanisms of action using literature and 3D structure studies.

In the case of predicting PTMs, we built high accuracy models of modifications. In comparison to other methods, we were able to resolve whether the closest neighborhood of a residue (the nanomer) is sufficient to determine its modification status. Importantly, the application of our method yields networks of interdependent physicochemical properties of amino acids that show how these properties collaborate in establishing a given modification.

We believe that the presented methods will help researchers to analyze a large class of important biological problems and will guide them in their research.

1. A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome
Open this publication in new window or tab >>A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome
2009 (English)In: Bioinformatics and Biology Insights, ISSN 1177-9322, Vol. 3, p. 109-127Article in journal (Refereed) Published
##### Abstract [en]

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and— more importantly—identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome.

##### Keywords
viral proteomics, bioinformatics, HIV-1 drug-resistance, viral complexity, resistance model
##### National Category
Bioinformatics and Systems Biology
##### Research subject
Infectious Diseases; Biology, with specialization in structural biology; Computer Science
##### Identifiers
urn:nbn:se:uu:diva-108925 (URN)
Available from: 2009-10-19 Created: 2009-10-05 Last updated: 2009-10-19Bibliographically approved
2. Monte Carlo feature selection and interdependency discovery in supervised classification
Open this publication in new window or tab >>Monte Carlo feature selection and interdependency discovery in supervised classification
2010 (English)In: Advances in Machine Learning: Dedicated to the memory of Professor Ryszard S. Michalski., Heidelberg: Springer , 2010Chapter in book (Other academic)
##### Abstract [en]

Applications of machine learning techniques in Life Sciences are the main applications forcing a paradigm shift in the way these techniques are used. Rather than obtaining the best possible supervised classiﬁer, the Life Scientist needs to know which features contribute best to classifying distinct classes and what are the interdependencies between the features. To this end we signiﬁcantly extend our earlier work [Dramiński et al. (2008)] that introduced an effective and reliable method for ranking features according to their importance for classiﬁcation. We begin with adding a method for ﬁnding a cut-off between informative and non-informative fea- tures and then continue with a development of a methodology and an implementa- tion of a procedure for determining interdependencies between informative features. The reliability of our approach rests on multiple construction of tree classiﬁers. Essentially, each classiﬁer is trained on a randomly chosen subset of the original data using only a fraction of all of the observed features. This approach is conceptually simple yet computer-intensive. The methodology is validated on a large and difﬁcult task of modelling HIV-1 reverse transcriptase resistance to drugs which is a good example of the aforementioned paradigm shift. We construct a classiﬁer but of the main interest is the identiﬁcation of mutation points (i.e. features) and their combinations that model drug resistance.

##### Place, publisher, year, edition, pages
Heidelberg: Springer, 2010
##### Series
Studies in Computational Intelligence, ISSN 1860-949X ; 263
##### National Category
Computer Sciences Microbiology in the medical area
##### Research subject
Computer Science
##### Identifiers
urn:nbn:se:uu:diva-109834 (URN)978-3-642-05178-4 (ISBN)
##### Projects
feature selection, interdependency discovery, MCFS-ID, biological sequence analysis Available from: 2009-11-05 Created: 2009-10-27 Last updated: 2018-01-12Bibliographically approved
3. Analysis of local molecular interaction networks underlying HIV-1 resistance to reverse transcriptase inhibitors.
Open this publication in new window or tab >>Analysis of local molecular interaction networks underlying HIV-1 resistance to reverse transcriptase inhibitors.
(English)Manuscript (preprint) (Refereed)
##### Abstract [en]

Rapid emergence of drug resistant HIV-1 mutants is the ma jor cause of many treatment failures. A number of individual drug resistance mutations is known but the way they interact to create resistance often remains an open question. So far this question could be answered in an experimental way only. Here we apply a novel Monte Carlo feature selection-based approach to uncover molecular interaction networks that form HIV-1 reverse transcriptase (RT) resistome. By considering mutation-induced changes in the physicochemical properties of mutating amino acids, we were able to elucidate interaction networks leading to resistance to six anti-viral drugs. We selected signiﬁcant properties (p − value <= 0.05) and analyzed the networks of the 20% strongest interdependencies between them. The topology of each network was validated by mapping it onto the 3D structure of RT and by relating the ﬁndings to the existing knowledge. The method can be easily applied to a wide range of similar problems in the domain of proteomics.

##### Keywords
HIV-1 resistance, interaction networks, resistome, MCFS-ID, feature selection, interdependency discovery
##### National Category
Bioinformatics and Systems Biology
##### Research subject
Biopharmaceutics; Biology, with specialization in structural biology
##### Identifiers
urn:nbn:se:uu:diva-109835 (URN)
Available from: 2009-10-29 Created: 2009-10-27 Last updated: 2010-01-14Bibliographically approved
4. A Monte Carlo approach to modeling post-translational modification sites using local physicochemical properties.
Open this publication in new window or tab >>A Monte Carlo approach to modeling post-translational modification sites using local physicochemical properties.
(English)Manuscript (preprint) (Other (popular science, discussion, etc.))
##### Abstract [en]

Many proteins undergo various chemical modiﬁcations during or shortly after translation. Post-translational modiﬁcations (PTM) greatly contribute to the diversity of protein functions and play crucial role in many cellular processes. Therefore understanding where and why certain protein is modiﬁed is an important issue in biomedical research. Mechanisms underlying some types of PTMs have been elucidated but many still remain unknown and a number of tools for predicting PTMs from short sequence fragments exists. While usually accurate at predicting modiﬁcation sites, these tools are not designed to increase the understanding of modiﬁcation mechanisms. Here we attempted at building easy-to-interpret models of PTMs and at identifying the physicochemical properties signiﬁcant for determining modiﬁcation status. To this end we applied our Monte Carlo feature selection and interdependency discovery (MCFS-ID) method. Considering 9 aa-long sequence fragments that were represented in terms of their physicochem- ical properties we analyzed 76 types of PTMs and for each type we identiﬁed the properties that played signiﬁcant (p ≤ 0.05) role in the classiﬁcation process. For 17 types of modiﬁcations no signiﬁcant prop- erty was found. For the remaining 59 types, we used the signiﬁcant properties to construct random forest-based high quality predictive models. We also showed an example of how to interpret the models by analyzing interdependency networks of signiﬁcant properties and how to complement the networks with decision rules inferred using rough set theory. The obtained results showed the necessity of applying feature selection prior to constructing a model that considers short sequence fragments. Interestingly, for some types of modiﬁcations we saw that models based on insigniﬁcant features can yield accurate results. This observation deserves further investigation. Among the examined PTMs we observed groups that share similar patterns of signiﬁcant properties. We also showed how to complement our models with decision rules that can guide life scientists in their research and to shed light on the actual molecular mechanisms determining modiﬁcation status.

##### National Category
Bioinformatics and Systems Biology
##### Identifiers
urn:nbn:se:uu:diva-109836 (URN)
Available from: 2009-10-29 Created: 2009-10-27 Last updated: 2010-01-13Bibliographically approved
• 110.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Institute of Computer Science, Polish Academy of Sciences. Institute of Computer Science, Polish Academy of Sciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Analysis of local molecular interaction networks underlying HIV-1 resistance to reverse transcriptase inhibitors.Manuscript (preprint) (Refereed)

Rapid emergence of drug resistant HIV-1 mutants is the ma jor cause of many treatment failures. A number of individual drug resistance mutations is known but the way they interact to create resistance often remains an open question. So far this question could be answered in an experimental way only. Here we apply a novel Monte Carlo feature selection-based approach to uncover molecular interaction networks that form HIV-1 reverse transcriptase (RT) resistome. By considering mutation-induced changes in the physicochemical properties of mutating amino acids, we were able to elucidate interaction networks leading to resistance to six anti-viral drugs. We selected signiﬁcant properties (p − value <= 0.05) and analyzed the networks of the 20% strongest interdependencies between them. The topology of each network was validated by mapping it onto the 3D structure of RT and by relating the ﬁndings to the existing knowledge. The method can be easily applied to a wide range of similar problems in the domain of proteomics.

• 111.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Interdisciplinary Centre for Mathematical and Computer Modeling, University of Warsaw, Poland. Institute of Computer Science, Polish Academy of Sciences. Institute of Computer Science, Polish Academy of Sciences. Interdisciplinary Centre for Mathematical and Computer Modeling, University of Warsaw, Poland. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome2009In: Bioinformatics and Biology Insights, ISSN 1177-9322, Vol. 3, p. 109-127Article in journal (Refereed)

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and— more importantly—identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome.

• 112.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Interdisciplinary Centre for Mathematical and Computer Modeling, University of Warsaw, Poland. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Interdisciplinary Centre for Mathematical and Computer Modeling, University of Warsaw, Poland. Institute of Computer Science, Polish Academy of Sciences. Institute of Computer Science, Polish Academy of Sciences. Interdisciplinary Centre for Mathematical and Computer Modeling, University of Warsaw, Poland. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
A Monte Carlo approach to modeling post-translational modification sites using local physicochemical properties.Manuscript (preprint) (Other (popular science, discussion, etc.))

Many proteins undergo various chemical modiﬁcations during or shortly after translation. Post-translational modiﬁcations (PTM) greatly contribute to the diversity of protein functions and play crucial role in many cellular processes. Therefore understanding where and why certain protein is modiﬁed is an important issue in biomedical research. Mechanisms underlying some types of PTMs have been elucidated but many still remain unknown and a number of tools for predicting PTMs from short sequence fragments exists. While usually accurate at predicting modiﬁcation sites, these tools are not designed to increase the understanding of modiﬁcation mechanisms. Here we attempted at building easy-to-interpret models of PTMs and at identifying the physicochemical properties signiﬁcant for determining modiﬁcation status. To this end we applied our Monte Carlo feature selection and interdependency discovery (MCFS-ID) method. Considering 9 aa-long sequence fragments that were represented in terms of their physicochem- ical properties we analyzed 76 types of PTMs and for each type we identiﬁed the properties that played signiﬁcant (p ≤ 0.05) role in the classiﬁcation process. For 17 types of modiﬁcations no signiﬁcant prop- erty was found. For the remaining 59 types, we used the signiﬁcant properties to construct random forest-based high quality predictive models. We also showed an example of how to interpret the models by analyzing interdependency networks of signiﬁcant properties and how to complement the networks with decision rules inferred using rough set theory. The obtained results showed the necessity of applying feature selection prior to constructing a model that considers short sequence fragments. Interestingly, for some types of modiﬁcations we saw that models based on insigniﬁcant features can yield accurate results. This observation deserves further investigation. Among the examined PTMs we observed groups that share similar patterns of signiﬁcant properties. We also showed how to complement our models with decision rules that can guide life scientists in their research and to shed light on the actual molecular mechanisms determining modiﬁcation status.

• 113.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Construction of Rough Set-Based Classifiers for Predicting HIV Resistance to Nucleoside Reverse Transcriptase Inhibitors2008In: GRANULAR COMPUTING: AT THE JUNCTION OF ROUGH SETS AND FUZZY SETS / [ed] Bello R, Falcon R, Pedrycz W, Kacprzy J, 2008, p. 249-258Conference paper (Refereed)

For more than two decades AIDS remains a terminal disease and no efficient therapy exists. The high mutability of HIV leads to serious problems in designing efficient anti-viral drugs. Soon after introducing a new drug, there appear HIV strains that are resistant to the applied agent. In order to help overcoming resistance, we constructed a classificatory model of genotype-resistance relationship. To derive our model, we use rough sets theory. Furthermore, by incorporating existing biochemical knowledge into our model, it gains biological meaning and becomes helpful in understanding drug resistance phenomenon. Our highly accurate classifiers are based on a number of explicit, easy-to-interpret IF-THEN rules. For every position in amino acid sequence of viral enzyme reverse transcriptase (one of two main targets for anti-viral drugs), the rules describe the way the biochemical properties of amino acid have to change in order to acquire drug resistance. Preliminary biomolecular analysis suggests the applicability of the model.

• 114.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Bacteriology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organism Biology, Systematic Biology. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. University of Aarhus. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Bacteriology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Bacteriology.
Mosaic structure of intragenic repetitive elements in histone H1-like protein Hc2 varies within serovars of Chlamydia trachomatis2010In: BMC Microbiology, ISSN 1471-2180, E-ISSN 1471-2180, Vol. 10, p. 81-Article in journal (Refereed)

BACKGROUND: The histone-like protein Hc2 binds DNA in Chlamydia trachomatis and is known to vary in size between 165 and 237 amino acids, which is caused by different numbers of lysine-rich pentamers. A more complex structure was seen in this study when sequences from 378 specimens covering the hctB gene, which encodes Hc2, were compared. RESULTS: This study shows that the size variation is due to different numbers of 36-amino acid long repetitive elements built up of five pentamers and one hexamer. Deletions and amino acid substitutions result in 14 variants of repetitive elements and these elements are combined into 22 configurations. A protein with similar structure has been described in Bordetella but was now also found in other genera, including Burkholderia, Herminiimonas, Minibacterium and Ralstonia.Sequence determination resulted in 41 hctB variants that formed four clades in phylogenetic analysis. Strains causing the eye disease trachoma and strains causing invasive lymphogranuloma venereum infections formed separate clades, while strains from urogenital infections were more heterogeneous. Three cases of recombination were identified. The size variation of Hc2 has previously been attributed to deletions of pentamers but we show that the structure is more complex with both duplication and deletions of 36-amino acid long elements. CONCLUSIONS: The polymorphisms in Hc2 need to be further investigated in experimental studies since DNA binding is essential for the unique biphasic life cycle of the Chlamydiacae. The high sequence variation in the corresponding hctB gene enables phylogenetic analysis and provides a suitable target for the genotyping of C. trachomatis.

• 115.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
[New knowledge derived from measurement of gene expression with the DNA microarray method]2001In: Tidsskr Nor Laegeforen, ISSN 0029-2001, Vol. 121, no 10, p. 1229-32Article in journal (Other (popular scientific, debate etc.))
• 116.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Modeling the Interaction Space of Biological Macromolecules: A Proteochemometric Approach: Applications for Drug Discovery and Development2008Doctoral thesis, comprehensive summary (Other academic)

Molecular interactions lie at the heart of myriad biological processes. Knowledge of molecular recognition processes and the ability to model and predict interactions of any biological molecule to any chemical compound are the key for better understanding of cell functions and discovery of more efficacious medicines.

This thesis presents contributions to the development of a novel chemo-bioinformatics approach called proteochemometrics; a general method for interaction space analysis of biological macromolecules and their ligands. In this work we explore proteochemometrics-based interaction models over broad groups of protein families, evaluate their validity and scope, and compare proteochemometrics to traditional modeling approaches.

Through the proteochemometric analysis of large interaction data sets of multiple retroviral proteases from various viral species we investigate complex mechanisms of drug resistance in HIV-1 and discover general physicochemical determinants of substrate cleavage efficiency and binding in retroviral proteases. We further demonstrate how global proteochemometric models can be used for design of protease inhibitors with broad activity on drug-resistant viral mutants, for monitoring drug resistance mechanisms in the physicochemical sense and prediction of potential HIV-1 evolution trajectories. We provide novel insights into the complexity of HIV-1 protease specificity by constructing a generalized IF-THEN rule model based on bioinformatics analysis of the largest set of HIV-1 protease substrates and non-substrates.

We discuss how proteochemometrics can be used to map recognition sites of entire protein families in great detail and demonstrate how it can incorporate target variability into drug discovery process. Finally, we assess the utility of the proteochemometric approach in evaluation of ADMET properties of drug candidates with a special focus on inhibition of cytochrome P450 enzymes and investigate application of the approach in the pharmacogenomics field.

1. Proteochemometric analysis of small cyclic peptides' interaction with wild-type and chimeric melanocortin receptors
Open this publication in new window or tab >>Proteochemometric analysis of small cyclic peptides' interaction with wild-type and chimeric melanocortin receptors
2007 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 69, no 1, p. 83-96Article in journal (Refereed) Published
##### Abstract [en]

The melanocortin (MC) system confines unique G-protein coupled receptor pathways, which include the MC1-5 receptors and their endogenous agonists and antagonists, the MCs and the agouti and agouti-related proteins. The MC4 receptor is an important target for development of drugs for treatment of obesity and cachexia. While natural MC peptides are selective for the MC1 receptor, some cyclic pentapeptides, such as the HS-129 peptide, show high selectivity for the MC4 receptor. Here we gained insight into the mechanisms for its recognition by MC receptors. To this end we correlated the interaction data of four HS peptide analogues with four wild-type and 14 multiple chimeric MC receptors to the binary and physicochemical descriptions of the studied entities by use of partial least squares regression, which resulted in highly valid proteochemometric models. Analysis of the models revealed that the recognition sites of the HS peptides are different from the earlier proteochemometrically mapped linear MSH peptides' recognitions sites, although they overlap partially. The analysis also revealed important amino acids that explain the selectivity of the HS-129 peptide for the MC4 receptor.

##### Keywords
proteochemometrics, G-protein coupled receptors, multipart chimeric receptors, melanocortin receptors, HS peptides, recognition site, selectivity
##### National Category
Pharmaceutical Sciences
##### Identifiers
urn:nbn:se:uu:diva-97331 (URN)10.1002/prot.21461 (DOI)000249189000009 ()17557335 (PubMedID)
Available from: 2008-05-15 Created: 2008-05-15 Last updated: 2018-01-26Bibliographically approved
2. Computational proteomics analysis of HIV-1 protease interactome
Open this publication in new window or tab >>Computational proteomics analysis of HIV-1 protease interactome
2007 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 68, no 1, p. 305-312Article in journal (Refereed) Published
##### Abstract [en]

HIV-1 protease is a small homodimeric enzyme that ensures maturation of HIV virions by cleaving the viral precursor Gag and Gag-Pol polyproteins into structural and functional elements. The cleavage sites in the viral polyproteins share neither sequence homology nor binding motif and the specificity of the HIV-1 protease is therefore only partially understood. Using an extensive data set collected from 16 years of HIV proteome research we have here created a general and predictive rule-based model for HIV-1 protease specificity based on rough sets. We demonstrate that HIV-1 protease specificity is much more complex than previously anticipated, which cannot be defined based solely on the amino acids at the substrate's scissile bond or by any other single substrate amino acid position only. Our results show that the combination of at least three particular amino acids is needed in the substrate for a cleavage event to occur. Only by combining and analyzing massive amounts of HIV proteome data it was possible to discover these novel and general patterns of physico-chemical substrate cleavage determinants. Our study is an example how computational biology methods can advance the understanding of the viral interactomes.

##### Keywords
viral proteomics, bioinformatics, protein-peptide interactions, HIV-1 protease specificity, viral complexity
##### National Category
Pharmaceutical Sciences Biological Sciences Engineering and Technology
##### Identifiers
urn:nbn:se:uu:diva-97332 (URN)10.1002/prot.21415 (DOI)000246894800031 ()17427231 (PubMedID)
Available from: 2008-05-15 Created: 2008-05-15 Last updated: 2018-01-26Bibliographically approved
3. A look inside HIV resistance through retroviral protease interaction maps
Open this publication in new window or tab >>A look inside HIV resistance through retroviral protease interaction maps
2007 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 3, no 3, p. 424-435Article in journal (Refereed) Published
##### Abstract [en]

Retroviruses affect a large number of species, from fish and birds to mammals and humans, with global socioeconomic negative impacts. Here the authors report and experimentally validate a novel approach for the analysis of the molecular networks that are involved in the recognition of substrates by retroviral proteases. Using multivariate analysis of the sequence-based physiochemical descriptions of 61 retroviral proteases comprising wild-type proteases, natural mutants, and drug-resistant forms of proteases from nine different viral species in relation to their ability to cleave 299 substrates, the authors mapped the physicochemical properties and cross-dependencies of the amino acids of the proteases and their substrates, which revealed a complex molecular interaction network of substrate recognition and cleavage. The approach allowed a detailed analysis of the molecular-chemical mechanisms involved in substrate cleavage by retroviral proteases.

##### Keywords
Proteochemometrics, HIV, resistance, drug discovery, PLS, protein-ligand interactions
##### National Category
Pharmaceutical Sciences
##### Identifiers
urn:nbn:se:uu:diva-97333 (URN)10.1371/journal.pcbi.0030048 (DOI)000246191000009 ()17352531 (PubMedID)
Available from: 2008-05-15 Created: 2008-05-15 Last updated: 2018-01-13Bibliographically approved
4. Exploring interaction space of retroviral proteases reveals general patterns for resistance-improved HIV retardants
Open this publication in new window or tab >>Exploring interaction space of retroviral proteases reveals general patterns for resistance-improved HIV retardants
Article in journal (Refereed) Submitted
##### Identifiers
urn:nbn:se:uu:diva-97334 (URN)
Available from: 2008-05-15 Created: 2008-05-15Bibliographically approved
5. Generalized proteochemometric model of multiple cytochrome P450 enzymes and their inhibitors
Open this publication in new window or tab >>Generalized proteochemometric model of multiple cytochrome P450 enzymes and their inhibitors
2008 (English)In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 48, no 9, p. 1840-1850Article in journal (Refereed) Published
##### Abstract [en]

Cytochrome P450 enzymes are a superfamily of heme-containing enzymes responsible for the oxidation of structurally diverse chemical Compounds. Inhibition of CYP enzymes is probably the most common mechanism underlying acute drug toxicity, loss of therapeutic drug efficacy, and drug-drug interactions. The presence of polymorphic genetic variants of CYPs among the population makes it difficult to foresee undesired effects of drugs and is a common cause of drug candidate failure. Computational models that can predict early drug failures due to the inhibition of CYP isoforms can substantially reduce the cost of drug development. Although several computational models for CYP inhibition have been developed recently, all were constructed for one CYP isoform at a time, thus limiting their use for comprehensive analysis and generalizations to other CYP isoforms and polymorphisms. Here we report a novel approach based on the principles of proteochemometrics for the generalized concomitant modeling of multiple CYP isoforms and their inhibitors. We created a predictive and statistically valid proteochemometric model for CYP enzymes by combining data from a large number of publicly available reports that describe the interactions of 14 CYP enzyme subtypes and 375 structurally diverse inhibitors. Our results demonstrate that Our model is capable of predicting the potential of new drug candidates to inhibit Multiple CYP enzymes. Analysis of the CYP model also revealed molecular properties of CYP enzymes and xenobiotics that are important for CYP inhibition. This approach may aid in the selection of novel drug, candidates that are unlikely to inhibit multiple CYP subtypes.

##### National Category
Pharmaceutical Sciences
##### Identifiers
urn:nbn:se:uu:diva-97335 (URN)10.1021/ci8000953 (DOI)000259398500011 ()18693719 (PubMedID)
Available from: 2008-05-15 Created: 2008-05-15 Last updated: 2018-01-13Bibliographically approved
• 117.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Generalized proteochemometric model of multiple cytochrome P450 enzymes and their inhibitors2008In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 48, no 9, p. 1840-1850Article in journal (Refereed)

Cytochrome P450 enzymes are a superfamily of heme-containing enzymes responsible for the oxidation of structurally diverse chemical Compounds. Inhibition of CYP enzymes is probably the most common mechanism underlying acute drug toxicity, loss of therapeutic drug efficacy, and drug-drug interactions. The presence of polymorphic genetic variants of CYPs among the population makes it difficult to foresee undesired effects of drugs and is a common cause of drug candidate failure. Computational models that can predict early drug failures due to the inhibition of CYP isoforms can substantially reduce the cost of drug development. Although several computational models for CYP inhibition have been developed recently, all were constructed for one CYP isoform at a time, thus limiting their use for comprehensive analysis and generalizations to other CYP isoforms and polymorphisms. Here we report a novel approach based on the principles of proteochemometrics for the generalized concomitant modeling of multiple CYP isoforms and their inhibitors. We created a predictive and statistically valid proteochemometric model for CYP enzymes by combining data from a large number of publicly available reports that describe the interactions of 14 CYP enzyme subtypes and 375 structurally diverse inhibitors. Our results demonstrate that Our model is capable of predicting the potential of new drug candidates to inhibit Multiple CYP enzymes. Analysis of the CYP model also revealed molecular properties of CYP enzymes and xenobiotics that are important for CYP inhibition. This approach may aid in the selection of novel drug, candidates that are unlikely to inhibit multiple CYP subtypes.

• 118.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Proteochemometric analysis of small cyclic peptides' interaction with wild-type and chimeric melanocortin receptors2007In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 69, no 1, p. 83-96Article in journal (Refereed)

The melanocortin (MC) system confines unique G-protein coupled receptor pathways, which include the MC1-5 receptors and their endogenous agonists and antagonists, the MCs and the agouti and agouti-related proteins. The MC4 receptor is an important target for development of drugs for treatment of obesity and cachexia. While natural MC peptides are selective for the MC1 receptor, some cyclic pentapeptides, such as the HS-129 peptide, show high selectivity for the MC4 receptor. Here we gained insight into the mechanisms for its recognition by MC receptors. To this end we correlated the interaction data of four HS peptide analogues with four wild-type and 14 multiple chimeric MC receptors to the binary and physicochemical descriptions of the studied entities by use of partial least squares regression, which resulted in highly valid proteochemometric models. Analysis of the models revealed that the recognition sites of the HS peptides are different from the earlier proteochemometrically mapped linear MSH peptides' recognitions sites, although they overlap partially. The analysis also revealed important amino acids that explain the selectivity of the HS-129 peptide for the MC4 receptor.

• 119.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Exploring interaction space of retroviral proteases reveals general patterns for resistance-improved HIV retardantsArticle in journal (Refereed)
• 120.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Proteochemometrics mapping of the interaction space for retroviral proteases and their substrates2009In: Bioorganic & Medicinal Chemistry, ISSN 0968-0896, E-ISSN 1464-3391, Vol. 17, no 14, p. 5229-5237Article in journal (Refereed)

Understanding the complex interactions of retroviral proteases with their ligands is an important scientific challenge in efforts to achieve control of retroviral infections. Development of drug resistance because of high mutation rates and extensive polymorphisms causes major problems in treating the deadly diseases these viruses cause, and prompts efforts to identify new strategies. Here we report a comprehensive analysis of the interaction of 63 retroviral proteases from nine different viral species with their substrates and inhibitors based on publicly available data from the past 17years of retroviral research. By correlating physico-chemical descriptions of retroviral proteases and substrates to their biological activities we constructed a highly statistically valid 'proteochemometric' model for the interactome of retroviral proteases. Analysis of the model indicated amino acid positions in retroviral proteases with the highest influence on ligand activity and revealed general physicochemical properties essential for tight binding of substrates across multiple retroviral proteases. Hexapeptide inhibitors developed based on the discovered general properties effectively inhibited HIV-1 proteases in vitro, and some exhibited uniformly high inhibitory activity against all HIV-1 proteases mutants evaluated. A generalized proteochemometric model for retroviral proteases interactome has been created and analysed in this study. Our results demonstrate the feasibility of using the developed general strategy in the design of inhibitory peptides that can potentially serve as templates for drug resistance-improved HIV retardants.

• 121.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
A look inside HIV resistance through retroviral protease interaction maps2007In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 3, no 3, p. 424-435Article in journal (Refereed)

Retroviruses affect a large number of species, from fish and birds to mammals and humans, with global socioeconomic negative impacts. Here the authors report and experimentally validate a novel approach for the analysis of the molecular networks that are involved in the recognition of substrates by retroviral proteases. Using multivariate analysis of the sequence-based physiochemical descriptions of 61 retroviral proteases comprising wild-type proteases, natural mutants, and drug-resistant forms of proteases from nine different viral species in relation to their ability to cleave 299 substrates, the authors mapped the physicochemical properties and cross-dependencies of the amino acids of the proteases and their substrates, which revealed a complex molecular interaction network of substrate recognition and cleavage. The approach allowed a detailed analysis of the molecular-chemical mechanisms involved in substrate cleavage by retroviral proteases.

• 122.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Computational proteomics analysis of HIV-1 protease interactome2007In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 68, no 1, p. 305-312Article in journal (Refereed)

HIV-1 protease is a small homodimeric enzyme that ensures maturation of HIV virions by cleaving the viral precursor Gag and Gag-Pol polyproteins into structural and functional elements. The cleavage sites in the viral polyproteins share neither sequence homology nor binding motif and the specificity of the HIV-1 protease is therefore only partially understood. Using an extensive data set collected from 16 years of HIV proteome research we have here created a general and predictive rule-based model for HIV-1 protease specificity based on rough sets. We demonstrate that HIV-1 protease specificity is much more complex than previously anticipated, which cannot be defined based solely on the amino acids at the substrate's scissile bond or by any other single substrate amino acid position only. Our results show that the combination of at least three particular amino acids is needed in the substrate for a cleavage event to occur. Only by combining and analyzing massive amounts of HIV proteome data it was possible to discover these novel and general patterns of physico-chemical substrate cleavage determinants. Our study is an example how computational biology methods can advance the understanding of the viral interactomes.

• 123. Koolen, J. H.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
There are finitely many triangle-free distance-regular graphs with degree 8, 9 or 102004In: Journal of Algebraic Combinatorics, Vol. 19, no 2, p. 205-217Article in journal (Refereed)

In this paper we prove that there are finitely many triangle-free distance-regular graphs with degree 8, 9 or 10.

• 124. Koolen, Jack
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Concerning the relationship between realizations and tight spans of finite metrics2007In: Discrete & Computational Geometry, ISSN 0179-5376, E-ISSN 1432-0444, Vol. 38, no 3, p. 605-614Article in journal (Refereed)

Given a metric d on a finite set X, a realization of d is a weighted graph $G=(V,E,w\colon \ E \to {\Bbb R}_{>0})$ with $X \subseteq V$ such that for all $x,y \in X$ the length of any shortest path in G between x and y equals d(x,y). In this paper we consider two special kinds of realizations, optimal realizations and hereditarily optimal realizations, and their relationship with the so-called tight span. In particular, we present an infinite family of metrics {dk}k≥1, and—using a new characterization for when the so-called underlying graph of a metric is an optimal realization that we also present—we prove that dk has (as a function of k) exponentially many optimal realizations with distinct degree sequences. We then show that this family of metrics provides counter-examples to a conjecture made by Dress in 1984 concerning the relationship between optimal realizations and the tight span, and a negative reply to a question posed by Althofer in 1988 on the relationship between optimal and hereditarily optimal realizations.

• 125. Koolen, Jack
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics. Mathematics and Computer Science, Department of Mathematics.
Optimal realizations of generic 5-point metrics2007Report (Other scientific)
• 126. Koolen, Jack
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Optimal realizations of generic 5-point metrics2009In: European journal of combinatorics (Print), ISSN 0195-6698, E-ISSN 1095-9971, Vol. 30, no 5, p. 1164-1171Article in journal (Refereed)

Given a metric cl oil a finite set X, a realization of d is a triple (G, phi, omega) consisting of a graph G = (V, E), a labeling phi : X -> V, and a weighting omega : E -> R->0 such that for all x, y is an element of X the length of any shortest path in G between phi(x) and phi(y) equals d(x, y). Such a realization is called optimal if parallel to G parallel to := Sigma(e is an element of E) omega(e) is minimal amongst all realizations of d. In this paper we will consider optimal realizations of generic five-point metric spaces. In particular, we show that there is a canonical subdivision C Of the metric fail of five-point metrics into cones such that (i) every metric d in the interior of a cone C is an element of C has a unique optimal realization (G, phi, omega), (ii) if d' is also in the interior of C with optimal realization (G', phi', omega') then (G, phi) and (G',  phi') are isomorphic as labeled graphs, and (iii) any labeled graph that underlies all optimal realizations of the metrics in the interior of some cone C e C must belong to one of three isomorphism classes.

• 127. Koolen, JH
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Hyperbolic bridged graphs2002In: European Journal of Combinatorics, ISSN 0195-6698, Vol. 23, no 6, p. 683-699Article in journal (Refereed)

Given a connected graph G, we take, as usual, the distance xy between any two vertices x, y of G to be the length of some geodesic between x and y. The graph G is said to be delta-hyperbolic, for some 3 : 0, if for all vertices x, y, u, v in G the inequality xy + uv :5 max{xu + yv, xv + yu} + delta holds, and G is bridged if it contains no finite isometric cycles of length four or more. In this paper, we will show that a finite connected bridged graph is 1-hyperbolic if and only if it does not contain any of a list of six graphs as an isometric subgraph.

• 128.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Maximal energy bipartite graphs2003In: Graphs and combinatorics, ISSN 0911-0119, Vol. 19, no 1, p. 131-135Article in journal (Refereed)
• 129. Koolen, JH
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
On a conjecture of Bannai and Ito: There are finitely many distance-regular graphs with degree 5, 6 or 72002In: European Journal of Combinatorics, Vol. 23, no 8, p. 987-1006Article in journal (Refereed)

Bannai and Ito conjectured in a 1987 paper that there are finitely many distance-regular graphs with fixed degree that is greater than two. In a series of papers they showed that their conjecture held for distance-regular graphs with degrees 3 or 4. In this paper we prove that the Bannai-Ito conjecture holds for degrees 5-7.

• 130. Koolen, JH
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
The structure of spherical graphs2004In: European Journal of Combinatorics, ISSN 0195-6698, Vol. 25, no 2, p. 299-310Article in journal (Refereed)

A spherical graph is a graph in which every interval is antipodal. Spherical graphs are an interesting generalization of hypercubes (a graph G is a hypercube if and only if G is spherical and bipartite). Besides hypercubes, there are many interesting examples of spherical graphs that appear in design theory, coding theory and geometry e.g., the Johnson graphs, the Gewirtz graph, the coset graph of the binary Golay code, the Gosset graph, and the Schlafli graph, to name a few. In this paper we study the structure of spherical graphs. In particular, we classify a subclass of these graphs consisting of what we call the strongly spherical graphs. This allows us to prove that if G is a triangle-free spherical graph then any interval in G must induce a hypercube, thus providing a proof for a conjecture due to Berrachedi, Havel and Mulder.

• 131.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
Nitrogen depletion in the fission yeast Schizosaccharomyces pombe causes nucleosome loss in both promoters and coding regions of activated genes2010In: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 20, no 3, p. 361-371Article in journal (Refereed)

Gene transcription is associated with local changes in chromatin, both in nucleosome positions and in chemical modifications of the histones. Chromatin dynamics has mostly been studied on a single-gene basis. Those genome-wide studies that have been made primarily investigated steady-state transcription. However, three studies of genome-wide changes in chromatin during the transcriptional response to heat shock in the budding yeast Saccharomyces cerevisiae revealed nucleosome eviction in promoter regions but only minor effects in coding regions. Here, we describe the short-term response to nitrogen starvation in the fission yeast Schizosaccharomyces pombe. Nitrogen depletion leads to a fast induction of a large number of genes in S. pombe and is thus suitable for genome-wide studies of chromatin dynamics during gene regulation. After 20 min of nitrogen removal, 118 transcripts were up-regulated. The distribution of regulated genes throughout the genome was not random; many up-regulated genes were found in clusters, while large parts of the genome were devoid of up-regulated genes. Surprisingly, this up-regulation was associated with nucleosome eviction of equal magnitudes in the promoters and in the coding regions. The nucleosome loss was not limited to induction by nitrogen depletion but also occurred during cadmium treatment. Furthermore, the lower nucleosome density persisted for at least 60 min after induction. Two highly induced genes, urg1(+) and urg2(+), displayed a substantial nucleosome loss, with only 20% of the nucleosomes being left in the coding region. We conclude that nucleosome loss during transcriptional activation is not necessarily limited to promoter regions.

• 132.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
Genome-wide chromatin profiling of the response to nitrogen starvation in the fission yeast Schizosaccharomyces pombeManuscript (Other (popular science, discussion, etc.))
• 133.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Fold Recognition Using Sequence Fingerprints of Protein Local Substructures2003In: IEEE Computer Society Bioinformatics Conference (CSB2003) Stanford, CA, USA, August 11-14, 2003, p. 517-518Conference paper (Other scientific)
• 134.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Early transcriptional responses in mouse embryos as a basis for selection of molecular markers predictive of valproic acid teratogenicity2010In: Reproductive Toxicology, ISSN 0890-6238, E-ISSN 1873-1708, Vol. 30, no 3, p. 457-468Article in journal (Refereed)

Cell-based in vitro assays would potentially reduce animal testing in preclinical drug development. Mouse embryos exposed to the teratogenic drug valproic acid (VPA) in utero for 1.5, 3 or 6h on gestational day 8 were analyzed using microarrays. Significant effects on gene expression were observed already at 1.5h, and 85 probes were deregulated across all time points. To find transcriptional markers of VPA-induced developmental toxicity, the in vivo data were compared to previous in vitro data on embryonal carcinoma P19 cells exposed to VPA for 1.5, 6 or 24h. Maximal concordance between embryos and cells was at the 6-h time points, with 163 genes showing similar deregulation. Developmentally important Gene Ontology terms, such as "organ morphogenesis" and "tube development" were overrepresented among putative VPA target genes. The genes Gja1, Hap1, Sall2, H1f0,Cyp26a1, Fgf15, Otx2, and Lin7b emerged as candidate in vitro markers of potential VPA-induced teratogenicity.

• 135.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
Predicting gene ontology biological process from temporal gene expression patterns2003In: Genome research, ISSN 1088-9051, Vol. 13, no 5, p. 965-979Article in journal (Refereed)
• 136. Lagreid, Astrid