DOCSLIB.ORG

Annual Report 2020

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Annual Report 2020 Research Summaries/Scholarly Activities……………………….12 Grants Awarded……………………………………………..…….100 CPCB Seminar Series………………….……………………….118 Training Grant Publications…………………………………….116 Administrative Duties……………………………………………117 Postdoctoral Associates………………………………………...121 Graduate Students………………………….…………….…..…122 Department Seminar Series……………………...……..….…..125 Primary Faculty…….……………………………...……..…........126 Joint Appointments...……………………………...……...….…..127 U of Pitt School of Medicine Committee Service….…..………128 University of Pittsburgh Committee Service..……….…...…....130 Other Services Outside of the University of Pittsburgh.….…..131 Tenure Track Faculty……………………………...……..….......138 Non-Tenure Track Faculty.………………………...……......….152 Executive Summary...……………………………...……..…......160 Resources..…………...……………………………...……...…...162 Budget………………...……………………………...……...…....164 Computational and Systems Biology – Who We Are The Department of Computational and Systems Biology (CSB) has continued to be a leader in the field. Our increasing focus on multi-scale interactions in biological systems enabled us to tackle more research at an integrated, Systems level. The post-genomic and big data era has brought a new excitement to science. With this excitement also comes new challenges that require new and innovative approaches with which to tackle them. CSB continues to play a leading role in driving discovery through continued interdisciplinary efforts to answer the research challenges of today and define the new approaches to address the questions of tomorrow. Concomitant, with our research foci, we also make concerted efforts in educating the next generation of scientists, who will continue this work. The unique, hands-on research opportunities that we provide to students at the graduate, undergraduate, and even high school levels provide these students with the necessary interdisciplinary and hands-on training to prepare them for careers in these fields. At the same time, these efforts provide numerous opportunities for our postdoctoral fellows and junior faculty to serve as mentors for our scientists-in-training, especially those at the undergraduate and high school level. These early experiences in mentoring are valuable opportunities that allow these scientists to hone their mentoring skills, while working together with some of the best and brightest in the next generation. A Brief History Computational biology was established as a research discipline at the University of Pittsburgh with the vision of the Senior Vice Chancellor, Dr. Arthur S. Levine, who recruited Dr. Bahar to create a new Center for Computational Biology and Bioinformatics in March 2001, which soon became a highly visible center. The creation of the Department of Computational Biology within the School of Medicine followed in October 2004. Built on the premise of the founding Center for Computational Biology and Bioinformatics, the department was created with Dr. Ivet Bahar as the Founding Chair, to establish a home uniquely welcoming to highly interdisciplinary faculty, and to firmly establish the University of Pittsburgh in a nationally recognized leadership role in a field of tremendous growth and excitement. The department name was changed to Department of Computational and Systems Biology in 2009, to reflect the expansion in research and educational goals and activities of the department. In 2020, the contributions of our Faculty were highlighted by various awards and promotions. Within our department: Dr. Ivet Bahar was elected to the National Academy of Sciences Dr. Ivet Bahar and she and her colleagues were named recipients of awards by the 2020 Human Frontier Science Program (HFSP); Dr. Robin Lee was promoted to Associate Professor with Tenure and was named one of the 126 Sloan Research Fellows for 2020.; Dr. Nathan Lord from Harvard Medical School joined the department as an Assistant Professor in the Tenure Stream; Dr. Maria Chikina to receive 2020 Chancellor’s Distinguished Research Award that annually recognizes outstanding scholarly accomplishments of members of the University of Pittsburgh’s faculty; Dr. Anne-Ruxandra Carvunis was featured in Nature and Science magazine articles which highlighted her work on de novo gene birth, was named one of the awardees of the highly prestigious NIH Director’s New Innovator Award for 2019 and was nominated Outstanding New PI of the Month by New PI Slack (NPIS), an international group of early stage faculty.; Dr. Jianhua Xing co- authored a paper published in PNAS. We are committed to providing a home that welcomes highly interdisciplinary faculty, and that firmly establishes the University of Pittsburgh in nationally recognized leadership roles in rapidly growing and evolving fields that are driving the next wave of scientific discovery. 6 CSB Research – Leading the Field with Cutting-edge Science Increasingly complex biological problems and extremely large biological data sets have necessitated new approaches to answer many of today’s current research challenges. As one of the fields in what are now being called the “New Biologies”, Computational and Systems Biology encompasses an interdisciplinary approach to harness the power of computation to tackle the emerging big data challenges and to answer questions in biology not amenable to traditional approaches. Thus, to rise to these challenges and to continue to be a leader in this newly emerging field, CSB has set our research mission to: Advance the scientific understanding of biological systems through computational tools and theoretical approaches based on the fundamental principles of physical sciences. Design new computational/mathematical models and methods for simulating complex biological processes and for extracting useful information from accumulating data. CSB Educational Endeavors – Training the Next Generation of Scientists As one of the first departments in computational and systems biology in the nation, the faculty and staff of the department strive to establish a successful model for inter-disciplinary research by collaborating with experimentalists and theoreticians alike and training a new generation of researchers capable of meeting the next wave of scientific challenges and thereby propagating scientific advances and breakthroughs. To this end, we have also set forth the educational goals of offering first-class programs at the graduate, undergraduate, and high school levels in computational biology, all of which serve multiple purposes: (i) to introduce computational biology problems and methods to students from chemistry, physics, engineering, mathematics, and computer sciences, (ii) to teach fundamental concepts of the quantitative and physical sciences to biology and biomedical sciences students, (iii) to prepare and encourage students at all levels for a career in computational biology research, and (iv) to reach out to and recruit students from backgrounds that are underrepresented in the sciences. In all cases, the aim is to train students and postdoctoral researchers to identify and tackle complex biological problems that are beyond the reach of traditional approaches. Joint CMU-Pitt Ph.D. Program in Computational Biology To support this educational mission, the Joint CMU-Pitt Ph.D. Program in Computational Biology (CPCB) was developed as a collaborative effort between the University of Pittsburgh and Carnegie Mellon University. This program was conceived as a cross-institutional (Pitt and CMU) and cross-campus (Pitt SOM and SAS) Ph.D. program that would exploit the complementing strengths of the different universities or schools. (For additional details about this program please refer to the Teaching Activities section of the department’s Annual Report.) The CPCB is supported through the Graduate Studies Office of the School of Medicine. In the first year of the program, students’ stipends and tuition expenses are covered by their respective universities. Beginning in their second year, students’ stipend costs are supported by their respective mentors through research dollars and/or other funding sources. Tuition costs for those students enrolled in the University of Pittsburgh are allocated to their respective departments via the university’s annual step-down cost model. In its first year, the CPCB enrolled six students (4 at Pitt and 2 at CMU). Including the 13 students who joined the program in Fall 2020, the program has a total of 84 students, of which 50% are domestic. About 37% of our students are female. Through aggressive recruiting efforts and wide dissemination of program information, CPCB expects to steadily increase enrollment each year. In FY20, the following activities are planned: Creation and distribution of a more dynamic program flyer; attendance at SACNAS and ABRCMS conferences to reach out to students underrepresented in the sciences; increased recruitment at research conferences and workshops; complete overhaul of program website; social networking, and a virtual fall open house. In FY21, we anticipate that 5-6students will apply for graduation to complete their thesis requirements. In November 2005, Carnegie Mellon University, in partnership with the University of Pittsburgh, was selected to receive a $1 million grant from the Howard Hughes Medical Institute (HHMI) to support the development of the CPCB. In FY09, our program was awarded an NIBIB T32 Training Grant: Integrated, Interdisciplinary, Inter-University Ph.D. Program in Computational Biology (PIs: Ivet Bahar and Robert Murphy). This training grant was
Load more

Recommended publications
  • Chemical Genomics 33
    Curr. Issues Mol. Biol. (2002) 4: 33-43. Chemical Genomics 33 Chemical Genomics: A Systematic Approach in Biological Research and Drug Discovery X.F. Steven Zheng1* and Ting-Fung Chan2 synthesis (Russell and Eggleston 2000) and new screening technologies such as small chemical compound (MacBeath 1Department of Pathology and Immunology and 2Molecular et al. 1999) and protein microarrays (MacBeath and and Cellular Biology Program, Campus Box 8069 Schreiber 2000; Zhu et al. 2000; Haab et al. 2001). In this Washington University School of Medicine article, we will provide a detailed analysis of the current 660 South Euclid Avenue, St. Louis, Missouri 63110 USA state of chemical genomics and its potential impact on biological and medical research, and pharmaceutical development. Abstract Chemical Biology or Genetics The knowledge of complete sequences of different organisms is dramatically changing the landscape of Since the seminal study of pea genetics by Mendal in 1865, biological research and pharmaceutical development. genetic analysis has been the benchmark for understanding We are experiencing a transition from a trial-and-error gene or protein functions. In classical genetics or forward approach in traditional biological research and natural genetics, the genomic DNA of a model organism or cell is product drug discovery to a systematic operation in randomly mutagenized to generate large numbers of genomics and target-specific drug design and mutants, which are screened for a desirable phenotype or selection. Small, cell-permeable and target-specific trait, such as alteration in growth, appearance or behavior. chemical ligands are particularly useful in systematic The phenotypes are then used to identify the responsible genomic approaches to study biological questions.
    [Show full text]
  • Link Mining for Kernel-Based Compound-Protein Interaction Predictions Using a Chemogenomics Approach
    Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach Masahito Ohue1,2,3,4*, Takuro Yamazaki3, Tomohiro Ban4, and Yutaka Akiyama1,2,3,4* 1Department of Computer Science, School of Computing, Tokyo Institute of Technology, Japan 2Advanced Computational Drug Discovery Unit, Institute of Innovative Research, Tokyo Institute of Technology, Japan 3Department of Computer Science, Faculty of Engineering, Tokyo Institute of Technology, Japan 4Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Japan *[email protected], [email protected] Abstract. Virtual screening (VS) is widely used during computational drug dis- covery to reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to predict new compound-protein interactions (CPIs) from known CPI network data using several methods, including machine learning and data min- ing. Although CGBVS facilitates highly efficient and accurate CPI prediction, it has poor performance for prediction of new compounds for which CPIs are un- known. The pairwise kernel method (PKM) is a state-of-the-art CGBVS method and shows high accuracy for prediction of new compounds. In this study, on the basis of link mining, we improved the PKM by combining link indicator kernel (LIK) and chemical similarity and evaluated the accuracy of these methods. The proposed method obtained an average area under the precision-recall curve (AUPR) value of 0.562, which was higher than that achieved by the conven- tional Gaussian interaction profile (GIP) method (0.425), and the calculation time was only increased by a few percent. Keywords: virtual screening; compound-protein interactions (CPIs); pairwise kernel; link mining; link indicator kernels (LIKs) 1 Introduction Virtual screening (VS), in which drug candidate compounds are selected by a computational method, is one of the main processes in the early stages of drug dis- covery.
    [Show full text]
  • Endogenous Metabolites in Drug Discovery: from Plants to Humans
    Endogenous Metabolites in Drug Discovery: from Plants to Humans Joaquim Olivés Farrés TESI DOCTORAL UPF / ANY 201 6 DIRECTOR DE LA TESI: Dr. Jordi Mestres CEXS Department The research in this T hesis has been carried out at the Systems Pharmacolo gy Group , within the Research Programme on Biomedical Informatics (GRIB) at the Parc de Recerca Biomèdica de Barcelona (PRBB). The research presented in this T hesis has been supported by Ministerio de Ciencia e Innovación project BIO2014 - 54404 - R and BIO2011 - 26669 . Printing funded by the Fundació IMIM’s program “Convocatòria d'ajuts 2016 per a la finalització de tesis doctorals de la Fundació IMIM.” Agraïments Voldria donar les gràcies a tanta gent que em fa por deixar - me ningú. Però per c omençar haig agrair en especial al meu director la tesi, Jordi Mestres, per donar - me la oportunitat de formar part del seu laboratori i poder desenvolupar aquí el treball que aquí es presenta. A més d’oferir l’ajuda necessària sempre que ha calgut. També haig de donar les gràcies a tots els companys del grup de Farmacologia de Sistemes que he anat coneguent durants tots aquests anys en què he estat aquí, en especial en Xavi, a qui li he preguntat mil coses, en Nikita, pels sdfs que m’ha anat llençant a CTL ink, i la Irene i la Cristina, que els seus treballs també m’ajuden a completar la tesis. I cal agrair també a la resta de companys del laboratori, l’Albert, la Viktoria, la Mari Carmen, l’Andreas, en George, l’Eric i l’Andreu; de Chemotargets, en Ricard i en David; i altres membres del GRIB, com són l’Alfons, en Miguel, en Pau, l’Oriol i la Carina.
    [Show full text]
  • Chemogenomics: an Emerging Strategy for Rapid Target and Drug Discovery
    REVIEWS CHEMOGENOMICS: AN EMERGING STRATEGY FOR RAPID TARGET AND DRUG DISCOVERY Markus Bredel*‡ and Edgar Jacoby§ Chemogenomics is an emerging discipline that combines the latest tools of genomics and chemistry and applies them to target and drug discovery. Its strength lies in eliminating the bottleneck that currently occurs in target identification by measuring the broad, conditional effects of chemical libraries on whole biological systems or by screening large chemical libraries quickly and efficiently against selected targets. The hope is that chemogenomics will concurrently identify and validate therapeutic targets and detect drug candidates to rapidly and effectively generate new treatments for many human diseases. Over the past five decades, pharmacological compounds however, that owing to the emergence of various sub- TRANSCRIPTIONAL PROFILING The study of the transcriptome have been identified that collectively target the products specialties of chemogenomics (discussed in the next — the complete set of RNA of ~400–500 genes in the human body; however, only section) and the involvement of several disciplines, it is transcripts that are produced by ~120 of these genes have reached the market as the tar- currently almost impossible to give a simple and com- the genome at any one time — gets of drugs1,2.The Human Genome Project3,4 has mon definition for this research discipline (BOX 1). using high-throughput methods, such as microarray made available many potential new targets for drug In chemogenomics-based drug discovery, large col- analysis. intervention: several thousand of the approximately lections of chemical products are screened for the paral- 30,000–40,000 estimated human genes4 could be associ- lel identification of biological targets and biologically ated with disease and, similarly, several thousand active compounds.
    [Show full text]
  • Chemogenomics:Chemogenomics 19/4/07 16:30 Page 57
    Chemogenomics:Chemogenomics 19/4/07 16:30 Page 57 Genomics CHEMOGENOMICS a gene family approach to parallel drug discovery Currently available drugs only target around 500 different proteins4. Recent reports from efforts to sequence the human genome suggest there are tens of thousands of genes1,2 and many more different proteins. Popular estimates of the number of ‘new’ drug targets that will emerge from genomic research range from 2,000 to 5,0003. A critical question as we enter the post-genomic world is: how can the pharmaceutical industry rapidly discover and develop medicines for these new targets to improve the human condition? n the pharmaceutical industry to date, research QSAR, structure-based drug design and informat- By Dr Paul R. Caron, and early development activities have typically ics, have accelerated the drug discovery process4. Dr Michael D. Ibeen organised according to therapeutic area. Dramatically new and different drug discovery Mullican, Dr Robert In organising their drug discovery efforts in this approaches, however, are needed to take full D. Mashal, Dr Keith P. way, companies have sought to create efficiency by advantage of the massive influx of targets being Wilson, Dr Michael S. building a critical mass of expertise and experience elucidated through genomic research. Simply stat- Su and Dr Mark A. in the biology of related diseases. Over the past 20- ed, a therapeutic area focus and a single target Murcko 30 years this organisational approach has proved drug discovery approach do not create enough effi- successful for many companies. While there is no ciency to allow companies to keep pace with the doubt that this strategy produces some synergies in massive inflows of new target information.
    [Show full text]
  • An Emerging Strategy for Rapid Target and Drug Discovery
    R E V I E W S CHEMOGENOMICS: AN EMERGING STRATEGY FOR RAPID TARGET AND DRUG DISCOVERY Markus Bredel*‡ and Edgar Jacoby§ Chemogenomics is an emerging discipline that combines the latest tools of genomics and chemistry and applies them to target and drug discovery. Its strength lies in eliminating the bottleneck that currently occurs in target identification by measuring the broad, conditional effects of chemical libraries on whole biological systems or by screening large chemical libraries quickly and efficiently against selected targets. The hope is that chemogenomics will concurrently identify and validate therapeutic targets and detect drug candidates to rapidly and effectively generate new treatments for many human diseases. Over the past five decades, pharmacological compounds however, that owing to the emergence of various sub- TRANSCRIPTIONAL PROFILING The study of the transcriptome have been identified that collectively target the products specialties of chemogenomics (discussed in the next — the complete set of RNA of ~400–500 genes in the human body; however, only section) and the involvement of several disciplines, it is transcripts that are produced by ~120 of these genes have reached the market as the tar- currently almost impossible to give a simple and com- the genome at any one time — gets of drugs1,2. The Human Genome Project3,4 has mon definition for this research discipline (BOX 1). using high-throughput methods, such as microarray made available many potential new targets for drug In chemogenomics-based drug discovery, large col- analysis. intervention: several thousand of the approximately lections of chemical products are screened for the paral- 30,000–40,000 estimated human genes4 could be associ- lel identification of biological targets and biologically ated with disease and, similarly, several thousand active compounds.
    [Show full text]
  • From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems
    molecules Review From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems Quentin T. L. Pasquer , Ioannis A. Tsakoumagkos and Sascha Hoogendoorn * Department of Organic Chemistry, University of Geneva, Quai Ernest-Ansermet 30, 1211 Genève, Switzerland; [email protected] (Q.T.L.P.); [email protected] (I.A.T.) * Correspondence: [email protected]; Tel.: +41-223796085 Academic Editor: Steven Verhelst Received: 9 November 2020; Accepted: 1 December 2020; Published: 3 December 2020 Abstract: Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes. Keywords: phenotypic screening; target identification; mechanism of action; drug discovery; chemical probes; photo-affinity labeling; proteomics; genetic screens; resistance cloning 1.
    [Show full text]
  • Network-Based Characterization of Drug-Protein Interaction Signatures
    Tabei et al. BMC Systems Biology 2019, 13(Suppl 2):39 https://doi.org/10.1186/s12918-019-0691-1 RESEARCH Open Access Network-based characterization of drug-protein interaction signatures with a space-efficient approach Yasuo Tabei1*, Masaaki Kotera2, Ryusuke Sawada3 and Yoshihiro Yamanishi3,4 From The 17th Asia Pacific Bioinformatics Conference (APBC 2019) Wuhan, China. 14–16 January 2019 Abstract Background: Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology. Results: We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call “drug-protein interaction signatures” from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug- protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers. Conclusions: Our method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions and are expected to contribute to rational drug design. Keywords: Drug-protein interaction prediction, Drug discovery, Large-scale prediction Background similar drugs are expected to interact with similar pro- Target proteins of drug molecules are classified into a pri- teins, with which the similarity of drugs and proteins are mary target and off-targets.
    [Show full text]
  • From Chemical to Systems Biology: How Chemoinformatics Can Contribute?
    From Chemical to Systems Biology: How Chemoinformatics can contribute? Olivier Taboureau, Computational Chemical Biology group SSSC, June 27, 2012 Computational Chemical Biology Objective: Understand the relationship between chemical actions (environmental chemicals, drugs, natural products) and disease susceptibility genes. Chemoinformatics Biological networks Gene expression data analysis Functional human variation Toxicogenomics Integrative Chemical Biology From Chemical to Systems Biology POLYPHARMACOLOGY CHEMOGENOMICS NETWORK PHARMACOLOGY SYSTEMS PHARMACOLOGY Small compounds Human body Structural information Biological pathways Bioactivity information Protein-protein interactions Gene expression data Disease phenotypes Side effect data, etc... etc... Oprea et al. Nature Chem Biol (2007) 3, 447-450 How can we do that? Many possibilities… Butcher EC, Berg EL, Kunkel EJ. Systems biology in drug discovery. Nat Biotech 2004; 22: 1253-9. Where to start? We hope for a simple concept… Drug Gene Phenotype But in reality it is not so simple Phenotype Phenotype Gene Phenotype Side Effects Side Effects Gene Gene Phenotype Phenotype Gene Drug Gene Gene Gene Side Effects Gene Phenotype Phenotype Side Effects What is the number of targets for a drug? 4400 drugs, 2.7 targets/drug in average 1081 drugs, 5.69 targets/ drug in average Wombat-PK The pharmacology of a drug is still sparse Chemical similarity Proteins Garcia-Serna R et al. Nat. Bioinformacs 2010 Compounds Keiser MJ et al. Nat. Biotech 2007 Drug-target network Yildirim M et al. Nat. Biotech 2007 Genes-tissues specificity What about phenotypes? Protein-Protein interactions Network (PPI) disease Especially genetic disorders (color blindness, Huntington’s disease, Cystic fibrosis) disease Prader-Willi syndrome (7 genes) disease Cancer, diabetes, mental illness A quality-controlled human protein interaction network 500 000 interactions Download and between 10,300 Trans organism Automated scoring reformat PPI human proteins databases ppi transferral of all interactions Lage et al.
    [Show full text]
  • Bioinformatics Mining of the Dark Matter Proteome For
    BIOINFORMATICS MINING OF THE DARK MATTER PROTEOME FOR CANCER TARGETS DISCOVERY by Ana Paula Delgado A Thesis Submitted to the Faculty of The Charles E. Schmidt College of Science In Partial Fulfillment of the Requirements for the Degree of Master of Science Florida Atlantic University Boca Raton, Florida May 2015 Copyright 2015 by Ana Paula Delgado ii ACKNOWLEDGEMENTS I would first like to thank Dr. Narayanan for his continuous encouragement, guidance, and support during the past two years of my graduate education. It has truly been an unforgettable experience working in his laboratory. I also want to express gratitude to my external advisor Professor Van de Ven from the University of Leuven, Belgium for his constant involvement and assistance on my project. Moreover, I would like to thank Dr. Binninger and Dr. Dawson-Scully for their advice and for agreeing to serve on my thesis committee. I also thank provost Dr. Perry for his involvement in my project. I thank Jeanine Narayanan for editorial assistance with the publications and with this dissertation. It has been a pleasure working with various undergraduate students some of whom became lab mates including Pamela Brandao, Maria Julia Chapado and Sheilin Hamid. I thank them for their expert help in the projects we were involved in. Lastly, I want to express my profound thanks to my parents and brother for their unconditional love, support and guidance over the last couple of years. They were my rock when I was in doubt and never let me give up. I would also like to thank my boyfriend Spencer Daniel and best friends for being part of an incredible support system.
    [Show full text]
  • Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos
    Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos Julian E. Fuchs, Susanne von Grafenstein, Roland G. Huber, Christian Kramer, Klaus R. Liedl* Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria Abstract Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides.
    [Show full text]
  • Assessing Drug Target Suitability Using Targetmine[Version 1; Peer Review
    F1000Research 2019, 8:233 Last updated: 28 MAY 2019 SOFTWARE TOOL ARTICLE Assessing drug target suitability using TargetMine [version 1; peer review: 2 approved] Yi-An Chen 1, Erika Yogo2, Naoko Kurihara2, Tomoshige Ohno2, Chihiro Higuchi1, Masatomo Rokushima2, Kenji Mizuguchi 1 1National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, 5670085, Japan 2Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., Toyonaka, Osaka, 5610825, Japan First published: 28 Feb 2019, 8:233 ( Open Peer Review v1 https://doi.org/10.12688/f1000research.18214.1) Latest published: 28 May 2019, 8:233 ( https://doi.org/10.12688/f1000research.18214.2) Reviewer Status Abstract Invited Reviewers In selecting drug target candidates for pharmaceutical research, the linkage 1 2 to disease and the tractability of the target are two important factors that can ultimately determine the drug efficacy. Several existing resources can provide gene-disease associations, but determining whether such a list of version 2 genes are attractive drug targets often requires further information published gathering and analysis. In addition, few resources provide the information 28 May 2019 required to evaluate the tractability of a target. To address these issues, we have updated TargetMine, a data warehouse for assisting target version 1 prioritization, by integrating new data sources for gene-disease published report report associations and enhancing functionalities for target assessment. As a data 28 Feb 2019 mining platform that integrates a variety of data sources, including protein structures and chemical compounds, TargetMine now offers a powerful and Rachel Lyne , University of Cambridge, flexible interface for constructing queries to check genetic evidence, 1 tractability and other relevant features for the candidate genes.
    [Show full text]