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Kennedy EN, Foster CA, Barr SA, Bourret RB. General strategies for using amino acid sequence data to guide biochemical investigation of protein function. Biochem Soc Trans 2022; 50:1847-1858. [PMID: 36416676 PMCID: PMC10257402 DOI: 10.1042/bst20220849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 11/24/2022]
Abstract
The rapid increase of '-omics' data warrants the reconsideration of experimental strategies to investigate general protein function. Studying individual members of a protein family is likely insufficient to provide a complete mechanistic understanding of family functions, especially for diverse families with thousands of known members. Strategies that exploit large amounts of available amino acid sequence data can inspire and guide biochemical experiments, generating broadly applicable insights into a given family. Here we review several methods that utilize abundant sequence data to focus experimental efforts and identify features truly representative of a protein family or domain. First, coevolutionary relationships between residues within primary sequences can be successfully exploited to identify structurally and/or functionally important positions for experimental investigation. Second, functionally important variable residue positions typically occupy a limited sequence space, a property useful for guiding biochemical characterization of the effects of the most physiologically and evolutionarily relevant amino acids. Third, amino acid sequence variation within domains shared between different protein families can be used to sort a particular domain into multiple subtypes, inspiring further experimental designs. Although generally applicable to any kind of protein domain because they depend solely on amino acid sequences, the second and third approaches are reviewed in detail because they appear to have been used infrequently and offer immediate opportunities for new advances. Finally, we speculate that future technologies capable of analyzing and manipulating conserved and variable aspects of the three-dimensional structures of a protein family could lead to broad insights not attainable by current methods.
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Affiliation(s)
- Emily N. Kennedy
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
| | - Clay A. Foster
- Department of Pediatrics, Section Hematology/Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Sarah A. Barr
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
| | - Robert B. Bourret
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
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2
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Sherill-Rofe D, Raban O, Findlay S, Rahat D, Unterman I, Samiei A, Yasmeen A, Kaiser Z, Kuasne H, Park M, Foulkes WD, Bloch I, Zick A, Gotlieb WH, Tabach Y, Orthwein A. Multi-omics data integration analysis identifies the spliceosome as a key regulator of DNA double-strand break repair. NAR Cancer 2022; 4:zcac013. [PMID: 35399185 PMCID: PMC8991968 DOI: 10.1093/narcan/zcac013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/25/2022] [Accepted: 03/23/2022] [Indexed: 11/14/2022] Open
Abstract
DNA repair by homologous recombination (HR) is critical for the maintenance of genome stability. Germline and somatic mutations in HR genes have been associated with an increased risk of developing breast (BC) and ovarian cancers (OvC). However, the extent of factors and pathways that are functionally linked to HR with clinical relevance for BC and OvC remains unclear. To gain a broader understanding of this pathway, we used multi-omics datasets coupled with machine learning to identify genes that are associated with HR and to predict their sub-function. Specifically, we integrated our phylogenetic-based co-evolution approach (CladePP) with 23 distinct genetic and proteomic screens that monitored, directly or indirectly, DNA repair by HR. This omics data integration analysis yielded a new database (HRbase) that contains a list of 464 predictions, including 76 gold standard HR genes. Interestingly, the spliceosome machinery emerged as one major pathway with significant cross-platform interactions with the HR pathway. We functionally validated 6 spliceosome factors, including the RNA helicase SNRNP200 and its co-factor SNW1. Importantly, their RNA expression correlated with BC/OvC patient outcome. Altogether, we identified novel clinically relevant DNA repair factors and delineated their specific sub-function by machine learning. Our results, supported by evolutionary and multi-omics analyses, suggest that the spliceosome machinery plays an important role during the repair of DNA double-strand breaks (DSBs).
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Affiliation(s)
- Dana Sherill-Rofe
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University of Jerusalem-Hadassah Medical School, Jerusalem 91120, Israel
| | - Oded Raban
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada
| | - Steven Findlay
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada
| | - Dolev Rahat
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University of Jerusalem-Hadassah Medical School, Jerusalem 91120, Israel
| | - Irene Unterman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University of Jerusalem-Hadassah Medical School, Jerusalem 91120, Israel
| | - Arash Samiei
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada
| | - Amber Yasmeen
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada
| | - Zafir Kaiser
- Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Hellen Kuasne
- Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Morag Park
- Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - William D Foulkes
- The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Idit Bloch
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University of Jerusalem-Hadassah Medical School, Jerusalem 91120, Israel
| | - Aviad Zick
- Department of Oncology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Ein-Kerem, Jerusalem 91120, Israel
| | - Walter H Gotlieb
- Division of Gynecology Oncology, Segal Cancer Center, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada
| | - Yuval Tabach
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University of Jerusalem-Hadassah Medical School, Jerusalem 91120, Israel
| | - Alexandre Orthwein
- Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada
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3
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Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs. Methods Mol Biol 2022; 2499:155-176. [PMID: 35696080 PMCID: PMC9844236 DOI: 10.1007/978-1-0716-2317-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Peroxiredoxins (Prxs) are a protein superfamily, present in all organisms, that play a critical role in protecting cellular macromolecules from oxidative damage but also regulate intracellular and intercellular signaling processes involving redox-regulated proteins and pathways. Bioinformatic approaches using computational tools that focus on active site-proximal sequence fragments (known as active site signatures) and iterative clustering and searching methods (referred to as TuLIP and MISST) have recently enabled the recognition of over 38,000 peroxiredoxins, as well as their classification into six functionally relevant groups. With these data providing so many examples of Prxs in each class, machine learning approaches offer an opportunity to extract additional information about features characteristic of these protein groups.In this study, we developed a novel computational method named "RF-Prx" based on a random forest (RF) approach integrated with K-space amino acid pairs (KSAAP) to identify peroxiredoxins and classify them into one of six subgroups. Our process performed in a superior manner compared to other machine learning classifiers. Thus the RF approach integrated with K-space amino acid pairs enabled the detection of class-specific conserved sequences outside the known functional centers and with potential importance. For example, drugs designed to target Prx proteins would likely suffer from cross-reactivity among distinct Prxs if targeted to conserved active sites, but this may be avoidable if remote, class-specific regions could be targeted instead.
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Rauer C, Sen N, Waman VP, Abbasian M, Orengo CA. Computational approaches to predict protein functional families and functional sites. Curr Opin Struct Biol 2021; 70:108-122. [PMID: 34225010 DOI: 10.1016/j.sbi.2021.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/13/2021] [Accepted: 05/25/2021] [Indexed: 01/06/2023]
Abstract
Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features.
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Affiliation(s)
- Clemens Rauer
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Vaishali P Waman
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Mahnaz Abbasian
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Christine A Orengo
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.
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5
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Rosen MR, Leuthaeuser JB, Parish CA, Fetrow JS. Isofunctional Clustering and Conformational Analysis of the Arsenate Reductase Superfamily Reveals Nine Distinct Clusters. Biochemistry 2020; 59:4262-4284. [PMID: 33135415 DOI: 10.1021/acs.biochem.0c00651] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Arsenate reductase (ArsC) is a superfamily of enzymes that reduce arsenate. Due to active site similarities, some ArsC can function as low-molecular weight protein tyrosine phosphatases (LMW-PTPs). Broad superfamily classifications align with redox partners (Trx- or Grx-linked). To understand this superfamily's mechanistic diversity, the ArsC superfamily is classified on the basis of active site features utilizing the tools TuLIP (two-level iterative clustering process) and autoMISST (automated multilevel iterative sequence searching technique). This approach identified nine functionally relevant (perhaps isofunctional) protein groups. Five groups exhibit distinct ArsC mechanisms. Three are Grx-linked: group 4AA (classical ArsC), group 3AAA (YffB-like), and group 5BAA. Two are Trx-linked: groups 6AAAAA and 7AAAAAAAA. One is an Spx-like transcriptional regulatory group, group 5AAA. Three are potential LMW-PTP groups: groups 7BAAAA, and 7AAAABAA, which have not been previously identified, and the well-studied LMW-PTP family group 8AAA. Molecular dynamics simulations were utilized to explore functional site details. In several families, we confirm and add detail to literature-based mechanistic information. Mechanistic roles are hypothesized for conserved active site residues in several families. In three families, simulations of the unliganded structure sample specific conformational ensembles, which are proposed to represent either a more ligand-binding-competent conformation or a pathway toward a more binding-competent state; these active sites may be designed to traverse high-energy barriers to the lower-energy conformations necessary to more readily bind ligands. This more detailed biochemical understanding of ArsC and ArsC-like PTP mechanisms opens possibilities for further understanding of arsenate bioremediation and the LMW-PTP mechanism.
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Affiliation(s)
- Mikaela R Rosen
- Department of Chemistry, Gottwald Center for the Sciences, University of Richmond, Richmond, Virginia 23713, United States
| | - Janelle B Leuthaeuser
- Department of Chemistry, Gottwald Center for the Sciences, University of Richmond, Richmond, Virginia 23713, United States
| | - Carol A Parish
- Department of Chemistry, Gottwald Center for the Sciences, University of Richmond, Richmond, Virginia 23713, United States
| | - Jacquelyn S Fetrow
- Department of Chemistry, Gottwald Center for the Sciences, University of Richmond, Richmond, Virginia 23713, United States
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6
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Domain-mediated interactions for protein subfamily identification. Sci Rep 2020; 10:264. [PMID: 31937869 PMCID: PMC6959277 DOI: 10.1038/s41598-019-57187-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 12/23/2019] [Indexed: 11/24/2022] Open
Abstract
Within a protein family, proteins with the same domain often exhibit different cellular functions, despite the shared evolutionary history and molecular function of the domain. We hypothesized that domain-mediated interactions (DMIs) may categorize a protein family into subfamilies because the diversified functions of a single domain often depend on interacting partners of domains. Here we systematically identified DMI subfamilies, in which proteins share domains with DMI partners, as well as with various functional and physical interaction networks in individual species. In humans, DMI subfamily members are associated with similar diseases, including cancers, and are frequently co-associated with the same diseases. DMI information relates to the functional and evolutionary subdivisions of human kinases. In yeast, DMI subfamilies contain proteins with similar phenotypic outcomes from specific chemical treatments. Therefore, the systematic investigation here provides insights into the diverse functions of subfamilies derived from a protein family with a link-centric approach and suggests a useful resource for annotating the functions and phenotypic outcomes of proteins.
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7
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Exploring the sequence, function, and evolutionary space of protein superfamilies using sequence similarity networks and phylogenetic reconstructions. Methods Enzymol 2019; 620:315-347. [PMID: 31072492 DOI: 10.1016/bs.mie.2019.03.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions by enabling the observation and investigation of complex sequence-structure-function and evolutionary relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) and phylogenetic reconstructions to map the functional divergence and evolutionary history of protein superfamilies. We exemplify this approach using the nitroreductase (NTR) flavoenzyme superfamily, demonstrating that SSN investigations can provide a rapid and effective means to classify groups of proteins, expose sequence similarity relationships across the global scale of a protein superfamily, and efficiently support detailed phylogenetic analyses. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes, their evolution, and their associated physiological roles.
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8
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Copp JN, Akiva E, Babbitt PC, Tokuriki N. Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks. Biochemistry 2018; 57:4651-4662. [PMID: 30052428 DOI: 10.1021/acs.biochem.8b00473] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire that exists in nature. Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions through the observation and investigation of the complex sequence-structure-function relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) to identify previously unexplored sequence and function space. We exemplify this approach using the nitroreductase (NTR) superfamily. We demonstrate that SSN investigations can provide a rapid and effective means to classify groups of proteins, therefore exposing experimentally unexplored sequences that may exhibit novel functionality. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes and their associated physiological roles.
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Affiliation(s)
- Janine N Copp
- Michael Smith Laboratories , University of British Columbia , 2185 East Mall , Vancouver , British Columbia V6T 1Z4 , Canada
| | - Eyal Akiva
- Department of Bioengineering and Therapeutic Sciences , University of California , San Francisco , California 94158 , United States.,Quantitative Biosciences Institute , University of California , San Francisco , California 94143 , United States
| | - Patricia C Babbitt
- Department of Bioengineering and Therapeutic Sciences , University of California , San Francisco , California 94158 , United States.,Quantitative Biosciences Institute , University of California , San Francisco , California 94143 , United States
| | - Nobuhiko Tokuriki
- Michael Smith Laboratories , University of British Columbia , 2185 East Mall , Vancouver , British Columbia V6T 1Z4 , Canada
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9
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Noda-Garcia L, Liebermeister W, Tawfik DS. Metabolite–Enzyme Coevolution: From Single Enzymes to Metabolic Pathways and Networks. Annu Rev Biochem 2018; 87:187-216. [DOI: 10.1146/annurev-biochem-062917-012023] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
How individual enzymes evolved is relatively well understood. However, individual enzymes rarely confer a physiological advantage on their own. Judging by its current state, the emergence of metabolism seemingly demanded the simultaneous emergence of many enzymes. Indeed, how multicomponent interlocked systems, like metabolic pathways, evolved is largely an open question. This complexity can be unlocked if we assume that survival of the fittest applies not only to genes and enzymes but also to the metabolites they produce. This review develops our current knowledge of enzyme evolution into a wider hypothesis of pathway and network evolution. We describe the current models for pathway evolution and offer an integrative metabolite–enzyme coevolution hypothesis. Our hypothesis addresses the origins of new metabolites and of new enzymes and the order of their recruitment. We aim to not only survey established knowledge but also present open questions and potential ways of addressing them.
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Affiliation(s)
- Lianet Noda-Garcia
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;,
| | - Wolfram Liebermeister
- INRA, Unité MaIAGE, 78352 Jouy en Josas, France
- Institute of Biochemistry, Charité Universitätsmedizin, Berlin, 10117 Berlin, Germany
| | - Dan S. Tawfik
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;,
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10
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Affiliation(s)
- Jacquelyn S. Fetrow
- Office of the President, Albright College, Reading, Pennsylvania, United States of America
- * E-mail:
| | - Patricia C. Babbitt
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
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11
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Harper AF, Leuthaeuser JB, Babbitt PC, Morris JH, Ferrin TE, Poole LB, Fetrow JS. An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins. PLoS Comput Biol 2017; 13:e1005284. [PMID: 28187133 PMCID: PMC5302317 DOI: 10.1371/journal.pcbi.1005284] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 12/06/2016] [Indexed: 12/15/2022] Open
Abstract
Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially-MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method's novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences.
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Affiliation(s)
- Angela F. Harper
- Department of Physics, Wake Forest University, Winston-Salem, North Carolina, United States of America
| | - Janelle B. Leuthaeuser
- Department of Molecular Genetics and Genomics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Patricia C. Babbitt
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco School of Pharmacy, San Francisco, California, United States of America
| | - John H. Morris
- Department of Pharmaceutical Chemistry, University of California San Francisco School of Pharmacy, San Francisco, California, United States of America
| | - Thomas E. Ferrin
- Department of Pharmaceutical Chemistry, University of California San Francisco School of Pharmacy, San Francisco, California, United States of America
| | - Leslie B. Poole
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Jacquelyn S. Fetrow
- Department of Chemistry, University of Richmond, Richmond, Virginia, United States of America
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