1
|
Konecki DM, Hamrick S, Wang C, Agosto MA, Wensel TG, Lichtarge O. CovET: A covariation-evolutionary trace method that identifies protein structure-function modules. J Biol Chem 2023; 299:104896. [PMID: 37290531 PMCID: PMC10338321 DOI: 10.1016/j.jbc.2023.104896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 06/10/2023] Open
Abstract
Measuring the relative effect that any two sequence positions have on each other may improve protein design or help better interpret coding variants. Current approaches use statistics and machine learning but rarely consider phylogenetic divergences which, as shown by Evolutionary Trace studies, provide insight into the functional impact of sequence perturbations. Here, we reframe covariation analyses in the Evolutionary Trace framework to measure the relative tolerance to perturbation of each residue pair during evolution. This approach (CovET) systematically accounts for phylogenetic divergences: at each divergence event, we penalize covariation patterns that belie evolutionary coupling. We find that while CovET approximates the performance of existing methods to predict individual structural contacts, it performs significantly better at finding structural clusters of coupled residues and ligand binding sites. For example, CovET found more functionally critical residues when we examined the RNA recognition motif and WW domains. It correlates better with large-scale epistasis screen data. In the dopamine D2 receptor, top CovET residue pairs recovered accurately the allosteric activation pathway characterized for Class A G protein-coupled receptors. These data suggest that CovET ranks highest the sequence position pairs that play critical functional roles through epistatic and allosteric interactions in evolutionarily relevant structure-function motifs. CovET complements current methods and may shed light on fundamental molecular mechanisms of protein structure and function.
Collapse
Affiliation(s)
- Daniel M Konecki
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Spencer Hamrick
- Chemical, Physical, and Structural Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Chen Wang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Melina A Agosto
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Theodore G Wensel
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA; Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA
| | - Olivier Lichtarge
- Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA; Cancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, Texas, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA.
| |
Collapse
|
2
|
Bourquard T, Lee K, Al-Ramahi I, Pham M, Shapiro D, Lagisetty Y, Soleimani S, Mota S, Wilhelm K, Samieinasab M, Kim YW, Huh E, Asmussen J, Katsonis P, Botas J, Lichtarge O. Functional variants identify sex-specific genes and pathways in Alzheimer's Disease. Nat Commun 2023; 14:2765. [PMID: 37179358 PMCID: PMC10183026 DOI: 10.1038/s41467-023-38374-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
The incidence of Alzheimer's Disease in females is almost double that of males. To search for sex-specific gene associations, we build a machine learning approach focused on functionally impactful coding variants. This method can detect differences between sequenced cases and controls in small cohorts. In the Alzheimer's Disease Sequencing Project with mixed sexes, this approach identified genes enriched for immune response pathways. After sex-separation, genes become specifically enriched for stress-response pathways in male and cell-cycle pathways in female. These genes improve disease risk prediction in silico and modulate Drosophila neurodegeneration in vivo. Thus, a general approach for machine learning on functionally impactful variants can uncover sex-specific candidates towards diagnostic biomarkers and therapeutic targets.
Collapse
Affiliation(s)
- Thomas Bourquard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kwanghyuk Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Ismael Al-Ramahi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, 77030, USA
- Center for Alzheimer's and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Minh Pham
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Dillon Shapiro
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yashwanth Lagisetty
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Department of Biology and Pharmacology, UTHealth McGovern Medical School, Houston, TX, 77030, USA
| | - Shirin Soleimani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Samantha Mota
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kevin Wilhelm
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Maryam Samieinasab
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Young Won Kim
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Eunna Huh
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jennifer Asmussen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Juan Botas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX, 77030, USA
- Center for Alzheimer's and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
- Center for Alzheimer's and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX, 77030, USA.
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, 77030, USA.
| |
Collapse
|
3
|
Lin CH, Konecki DM, Liu M, Wilson SJ, Nassar H, Wilkins AD, Gleich DF, Lichtarge O. Multimodal network diffusion predicts future disease-gene-chemical associations. Bioinformatics 2020; 35:1536-1543. [PMID: 30304494 DOI: 10.1093/bioinformatics/bty858] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 09/14/2018] [Accepted: 10/08/2018] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction. RESULTS We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies. AVAILABILITY AND IMPLEMENTATION Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Chih-Hsu Lin
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Daniel M Konecki
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Meng Liu
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Stephen J Wilson
- Department of Biochemistry and Molecular Biology, Houston, TX, USA
| | - Huda Nassar
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Angela D Wilkins
- Departments of Molecular and Human Genetics, and Pharmacology, Houston, TX, USA.,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA
| | - David F Gleich
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Olivier Lichtarge
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA.,Department of Biochemistry and Molecular Biology, Houston, TX, USA.,Departments of Molecular and Human Genetics, and Pharmacology, Houston, TX, USA.,Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA
| |
Collapse
|
4
|
Pham M, Lichtarge O. Graph-based information diffusion method for prioritizing functionally related genes in protein-protein interaction networks. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:439-450. [PMID: 31797617 PMCID: PMC7043368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Shortest path length methods are routinely used to validate whether genes of interest are functionally related to each other based on biological network information. However, the methods are computationally intensive, impeding extensive utilization of network information. In addition, non-weighted shortest path length approach, which is more frequently used, often treat all network connections equally without taking into account of confidence levels of the associations. On the other hand, graph-based information diffusion method, which employs both the presence and confidence weights of network edges, can efficiently explore large networks and has previously detected meaningful biological patterns. Therefore, in this study, we hypothesized that the graph-based information diffusion method could prioritize genes with relevant functions more efficiently and accurately than the shortest path length approaches. We demonstrated that the graph-based information diffusion method substantially differentiated not only genes participating in same biological pathways (p << 0.0001) but also genes associated with specific human drug-induced clinical symptoms (p << 0.0001) from random. Furthermore, the diffusion method prioritized these functionally related genes faster and more accurately than the shortest path length approaches (pathways: p = 2.7e-28, clinical symptoms: p = 0.032). These data show the graph-based information diffusion method can be routinely used for robust prioritization of functionally related genes, facilitating efficient network validation and hypothesis generation, especially for human phenotype-specific genes.
Collapse
Affiliation(s)
- Minh Pham
- Integrative Molecular and Biomedical Sciences Graduate Program, and Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA,
| | | |
Collapse
|
5
|
Lisewski AM, Quiros JP, Ng CL, Adikesavan AK, Miura K, Putluri N, Eastman RT, Scanfeld D, Regenbogen SJ, Altenhofen L, Llinás M, Sreekumar A, Long C, Fidock DA, Lichtarge O. Supergenomic network compression and the discovery of EXP1 as a glutathione transferase inhibited by artesunate. Cell 2014; 158:916-928. [PMID: 25126794 DOI: 10.1016/j.cell.2014.07.011] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Revised: 01/02/2014] [Accepted: 07/07/2014] [Indexed: 01/01/2023]
Abstract
A central problem in biology is to identify gene function. One approach is to infer function in large supergenomic networks of interactions and ancestral relationships among genes; however, their analysis can be computationally prohibitive. We show here that these biological networks are compressible. They can be shrunk dramatically by eliminating redundant evolutionary relationships, and this process is efficient because in these networks the number of compressible elements rises linearly rather than exponentially as in other complex networks. Compression enables global network analysis to computationally harness hundreds of interconnected genomes and to produce functional predictions. As a demonstration, we show that the essential, but functionally uncharacterized Plasmodium falciparum antigen EXP1 is a membrane glutathione S-transferase. EXP1 efficiently degrades cytotoxic hematin, is potently inhibited by artesunate, and is associated with artesunate metabolism and susceptibility in drug-pressured malaria parasites. These data implicate EXP1 in the mode of action of a frontline antimalarial drug.
Collapse
Affiliation(s)
- Andreas Martin Lisewski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Joel P Quiros
- Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Caroline L Ng
- Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY 10032, USA
| | - Anbu Karani Adikesavan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kazutoyo Miura
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA
| | - Nagireddy Putluri
- Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Richard T Eastman
- Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY 10032, USA; Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA
| | - Daniel Scanfeld
- Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY 10032, USA
| | - Sam J Regenbogen
- Department of Pharmacology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lindsey Altenhofen
- Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Biochemistry and Molecular Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA 16802, USA
| | - Manuel Llinás
- Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Biochemistry and Molecular Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, State College, PA 16802, USA
| | - Arun Sreekumar
- Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX 77030, USA; Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Carole Long
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA
| | - David A Fidock
- Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY 10032, USA; Division of Infectious Diseases, Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pharmacology, Baylor College of Medicine, Houston, TX 77030, USA.
| |
Collapse
|
6
|
Lua RC, Marciano DC, Katsonis P, Adikesavan AK, Wilkins AD, Lichtarge O. Prediction and redesign of protein-protein interactions. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:194-202. [PMID: 24878423 DOI: 10.1016/j.pbiomolbio.2014.05.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 05/02/2014] [Accepted: 05/17/2014] [Indexed: 12/14/2022]
Abstract
Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.
Collapse
Affiliation(s)
- Rhonald C Lua
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - David C Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Anbu K Adikesavan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Angela D Wilkins
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA.
| |
Collapse
|
7
|
Nemoto W, Saito A, Oikawa H. Recent advances in functional region prediction by using structural and evolutionary information - Remaining problems and future extensions. Comput Struct Biotechnol J 2013; 8:e201308007. [PMID: 24688747 PMCID: PMC3962155 DOI: 10.5936/csbj.201308007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2013] [Revised: 11/12/2013] [Accepted: 11/13/2013] [Indexed: 11/22/2022] Open
Abstract
Structural genomics projects have solved many new structures with unknown functions. One strategy to investigate the function of a structure is to computationally find the functionally important residues or regions on it. Therefore, the development of functional region prediction methods has become an important research subject. An effective approach is to use a method employing structural and evolutionary information, such as the evolutionary trace (ET) method. ET ranks the residues of a protein structure by calculating the scores for relative evolutionary importance, and locates functionally important sites by identifying spatial clusters of highly ranked residues. After ET was developed, numerous ET-like methods were subsequently reported, and many of them are in practical use, although they require certain conditions. In this mini review, we first introduce the remaining problems and the recent improvements in the methods using structural and evolutionary information. We then summarize the recent developments of the methods. Finally, we conclude by describing possible extensions of the evolution- and structure-based methods.
Collapse
Affiliation(s)
- Wataru Nemoto
- Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-cho, Hiki-gun, Saitama, 350-0394, Japan
| | - Akira Saito
- Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-cho, Hiki-gun, Saitama, 350-0394, Japan
| | - Hayato Oikawa
- Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-cho, Hiki-gun, Saitama, 350-0394, Japan
| |
Collapse
|
8
|
Prediction and experimental validation of enzyme substrate specificity in protein structures. Proc Natl Acad Sci U S A 2013; 110:E4195-202. [PMID: 24145433 DOI: 10.1073/pnas.1305162110] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.
Collapse
|
9
|
Wilkins AD, Venner E, Marciano DC, Erdin S, Atri B, Lua RC, Lichtarge O. Accounting for epistatic interactions improves the functional analysis of protein structures. Bioinformatics 2013; 29:2714-21. [PMID: 24021383 PMCID: PMC3799481 DOI: 10.1093/bioinformatics/btt489] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Motivation: The constraints under which sequence, structure and function coevolve are not fully understood. Bringing this mutual relationship to light can reveal the molecular basis of binding, catalysis and allostery, thereby identifying function and rationally guiding protein redesign. Underlying these relationships are the epistatic interactions that occur when the consequences of a mutation to a protein are determined by the genetic background in which it occurs. Based on prior data, we hypothesize that epistatic forces operate most strongly between residues nearby in the structure, resulting in smooth evolutionary importance across the structure. Methods and Results: We find that when residue scores of evolutionary importance are distributed smoothly between nearby residues, functional site prediction accuracy improves. Accordingly, we designed a novel measure of evolutionary importance that focuses on the interaction between pairs of structurally neighboring residues. This measure that we term pair-interaction Evolutionary Trace yields greater functional site overlap and better structure-based proteome-wide functional predictions. Conclusions: Our data show that the structural smoothness of evolutionary importance is a fundamental feature of the coevolution of sequence, structure and function. Mutations operate on individual residues, but selective pressure depends in part on the extent to which a mutation perturbs interactions with neighboring residues. In practice, this principle led us to redefine the importance of a residue in terms of the importance of its epistatic interactions with neighbors, yielding better annotation of functional residues, motivating experimental validation of a novel functional site in LexA and refining protein function prediction. Contact:lichtarge@bcm.edu Supplementary information:Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Angela D Wilkins
- Department of Molecular and Human Genetics, CIBR Center for Computational and Integrative Biomedical Research and Program in Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030 and Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | | | | | | | | | | | | |
Collapse
|
10
|
Erdin S, Venner E, Lisewski AM, Lichtarge O. Function prediction from networks of local evolutionary similarity in protein structure. BMC Bioinformatics 2013; 14 Suppl 3:S6. [PMID: 23514548 PMCID: PMC3584919 DOI: 10.1186/1471-2105-14-s3-s6] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background Annotating protein function with both high accuracy and sensitivity remains a major challenge in structural genomics. One proven computational strategy has been to group a few key functional amino acids into templates and search for these templates in other protein structures, so as to transfer function when a match is found. To this end, we previously developed Evolutionary Trace Annotation (ETA) and showed that diffusing known annotations over a network of template matches on a structural genomic scale improved predictions of function. In order to further increase sensitivity, we now let each protein contribute multiple templates rather than just one, and also let the template size vary. Results Retrospective benchmarks in 605 Structural Genomics enzymes showed that multiple templates increased sensitivity by up to 14% when combined with single template predictions even as they maintained the accuracy over 91%. Diffusing function globally on networks of single and multiple template matches marginally increased the area under the ROC curve over 0.97, but in a subset of proteins that could not be annotated by ETA, the network approach recovered annotations for the most confident 20-23 of 91 cases with 100% accuracy. Conclusions We improve the accuracy and sensitivity of predictions by using multiple templates per protein structure when constructing networks of ETA matches and diffusing annotations.
Collapse
Affiliation(s)
- Serkan Erdin
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA
| | | | | | | |
Collapse
|
11
|
Aguilar D, Oliva B, Marino Buslje C. Mapping the mutual information network of enzymatic families in the protein structure to unveil functional features. PLoS One 2012; 7:e41430. [PMID: 22848494 PMCID: PMC3405127 DOI: 10.1371/journal.pone.0041430] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Accepted: 06/26/2012] [Indexed: 11/24/2022] Open
Abstract
Amino acids committed to a particular function correlate tightly along evolution and tend to form clusters in the 3D structure of the protein. Consequently, a protein can be seen as a network of co-evolving clusters of residues. The goal of this work is two-fold: first, we have combined mutual information and structural data to describe the amino acid networks within a protein and their interactions. Second, we have investigated how this information can be used to improve methods of prediction of functional residues by reducing the search space. As a main result, we found that clusters of co-evolving residues related to the catalytic site of an enzyme have distinguishable topological properties in the network. We also observed that these clusters usually evolve independently, which could be related to a fail-safe mechanism. Finally, we discovered a significant enrichment of functional residues (e.g. metal binding, susceptibility to detrimental mutations) in the clusters, which could be the foundation of new prediction tools.
Collapse
Affiliation(s)
- Daniel Aguilar
- Structural Bioinformatics Group, Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain.
| | | | | |
Collapse
|
12
|
Bachman BJ, Venner E, Lua RC, Erdin S, Lichtarge O. ETAscape: analyzing protein networks to predict enzymatic function and substrates in Cytoscape. Bioinformatics 2012; 28:2186-8. [PMID: 22689386 DOI: 10.1093/bioinformatics/bts331] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
UNLABELLED Most proteins lack experimentally validated functions. To address this problem, we implemented the Evolutionary Trace Annotation (ETA) method in the Cytoscape network visualization environment. The result is the ETAscape plugin, which builds a structural genomics network based on local structural and evolutionary similarities among proteins and then globally diffuses known annotations across the resulting network. The plugin displays these novel functional annotations, their confidence, the molecular basis for individual matches and the set of matches that lead to a prediction. AVAILABILITY The ETA Network Plugin is available publicly for download at http://mammoth.bcm.tmc.edu/networks/.
Collapse
Affiliation(s)
- Benjamin J Bachman
- Departments of Molecular and Human Genetics, Program Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | | | | | | | | |
Collapse
|
13
|
Wilkins AD, Bachman BJ, Erdin S, Lichtarge O. The use of evolutionary patterns in protein annotation. Curr Opin Struct Biol 2012; 22:316-25. [PMID: 22633559 DOI: 10.1016/j.sbi.2012.05.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Accepted: 05/01/2012] [Indexed: 01/13/2023]
Abstract
With genomic data skyrocketing, their biological interpretation remains a serious challenge. Diverse computational methods address this problem by pointing to the existence of recurrent patterns among sequence, structure, and function. These patterns emerge naturally from evolutionary variation, natural selection, and divergence--the defining features of biological systems--and they identify molecular events and shapes that underlie specificity of function and allosteric communication. Here we review these methods, and the patterns they identify in case studies and in proteome-wide applications, to infer and rationally redesign function.
Collapse
Affiliation(s)
- Angela D Wilkins
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | | | | |
Collapse
|
14
|
Abstract
The evolutionary trace (ET) is the single most validated approach to identify protein functional determinants and to target mutational analysis, protein engineering and drug design to the most relevant sites of a protein. It applies to the entire proteome; its predictions come with a reliability score; and its results typically reach significance in most protein families with 20 or more sequence homologs. In order to identify functional hot spots, ET scans a multiple sequence alignment for residue variations that correlate with major evolutionary divergences. In case studies this enables the selective separation, recoding, or mimicry of functional sites and, on a large scale, this enables specific function predictions based on motifs built from select ET-identified residues. ET is therefore an accurate, scalable and efficient method to identify the molecular determinants of protein function and to direct their rational perturbation for therapeutic purposes. Public ET servers are located at: http://mammoth.bcm.tmc.edu/.
Collapse
|
15
|
Erdin S, Lisewski AM, Lichtarge O. Protein function prediction: towards integration of similarity metrics. Curr Opin Struct Biol 2011; 21:180-8. [PMID: 21353529 DOI: 10.1016/j.sbi.2011.02.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2011] [Accepted: 02/03/2011] [Indexed: 11/16/2022]
Abstract
Genomic centers discover increasingly many protein sequences and structures, but not necessarily their full biological functions. Thus, currently, less than one percent of proteins have experimentally verified biochemical activities. To fill this gap, function prediction algorithms apply metrics of similarity between proteins on the premise that those sufficiently alike in sequence, or structure, will perform identical functions. Although high sensitivity is elusive, network analyses that integrate these metrics together hold the promise of rapid gains in function prediction specificity.
Collapse
Affiliation(s)
- Serkan Erdin
- Department of Molecular and Human Genetics, 1 Baylor Plaza, Baylor College of Medicine, Houston, TX 77030, USA
| | | | | |
Collapse
|