51
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Su Z, Wu Y. Computational studies of protein-protein dissociation by statistical potential and coarse-grained simulations: a case study on interactions between colicin E9 endonuclease and immunity proteins. Phys Chem Chem Phys 2019; 21:2463-2471. [PMID: 30652698 DOI: 10.1039/c8cp05644g] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Proteins carry out their diverse functions in cells by forming interactions with each other. The dynamics of these interactions are quantified by the measurement of association and dissociation rate constants. Relative to the efforts made to model the association of biomolecules, little has been studied to understand the principles of protein complex dissociation. Using the interaction between colicin E9 endonucleases and immunity proteins as a test system, here we develop a coarse-grained simulation method to explore the dissociation mechanisms of protein complexes. The interactions between proteins in the complex are described by the knowledge-based potential that was constructed by the statistics from available protein complexes in the structural database. Our study provides the supportive evidences to the dual recognition mechanism for the specificity of binding between E9 DNase and immunity proteins, in which the conserved residues of helix III of Im2 and Im9 proteins act as the anchor for binding, while the sequence variations in helix II make positive or negative contributions to specificity. Beyond that, we further suggest that this binding specificity is rooted in the process of complex dissociation instead of association. While we increased the flexibility of protein complexes, we further found that they are less prone to dissociation, suggesting that conformational fluctuations of protein complexes play important functional roles in regulating their binding and dissociation. Our studies therefore bring new insights to the molecule mechanisms of protein-protein interactions, while the method can serve as a new addition to a suite of existing computational tools for the simulations of protein complexes.
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Affiliation(s)
- Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
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52
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Ashford P, Pang CSM, Moya-García AA, Adeyelu T, Orengo CA. A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations. Sci Rep 2019; 9:263. [PMID: 30670742 PMCID: PMC6343001 DOI: 10.1038/s41598-018-36401-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 11/13/2018] [Indexed: 12/31/2022] Open
Abstract
Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated. Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer associated genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.
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Affiliation(s)
- Paul Ashford
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Camilla S M Pang
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Aurelio A Moya-García
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.,Laboratorio de Biología Molecular del Cáncer, Centro de Investigaciones Médico-Sanitarias (CIMES), Universidad de Málaga, Málaga, Spain
| | - Tolulope Adeyelu
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Christine A Orengo
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.
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53
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Maheepala DC, Emerling CA, Rajewski A, Macon J, Strahl M, Pabón-Mora N, Litt A. Evolution and Diversification of FRUITFULL Genes in Solanaceae. FRONTIERS IN PLANT SCIENCE 2019; 10:43. [PMID: 30846991 PMCID: PMC6394111 DOI: 10.3389/fpls.2019.00043] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 01/11/2019] [Indexed: 05/12/2023]
Abstract
Ecologically and economically important fleshy edible fruits have evolved from dry fruit numerous times during angiosperm diversification. However, the molecular mechanisms that underlie these shifts are unknown. In the Solanaceae there has been a major shift to fleshy fruits in the subfamily Solanoideae. Evidence suggests that an ortholog of FRUITFULL (FUL), a transcription factor that regulates cell proliferation and limits the dehiscence zone in the silique of Arabidopsis, plays a similar role in dry-fruited Solanaceae. However, studies have shown that FUL orthologs have taken on new functions in fleshy fruit development, including regulating elements of tomato ripening such as pigment accumulation. FUL belongs to the core eudicot euFUL clade of the angiosperm AP1/FUL gene lineage. The euFUL genes fall into two paralogous clades, euFULI and euFULII. While most core eudicots have one gene in each clade, Solanaceae have two: FUL1 and FUL2 in the former, and MBP10 and MBP20 in the latter. We characterized the evolution of the euFUL genes to identify changes that might be correlated with the origin of fleshy fruit in Solanaceae. Our analyses revealed that the Solanaceae FUL1 and FUL2 clades probably originated through an early whole genome multiplication event. By contrast, the data suggest that the MBP10 and MBP20 clades are the result of a later tandem duplication event. MBP10 is expressed at weak to moderate levels, and its atypical short first intron lacks putative transcription factor binding sites, indicating possible pseudogenization. Consistent with this, our analyses show that MBP10 is evolving at a faster rate compared to MBP20. Our analyses found that Solanaceae euFUL gene duplications, evolutionary rates, and changes in protein residues and expression patterns are not correlated with the shift in fruit type. This suggests deeper analyses are needed to identify the mechanism underlying the change in FUL ortholog function.
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Affiliation(s)
- Dinusha C. Maheepala
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Christopher A. Emerling
- Institut des Sciences de l’Évolution de Montpellier, Université de Montpellier, Centre National de la Recherche Scientifique, Institut de Recherche pour le Développement, École Pratique des Hautes Études, Montpellier, France
| | - Alex Rajewski
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Jenna Macon
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Maya Strahl
- The New York Botanical Garden, Bronx, NY, United States
| | | | - Amy Litt
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
- *Correspondence: Amy Litt,
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54
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Abstract
Human cancers often harbor large numbers of somatic mutations. However, only a small proportion of these mutations are expected to contribute to tumor growth and progression. Therefore, determining causal driver mutations and the genes they target is becoming an important challenge in cancer genomics. Here we describe an approach for mapping somatic mutations onto 3D structures of human proteins in complex to identify "driver interfaces." Our strategy relies on identifying protein-interaction interfaces that are unexpectedly biased toward nonsynonymous mutations, which suggests that these interfaces are subject to positive selection during tumorigenesis, implicating the interacting proteins as candidate drivers.
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Affiliation(s)
- Kivilcim Ozturk
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics Program, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics Program, University of California San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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55
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Capriotti E, Ozturk K, Carter H. Integrating molecular networks with genetic variant interpretation for precision medicine. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1443. [PMID: 30548534 PMCID: PMC6450710 DOI: 10.1002/wsbm.1443] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/23/2018] [Accepted: 10/30/2018] [Indexed: 02/01/2023]
Abstract
More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under:
Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods
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Affiliation(s)
- Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Kivilcim Ozturk
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, California
| | - Hannah Carter
- Department of Medicine and Institute for Genomic Medicine, University of California, San Diego, La Jolla, California
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56
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Raimondi D, Orlando G, Tabaro F, Lenaerts T, Rooman M, Moreau Y, Vranken WF. Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome. Sci Rep 2018; 8:16980. [PMID: 30451933 PMCID: PMC6242909 DOI: 10.1038/s41598-018-34959-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 10/26/2018] [Indexed: 12/18/2022] Open
Abstract
Next generation sequencing technologies are providing increasing amounts of sequencing data, paving the way for improvements in clinical genetics and precision medicine. The interpretation of the observed genomic variants in the light of their phenotypic effects is thus emerging as a crucial task to solve in order to advance our understanding of how exomic variants affect proteins and how the proteins' functional changes affect human health. Since the experimental evaluation of the effects of every observed variant is unfeasible, Bioinformatics methods are being developed to address this challenge in-silico, by predicting the impact of millions of variants, thus providing insight into the deleteriousness landscape of entire proteomes. Here we show the feasibility of this approach by using the recently developed DEOGEN2 variant-effect predictor to perform the largest in-silico mutagenesis scan to date. We computed the deleteriousness score of 170 million variants over 15000 human proteins and we analysed the results, investigating how the predicted deleteriousness landscape of the proteins relates to known functionally and structurally relevant protein regions and biophysical properties. Moreover, we qualitatively validated our results by comparing them with two mutagenesis studies targeting two specific proteins, showing the consistency of DEOGEN2 predictions with respect to experimental data.
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Affiliation(s)
- Daniele Raimondi
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan, 1050, Brussels, Belgium
- ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Gabriele Orlando
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan, 1050, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Francesco Tabaro
- Institute of Biosciences and Medical Technology, Arvo Ylpőn katu 34, 33520, Tampere, Finland
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan, 1050, Brussels, Belgium
- Machine Learning Group, ULB, La Plaine Campus, 1050, Brussels, Belgium
| | - Marianne Rooman
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan, 1050, Brussels, Belgium
- Department of BioModeling, BioInformatics & BioProcesses, Université Libre de Bruxelles, 1050, Brussels, Belgium
| | - Yves Moreau
- ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium
- Imec, 3001, Leuven, Belgium
| | - Wim F Vranken
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan, 1050, Brussels, Belgium.
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.
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57
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Goswami AM. α-Adducin nsSNPs affect mRNA secondary structure, protein modification and stability. Meta Gene 2018. [DOI: 10.1016/j.mgene.2018.06.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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58
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Ittisoponpisan S, David A. Structural Biology Helps Interpret Variants of Uncertain Significance in Genes Causing Endocrine and Metabolic Disorders. J Endocr Soc 2018; 2:842-854. [PMID: 30019023 PMCID: PMC6041779 DOI: 10.1210/js.2018-00077] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 06/08/2018] [Indexed: 12/22/2022] Open
Abstract
CONTEXT Variants of uncertain significance (VUSs) lack sufficient evidence, in terms of statistical power or experimental studies, to allow unequivocal determination of their damaging effect. VUSs are a major burden in performing genetic analysis. Although in silico prediction tools are widely used, their specificity is low, thus urgently calling for methods for prioritizing and characterizing variants. OBJECTIVE To assess the frequency of VUSs in genes causing endocrine and metabolic disorders, the concordance rate of predictions from different in silico methods, and the added value of three-dimensional protein structure analysis in discerning and prioritizing damaging variants. RESULTS A total of 12,266 missense variants reported in 641 genes causing endocrine and metabolic disorders were analyzed. Among these, 4123 (33.7%) were VUSs, of which 2010 (48.8%) were predicted to be damaging and 1452 (35.2%) were predicted to be tolerated according to in silico tools. A total of 5383 (87.7%) of 6133 disease-causing variants and 823 (55.8%) of 1474 benign variants were correctly predicted. In silico predictions were noninformative in 5.7%, 14.4%, and 16% of damaging, benign, and VUSs, respectively. A damaging effect on 3D protein structure was present in 240 (30.9%) of predicted damaging and 40 (9.7%) of predicted tolerated VUSs (P < 0.001). An in-depth analysis of nine VUSs occurring in TSHR, LDLR, CASR, and APOE showed that they greatly affect protein stability and are therefore strong candidates for disease. CONCLUSIONS In our dataset, we confirmed the high sensitivity but low specificity of in silico predictions tools. 3D protein structural analysis is a compelling tool for characterizing and prioritizing VUSs and should be a part of genetic variant analysis.
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Affiliation(s)
- Sirawit Ittisoponpisan
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Alessia David
- Structural Bioinformatics Group, Department of Life Sciences, Imperial College London, London, United Kingdom
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59
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Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins 2018; 86 Suppl 1:7-15. [PMID: 29082672 PMCID: PMC5897042 DOI: 10.1002/prot.25415] [Citation(s) in RCA: 245] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 10/25/2017] [Accepted: 10/27/2017] [Indexed: 12/24/2022]
Abstract
This article reports the outcome of the 12th round of Critical Assessment of Structure Prediction (CASP12), held in 2016. CASP is a community experiment to determine the state of the art in modeling protein structure from amino acid sequence. Participants are provided sequence information and in turn provide protein structure models and related information. Analysis of the submitted structures by independent assessors provides a comprehensive picture of the capabilities of current methods, and allows progress to be identified. This was again an exciting round of CASP, with significant advances in 4 areas: (i) The use of new methods for predicting three-dimensional contacts led to a two-fold improvement in contact accuracy. (ii) As a consequence, model accuracy for proteins where no template was available improved dramatically. (iii) Models based on a structural template showed overall improvement in accuracy. (iv) Methods for estimating the accuracy of a model continued to improve. CASP continued to develop new areas: (i) Assessing methods for building quaternary structure models, including an expansion of the collaboration between CASP and CAPRI. (ii) Modeling with the aid of experimental data was extended to include SAXS data, as well as again using chemical cross-linking information. (iii) A team of assessors evaluated the suitability of models for a range of applications, including mutation interpretation, analysis of ligand binding properties, and identification of interfaces. This article describes the experiment and summarizes the results. The rest of this special issue of PROTEINS contains papers describing CASP12 results and assessments in more detail.
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Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research and Department of Cell Biology and Molecular Genetics, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Torsten Schwede
- University of Basel, Biozentrum & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Anna Tramontano
- Department of Physics and Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
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60
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Škrlj B, Kunej T, Konc J. Insights from Ion Binding Site Network Analysis into Evolution and Functions of Proteins. Mol Inform 2018; 37:e1700144. [PMID: 29418080 DOI: 10.1002/minf.201700144] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 02/01/2018] [Indexed: 01/05/2023]
Abstract
Many biological phenomena can be represented as complex networks. Using a protein binding site comparison approach, we generated a network of ion binding sites on the scale of all known protein structures from the Protein Data Bank. We found that this ion binding site similarity network is scale-free, indicating a network in which a few ion binding site scaffolds are the network hubs, and these are connected to hundreds of nodes, whereas the vast majority of nodes have only a few neighbors. Enrichment and statistical analysis of the network components and communities yielded insights into underlying processes from the functional and the structural perspective. Largest components and communities were observed to be closely related to basic metabolic processes and some of the most common structural folds, which, from the evolutionary point of view, indicates that they may be the oldest ones. Further, we derived the first comprehensive map of ion interchangeability, based on binding site similarity. Several highly interchangeable protein-ion binding site pairs emerged (e.g., Ca2+ and Mg2+ ), as well as structurally distinct ones. The constructed network of ion binding site similarities will aid in understanding the general principles of protein-ion binding sites structure, function and evolution. We demonstrate potential uses of the network on proteins involved in cancer development and immune response, where individual ions play prominent roles in disease development.
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Affiliation(s)
- Blaž Škrlj
- Department of molecular modeling, National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia.,Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Tanja Kunej
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Slovenia
| | - Janez Konc
- Department of molecular modeling, National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia
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61
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Rosell M, Fernández-Recio J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opin Drug Discov 2018; 13:327-338. [PMID: 29376444 DOI: 10.1080/17460441.2018.1430763] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
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Affiliation(s)
- Mireia Rosell
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain
| | - Juan Fernández-Recio
- a Department of Life Sciences , Barcelona Supercomputing Center (BSC) , Barcelona , Spain.,b Structural Biology Unit , Institut de Biologia Molecular de Barcelona (IBMB), CSIC , Barcelona , Spain
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62
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Zhou HX, Pang X. Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation. Chem Rev 2018; 118:1691-1741. [PMID: 29319301 DOI: 10.1021/acs.chemrev.7b00305] [Citation(s) in RCA: 454] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.
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Affiliation(s)
- Huan-Xiang Zhou
- Department of Chemistry and Department of Physics, University of Illinois at Chicago , Chicago, Illinois 60607, United States.,Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
| | - Xiaodong Pang
- Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
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63
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Laddach A, Ng JCF, Chung SS, Fraternali F. Genetic variants and protein-protein interactions: a multidimensional network-centric view. Curr Opin Struct Biol 2018; 50:82-90. [PMID: 29306755 DOI: 10.1016/j.sbi.2017.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 12/19/2017] [Accepted: 12/20/2017] [Indexed: 01/18/2023]
Abstract
We review recent progress in the mapping of genetic variants to proteins, in the context of their interactions, as measured from experiments and/or computational predictions. Such variants can impact on the molecular mechanisms underlying an interaction and its stability. We highlight recent work which relies on the effective use of protein-protein interaction networks (PPINs), integrated with 3D structural information, for evaluating disease-associated variants. Furthermore, we discuss how the integration of multiple layers of biological information, in the context of PPINs, can improve the interpretation of genetic variants and inspire new therapeutic strategies.
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Affiliation(s)
- Anna Laddach
- Randall Division of Cell and Molecular Biophysics, King's College London, UK
| | - Joseph Chi-Fung Ng
- Randall Division of Cell and Molecular Biophysics, King's College London, UK
| | - Sun Sook Chung
- Randall Division of Cell and Molecular Biophysics, King's College London, UK; Department of Haematological Medicine, King's College London, UK
| | - Franca Fraternali
- Randall Division of Cell and Molecular Biophysics, King's College London, UK.
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64
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Barradas-Bautista D, Rosell M, Pallara C, Fernández-Recio J. Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems. PROTEIN-PROTEIN INTERACTIONS IN HUMAN DISEASE, PART A 2018; 110:203-249. [DOI: 10.1016/bs.apcsb.2017.06.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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65
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Meyer MJ, Beltrán JF, Liang S, Fragoza R, Rumack A, Liang J, Wei X, Yu H. Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 2018; 15:107-114. [PMID: 29355848 PMCID: PMC6026581 DOI: 10.1038/nmeth.4540] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 10/22/2017] [Indexed: 02/07/2023]
Abstract
We present Interactome INSIDER, a tool to link genomic variant information with
structural protein-protein interactomes. Underlying this tool is the application of
machine learning to predict protein interaction interfaces for 185,957 protein
interactions with previously unresolved interfaces, in human and 7 model organisms,
including the entire experimentally determined human binary interactome. Predicted
interfaces exhibit similar functional properties as known interfaces, including enrichment
for disease mutations and recurrent cancer mutations. Through 2,164 de
novo mutagenesis experiments, we show that mutations of predicted and known
interface residues disrupt interactions at a similar rate, and much more frequently than
mutations outside of predicted interfaces. To spur functional genomic studies, Interactome
INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether
variants or disease mutations are enriched in known and predicted interaction interfaces
at various resolutions. Users may explore known population variants, disease mutations,
and somatic cancer mutations, or upload their own set of mutations for this purpose.
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Affiliation(s)
- Michael J Meyer
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA
| | - Juan Felipe Beltrán
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Siqi Liang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Robert Fragoza
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
| | - Aaron Rumack
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Jin Liang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Xiaomu Wei
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Department of Medicine, Weill Cornell College of Medicine, New York, New York, USA
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
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66
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Yefremova Y, Danquah BD, Opuni KF, El-Kased R, Koy C, Glocker MO. Mass spectrometric characterization of protein structures and protein complexes in condensed and gas phase. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2017; 23:445-459. [PMID: 29183193 DOI: 10.1177/1469066717722256] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Proteins are essential for almost all physiological processes of life. They serve a myriad of functions which are as varied as their unique amino acid sequences and their corresponding three-dimensional structures. To fulfill their tasks, most proteins depend on stable physical associations, in the form of protein complexes that evolved between themselves and other proteins. In solution (condensed phase), proteins and/or protein complexes are in constant energy exchange with the surrounding solvent. Albeit methods to describe in-solution thermodynamic properties of proteins and of protein complexes are well established and broadly applied, they do not provide a broad enough access to life-science experimentalists to study all their proteins' properties at leisure. This leaves great desire to add novel methods to the analytical biochemist's toolbox. The development of electrospray ionization created the opportunity to characterize protein higher order structures and protein complexes rather elegantly by simultaneously lessening the need of sophisticated sample preparation steps. Electrospray mass spectrometry enabled us to translate proteins and protein complexes very efficiently into the gas phase under mild conditions, retaining both, intact protein complexes, and gross protein structures upon phase transition. Moreover, in the environment of the mass spectrometer (gas phase, in vacuo), analyte molecules are free of interactions with surrounding solvent molecules and, therefore, the energy of inter- and intramolecular forces can be studied independently from interference of the solvating environment. Provided that gas phase methods can give information which is relevant for understanding in-solution processes, gas phase protein structure studies and/or investigations on the characterization of protein complexes has rapidly gained more and more attention from the bioanalytical scientific community. Recent reports have shown that electrospray mass spectrometry provides direct access to six prime protein complex properties: stabilities, compositions, binding surfaces (epitopes), disassembly processes, stoichiometries, and thermodynamic parameters.
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Affiliation(s)
- Yelena Yefremova
- 1 Proteome Center Rostock, University of Rostock, Rostock, Germany
| | - Bright D Danquah
- 1 Proteome Center Rostock, University of Rostock, Rostock, Germany
| | | | - Reham El-Kased
- 3 Microbiology and Immunology, Faculty of Pharmacy, The British University in Egypt, Cairo, Egypt
| | - Cornelia Koy
- 1 Proteome Center Rostock, University of Rostock, Rostock, Germany
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67
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Deng N, Zhou H, Fan H, Yuan Y. Single nucleotide polymorphisms and cancer susceptibility. Oncotarget 2017; 8:110635-110649. [PMID: 29299175 PMCID: PMC5746410 DOI: 10.18632/oncotarget.22372] [Citation(s) in RCA: 190] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 10/03/2017] [Indexed: 12/12/2022] Open
Abstract
A large number of genes associated with various cancer types contain single nucleotide polymorphisms (SNPs). SNPs are located in gene promoters, exons, introns as well as 5'- and 3'- untranslated regions (UTRs) and affect gene expression by different mechanisms. These mechanisms depend on the role of the genetic elements in which the individual SNPs are located. Moreover, alterations in epigenetic regulation due to gene polymorphisms add to the complexity underlying cancer susceptibility related to SNPs. In this systematic review, we discuss the various genetic and epigenetic mechanisms involved in determining cancer susceptibility related to various SNPs located in different genetic elements. We also discuss the diagnostic potential of these SNPs and the focus for future studies.
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Affiliation(s)
- Na Deng
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, and Key Laboratory of Cancer Etiology and Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, China.,Department of Hematology, The Fourth Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Heng Zhou
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, and Key Laboratory of Cancer Etiology and Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, China
| | - Hua Fan
- Department of Hematology, The Fourth Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Yuan Yuan
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Affiliated Hospital of China Medical University, and Key Laboratory of Cancer Etiology and Prevention (China Medical University), Liaoning Provincial Education Department, Shenyang 110001, China.,National Clinical Research Center for Digestive Diseases, Xi'an 110001, China
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68
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Yefremova Y, Melder FTI, Danquah BD, Opuni KFM, Koy C, Ehrens A, Frommholz D, Illges H, Koelbel K, Sobott F, Glocker MO. Apparent activation energies of protein-protein complex dissociation in the gas-phase determined by electrospray mass spectrometry. Anal Bioanal Chem 2017; 409:6549-6558. [PMID: 28900708 DOI: 10.1007/s00216-017-0603-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 08/09/2017] [Accepted: 08/23/2017] [Indexed: 11/30/2022]
Abstract
We have developed a method to determine apparent activation energies of dissociation for ionized protein-protein complexes in the gas phase using electrospray ionization mass spectrometry following the Rice-Ramsperger-Kassel-Marcus quasi-equilibrium theory. Protein-protein complexes were formed in solution, transferred into the gas phase, and separated from excess free protein by ion mobility filtering. Afterwards, complex disassembly was initiated by collision-induced dissociation with step-wise increasing energies. Relative intensities of ion signals were used to calculate apparent activation energies of dissociation in the gas phase by applying linear free energy relations. The method was developed using streptavidin tetramers. Experimentally determined apparent gas-phase activation energies for dissociation ([Formula: see text]) of complexes consisting of Fc parts from immunoglobulins (IgG-Fc) and three closely related protein G' variants (IgG-Fc•protein G'e, IgG-Fc•protein G'f, and IgG-Fc•protein G'g) show the same order of stabilities as can be inferred from their in-solution binding constants. Differences in stabilities between the protein-protein complexes correspond to single amino acid residue exchanges in the IgG-binding regions of the protein G' variants. Graphical abstract Electrospray mass spectrometry and collision-induced dissociation delivers apparent activation energies and supramolecular bond force constants of protein-protein complexes in the gas phase.
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Affiliation(s)
- Yelena Yefremova
- Proteome Center Rostock, University Rostock Medical Center, Schillingallee 69, 18059, Rostock, Germany
| | - F Teresa I Melder
- Proteome Center Rostock, University Rostock Medical Center, Schillingallee 69, 18059, Rostock, Germany
| | - Bright D Danquah
- Proteome Center Rostock, University Rostock Medical Center, Schillingallee 69, 18059, Rostock, Germany
| | - Kwabena F M Opuni
- Proteome Center Rostock, University Rostock Medical Center, Schillingallee 69, 18059, Rostock, Germany.,School of Pharmacy, University of Ghana, P.O. Box LG43, Legon Accra, Ghana
| | - Cornelia Koy
- Proteome Center Rostock, University Rostock Medical Center, Schillingallee 69, 18059, Rostock, Germany
| | - Alexandra Ehrens
- University of Applied Sciences Bonn-Rhein-Sieg, von-Liebig-Str. 20, 53359, Rheinbach, Germany.,University Hospital of Bonn, Sigmung-Freud-Str. 25, 53105, Bonn, Germany
| | - David Frommholz
- University of Applied Sciences Bonn-Rhein-Sieg, von-Liebig-Str. 20, 53359, Rheinbach, Germany
| | - Harald Illges
- University of Applied Sciences Bonn-Rhein-Sieg, von-Liebig-Str. 20, 53359, Rheinbach, Germany
| | - Knut Koelbel
- Department of Chemistry, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Frank Sobott
- Department of Chemistry, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium.,Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK.,School of Molecular and Cellular Biology, University of Leeds, Leeds, LS2 9JT, UK
| | - Michael O Glocker
- Proteome Center Rostock, University Rostock Medical Center, Schillingallee 69, 18059, Rostock, Germany.
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69
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Salvatori R, Radian S, Diekmann Y, Iacovazzo D, David A, Gabrovska P, Grassi G, Bussell AM, Stals K, Weber A, Quinton R, Crowne EC, Corazzini V, Metherell L, Kearney T, Du Plessis D, Sinha AK, Baborie A, Lecoq AL, Chanson P, Ansorge O, Ellard S, Trainer PJ, Balding D, Thomas MG, Korbonits M. In-frame seven amino-acid duplication in AIP arose over the last 3000 years, disrupts protein interaction and stability and is associated with gigantism. Eur J Endocrinol 2017; 177. [PMID: 28634279 PMCID: PMC5510572 DOI: 10.1530/eje-17-0293] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene are associated with pituitary adenoma, acromegaly and gigantism. Identical alleles in unrelated pedigrees could be inherited from a common ancestor or result from recurrent mutation events. DESIGN AND METHODS Observational, inferential and experimental study, including: AIP mutation testing; reconstruction of 14 AIP-region (8.3 Mbp) haplotypes; coalescent-based approximate Bayesian estimation of the time to most recent common ancestor (tMRCA) of the derived allele; forward population simulations to estimate current number of allele carriers; proposal of mutation mechanism; protein structure predictions; co-immunoprecipitation and cycloheximide chase experiments. RESULTS Nine European-origin, unrelated c.805_825dup-positive pedigrees (four familial, five sporadic from the UK, USA and France) included 16 affected (nine gigantism/four acromegaly/two non-functioning pituitary adenoma patients and one prospectively diagnosed acromegaly patient) and nine unaffected carriers. All pedigrees shared a 2.79 Mbp haploblock around AIP with additional haploblocks privately shared between subsets of the pedigrees, indicating the existence of an evolutionarily recent common ancestor, the 'English founder', with an estimated median tMRCA of 47 generations (corresponding to 1175 years) with a confidence interval (9-113 generations, equivalent to 225-2825 years). The mutation occurred in a small tandem repeat region predisposed to slipped strand mispairing. The resulting seven amino-acid duplication disrupts interaction with HSP90 and leads to a marked reduction in protein stability. CONCLUSIONS The c.805_825dup allele, originating from a common ancestor, associates with a severe clinical phenotype and a high frequency of gigantism. The mutation is likely to be the result of slipped strand mispairing and affects protein-protein interactions and AIP protein stability.
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Affiliation(s)
| | - Serban Radian
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
- Department of EndocrinologyC.I. Parhon National Institute of Endocrinology, ‘C. Davila’ University of Medicine and Pharmacy, Bucharest, Romania
| | - Yoan Diekmann
- Research Department of GeneticsEvolution and Environment, University College London, London, UK
| | - Donato Iacovazzo
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Alessia David
- Centre of Bioinformatics and System BiologyDepartment of Life Sciences, Imperial College London, London, UK
| | - Plamena Gabrovska
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Giorgia Grassi
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Anna-Marie Bussell
- Department of Molecular GeneticsRoyal Devon and Exeter Foundation Trust, Exeter, UK
| | - Karen Stals
- Department of Molecular GeneticsRoyal Devon and Exeter Foundation Trust, Exeter, UK
| | - Astrid Weber
- Department of Clinical GeneticsLiverpool Women’s Hospital, Liverpool, UK
| | - Richard Quinton
- Department of EndocrinologyNewcastle-upon-Tyne Hospitals & Institute of Genetic Medicine, Newcastle University, Newcastle, UK
| | - Elizabeth C Crowne
- Bristol Royal Hospital for ChildrenUniversity Hospitals Bristol Foundation Trust, Bristol, UK
| | | | - Lou Metherell
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
| | - Tara Kearney
- Endocrinology and Neuropathology UnitSalford Royal Hospital, Manchester, UK
| | - Daniel Du Plessis
- Endocrinology and Neuropathology UnitSalford Royal Hospital, Manchester, UK
| | | | - Atik Baborie
- The Walton Centre for Neurology and NeurosurgeryLiverpool, UK
| | - Anne-Lise Lecoq
- Assistance Publique-Hôpitaux de ParisHôpital de Bicêtre, Service d’Endocrinologie et des Maladies de la Reproduction and Centre de Référence des Maladies Endocriniennes Rares de la Croissance, Le Kremlin-Bicêtre, France
- Inserm 1185Fac Med Paris Sud, Univ Paris-Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Philippe Chanson
- Assistance Publique-Hôpitaux de ParisHôpital de Bicêtre, Service d’Endocrinologie et des Maladies de la Reproduction and Centre de Référence des Maladies Endocriniennes Rares de la Croissance, Le Kremlin-Bicêtre, France
- Inserm 1185Fac Med Paris Sud, Univ Paris-Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | | | - Sian Ellard
- Department of Molecular GeneticsRoyal Devon and Exeter Foundation Trust, Exeter, UK
- Institute of Biomedical and Clinical ScienceUniversity of Exeter Medical School, Exeter, UK
| | - Peter J Trainer
- Department of EndocrinologyChristie Hospital, Manchester, UK
| | - David Balding
- Research Department of GeneticsEvolution and Environment, University College London, London, UK
- Centre for Systems GenomicsSchools of Biosciences and of Mathematics & Statistics, University of Melbourne, Melbourne, Australia
| | - Mark G Thomas
- Research Department of GeneticsEvolution and Environment, University College London, London, UK
| | - Márta Korbonits
- William Harvey Research InstituteBarts and the London School of Medicine, Queen Mary University of London, London, UK
- Correspondence should be addressed to M Korbonits;
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70
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Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:3-13. [DOI: 10.1016/j.pbiomolbio.2016.10.002] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 10/15/2016] [Accepted: 10/19/2016] [Indexed: 12/22/2022]
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71
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Goodacre N, Edwards N, Danielsen M, Uetz P, Wu C. Predicting nsSNPs that Disrupt Protein-Protein Interactions Using Docking. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1082-1093. [PMID: 26812731 DOI: 10.1109/tcbb.2016.2520931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The human genome contains a large number of protein polymorphisms due to individual genome variation. How many of these polymorphisms lead to altered protein-protein interaction is unknown. We have developed a method to address this question. The intersection of the SKEMPI database (of affinity constants among interacting proteins) and CAPRI 4.0 docking benchmark was docked using HADDOCK, leading to a training set of 166 mutant pairs. A random forest classifier based on the differences in resulting docking scores between the 166 mutant pairs and their wild-types was used, to distinguish between variants that have either completely or partially lost binding ability. Fifty percent of non-binders were correctly predicted with a false discovery rate of only 2 percent. The model was tested on a set of 15 HIV-1 - human, as well as seven human- human glioblastoma-related, mutant protein pairs: 50 percent of combined non-binders were correctly predicted with a false discovery rate of 10 percent. The model was also used to identify 10 protein-protein interactions between human proteins and their HIV-1 partners that are likely to be abolished by rare non-synonymous single-nucleotide polymorphisms (nsSNPs). These nsSNPs may represent novel and potentially therapeutically-valuable targets for anti-viral therapy by disruption of viral binding.
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72
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Latysheva NS, Oates ME, Maddox L, Flock T, Gough J, Buljan M, Weatheritt RJ, Babu MM. Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer. Mol Cell 2017; 63:579-592. [PMID: 27540857 PMCID: PMC5003813 DOI: 10.1016/j.molcel.2016.07.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 06/14/2016] [Accepted: 07/14/2016] [Indexed: 11/26/2022]
Abstract
Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer.
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Affiliation(s)
- Natasha S Latysheva
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.
| | - Matt E Oates
- Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK
| | - Louis Maddox
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Tilman Flock
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Julian Gough
- Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK
| | - Marija Buljan
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Robert J Weatheritt
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK; The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - M Madan Babu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.
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73
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Barradas-Bautista D, Fernández-Recio J. Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations. PLoS One 2017; 12:e0183643. [PMID: 28841721 PMCID: PMC5571915 DOI: 10.1371/journal.pone.0183643] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 08/08/2017] [Indexed: 01/23/2023] Open
Abstract
Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.
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Affiliation(s)
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona, Spain
- * E-mail:
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74
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Azani A, Hosseinzadeh A, Azadkhah R, Zonouzi AAP, Zonouzi AP, Aftabi Y, Khani H, Heidary L, Danaii S, Bargahi N, Pouladi N, Hosseini SM. Association of endothelial nitric oxide synthase gene variants (-786 T>C, intron 4 b/a VNTR and 894 G>T) with idiopathic recurrent pregnancy loss: A case-control study with haplotype and in silico analysis. Eur J Obstet Gynecol Reprod Biol 2017; 215:93-100. [PMID: 28605668 DOI: 10.1016/j.ejogrb.2017.05.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 04/22/2017] [Accepted: 05/25/2017] [Indexed: 01/05/2023]
Abstract
OBJECTIVE(S) Many lines of evidence suggest that reduced production of nitric oxide (NO) due to single nucleotide polymorphisms in endothelial nitric oxide synthase (eNOS) gene may affect the implantation and maintenance of pregnancy. Accordingly, our objective was to investigate whether the eNOS polymorphisms (-786 T>C, intron 4 b/a VNTR and 894 G>T) and haplotypes may be associated with increased susceptibility to recurrent pregnancy loss (RPL). STUDY DESIGN A total of 130 women with a history of two or more unexplained consecutive first trimester miscarriages and 110 ethnically matched women with at least two normal pregnancies and no history of pregnancy loss were included in the study as cases and controls, respectively. To identify the genotypes, we used polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (PCR-RFLP) methods In addition, an in silico analysis was conducted to predict the possible effects of the eNOS 894 G>T polymorphism on the structure and function of eNOS mRNA and protein using prediction servers. RESULTS Our findings revealed that the prevalence of eNOS -786 T>C polymorphism, eNOS -786C allele and TC+CC genotype in cases were significantly higher than those in healthy controls (p<0.05). Also, the combination genotypes -786TT/4b4a and -786TT/894GG were significantly associated with reduced risk of RPL. We also found that the C-4a-G haplotype of the eNOS gene studied polymorphisms was significantly associated with a predisposition to RPL (odds ratio, 3.219; 95% confidence interval, 1.649-6.282; p=0.0003). The in silico analysis showed that the eNOS 894 G>T polymorphism couldn't affects eNOS mRNA and protein significantly. CONCLUSION Our findings provide evidence to support the hypothesis that eNOS -786 T>C polymorphism and the -786C-4a-894G haplotype are associated with the high risk of RPL.
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Affiliation(s)
- Alireza Azani
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Roya Azadkhah
- Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Ali Akbar Poursadegh Zonouzi
- Department of Genetics and Medical Biotechnology, Faculty of Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | | | - Younes Aftabi
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Hourieh Khani
- Laboratory of Medical Genetic, ART and Stem Cell Research Center (ACECR), Tabriz, Iran
| | - Leida Heidary
- Laboratory of Medical Genetic, ART and Stem Cell Research Center (ACECR), Tabriz, Iran
| | - Shahla Danaii
- Laboratory of Medical Genetic, ART and Stem Cell Research Center (ACECR), Tabriz, Iran
| | - Nasrin Bargahi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nasser Pouladi
- Department of Cellular and Molecular Biology, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Sayed Mostafa Hosseini
- Human Genetic Research Center, Baqiyatallah university of Medical Science, Tehran, Iran.
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Skrlj B, Konc J, Kunej T. Identification of Sequence Variants within Experimentally Validated Protein Interaction Sites Provides New Insights into Molecular Mechanisms of Disease Development. Mol Inform 2017; 36. [DOI: 10.1002/minf.201700017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Accepted: 04/17/2017] [Indexed: 11/07/2022]
Affiliation(s)
- Blaz Skrlj
- Department of molecular modeling; National Institute of Chemistry; Hajdrihova 19 Ljubljana Slovenia
- Jožef Stefan International Postgraduate School; Jamova cesta 39 1000 Ljubljana Slovenia
| | - Janez Konc
- Department of molecular modeling; National Institute of Chemistry; Hajdrihova 19 Ljubljana Slovenia
| | - Tanja Kunej
- Department of Animal Science, Biotechnical Faculty; University of Ljubljana
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76
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Chen C, Yang L, Villoutreix BO, Wang X, Ding Q, Rezaie AR. Gly74Ser mutation in protein C causes thrombosis due to a defect in protein S-dependent anticoagulant function. Thromb Haemost 2017; 117:1358-1369. [PMID: 28405673 DOI: 10.1160/th17-01-0043] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 03/25/2017] [Indexed: 12/30/2022]
Abstract
Protein C is a vitamin K-dependent serine protease zymogen in plasma which upon activation by thrombin in complex with thrombomodulin (TM) down-regulates the clotting cascade by a feedback loop inhibition mechanism. Activated protein C (APC) exerts its anticoagulant function through protein S-dependent degradation of factors Va and VIIIa. We recently identified a venous thrombosis patient whose plasma level of protein C antigen is normal, but its anticoagulant activity is only 34 % of the normal level. Genetic analysis revealed that the proband and her younger brother carry a novel heterozygous mutation c.346G>A, p.Gly74Ser (G74S) in PROC. Thrombin generation assay indicated that the TM-dependent anticoagulant activity of the proband's plasma has been significantly impaired. We expressed protein C-G74S in mammalian cells and characterised its properties in established coagulation assays. We demonstrate that the protein C variant can be normally activated by the thrombin-TM complex and the resulting APC mutant also exhibits normal amidolytic and proteolytic activities toward both FVa and FVIIIa. However, it was discovered the protein S-dependent catalytic activity of APC variant toward both procoagulant cofactors has been significantly impaired. Protein S concentration-dependence of FVa degradation revealed that the capacity of APC variant to interact with the cofactor has been markedly impaired. The same results were obtained for inactivation of FVa-Leiden suggesting that the protein S-dependent activity of APC variant toward cleavage of Arg-306 site has been adversely affected. These results provide insight into the mechanism through which G74S substitution in APC causes thrombosis in the proband carrying this mutation.
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Affiliation(s)
| | | | | | | | | | - Alireza R Rezaie
- Alireza R. Rezaie, PhD, Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA, Tel: +1 405 271 4711, E-mail: , or, Qiulan Ding, PhD, Department of Laboratory Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, No. 197 Ruijin Second Road, Shanghai, 200025 China, Tel.: +86 21 54667770, Fax: +86 21 64333548, E-mail:
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77
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Vishwanath S, Sukhwal A, Sowdhamini R, Srinivasan N. Specificity and stability of transient protein-protein interactions. Curr Opin Struct Biol 2017; 44:77-86. [PMID: 28088083 DOI: 10.1016/j.sbi.2016.12.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 11/03/2016] [Accepted: 12/19/2016] [Indexed: 11/18/2022]
Abstract
Remarkable features that are achieved in a protein-protein complex to precise levels are stability and specificity. Deviation from the normal levels of specificity and stability, which is often caused by mutations, could result in disease conditions. Chemical nature, 3-D arrangement and dynamics of interface residues code for both specificity and stability. This article reviews roles of interfacial residues in transient protein-protein complexes. It is proposed that aside from hotspot residues conferring stability to the complex, a small set of 'rigid' residues at the interface that maintain conformation between complexed and uncomplexed forms, play a major role in conferring specificity. Exceptionally, 'super hotspot' residues, which confer both stability and specificity, are attractive sites for interaction with small molecule inhibitors.
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Affiliation(s)
- Sneha Vishwanath
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India
| | - Anshul Sukhwal
- National Centre for Biological Sciences, TIFR, UAS-GKVK Campus, Bellary road, Bangalore 560065, India; SASTRA Deemed University, Tirumalai Samudram, Thanjavur 613402, India
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences, TIFR, UAS-GKVK Campus, Bellary road, Bangalore 560065, India
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78
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Awan FM, Obaid A, Ikram A, Janjua HA. Mutation-Structure-Function Relationship Based Integrated Strategy Reveals the Potential Impact of Deleterious Missense Mutations in Autophagy Related Proteins on Hepatocellular Carcinoma (HCC): A Comprehensive Informatics Approach. Int J Mol Sci 2017; 18:ijms18010139. [PMID: 28085066 PMCID: PMC5297772 DOI: 10.3390/ijms18010139] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 11/14/2016] [Accepted: 11/16/2016] [Indexed: 12/13/2022] Open
Abstract
Autophagy, an evolutionary conserved multifaceted lysosome-mediated bulk degradation system, plays a vital role in liver pathologies including hepatocellular carcinoma (HCC). Post-translational modifications (PTMs) and genetic variations in autophagy components have emerged as significant determinants of autophagy related proteins. Identification of a comprehensive spectrum of genetic variations and PTMs of autophagy related proteins and their impact at molecular level will greatly expand our understanding of autophagy based regulation. In this study, we attempted to identify high risk missense mutations that are highly damaging to the structure as well as function of autophagy related proteins including LC3A, LC3B, BECN1 and SCD1. Number of putative structural and functional residues, including several sites that undergo PTMs were also identified. In total, 16 high-risk SNPs in LC3A, 18 in LC3B, 40 in BECN1 and 43 in SCD1 were prioritized. Out of these, 2 in LC3A (K49A, K51A), 1 in LC3B (S92C), 6 in BECN1 (S113R, R292C, R292H, Y338C, S346Y, Y352H) and 6 in SCD1 (Y41C, Y55D, R131W, R135Q, R135W, Y151C) coincide with potential PTM sites. Our integrated analysis found LC3B Y113C, BECN1 I403T, SCD1 R126S and SCD1 Y218C as highly deleterious HCC-associated mutations. This study is the first extensive in silico mutational analysis of the LC3A, LC3B, BECN1 and SCD1 proteins. We hope that the observed results will be a valuable resource for in-depth mechanistic insight into future investigations of pathological missense SNPs using an integrated computational platform.
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Affiliation(s)
- Faryal Mehwish Awan
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12 Islamabad 44000, Pakistan.
| | - Ayesha Obaid
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12 Islamabad 44000, Pakistan.
| | - Aqsa Ikram
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12 Islamabad 44000, Pakistan.
| | - Hussnain Ahmed Janjua
- Department of Industrial Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), H-12 Islamabad 44000, Pakistan.
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79
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Abrusán G, Marsh JA. Alpha Helices Are More Robust to Mutations than Beta Strands. PLoS Comput Biol 2016; 12:e1005242. [PMID: 27935949 PMCID: PMC5147804 DOI: 10.1371/journal.pcbi.1005242] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 11/08/2016] [Indexed: 12/30/2022] Open
Abstract
The rapidly increasing amount of data on human genetic variation has resulted in a growing demand to identify pathogenic mutations computationally, as their experimental validation is currently beyond reach. Here we show that alpha helices and beta strands differ significantly in their ability to tolerate mutations: helices can accumulate more mutations than strands without change, due to the higher numbers of inter-residue contacts in helices. This results in two patterns: a) the same number of mutations causes less structural change in helices than in strands; b) helices diverge more rapidly in sequence than strands within the same domains. Additionally, both helices and strands are significantly more robust than coils. Based on this observation we show that human missense mutations that change secondary structure are more likely to be pathogenic than those that do not. Moreover, inclusion of predicted secondary structure changes shows significant utility for improving upon state-of-the-art pathogenicity predictions. The factors that determine the robustness and evolvability of proteins are still largely unknown. In this work the authors show that different secondary structure elements of proteins (helices and strands) differ in their ability to tolerate mutations, and demonstrate that it is caused by differences in the number of non-covalent residue interactions within these secondary structure units. The results suggest that engineering de novo all-alpha proteins should be easier than all-beta ones, as more sequences can to fold to the same topology. Additionally, secondary structure can be used to improve current methods of pathogenicity predictions; mutations that change secondary structure are more likely to be pathogenic than mutations that do not, due to their strong destabilizing effect on protein structure.
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Affiliation(s)
- György Abrusán
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom
- Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Temesvári krt. 62, Hungary
- * E-mail:
| | - Joseph A. Marsh
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom
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80
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Kawabata T. HOMCOS: an updated server to search and model complex 3D structures. ACTA ACUST UNITED AC 2016; 17:83-99. [PMID: 27522608 PMCID: PMC5274653 DOI: 10.1007/s10969-016-9208-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 08/05/2016] [Indexed: 01/18/2023]
Abstract
The HOMCOS server (http://homcos.pdbj.org) was updated for both searching and modeling the 3D complexes for all molecules in the PDB. As compared to the previous HOMCOS server, the current server targets all of the molecules in the PDB including proteins, nucleic acids, small compounds and metal ions. Their binding relationships are stored in the database. Five services are available for users. For the services “Modeling a Homo Protein Multimer” and “Modeling a Hetero Protein Multimer”, a user can input one or two proteins as the queries, while for the service “Protein-Compound Complex”, a user can input one chemical compound and one protein. The server searches similar molecules by BLAST and KCOMBU. Based on each similar complex found, a simple sequence-replaced model is quickly generated by replacing the residue names and numbers with those of the query protein. A target compound is flexibly superimposed onto the template compound using the program fkcombu. If monomeric 3D structures are input as the query, then template-based docking can be performed. For the service “Searching Contact Molecules for a Query Protein”, a user inputs one protein sequence as the query, and then the server searches for its homologous proteins in PDB and summarizes their contacting molecules as the predicted contacting molecules. The results are summarized in “Summary Bars” or “Site Table”display. The latter shows the results as a one-site-one-row table, which is useful for annotating the effects of mutations. The service “Searching Contact Molecules for a Query Compound” is also available.
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Affiliation(s)
- Takeshi Kawabata
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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81
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Metherell LA, Guerra-Assunção JA, Sternberg MJ, David A. Three-Dimensional Model of Human Nicotinamide Nucleotide Transhydrogenase (NNT) and Sequence-Structure Analysis of its Disease-Causing Variations. Hum Mutat 2016; 37:1074-84. [PMID: 27459240 PMCID: PMC5026163 DOI: 10.1002/humu.23046] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 06/23/2016] [Accepted: 06/28/2016] [Indexed: 12/22/2022]
Abstract
Defective mitochondrial proteins are emerging as major contributors to human disease. Nicotinamide nucleotide transhydrogenase (NNT), a widely expressed mitochondrial protein, has a crucial role in the defence against oxidative stress. NNT variations have recently been reported in patients with familial glucocorticoid deficiency (FGD) and in patients with heart failure. Moreover, knockout animal models suggest that NNT has a major role in diabetes mellitus and obesity. In this study, we used experimental structures of bacterial transhydrogenases to generate a structural model of human NNT (H‐NNT). Structure‐based analysis allowed the identification of H‐NNT residues forming the NAD binding site, the proton canal and the large interaction site on the H‐NNT dimer. In addition, we were able to identify key motifs that allow conformational changes adopted by domain III in relation to its functional status, such as the flexible linker between domains II and III and the salt bridge formed by H‐NNT Arg882 and Asp830. Moreover, integration of sequence and structure data allowed us to study the structural and functional effect of deleterious amino acid substitutions causing FGD and left ventricular non‐compaction cardiomyopathy. In conclusion, interpretation of the function–structure relationship of H‐NNT contributes to our understanding of mitochondrial disorders.
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Affiliation(s)
- Louise A Metherell
- Centre for Endocrinology, William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - José Afonso Guerra-Assunção
- Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, UK
| | - Michael J Sternberg
- Centre for Integrative System Biology and Bioinformatics, Imperial College London, London, UK
| | - Alessia David
- Centre for Integrative System Biology and Bioinformatics, Imperial College London, London, UK.
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82
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Shirian J, Sharabi O, Shifman JM. Cold Spots in Protein Binding. Trends Biochem Sci 2016; 41:739-745. [PMID: 27477052 DOI: 10.1016/j.tibs.2016.07.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 06/22/2016] [Accepted: 07/01/2016] [Indexed: 11/19/2022]
Abstract
Understanding the energetics and architecture of protein-binding interfaces is important for basic research and could potentially facilitate the design of novel binding domains for biotechnological applications. It is well accepted that a few key residues at binding interfaces (binding hot spots) are responsible for contributing most to the free energy of binding. In this opinion article, we introduce a new concept of 'binding cold spots', or interface positions occupied by suboptimal amino acids. Such positions exhibit a potential for affinity enhancement through various mutations. We give several examples of cold spots from different protein-engineering studies and argue that identification of such positions is crucial for studies of protein evolution and protein design.
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Affiliation(s)
- Jason Shirian
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Oz Sharabi
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
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83
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Martelli PL, Fariselli P, Savojardo C, Babbi G, Aggazio F, Casadio R. Large scale analysis of protein stability in OMIM disease related human protein variants. BMC Genomics 2016; 17 Suppl 2:397. [PMID: 27356511 PMCID: PMC4928156 DOI: 10.1186/s12864-016-2726-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Background Modern genomic techniques allow to associate several Mendelian human diseases to single residue variations in different proteins. Molecular mechanisms explaining the relationship among genotype and phenotype are still under debate. Change of protein stability upon variation appears to assume a particular relevance in annotating whether a single residue substitution can or cannot be associated to a given disease. Thermodynamic properties of human proteins and of their disease related variants are lacking. In the present work, we take advantage of the available three dimensional structure of human proteins for predicting the role of disease related variations on the perturbation of protein stability. Results We develop INPS3D, a new predictor based on protein structure for computing the effect of single residue variations on protein stability (ΔΔG), scoring at the state-of-the-art (Pearson’s correlation value of the regression is equal to 0.72 with mean standard error of 1.15 kcal/mol on a blind test set comprising 351 variations in 60 proteins). We then filter 368 OMIM disease related proteins known with atomic resolution (where the three dimensional structure covers at least 70 % of the sequence) with 4717 disease related single residue variations and 685 polymorphisms without clinical consequence. We find that the effect on protein stability of disease related variations is larger than the effect of polymorphisms: in particular, by setting to |1 kcal/mol| the threshold between perturbing and not perturbing variations of the protein stability, about 44 % of disease related variations and 20 % of polymorphisms are predicted with |ΔΔG| > 1 kcal/mol, respectively. A consistent fraction of OMIM disease related variations is however predicted to promote |ΔΔG| ≤ 1 kcal/mol and we focus here on detecting features that can be associated to the thermodynamic property of the protein variant. Our analysis reveals that some 47 % of disease related variations promoting |ΔΔG| ≤ 1 are located in solvent exposed sites of the protein structure. We also find that the increase of the fraction of variations that in proteins are predicted with |ΔΔG| ≤ 1 kcal/mol, partially relates with the increasing number of the protein interacting partners, corroborating the notion that disease related, non-perturbing variations are likely to impair protein-protein interaction (70 % of the disease causing variations, with high accessible surface are indeed predicted in interacting sites). The set of OMIM surface accessible variations with |ΔΔG| ≤ 1 kcal/mol and located in interaction sites are 23 % of the total in 161 proteins. Among these, 43 proteins with some 327 disease causing variations are involved in signalling, structural biological processes, development and differentiation. Conclusions We compute the effect of disease causing variations on protein stability with INPS3D, a new state-of-the-art tool for predicting the change in ΔΔG value associated to single residue substitution in protein structures. The analysis indicates that OMIM disease related variations in proteins promote a much larger effect on protein stability than polymorphisms non-associated to diseases. Disease related variations with a slight effect on protein stability (|ΔΔG| < 1 kcal/mol) frequently occur at the protein accessible surface suggesting that they are located in protein-protein interactions patches in putative human biological functional networks. The hypothesis is corroborated by proving that proteins with many disease related variations that slightly perturb protein stability are on average more connected in the human physical interactome (IntAct) than proteins with variations predicted with |ΔΔG| > 1 kcal/mol.
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Affiliation(s)
- Pier Luigi Martelli
- Biocomputing Group, University of Bologna, Via San Giacomo 9/2, 40126, Bologna, Italy. .,Department BiGeA, University of Bologna, Via Selmi 3, 40126, Bologna, Italy.
| | - Piero Fariselli
- Biocomputing Group, University of Bologna, Via San Giacomo 9/2, 40126, Bologna, Italy.,Department BCA, University of Padova, Viale Università 16, 35020, Legnaro (PD), Italy
| | - Castrense Savojardo
- Biocomputing Group, University of Bologna, Via San Giacomo 9/2, 40126, Bologna, Italy.,Department BiGeA, University of Bologna, Via Selmi 3, 40126, Bologna, Italy
| | - Giulia Babbi
- Biocomputing Group, University of Bologna, Via San Giacomo 9/2, 40126, Bologna, Italy.,Department BiGeA, University of Bologna, Via Selmi 3, 40126, Bologna, Italy
| | - Francesco Aggazio
- Biocomputing Group, University of Bologna, Via San Giacomo 9/2, 40126, Bologna, Italy.,Department BiGeA, University of Bologna, Via Selmi 3, 40126, Bologna, Italy
| | - Rita Casadio
- Biocomputing Group, University of Bologna, Via San Giacomo 9/2, 40126, Bologna, Italy.,Department BiGeA, University of Bologna, Via Selmi 3, 40126, Bologna, Italy
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84
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Engin HB, Kreisberg JF, Carter H. Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces. PLoS One 2016; 11:e0152929. [PMID: 27043210 PMCID: PMC4820104 DOI: 10.1371/journal.pone.0152929] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2016] [Accepted: 03/20/2016] [Indexed: 01/06/2023] Open
Abstract
Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10−4) and oncogenes (Odds Ratio 1.17, P-value < 10−3). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10−8). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer genes that encode proteins participating in interactions that are perturbed recurrently across tumors. In summary, mutation of specific protein interactions is an important contributor to tumor heterogeneity and may have important implications for clinical outcomes.
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Affiliation(s)
- H. Billur Engin
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, United States of America
| | - Jason F. Kreisberg
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, United States of America
| | - Hannah Carter
- Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, United States of America
- * E-mail:
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85
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Ames RM, Talavera D, Williams SG, Robertson DL, Lovell SC. Binding interface change and cryptic variation in the evolution of protein-protein interactions. BMC Evol Biol 2016; 16:40. [PMID: 26892785 PMCID: PMC4758157 DOI: 10.1186/s12862-016-0608-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 02/02/2016] [Indexed: 12/03/2022] Open
Abstract
Background Physical interactions between proteins are essential for almost all biological functions and systems. To understand the evolution of function it is therefore important to understand the evolution of molecular interactions. Of key importance is the evolution of binding specificity, the set of interactions made by a protein, since change in specificity can lead to “rewiring” of interaction networks. Unfortunately, the interfaces through which proteins interact are complex, typically containing many amino-acid residues that collectively must contribute to binding specificity as well as binding affinity, structural integrity of the interface and solubility in the unbound state. Results In order to study the relationship between interface composition and binding specificity, we make use of paralogous pairs of yeast proteins. Immediately after duplication these paralogues will have identical sequences and protein products that make an identical set of interactions. As the sequences diverge, we can correlate amino-acid change in the interface with any change in the specificity of binding. We show that change in interface regions correlates only weakly with change in specificity, and many variants in interfaces are functionally equivalent. We show that many of the residue replacements within interfaces are silent with respect to their contribution to binding specificity. Conclusions We conclude that such functionally-equivalent change has the potential to contribute to evolutionary plasticity in interfaces by creating cryptic variation, which in turn may provide the raw material for functional innovation and coevolution. Electronic supplementary material The online version of this article (doi:10.1186/s12862-016-0608-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ryan M Ames
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK. .,Current address: Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, RILD Level 3, Exeter, EX2 5DW, UK.
| | - David Talavera
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK. .,Current address: Institute of Cardiovascular Sciences, Faculty of Medical and Human Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
| | - Simon G Williams
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK. .,Current address: Institute of Cardiovascular Sciences, Faculty of Medical and Human Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
| | - David L Robertson
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
| | - Simon C Lovell
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
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86
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Aftabi Y, Colagar AH, Mehrnejad F. An in silico approach to investigate the source of the controversial interpretations about the phenotypic results of the human AhR-gene G1661A polymorphism. J Theor Biol 2016; 393:1-15. [PMID: 26776670 DOI: 10.1016/j.jtbi.2016.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Revised: 12/11/2015] [Accepted: 01/01/2016] [Indexed: 12/21/2022]
Abstract
Aryl hydrocarbon receptor (AhR) acts as an enhancer binding ligand-activated intracellular receptor. Chromatin remodeling components and general transcription factors such as TATA-binding protein (TBP) are evoked on AhR-target genes by interaction with its flexible transactivation domain (TAD). AhR-G1661A single nucleotide polymorphism (SNP: rs2066853) causes an arginine to lysine substitution in the acidic sub-domain of TAD at position 554 (R554K). Although, numerous studies associate the SNP with some abnormalities such as cancer, other reliable investigations refuse the associations. Consequently, the interpretation of the phenotypic results of G1661A-transition has been controversial. In this study, an in silico analysis were performed to investigate the possible effects of the transition on AhR-mRNA, protein structure, interaction properties and modifications. The analysis revealed that the R554K substitution affects secondary structure and solvent accessibility of adjacent residues. Also, it causes to decreasing of the AhR stability; altering the hydropathy features of the local sequence and changing the pattern of the residues at the binding site of the TAD-acidic sub-domain. Generating of new sites for ubiquitination and acetylation for AhR-K554 variant respectively at positions 544 and 560 was predicted. Our findings intensify the idea that the AhR-G1661A transition may affects AhR-TAD interactions, especially with the TBP, which influence AhR-target genes expression. However, the previously reported flexibility of the modular TAD could act as an intervening factor, moderate the SNP effects and causes distinct outcomes in different individuals and tissues.
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Affiliation(s)
- Younes Aftabi
- Department of Molecular and Cell Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, Post Code: 47416-95447, Mazandaran, Iran
| | - Abasalt Hosseinzadeh Colagar
- Department of Molecular and Cell Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, Post Code: 47416-95447, Mazandaran, Iran.
| | - Faramarz Mehrnejad
- Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, P.O. Box: 14395-1561, Tehran, Iran
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87
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Hu G, Xiao F, Li Y, Li Y, Vongsangnak W. Protein-Protein Interface and Disease: Perspective from Biomolecular Networks. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:57-74. [PMID: 27928579 DOI: 10.1007/10_2016_40] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Protein-protein interactions are involved in many important biological processes and molecular mechanisms of disease association. Structural studies of interfacial residues in protein complexes provide information on protein-protein interactions. Characterizing protein-protein interfaces, including binding sites and allosteric changes, thus pose an imminent challenge. With special focus on protein complexes, approaches based on network theory are proposed to meet this challenge. In this review we pay attention to protein-protein interfaces from the perspective of biomolecular networks and their roles in disease. We first describe the different roles of protein complexes in disease through several structural aspects of interfaces. We then discuss some recent advances in predicting hot spots and communication pathway analysis in terms of amino acid networks. Finally, we highlight possible future aspects of this area with respect to both methodology development and applications for disease treatment.
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Affiliation(s)
- Guang Hu
- Center for Systems Biology, School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China.
| | - Fei Xiao
- School of Basic Medicine and Biological Sciences, Medical College of Soochow University, Suzhou, 215123, China
| | - Yuqian Li
- School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuan Li
- Center for Systems Biology, School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Wanwipa Vongsangnak
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
- Computational Biomodelling Laboratory for Agricultural Science and Technology (CBLAST), Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
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88
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Kumar A, Butler BM, Kumar S, Ozkan SB. Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine. Curr Opin Struct Biol 2015; 35:135-42. [PMID: 26684487 PMCID: PMC4856467 DOI: 10.1016/j.sbi.2015.11.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 11/03/2015] [Accepted: 11/05/2015] [Indexed: 01/08/2023]
Abstract
Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine.
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Affiliation(s)
- Avishek Kumar
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, AZ 85281, United States
| | - Brandon M Butler
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, AZ 85281, United States
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, United States; Department of Biology, Temple University, Philadelphia, PA 19122, United States; Center for Genomic Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - S Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, AZ 85281, United States.
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89
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Tsuji T, Yoda T, Shirai T. Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling. Sci Rep 2015; 5:16341. [PMID: 26549015 PMCID: PMC4637837 DOI: 10.1038/srep16341] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 10/09/2015] [Indexed: 11/30/2022] Open
Abstract
Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures.
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Affiliation(s)
- Toshiyuki Tsuji
- Nagahama Institute of Bio-Science and Technology, and Japan Science and Technology Agency, Bioinformatics Research Division, Nagahama, Shiga 526-0829, Japan
| | - Takao Yoda
- Nagahama Institute of Bio-Science and Technology, and Japan Science and Technology Agency, Bioinformatics Research Division, Nagahama, Shiga 526-0829, Japan
| | - Tsuyoshi Shirai
- Nagahama Institute of Bio-Science and Technology, and Japan Science and Technology Agency, Bioinformatics Research Division, Nagahama, Shiga 526-0829, Japan
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90
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Abstract
The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype.
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Affiliation(s)
- Emily Bowler
- Centre for Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Zhenghe Wang
- Department of Genetics and Genome Science, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Rob M. Ewing
- Centre for Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK
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91
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David A, Sternberg MJE. The Contribution of Missense Mutations in Core and Rim Residues of Protein-Protein Interfaces to Human Disease. J Mol Biol 2015; 427:2886-98. [PMID: 26173036 PMCID: PMC4548493 DOI: 10.1016/j.jmb.2015.07.004] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 06/19/2015] [Accepted: 07/06/2015] [Indexed: 01/21/2023]
Abstract
Missense mutations at protein–protein interaction sites, called interfaces, are important contributors to human disease. Interfaces are non-uniform surface areas characterized by two main regions, “core” and “rim”, which differ in terms of evolutionary conservation and physicochemical properties. Moreover, within interfaces, only a small subset of residues (“hot spots”) is crucial for the binding free energy of the protein–protein complex. We performed a large-scale structural analysis of human single amino acid variations (SAVs) and demonstrated that disease-causing mutations are preferentially located within the interface core, as opposed to the rim (p < 0.01). In contrast, the interface rim is significantly enriched in polymorphisms, similar to the remaining non-interacting surface. Energetic hot spots tend to be enriched in disease-causing mutations compared to non-hot spots (p = 0.05), regardless of their occurrence in core or rim residues. For individual amino acids, the frequency of substitution into a polymorphism or disease-causing mutation differed to other amino acids and was related to its structural location, as was the type of physicochemical change introduced by the SAV. In conclusion, this study demonstrated the different distribution and properties of disease-causing SAVs and polymorphisms within different structural regions and in relation to the energetic contribution of amino acid in protein–protein interfaces, thus highlighting the importance of a structural system biology approach for predicting the effect of SAVs. Protein–protein interactions are fundamental in all biological processes. The distribution of deleterious and non-SAVs within protein interfaces is unknown. The distribution of deleterious SAVs differs within different interface structural regions. The distribution of SAVs differs in relation to interface residues energetic contribution. Structural analysis of protein complexes enhances the understanding of deleterious SAVs.
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Affiliation(s)
- Alessia David
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, SW7 2AZ London, United Kingdom.
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, SW7 2AZ London, United Kingdom.
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92
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Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification. BMC Med Genomics 2015; 8 Suppl 2:S7. [PMID: 26043787 PMCID: PMC4460713 DOI: 10.1186/1755-8794-8-s2-s7] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Post-translational modification (PTM) plays a crucial role in biological functions and corresponding disease developments. Discovering disease-associated non-synonymous SNPs (nsSNPs) altering PTM sites can help to estimate the various PTM candidates involved in diseases, therefore, an integrated analysis between SNPs, PTMs and diseases is necessary. However, only a few types of PTMs affected by nsSNPs have been studied without considering disease-association until now. In this study, we developed a new database called PTM-SNP which contains a comprehensive collection of human nsSNPs that affect PTM sites, together with disease information. Total 179,325 PTM-SNPs were collected by aligning missense SNPs and stop-gain SNPs on PTM sites (position 0) or their flanking region (position -7 to 7). Disease-associated SNPs from GWAS catalogs were also matched with detected PTM-SNP to find disease associated PTM-SNPs. Our result shows PTM-SNPs are highly associated with diseases, compared with other nsSNP sites and functional classes including near gene, intron and so on. PTM-SNP can provide an insight about discovering important PTMs involved in the diseases easily through the web site. PTM-SNP is freely available at http://gcode.kaist.ac.kr/ptmsnp.
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93
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Butler BM, Gerek ZN, Kumar S, Ozkan SB. Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association. Proteins 2015; 83:428-35. [PMID: 25546381 DOI: 10.1002/prot.24748] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Revised: 11/20/2014] [Accepted: 12/10/2014] [Indexed: 12/12/2022]
Abstract
Recent studies have shown that the protein interface sites between individual monomeric units in biological assemblies are enriched in disease-associated non-synonymous single nucleotide variants (nsSNVs). To elucidate the mechanistic underpinning of this observation, we investigated the conformational dynamic properties of protein interface sites through a site-specific structural dynamic flexibility metric (dfi) for 333 multimeric protein assemblies. dfi measures the dynamic resilience of a single residue to perturbations that occurred in the rest of the protein structure and identifies sites contributing the most to functionally critical dynamics. Analysis of dfi profiles of over a thousand positions harboring variation revealed that amino acid residues at interfaces have lower average dfi (31%) than those present at non-interfaces (50%), which means that protein interfaces have less dynamic flexibility. Interestingly, interface sites with disease-associated nsSNVs have significantly lower average dfi (23%) as compared to those of neutral nsSNVs (42%), which directly relates structural dynamics to functional importance. We found that less conserved interface positions show much lower dfi for disease nsSNVs as compared to neutral nsSNVs. In this case, dfi is better as compared to the accessible surface area metric, which is based on the static protein structure. Overall, our proteome-wide conformational dynamic analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease.
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94
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ENGIN HBILLUR, HOFREE MATAN, CARTER HANNAH. Identifying mutation specific cancer pathways using a structurally resolved protein interaction network. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2015; 20:84-95. [PMID: 25592571 PMCID: PMC4299875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Here we present a method for extracting candidate cancer pathways from tumor 'omics data while explicitly accounting for diverse consequences of mutations for protein interactions. Disease-causing mutations are frequently observed at either core or interface residues mediating protein interactions. Mutations at core residues frequently destabilize protein structure while mutations at interface residues can specifically affect the binding energies of protein-protein interactions. As a result, mutations in a protein may result in distinct interaction profiles and thus have different phenotypic consequences. We describe a protein structure-guided pipeline for extracting interacting protein sets specific to a particular mutation. Of 59 cancer genes with 3D co-complexed structures in the Protein Data Bank, 43 showed evidence of mutations with different functional consequences. Literature survey reciprocated functional predictions specific to distinct mutations on APC, ATRX, BRCA1, CBL and HRAS. Our analysis suggests that accounting for mutation-specific perturbations to cancer pathways will be essential for personalized cancer therapy.
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Affiliation(s)
- H. BILLUR ENGIN
- School of Medicine, University of California San Diego, 9500 Gilman Dr. San Diego, CA 92093, USA
| | - MATAN HOFREE
- Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Dr. San Diego, CA 92093, USA
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95
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Abstract
The assembly of individual proteins into functional complexes is fundamental to nearly all biological processes. In recent decades, many thousands of homomeric and heteromeric protein complex structures have been determined, greatly improving our understanding of the fundamental principles that control symmetric and asymmetric quaternary structure organization. Furthermore, our conception of protein complexes has moved beyond static representations to include dynamic aspects of quaternary structure, including conformational changes upon binding, multistep ordered assembly pathways, and structural fluctuations occurring within fully assembled complexes. Finally, major advances have been made in our understanding of protein complex evolution, both in reconstructing evolutionary histories of specific complexes and in elucidating general mechanisms that explain how quaternary structure tends to evolve. The evolution of quaternary structure occurs via changes in self-assembly state or through the gain or loss of protein subunits, and these processes can be driven by both adaptive and nonadaptive influences.
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Affiliation(s)
- Joseph A Marsh
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, United Kingdom;
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96
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Betts MJ, Lu Q, Jiang Y, Drusko A, Wichmann O, Utz M, Valtierra-Gutiérrez IA, Schlesner M, Jaeger N, Jones DT, Pfister S, Lichter P, Eils R, Siebert R, Bork P, Apic G, Gavin AC, Russell RB. Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions. Nucleic Acids Res 2014; 43:e10. [PMID: 25392414 PMCID: PMC4333368 DOI: 10.1093/nar/gku1094] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein–protein, protein–nucleic acid and a subset of protein–chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.
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Affiliation(s)
- Matthew J Betts
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Qianhao Lu
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - YingYing Jiang
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Armin Drusko
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Oliver Wichmann
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Mathias Utz
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Ilse A Valtierra-Gutiérrez
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Matthias Schlesner
- Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Natalie Jaeger
- Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - David T Jones
- Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Stefan Pfister
- Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Peter Lichter
- Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Roland Eils
- Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB), University of Heidelberg, Heidelberg, Germany
| | - Reiner Siebert
- Institut für Humangenetik, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Arnold Heller Straße 3, 24105 Kiel, Germany
| | - Peer Bork
- EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Gordana Apic
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Cambridge Cell Networks Ltd, St John's Innovation Centre, Cowley Road, CB3 0WS, Cambridge, UK
| | | | - Robert B Russell
- Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
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97
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Cukuroglu E, Engin HB, Gursoy A, Keskin O. Hot spots in protein–protein interfaces: Towards drug discovery. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:165-73. [DOI: 10.1016/j.pbiomolbio.2014.06.003] [Citation(s) in RCA: 113] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Revised: 05/30/2014] [Accepted: 06/12/2014] [Indexed: 11/16/2022]
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98
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Berliner N, Teyra J, Çolak R, Garcia Lopez S, Kim PM. Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation. PLoS One 2014; 9:e107353. [PMID: 25243403 PMCID: PMC4170975 DOI: 10.1371/journal.pone.0107353] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 07/21/2014] [Indexed: 12/04/2022] Open
Abstract
Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.
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Affiliation(s)
- Niklas Berliner
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
| | - Recep Çolak
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Sebastian Garcia Lopez
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
- Universidad Nacional de Colombia, Manizales, Colombia
| | - Philip M. Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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99
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Sudha G, Nussinov R, Srinivasan N. An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:141-50. [PMID: 25077409 DOI: 10.1016/j.pbiomolbio.2014.07.004] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 07/13/2014] [Indexed: 12/20/2022]
Abstract
Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.
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Affiliation(s)
- Govindarajan Sudha
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., National Cancer Institute, Frederick, MD 21702, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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100
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Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine. ACTA ACUST UNITED AC 2014. [DOI: 10.1155/2014/471836] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.
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