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Parvathy J, Yazhini A, Srinivasan N, Sowdhamini R. Interfacial residues in protein-protein complexes are in the eyes of the beholder. Proteins 2024; 92:509-528. [PMID: 37982321 DOI: 10.1002/prot.26628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 11/21/2023]
Abstract
Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.
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Affiliation(s)
- Jayadevan Parvathy
- Interdisciplinary Mathematical Sciences Initiative (IMI), Indian Institute of Science, Bangalore, India
- Molecular Biophysics Unit (MBU), Indian Institute of Science, Bangalore, India
| | | | | | - Ramanathan Sowdhamini
- Molecular Biophysics Unit (MBU), Indian Institute of Science, Bangalore, India
- National Center for Biological Sciences (NCBS), Bangalore, India
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2
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Guven-Maiorov E, Sakakibara N, Ponnamperuma RM, Dong K, Matar H, King KE, Weinberg WC. Delineating functional mechanisms of the p53/p63/p73 family of transcription factors through identification of protein-protein interactions using interface mimicry. Mol Carcinog 2022; 61:629-642. [PMID: 35560453 PMCID: PMC9949960 DOI: 10.1002/mc.23405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 11/08/2022]
Abstract
Members of the p53 family of transcription factors-p53, p63, and p73-share a high degree of homology; however, members can be activated in response to different stimuli, perform distinct (sometimes opposing) roles and are expressed in different tissues. The level of complexity is increased further by the transcription of multiple isoforms of each homolog, which may interact or interfere with each other and can impact cellular outcome. Proteins perform their functions through interacting with other proteins (and/or with nucleic acids). Therefore, identification of the interactors of a protein and how they interact in 3D is essential to fully comprehend their roles. By utilizing an in silico protein-protein interaction prediction method-HMI-PRED-we predicted interaction partners of p53 family members and modeled 3D structures of these protein interaction complexes. This method recovered experimentally known interactions while identifying many novel candidate partners. We analyzed the similarities and differences observed among the interaction partners to elucidate distinct functions of p53 family members and provide examples of how this information may yield mechanistic insight to explain their overlapping versus distinct/opposing outcomes in certain contexts. While some interaction partners are common to p53, p63, and p73, the majority are unique to each member. Nevertheless, most of the enriched pathways associated with these partners are common to all members, indicating that the members target the same biological pathways but through unique mediators. p63 and p73 have more common enriched pathways compared to p53, supporting their similar developmental roles in different tissues.
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Affiliation(s)
- Emine Guven-Maiorov
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.,National Cancer Institute, Bethesda, MD, United States.,Postal and email addresses of corresponding authors FDA/CDER/OPQ/OBP, Building 52-72/2306, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States, ,
| | - Nozomi Sakakibara
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Roshini M. Ponnamperuma
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Kun Dong
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.,National Cancer Institute, Bethesda, MD, United States
| | - Hector Matar
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Kathryn E. King
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Wendy C. Weinberg
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.,Postal and email addresses of corresponding authors FDA/CDER/OPQ/OBP, Building 52-72/2306, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States, ,
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3
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Elhabashy H, Merino F, Alva V, Kohlbacher O, Lupas AN. Exploring protein-protein interactions at the proteome level. Structure 2022; 30:462-475. [DOI: 10.1016/j.str.2022.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/26/2021] [Accepted: 02/02/2022] [Indexed: 02/08/2023]
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Guven-Maiorov E, Tsai CJ, Nussinov R. Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry. Front Oncol 2019; 9:1236. [PMID: 31803618 PMCID: PMC6872517 DOI: 10.3389/fonc.2019.01236] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/28/2019] [Indexed: 01/17/2023] Open
Abstract
Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.
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Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
- Department of Human Genetics and Molecular Medicine, Sackler Institute of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. Interface-Based Structural Prediction of Novel Host-Pathogen Interactions. Methods Mol Biol 2019; 1851:317-335. [PMID: 30298406 PMCID: PMC8192064 DOI: 10.1007/978-1-4939-8736-8_18] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.
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Affiliation(s)
- Emine Guven-Maiorov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Buyong Ma
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
- Department of Human Genetics and Molecular Medicine, Sackler Inst. of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
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Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer. J Mol Biol 2017; 429:3925-3941. [PMID: 29106933 PMCID: PMC7906438 DOI: 10.1016/j.jmb.2017.10.023] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/16/2017] [Accepted: 10/16/2017] [Indexed: 02/07/2023]
Abstract
There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.
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Affiliation(s)
- Emine Guven-Maiorov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Buyong Ma
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, 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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Abstract
Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.
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Affiliation(s)
- Emine Guven-Maiorov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States of America
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States of America
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc. Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, United States of America
- Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Tuncbag N, Keskin O, Nussinov R, Gursoy A. Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information. Methods Mol Biol 2017; 1558:255-270. [PMID: 28150242 PMCID: PMC7900904 DOI: 10.1007/978-1-4939-6783-4_12] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Structural details of protein interactions are invaluable to the understanding of cellular processes. However, the identification of interactions at atomic resolution is a continuing challenge in the systems biology era. Although the number of structurally resolved complexes in the Protein Databank increases exponentially, the complexes only cover a small portion of the known structural interactome. In this chapter, we review the PRISM system that is a protein-protein interaction (PPI) prediction tool-its rationale, principles, and applications. We further discuss its extensions to discover the effect of single residue mutations, to model large protein assemblies, to improve its performance by exploiting conformational protein ensembles, and to reconstruct large PPI networks or pathway maps.
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Affiliation(s)
- Nurcan Tuncbag
- Graduate School of Informatics, Department of Health Informatics, Middle East Technical University, 06800, Ankara, Turkey
| | - Ozlem Keskin
- Chemical and Biological Engineering, College of Engineering, Koc University, 34450, Istanbul, Turkey.
- Center for Computational Biology and Bioinformatics, Koc University, Rumelifeneri Yolu Sariyer, 34450, Istanbul, Turkey.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory, National Cancer Institute, Frederick, MD, 21702, USA
- Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Sackler Institute of Molecular Medicine, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics, Koc University, Rumelifeneri Yolu Sariyer, 34450, Istanbul, Turkey.
- Computer Engineering, College of Engineering, Koc University, 34450, Istanbul, Turkey.
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Neutralization mechanism of a highly potent antibody against Zika virus. Nat Commun 2016; 7:13679. [PMID: 27882950 PMCID: PMC5123051 DOI: 10.1038/ncomms13679] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 10/24/2016] [Indexed: 12/31/2022] Open
Abstract
The rapid spread of Zika virus (ZIKV), which causes microcephaly and Guillain-Barré syndrome, signals an urgency to identify therapeutics. Recent efforts to rescreen dengue virus human antibodies for ZIKV cross-neutralization activity showed antibody C10 as one of the most potent. To investigate the ability of the antibody to block fusion, we determined the cryoEM structures of the C10-ZIKV complex at pH levels mimicking the extracellular (pH8.0), early (pH6.5) and late endosomal (pH5.0) environments. The 4.0 Å resolution pH8.0 complex structure shows that the antibody binds to E proteins residues at the intra-dimer interface, and the virus quaternary structure-dependent inter-dimer and inter-raft interfaces. At pH6.5, antibody C10 locks all virus surface E proteins, and at pH5.0, it locks the E protein raft structure, suggesting that it prevents the structural rearrangement of the E proteins during the fusion event-a vital step for infection. This suggests antibody C10 could be a good therapeutic candidate.
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10
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Guven-Maiorov E, Tsai CJ, Nussinov R. Pathogen mimicry of host protein-protein interfaces modulates immunity. Semin Cell Dev Biol 2016; 58:136-45. [DOI: 10.1016/j.semcdb.2016.06.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 06/02/2016] [Accepted: 06/06/2016] [Indexed: 12/21/2022]
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Krull F, Korff G, Elghobashi-Meinhardt N, Knapp EW. ProPairs: A Data Set for Protein–Protein Docking. J Chem Inf Model 2015; 55:1495-507. [DOI: 10.1021/acs.jcim.5b00082] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Florian Krull
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
| | - Gerrit Korff
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
| | - Nadia Elghobashi-Meinhardt
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
| | - Ernst-Walter Knapp
- Institute of Chemistry and
Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, 14195 Berlin, Germany
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12
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Li Z, He Y, Wong L, Li J. Burial Level Change Defines a High Energetic Relevance for Protein Binding Interfaces. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:410-421. [PMID: 26357227 DOI: 10.1109/tcbb.2014.2361355] [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
Protein-protein interfaces defined through atomic contact or solvent accessibility change are widely adopted in structural biology studies. But, these definitions cannot precisely capture energetically important regions at protein interfaces. The burial depth of an atom in a protein is related to the atom's energy. This work investigates how closely the change in burial level of an atom/residue upon complexation is related to the binding. Burial level change is different from burial level itself. An atom deeply buried in a monomer with a high burial level may not change its burial level after an interaction and it may have little burial level change. We hypothesize that an interface is a region of residues all undergoing burial level changes after interaction. By this definition, an interface can be decomposed into an onion-like structure according to the burial level change extent. We found that our defined interfaces cover energetically important residues more precisely, and that the binding free energy of an interface is distributed progressively from the outermost layer to the core. These observations are used to predict binding hot spots. Our approach's F-measure performance on a benchmark dataset of alanine mutagenesis residues is much superior or similar to those by complicated energy modeling or machine learning approaches.
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Cheng S, Zhang Y, Brooks CL. PCalign: a method to quantify physicochemical similarity of protein-protein interfaces. BMC Bioinformatics 2015; 16:33. [PMID: 25638036 PMCID: PMC4339745 DOI: 10.1186/s12859-015-0471-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 01/15/2015] [Indexed: 02/07/2023] Open
Abstract
Background Structural comparison of protein-protein interfaces provides valuable insights into the functional relationship between proteins, which may not solely arise from shared evolutionary origin. A few methods that exist for such comparative studies have focused on structural models determined at atomic resolution, and may miss out interesting patterns present in large macromolecular complexes that are typically solved by low-resolution techniques. Results We developed a coarse-grained method, PCalign, to quantitatively evaluate physicochemical similarities between a given pair of protein-protein interfaces. This method uses an order-independent algorithm, geometric hashing, to superimpose the backbone atoms of a given pair of interfaces, and provides a normalized scoring function, PC-score, to account for the extent of overlap in terms of both geometric and chemical characteristics. We demonstrate that PCalign outperforms existing methods, and additionally facilitates comparative studies across models of different resolutions, which are not accommodated by existing methods. Furthermore, we illustrate potential application of our method to recognize interesting biological relationships masked by apparent lack of structural similarity. Conclusions PCalign is a useful method in recognizing shared chemical and spatial patterns among protein-protein interfaces. It outperforms existing methods for high-quality data, and additionally facilitates comparison across structural models with different levels of details with proven robustness against noise. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0471-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shanshan Cheng
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA. .,Department of Biological Chemistry, Medical School, University of Michigan, Ann Arbor, MI, USA.
| | - Charles L Brooks
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA. .,Department of Chemistry, University of Michigan, Ann Arbor, MI, USA. .,Biophysics Program, University of Michigan, Ann Arbor, MI, USA.
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Liu Z, Li Y, Han L, Li J, Liu J, Zhao Z, Nie W, Liu Y, Wang R. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics 2014; 31:405-12. [DOI: 10.1093/bioinformatics/btu626] [Citation(s) in RCA: 264] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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15
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Skolnick J, Gao M, Zhou H. On the role of physics and evolution in dictating protein structure and function. Isr J Chem 2014; 54:1176-1188. [PMID: 25484448 PMCID: PMC4255337 DOI: 10.1002/ijch.201400013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
How many of the structural and functional properties of proteins are inherent? Computer simulations provide a powerful tool to address this question. A series of studies on QS, quasi-spherical, compact polypeptides which lack any secondary structure; ART, artificial, proteins comprised of compact homopolypeptides with protein-like secondary structure; and PDB, native, single domain proteins shows that essentially all native global folds, pockets and protein-protein interfaces are in the ART library. This suggests that many protein properties are inherent and that evolution is involved in fine-tuning. The completeness of the space of ligand binding pockets and protein-protein interfaces suggests that promiscuous interactions are intrinsic to proteins and that the capacity to perform the biochemistry of life at low level does not require evolution. If so, this has profound consequences for the origin of life.
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Affiliation(s)
- Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
| | - Mu Gao
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street NW, Atlanta, GA 30318, USA
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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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17
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Lua RC, Marciano DC, Katsonis P, Adikesavan AK, Wilkins AD, Lichtarge O. Prediction and redesign of protein-protein interactions. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 116:194-202. [PMID: 24878423 DOI: 10.1016/j.pbiomolbio.2014.05.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 05/02/2014] [Accepted: 05/17/2014] [Indexed: 12/14/2022]
Abstract
Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.
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Affiliation(s)
- Rhonald C Lua
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - David C Marciano
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Anbu K Adikesavan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Angela D Wilkins
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX 77030, USA.
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18
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Intermolecular β-strand networks avoid hub residues and favor low interconnectedness: a potential protection mechanism against chain dissociation upon mutation. PLoS One 2014; 9:e94745. [PMID: 24733378 PMCID: PMC3986249 DOI: 10.1371/journal.pone.0094745] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Accepted: 03/19/2014] [Indexed: 01/11/2023] Open
Abstract
Altogether few protein oligomers undergo a conformational transition to a state that impairs their function and leads to diseases. But when it happens, the consequences are not harmless and the so-called conformational diseases pose serious public health problems. Notorious examples are the Alzheimer's disease and some cancers associated with a conformational change of the amyloid precursor protein (APP) and of the p53 tumor suppressor, respectively. The transition is linked with the propensity of β-strands to aggregate into amyloid fibers. Nevertheless, a huge number of protein oligomers associate chains via β-strand interactions (intermolecular β-strand interface) without ever evolving into fibers. We analyzed the layout of 1048 intermolecular β-strand interfaces looking for features that could provide the β-strands resistance to conformational transitions. The interfaces were reconstructed as networks with the residues as the nodes and the interactions between residues as the links. The networks followed an exponential decay degree distribution, implying an absence of hubs and nodes with few links. Such layout provides robustness to changes. Few links per nodes do not restrict the choices of amino acids capable of making an interface and maintain high sequence plasticity. Few links reduce the “bonding” cost of making an interface. Finally, few links moderate the vulnerability to amino acid mutation because it entails limited communication between the nodes. This confines the effects of a mutation to few residues instead of propagating them to many residues via hubs. We propose that intermolecular β-strand interfaces are organized in networks that tolerate amino acid mutation to avoid chain dissociation, the first step towards fiber formation. This is tested by looking at the intermolecular β-strand network of the p53 tetramer.
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19
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Cukuroglu E, Gursoy A, Nussinov R, Keskin O. Non-redundant unique interface structures as templates for modeling protein interactions. PLoS One 2014; 9:e86738. [PMID: 24475173 PMCID: PMC3903793 DOI: 10.1371/journal.pone.0086738] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2013] [Accepted: 12/18/2013] [Indexed: 01/16/2023] Open
Abstract
Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.
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Affiliation(s)
- Engin Cukuroglu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
| | - Ruth Nussinov
- National Cancer Institute, Cancer and Inflammation Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey
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20
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Li Y, Liu Z, Han L, Li C, Wang R. Mining the Characteristic Interaction Patterns on Protein–Protein Binding Interfaces. J Chem Inf Model 2013; 53:2437-47. [DOI: 10.1021/ci400241s] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yan Li
- State
Key Laboratory of Bioorganic and Natural
Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
| | - Zhihai Liu
- State
Key Laboratory of Bioorganic and Natural
Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
| | - Li Han
- State
Key Laboratory of Bioorganic and Natural
Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
| | - Chengke Li
- State
Key Laboratory of Bioorganic and Natural
Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
| | - Renxiao Wang
- State
Key Laboratory of Bioorganic and Natural
Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, People’s Republic of China
- State Key Laboratory of Quality Research
in Chinese
Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, People’s Republic of China
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21
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Kuzu G, Gursoy A, Nussinov R, Keskin O. Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale. J Proteome Res 2013; 12:2641-53. [PMID: 23590674 PMCID: PMC3685852 DOI: 10.1021/pr400006k] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than "blind" docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited an ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested "difficult" cases in a docking-benchmark data set, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26 to 66%, and 57% of the interactions were successfully predicted in an "unbiased" scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome scale. We constructed the structural network of ERK interacting proteins as a case study.
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Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
| | - Ruth Nussinov
- Basic Science Program, SAIC-Frederick, Inc. National Cancer Institute, Center for Cancer Research Nanobiology Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702
- Sackler Inst. of Molecular Medicine Department of Human Genetics and Molecular Medicine Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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22
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Beta atomic contacts: identifying critical specific contacts in protein binding interfaces. PLoS One 2013; 8:e59737. [PMID: 23630569 PMCID: PMC3632532 DOI: 10.1371/journal.pone.0059737] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Accepted: 02/21/2013] [Indexed: 11/19/2022] Open
Abstract
Specific binding between proteins plays a crucial role in molecular functions and biological processes. Protein binding interfaces and their atomic contacts are typically defined by simple criteria, such as distance-based definitions that only use some threshold of spatial distance in previous studies. These definitions neglect the nearby atomic organization of contact atoms, and thus detect predominant contacts which are interrupted by other atoms. It is questionable whether such kinds of interrupted contacts are as important as other contacts in protein binding. To tackle this challenge, we propose a new definition called beta (β) atomic contacts. Our definition, founded on the β-skeletons in computational geometry, requires that there is no other atom in the contact spheres defined by two contact atoms; this sphere is similar to the van der Waals spheres of atoms. The statistical analysis on a large dataset shows that β contacts are only a small fraction of conventional distance-based contacts. To empirically quantify the importance of β contacts, we design βACV, an SVM classifier with β contacts as input, to classify homodimers from crystal packing. We found that our βACV is able to achieve the state-of-the-art classification performance superior to SVM classifiers with distance-based contacts as input. Our βACV also outperforms several existing methods when being evaluated on several datasets in previous works. The promising empirical performance suggests that β contacts can truly identify critical specific contacts in protein binding interfaces. β contacts thus provide a new model for more precise description of atomic organization in protein quaternary structures than distance-based contacts.
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23
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Wilkinson IC, Fowler SB, Machiesky L, Miller K, Hayes DB, Adib M, Her C, Borrok MJ, Tsui P, Burrell M, Corkill DJ, Witt S, Lowe DC, Webster CI. Monovalent IgG4 molecules: immunoglobulin Fc mutations that result in a monomeric structure. MAbs 2013; 5:406-17. [PMID: 23567207 DOI: 10.4161/mabs.23941] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Antibodies have become the fastest growing class of biological therapeutics, in part due to their exquisite specificity and ability to modulate protein-protein interactions with a high biological potency. The relatively large size and bivalency of antibodies, however, limits their use as therapeutics in certain circumstances. Antibody fragments, such as single-chain variable fragments and antigen binding-fragments, have emerged as viable alternatives, but without further modifications these monovalent formats have reduced terminal serum half-lives because of their small size and lack of an Fc domain, which is required for FcRn-mediated recycling. Using rational engineering of the IgG4 Fc domain to disrupt key interactions at the CH3-CH3 interface, we identified a number of point mutations that abolish Fc dimerization and created half-antibodies, a novel monovalent antibody format that retains a monomeric Fc domain. Introduction of these mutations into an IgG1 framework also led to the creation of half-antibodies. These half-antibodies were shown to be soluble, thermodynamically stable and monomeric, characteristics that are favorable for use as therapeutic proteins. Despite significantly reduced FcRn binding in vitro, which suggests that avidity gains in a dimeric Fc are critical to optimal FcRn binding, this format demonstrated an increased terminal serum half-life compared with that expected for most alternative antibody fragments.
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Affiliation(s)
- Ian C Wilkinson
- MedImmune Ltd.; Department of Antibody Discovery and Protein Engineering; Cambridge, UK
| | - Susan B Fowler
- MedImmune Ltd.; Department of Antibody Discovery and Protein Engineering; Cambridge, UK
| | - Leeann Machiesky
- MedImmune LLC.; Departments of Antibody Discovery and Protein Engineering and Analytical Biochemistry; Gaithersburg, MD USA
| | - Kenneth Miller
- MedImmune LLC.; Departments of Antibody Discovery and Protein Engineering and Analytical Biochemistry; Gaithersburg, MD USA
| | - David B Hayes
- MedImmune LLC.; Departments of Antibody Discovery and Protein Engineering and Analytical Biochemistry; Gaithersburg, MD USA
| | - Morshed Adib
- MedImmune LLC.; Departments of Antibody Discovery and Protein Engineering and Analytical Biochemistry; Gaithersburg, MD USA
| | - Cheng Her
- MedImmune LLC.; Departments of Antibody Discovery and Protein Engineering and Analytical Biochemistry; Gaithersburg, MD USA
| | - M Jack Borrok
- MedImmune LLC.; Departments of Antibody Discovery and Protein Engineering and Analytical Biochemistry; Gaithersburg, MD USA
| | - Ping Tsui
- MedImmune LLC.; Departments of Antibody Discovery and Protein Engineering and Analytical Biochemistry; Gaithersburg, MD USA
| | - Matthew Burrell
- MedImmune Ltd.; Department of Antibody Discovery and Protein Engineering; Cambridge, UK
| | - Dominic J Corkill
- MedImmune Ltd.; Department of Antibody Discovery and Protein Engineering; Cambridge, UK
| | - Susanne Witt
- MedImmune Ltd.; Department of Antibody Discovery and Protein Engineering; Cambridge, UK
| | - David C Lowe
- MedImmune Ltd.; Department of Antibody Discovery and Protein Engineering; Cambridge, UK
| | - Carl I Webster
- MedImmune Ltd.; Department of Antibody Discovery and Protein Engineering; Cambridge, UK
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24
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Winter C, Henschel A, Tuukkanen A, Schroeder M. Protein interactions in 3D: From interface evolution to drug discovery. J Struct Biol 2012; 179:347-58. [DOI: 10.1016/j.jsb.2012.04.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Revised: 03/27/2012] [Accepted: 04/18/2012] [Indexed: 11/25/2022]
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25
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Liu Q, Wong L, Li J. Z-score biological significance of binding hot spots of protein interfaces by using crystal packing as the reference state. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2012; 1824:1457-67. [PMID: 22728649 DOI: 10.1016/j.bbapap.2012.05.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Revised: 05/12/2012] [Accepted: 05/31/2012] [Indexed: 11/19/2022]
Abstract
Characterization of binding hot spots of protein interfaces is a fundamental study in molecular biology. Many computational methods have been proposed to identify binding hot spots. However, there are few studies to assess the biological significance of binding hot spots. We introduce the notion of biological significance of a contact residue for capturing the probability of the residue occurring in or contributing to protein binding interfaces. We take a statistical Z-score approach to the assessment of the biological significance. The method has three main steps. First, the potential score of a residue is defined by using a knowledge-based potential function with relative accessible surface area calculations. A null distribution of this potential score is then generated from artifact crystal packing contacts. Finally, the Z-score significance of a contact residue with a specific potential score is determined according to this null distribution. We hypothesize that residues at binding hot spots have big absolute values of Z-score as they contribute greatly to binding free energy. Thus, we propose to use Z-score to predict whether a contact residue is a hot spot residue. Comparison with previously reported methods on two benchmark datasets shows that this Z-score method is mostly superior to earlier methods. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.
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Affiliation(s)
- Qian Liu
- BIRC, SCE, Nanyang Technological University, Singapore 639798, Singapore
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26
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Feverati G, Achoch M, Zrimi J, Vuillon L, Lesieur C. Beta-strand interfaces of non-dimeric protein oligomers are characterized by scattered charged residue patterns. PLoS One 2012; 7:e32558. [PMID: 22496732 PMCID: PMC3322119 DOI: 10.1371/journal.pone.0032558] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2011] [Accepted: 01/29/2012] [Indexed: 11/19/2022] Open
Abstract
Protein oligomers are formed either permanently, transiently or even by default. The protein chains are associated through intermolecular interactions constituting the protein interface. The protein interfaces of 40 soluble protein oligomers of stœchiometries above two are investigated using a quantitative and qualitative methodology, which analyzes the x-ray structures of the protein oligomers and considers their interfaces as interaction networks. The protein oligomers of the dataset share the same geometry of interface, made by the association of two individual β-strands (β-interfaces), but are otherwise unrelated. The results show that the β-interfaces are made of two interdigitated interaction networks. One of them involves interactions between main chain atoms (backbone network) while the other involves interactions between side chain and backbone atoms or between only side chain atoms (side chain network). Each one has its own characteristics which can be associated to a distinct role. The secondary structure of the β-interfaces is implemented through the backbone networks which are enriched with the hydrophobic amino acids favored in intramolecular β-sheets (MCWIV). The intermolecular specificity is provided by the side chain networks via positioning different types of charged residues at the extremities (arginine) and in the middle (glutamic acid and histidine) of the interface. Such charge distribution helps discriminating between sequences of intermolecular β-strands, of intramolecular β-strands and of β-strands forming β-amyloid fibers. This might open new venues for drug designs and predictive tool developments. Moreover, the β-strands of the cholera toxin B subunit interface, when produced individually as synthetic peptides, are capable of inhibiting the assembly of the toxin into pentamers. Thus, their sequences contain the features necessary for a β-interface formation. Such β-strands could be considered as ‘assemblons’, independent associating units, by homology to the foldons (independent folding unit). Such property would be extremely valuable in term of assembly inhibitory drug development.
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Affiliation(s)
| | - Mounia Achoch
- Université de Savoie, Annecy le Vieux Cedex, France
- Laboratoire de Chimie Bioorganique et Macromoléculaire (LCBM), Faculté des Sciences et Techniques-Guéliz, Université Cadi Ayyad, Marrakech, Maroc
| | - Jihad Zrimi
- Université de Savoie, Annecy le Vieux Cedex, France
- Laboratoire de Chimie Bioorganique et Macromoléculaire (LCBM), Faculté des Sciences et Techniques-Guéliz, Université Cadi Ayyad, Marrakech, Maroc
| | | | - Claire Lesieur
- Université de Savoie, Annecy le Vieux Cedex, France
- AGIM, Université Joseph Fourier, Archamps, France
- * E-mail:
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27
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Pang B, Zhao N, Korkin D, Shyu CR. Fast protein binding site comparisons using visual words representation. ACTA ACUST UNITED AC 2012; 28:1345-52. [PMID: 22492639 DOI: 10.1093/bioinformatics/bts138] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Finding geometrically similar protein binding sites is crucial for understanding protein functions and can provide valuable information for protein-protein docking and drug discovery. As the number of known protein-protein interaction structures has dramatically increased, a high-throughput and accurate protein binding site comparison method is essential. Traditional alignment-based methods can provide accurate correspondence between the binding sites but are computationally expensive. RESULTS In this article, we present a novel method for the comparisons of protein binding sites using a 'visual words' representation (PBSword). We first extract geometric features of binding site surfaces and build a vocabulary of visual words by clustering a large set of feature descriptors. We then describe a binding site surface with a high-dimensional vector that encodes the frequency of visual words, enhanced by the spatial relationships among them. Finally, we measure the similarity of binding sites by utilizing metric space operations, which provide speedy comparisons between protein binding sites. Our experimental results show that PBSword achieves a comparable classification accuracy to an alignment-based method and improves accuracy of a feature-based method by 36% on a non-redundant dataset. PBSword also exhibits a significant efficiency improvement over an alignment-based method.
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Affiliation(s)
- Bin Pang
- Informatics Institute, University of Missouri, Columbia, MO 65211, USA
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28
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Tuncbag N, Keskin O, Nussinov R, Gursoy A. Fast and accurate modeling of protein-protein interactions by combining template-interface-based docking with flexible refinement. Proteins 2012; 80:1239-49. [PMID: 22275112 PMCID: PMC7448677 DOI: 10.1002/prot.24022] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2011] [Revised: 11/29/2011] [Accepted: 12/13/2011] [Indexed: 11/06/2022]
Abstract
The similarity between folding and binding led us to posit the concept that the number of protein-protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein-protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge-based combinatorial method on the Docking Benchmark and show that it efficiently finds high-quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to "classical" docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large-scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high-quality models of protein-protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge-based methods will be increasingly used by the broad proteomics community.
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Affiliation(s)
- Nurcan Tuncbag
- Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, 34450 Sariyer, Istanbul, Turkey
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, 34450 Sariyer, Istanbul, Turkey
| | - Ruth Nussinov
- Basic Science Program, SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program, NCI-Frederick, Frederick, Maryland 21702
- Department of Human Genetics and Molecular Medicine, Sackler Institute of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, 34450 Sariyer, Istanbul, Turkey
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29
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Kuzu G, Keskin O, Gursoy A, Nussinov R. Expanding the conformational selection paradigm in protein-ligand docking. Methods Mol Biol 2012; 819:59-74. [PMID: 22183530 PMCID: PMC7455014 DOI: 10.1007/978-1-61779-465-0_5] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Conformational selection emerges as a theme in macromolecular interactions. Data validate it as a prevailing mechanism in protein-protein, protein-DNA, protein-RNA, and protein-small molecule drug recognition. This raises the question of whether this fundamental biomolecular binding mechanism can be used to improve drug docking and discovery. Actually, in practice this has already been taking place for some years in increasing numbers. Essentially, it argues for using not a single conformer, but an ensemble. The paradigm of conformational selection holds that because the ensemble is heterogeneous, within it there will be states whose conformation matches that of the ligand. Even if the population of this state is low, since it is favorable for binding the ligand, it will bind to it with a subsequent population shift toward this conformer. Here we suggest expanding it by first modeling all protein interactions in the cell by using Prism, an efficient motif-based protein-protein interaction modeling strategy, followed by ensemble generation. Such a strategy could be particularly useful for signaling proteins, which are major targets in drug discovery and bind multiple partners through a shared binding site, each with some-minor or major-conformational change.
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Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, Istanbul, Turkey
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30
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AUNG ZEYAR, TAN SOONHENG, NG SEEKIONG, TAN KIANLEE. PPiClust: EFFICIENT CLUSTERING OF 3D PROTEIN–PROTEIN INTERACTION INTERFACES. J Bioinform Comput Biol 2011; 6:415-33. [DOI: 10.1142/s0219720008003485] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2007] [Revised: 12/01/2007] [Accepted: 01/03/2008] [Indexed: 11/18/2022]
Abstract
The biological mechanisms through which proteins interact with one another are best revealed by studying the structural interfaces between interacting proteins. Protein–protein interfaces can be extracted from three-dimensional (3D) structural data of protein complexes and then clustered to derive biological insights. However, conventional protein interface clustering methods lack computational scalability and statistical support. In this work, we present a new method named "PPiClust" to systematically encode, cluster, and analyze similar 3D interface patterns in protein complexes efficiently. Experimental results showed that our method is effective in discovering visually consistent and statistically significant clusters of interfaces, and at the same time sufficiently time-efficient to be performed on a single computer. The interface clusters are also useful for uncovering the structural basis of protein interactions. Analysis of the resulting interface clusters revealed groups of structurally diverse proteins having similar interface patterns. We also found, in some of the interface clusters, the presence of well-known linear binding motifs which were noncontiguous in the primary sequences. These results suggest that PPiClust can discover not only statistically significant, but also biologically significant, protein interface clusters from protein complex structural data.
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Affiliation(s)
- ZEYAR AUNG
- Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore
| | - SOON-HENG TAN
- Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore
| | - SEE-KIONG NG
- Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore
| | - KIAN-LEE TAN
- School of Computing, National University of Singapore, Law Link, Singapore 117590, Singapore
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31
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Cukuroglu E, Gursoy A, Keskin O. HotRegion: a database of predicted hot spot clusters. Nucleic Acids Res 2011; 40:D829-33. [PMID: 22080558 PMCID: PMC3245113 DOI: 10.1093/nar/gkr929] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.
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Affiliation(s)
- Engin Cukuroglu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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Tuncbag N, Gursoy A, Nussinov R, Keskin O. Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nat Protoc 2011; 6:1341-54. [PMID: 21886100 PMCID: PMC7384353 DOI: 10.1038/nprot.2011.367] [Citation(s) in RCA: 206] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.
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Affiliation(s)
- Nurcan Tuncbag
- Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, Rumelifeneri Yolu, Sariyer Istanbul, Turkey
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Nguyen MN, Madhusudhan MS. Biological insights from topology independent comparison of protein 3D structures. Nucleic Acids Res 2011; 39:e94. [PMID: 21596786 PMCID: PMC3152366 DOI: 10.1093/nar/gkr348] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Comparing and classifying the three-dimensional (3D) structures of proteins is of crucial importance to molecular biology, from helping to determine the function of a protein to determining its evolutionary relationships. Traditionally, 3D structures are classified into groups of families that closely resemble the grouping according to their primary sequence. However, significant structural similarities exist at multiple levels between proteins that belong to these different structural families. In this study, we propose a new algorithm, CLICK, to capture such similarities. The method optimally superimposes a pair of protein structures independent of topology. Amino acid residues are represented by the Cartesian coordinates of a representative point (usually the Cα atom), side chain solvent accessibility, and secondary structure. Structural comparison is effected by matching cliques of points. CLICK was extensively benchmarked for alignment accuracy on four different sets: (i) 9537 pair-wise alignments between two structures with the same topology; (ii) 64 alignments from set (i) that were considered to constitute difficult alignment cases; (iii) 199 pair-wise alignments between proteins with similar structure but different topology; and (iv) 1275 pair-wise alignments of RNA structures. The accuracy of CLICK alignments was measured by the average structure overlap score and compared with other alignment methods, including HOMSTRAD, MUSTANG, Geometric Hashing, SALIGN, DALI, GANGSTA+, FATCAT, ARTS and SARA. On average, CLICK produces pair-wise alignments that are either comparable or statistically significantly more accurate than all of these other methods. We have used CLICK to uncover relationships between (previously) unrelated proteins. These new biological insights include: (i) detecting hinge regions in proteins where domain or sub-domains show flexibility; (ii) discovering similar small molecule binding sites from proteins of different folds and (iii) discovering topological variants of known structural/sequence motifs. Our method can generally be applied to compare any pair of molecular structures represented in Cartesian coordinates as exemplified by the RNA structure superimposition benchmark.
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Affiliation(s)
- Minh N Nguyen
- Bioinformatics Institute, 30 Biopolis Street, #07-01 Matrix, Singapore 138671
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Tuncbag N, Gursoy A, Keskin O. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces. Phys Biol 2011; 8:035006. [PMID: 21572173 DOI: 10.1088/1478-3975/8/3/035006] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.
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Affiliation(s)
- Nurcan Tuncbag
- Koc University, Center for Computational Biology and Bioinformatics, and College of Engineering, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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Zhao N, Pang B, Shyu CR, Korkin D. Structural similarity and classification of protein interaction interfaces. PLoS One 2011; 6:e19554. [PMID: 21589874 PMCID: PMC3093400 DOI: 10.1371/journal.pone.0019554] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2011] [Accepted: 04/05/2011] [Indexed: 11/25/2022] Open
Abstract
Interactions between proteins play a key role in many cellular processes.
Studying protein-protein interactions that share similar interaction interfaces
may shed light on their evolution and could be helpful in elucidating the
mechanisms behind stability and dynamics of the protein complexes. When two
complexes share structurally similar subunits, the similarity of the interaction
interfaces can be found through a structural superposition of the subunits.
However, an accurate detection of similarity between the protein complexes
containing subunits of unrelated structure remains an open problem. Here, we present an alignment-free machine learning approach to measure interface
similarity. The approach relies on the feature-based representation of protein
interfaces and does not depend on the superposition of the interacting subunit
pairs. Specifically, we develop an SVM classifier of similar and dissimilar
interfaces and derive a feature-based interface similarity measure. Next, the
similarity measure is applied to a set of 2,806×2,806 binary complex pairs
to build a hierarchical classification of protein-protein interactions. Finally,
we explore case studies of similar interfaces from each level of the hierarchy,
considering cases when the subunits forming interactions are either homologous
or structurally unrelated. The analysis has suggested that the positions of
charged residues in the homologous interfaces are not necessarily conserved and
may exhibit more complex conservation patterns.
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Affiliation(s)
- Nan Zhao
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
| | - Bin Pang
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
| | - Chi-Ren Shyu
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
| | - Dmitry Korkin
- Informatics Institute and Department of
Computer Science, University of Missouri, Columbia, Missouri, United States of
America
- Bond Life Science Center, University of
Missouri, Columbia, Missouri, United States of America
- * E-mail:
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Zhou P, Tian F, Ren Y, Shang Z. Systematic classification and analysis of themes in protein-DNA recognition. J Chem Inf Model 2010; 50:1476-88. [PMID: 20726602 DOI: 10.1021/ci100145d] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.
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Affiliation(s)
- Peng Zhou
- Department of Chemistry, Zhejiang University, Hangzhou 310027, China, College of Bioengineering, Chongqing University, Chongqing 400044, China
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Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected. Proc Natl Acad Sci U S A 2010; 107:22517-22. [PMID: 21149688 DOI: 10.1073/pnas.1012820107] [Citation(s) in RCA: 103] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
At the heart of protein-protein interactions are protein-protein interfaces where the direct physical interactions occur. By developing and applying an efficient structural alignment method, we study the structural similarity of representative protein-protein interfaces involving interactions between dimers. Even without structural similarity between individual monomers that form dimeric complexes, ∼90% of native interfaces have a close structural neighbor with similar backbone C(α) geometry and interfacial contact pattern. About 80% of the interfaces form a dense network, where any two interfaces are structurally related using a transitive set of at most seven intermediate interfaces. The degeneracy of interface space is largely due to the packing of compact, hydrogen-bonded secondary structure elements. This packing generates relatively flat interacting surfaces whose geometries are highly degenerate. Comparative study of artificial and native interfaces argues that the library of protein interfaces is close to complete and comprised of roughly 1,000 distinct interface types. In contrast, the number of possible quaternary structures of dimers is estimated to be about 10(4) times larger; thus, an experimentally determined database of all representative quaternary structures is not likely in the near future. Nevertheless, one could in principle exploit the completeness of protein interfaces to predict most dimeric quaternary structures. Finally, our results provide a structural explanation for the prevalence of promiscuous protein interactions. By side-chain packing adjustments, we illustrate how multiprotein specificity can be attained at a promiscuous interface.
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Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming. J Theor Biol 2010; 269:174-80. [PMID: 21035465 DOI: 10.1016/j.jtbi.2010.10.021] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2010] [Revised: 10/01/2010] [Accepted: 10/16/2010] [Indexed: 11/21/2022]
Abstract
Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.
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Rückert F, Dawelbait G, Winter C, Hartmann A, Denz A, Ammerpohl O, Schroeder M, Schackert HK, Sipos B, Klöppel G, Kalthoff H, Saeger HD, Pilarsky C, Grützmann R. Examination of apoptosis signaling in pancreatic cancer by computational signal transduction analysis. PLoS One 2010; 5:e12243. [PMID: 20808857 PMCID: PMC2924379 DOI: 10.1371/journal.pone.0012243] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2010] [Accepted: 07/20/2010] [Indexed: 02/06/2023] Open
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction prediction. Methodology/Principal Findings Apoptosis pathway related genes were assembled from electronic databases. To assess expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were analysed using public databases and computational prediction of protein interactions. Gene expression data were implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further showed possible new interactions between the single pathway members, which demonstrate the complexity of the apoptosis pathway. Conclusions/Significance Our data shows that by computational evaluation of public accessible data an acceptable virtual image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC.
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Affiliation(s)
- Felix Rückert
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden,
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Gao M, Skolnick J. iAlign: a method for the structural comparison of protein-protein interfaces. ACTA ACUST UNITED AC 2010; 26:2259-65. [PMID: 20624782 DOI: 10.1093/bioinformatics/btq404] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Protein-protein interactions play an essential role in many cellular processes. The rapid accumulation of protein-protein complex structures provides an unprecedented opportunity for comparative studies of protein-protein interactions. To facilitate such studies, it is necessary to develop an accurate and efficient computational algorithm for the comparison of protein-protein interaction modes. While there are many structural comparison approaches developed for individual proteins, very few methods are available for protein-protein complexes. RESULTS We present a novel interface alignment method, iAlign, for the structural alignment of protein-protein interfaces. New scoring schemes for measuring interface similarity are introduced, and an iterative dynamic programming algorithm is implemented. We find that the similarity scores follow extreme value distributions. Using statistical models, we empirically estimate their statistical significance, which is in good agreement with manual classifications by human experts. Large-scale tests of iAlign were conducted on both artificial docking models and experimental structures. In a benchmark test on 1517 dimers, iAlign successfully detects biologically related, structurally similar protein-protein interfaces at a coverage percentage of 90% and an error per query of 0.05. When compared against previously published methods, iAlign is substantially more accurate and efficient. AVAILABILITY The iAlign software package is freely available at http://cssb.biology.gatech.edu/iAlign.
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Affiliation(s)
- Mu Gao
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA, USA
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41
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Martin J. Beauty is in the eye of the beholder: proteins can recognize binding sites of homologous proteins in more than one way. PLoS Comput Biol 2010; 6:e1000821. [PMID: 20585553 PMCID: PMC2887470 DOI: 10.1371/journal.pcbi.1000821] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2010] [Accepted: 05/18/2010] [Indexed: 11/18/2022] Open
Abstract
Understanding the mechanisms of protein-protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein-protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein-protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution.
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Affiliation(s)
- Juliette Martin
- Université de Lyon, Lyon, France; Université Lyon 1, IFR 128, CNRS, UMR 5086 Institut de Biologie et Chimie des Protéines (IBCP), Lyon, France.
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Liu Q, Li J. Protein binding hot spots and the residue-residue pairing preference: a water exclusion perspective. BMC Bioinformatics 2010; 11:244. [PMID: 20462403 PMCID: PMC2882391 DOI: 10.1186/1471-2105-11-244] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2009] [Accepted: 05/12/2010] [Indexed: 11/30/2022] Open
Abstract
Background A protein binding hot spot is a small cluster of residues tightly packed at the center of the interface between two interacting proteins. Though a hot spot constitutes a small fraction of the interface, it is vital to the stability of protein complexes. Recently, there are a series of hypotheses proposed to characterize binding hot spots, including the pioneering O-ring theory, the insightful 'coupling' and 'hot region' principle, and our 'double water exclusion' (DWE) hypothesis. As the perspective changes from the O-ring theory to the DWE hypothesis, we examine the physicochemical properties of the binding hot spots under the new hypothesis and compare with those under the O-ring theory. Results The requirements for a cluster of residues to form a hot spot under the DWE hypothesis can be mathematically satisfied by a biclique subgraph if a vertex is used to represent a residue, an edge to indicate a close distance between two residues, and a bipartite graph to represent a pair of interacting proteins. We term these hot spots as DWE bicliques. We identified DWE bicliques from crystal packing contacts, obligate and non-obligate interactions. Our comparative study revealed that there are abundant unique bicliques to the biological interactions, indicating specific biological binding behaviors in contrast to crystal packing. The two sub-types of biological interactions also have their own signature bicliques. In our analysis on residue compositions and residue pairing preferences in DWE bicliques, the focus was on interaction-preferred residues (ipRs) and interaction-preferred residue pairs (ipRPs). It is observed that hydrophobic residues are heavily involved in the ipRs and ipRPs of the obligate interactions; and that aromatic residues are in favor in the ipRs and ipRPs of the biological interactions, especially in those of the non-obligate interactions. In contrast, the ipRs and ipRPs in crystal packing are dominated by hydrophilic residues, and most of the anti-ipRs of crystal packing are the ipRs of the obligate or non-obligate interactions. Conclusions These ipRs and ipRPs in our DWE bicliques describe a diverse binding features among the three types of interactions. They also highlight the specific binding behaviors of the biological interactions, sharply differing from the artifact interfaces in the crystal packing. It can be noted that DWE bicliques, especially the unique bicliques, can capture deep insights into the binding characteristics of protein interfaces.
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Affiliation(s)
- Qian Liu
- Bioinformatics Research Center & School of Computer Engineering, Nanyang Technological University, Singapore 639798
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43
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Evolution: a guide to perturb protein function and networks. Curr Opin Struct Biol 2010; 20:351-9. [PMID: 20444593 DOI: 10.1016/j.sbi.2010.04.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2010] [Accepted: 04/08/2010] [Indexed: 12/11/2022]
Abstract
Protein interactions give rise to networks that control cell fate in health and disease; selective means to probe these interactions are therefore of wide interest. We discuss here Evolutionary Tracing (ET), a comparative method to identify protein functional sites and to guide experiments that selectively block, recode, or mimic their amino acid determinants. These studies suggest, in principle, a scalable approach to perturb individual links in protein networks.
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44
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Analysis of hot region organization in hub proteins. Ann Biomed Eng 2010; 38:2068-78. [PMID: 20437205 DOI: 10.1007/s10439-010-0048-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2010] [Accepted: 04/19/2010] [Indexed: 01/28/2023]
Abstract
Protein interaction maps constructed from binary interactions reveal that some proteins are highly connected to others (acting as hub proteins), whereas some others have a few interactions (at the edges of the map). This paper addresses hub proteins from a structural point: interfaces. It investigates how hot spots are organized in hub proteins (hot regions). We annotate interfaces as the ones between two date-hubs (DD), two party hubs (PP), and two non-hubs (NN). We investigate the physico-chemical properties of these three types of interfaces focusing on the accessible surface area distribution, hot region organization, and amino acid composition differences. Results reveal that there are significant differences between DD and PP interfaces. More of the hot spots are organized into the hot regions in DD interfaces compared to PP ones. A high fraction of the interfaces are covered by hot regions in DD interfaces. There are more distinct hot regions in DDs. Since the same (or overlapping) DD interfaces should be used repeatedly, different hot regions can be used to bind to different partners. Further, these hot region characteristics can be used to predict whether a given hub interface is involved in a DD or a PP interface type with 80% accuracy.
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45
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Potential aggregation-prone regions in complementarity-determining regions of antibodies and their contribution towards antigen recognition: a computational analysis. Pharm Res 2010; 27:1512-29. [PMID: 20422267 PMCID: PMC7088613 DOI: 10.1007/s11095-010-0143-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2010] [Accepted: 03/30/2010] [Indexed: 11/03/2022]
Abstract
PURPOSE To analyze contribution of short aggregation-prone regions (APRs), which may self-associate via cross-beta motif and were earlier identified in therapeutic mAbs, towards antigen recognition via structural analyses of antibody-antigen complexes. METHODS A dataset of 29 publically available high-resolution crystal structures of Fab-antigen complexes was collected. Contribution of APRs towards the surface areas of the Fabs buried by the cognate antigens was computed. Propensities of amino acids to occur in APRs and to be involved in antigen binding were compared. Coincidence between APRs and individual CDR loops was examined. RESULTS All Fabs in the dataset contain at least one APR in CDR loops and adjacent framework beta-strands. The average contribution of APRs towards buried surface area of Fabs is 16.0 +/- 10.7%. Aggregation and antigen recognition may be coupled via aromatic residues (Tyr, Trp), which occur with high propensities in both APRs and antigen binding sites. APRs are infrequent in the heavy chain CDR 3 (H3) loops (7%), but are frequent in H2 loops (45%). CONCLUSIONS Co-incidence of APRs with antigen recognition sites can potentially lead to the loss of function upon aggregation. Rational structure-based design or selection strategies are suggested for biotherapeutics with improved druggability while maintaining potency.
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46
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Zhou P, Zou J, Tian F, Shang Z. Geometric similarity between protein-RNA interfaces. J Comput Chem 2010; 30:2738-51. [PMID: 19399760 DOI: 10.1002/jcc.21300] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A new method is described to measure the geometric similarity between protein-RNA interfaces quantitatively. The method is based on a procedure that dissects the interface geometry in terms of the spatial relationships between individual amino acid nucleotide pairs. Using this technique, we performed an all-on-all comparison of 586 protein-RNA interfaces deposited in the current Protein Data Bank, as the result, an interface-interface similarity score matrix was obtained. Based upon this matrix, hierarchical clustering was carried out which yielded a complete clustering tree for the 586 protein-RNA interfaces. By investigating the organizing behavior of the clustering tree and the SCOP classification of protein partners in complexes, a geometrically nonredundant, diverse data set (representative data set) consisting of 45 distinct protein-RNA interfaces was extracted for the purpose of studying protein-RNA interactions, RNA regulations, and drug design. We classified protein-RNA interfaces into three types. In type I, the families and interface structural classes of the protein partners, as well as the interface geometries are all similar. In type II, the interface geometries and the interface structural classes are similar, whereas the protein families are different. In type III, only the interface geometries are similar but the protein families and the interface structural classes are distinct. Furthermore, we also show two new RNA recognition themes derived from the representative data set.
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Affiliation(s)
- Peng Zhou
- Institute of Molecular Design and Molecular Thermodynamics, Department of Chemistry, Zhejiang University, Hangzhou 310027, China
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47
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Dey S, Pal A, Chakrabarti P, Janin J. The subunit interfaces of weakly associated homodimeric proteins. J Mol Biol 2010; 398:146-60. [PMID: 20156457 DOI: 10.1016/j.jmb.2010.02.020] [Citation(s) in RCA: 79] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2009] [Revised: 02/10/2010] [Accepted: 02/10/2010] [Indexed: 02/07/2023]
Abstract
We analyzed subunit interfaces in 315 homodimers with an X-ray structure in the Protein Data Bank, validated by checking the literature for data that indicate that the proteins are dimeric in solution and that, in the case of the "weak" dimers, the homodimer is in equilibrium with the monomer. The interfaces of the 42 weak dimers, which are smaller by a factor of 2.4 on average than in the remainder of the set, are comparable in size with antibody-antigen or protease-inhibitor interfaces. Nevertheless, they are more hydrophobic than in the average transient protein-protein complex and similar in amino acid composition to the other homodimer interfaces. The mean numbers of interface hydrogen bonds and hydration water molecules per unit area are also similar in homodimers and transient complexes. Parameters related to the atomic packing suggest that many of the weak dimer interfaces are loosely packed, and we suggest that this contributes to their low stability. To evaluate the evolutionary selection pressure on interface residues, we calculated the Shannon entropy of homologous amino acid sequences at 60% sequence identity. In 93% of the homodimers, the interface residues are better conserved than the residues on the protein surface. The weak dimers display the same high degree of interface conservation as other homodimers, but their homologs may be heterodimers as well as homodimers. Their interfaces may be good models in terms of their size, composition, and evolutionary conservation for the labile subunit contacts that allow protein assemblies to share and exchange components, allosteric proteins to undergo quaternary structure transitions, and molecular machines to operate in the cell.
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Affiliation(s)
- Sucharita Dey
- Bioinformatics Centre, Bose Institute, P-1/12 CIT Scheme VIIM, Calcutta 700 054, India
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48
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Abstract
The Protein Data Bank contains the description of approximately 27 000 protein-ligand binding sites. Most of the ligands at these sites are biologically active small molecules, affecting the biological function of the protein. The classification of their binding sites may lead to relevant results in drug discovery and design. Clusters of similar binding sites were created here by a hybrid, sequence and spatial structure-based approach, using the OPTICS clustering algorithm. A dissimilarity measure was defined: a distance function on the amino acid sequences of the binding sites. All the binding sites were clustered in the Protein Data Bank according to this distance function, and it was found that the clusters characterized well the Enzyme Commission numbers of the entries. The results, carefully color coded by the Enzyme Commission numbers of the proteins, containing the 20 967 binding sites clustered, are available as html files in three parts at http://pitgroup.org/seqclust/.
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Affiliation(s)
- Gábor Iván
- Protein Information Technology Group, Department of Computer Science, Eötvös University, Budapest, Hungary
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Conformational selection and induced fit mechanism underlie specificity in noncovalent interactions with ubiquitin. Proc Natl Acad Sci U S A 2009; 106:19346-51. [PMID: 19887638 DOI: 10.1073/pnas.0906966106] [Citation(s) in RCA: 156] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Noncovalent binding interactions between proteins are the central physicochemical phenomenon underlying biological signaling and functional control on the molecular level. Here, we perform an extensive structural analysis of a large set of bound and unbound ubiquitin conformers and study the level of residual induced fit after conformational selection in the binding process. We show that the region surrounding the binding site in ubiquitin undergoes conformational changes that are significantly more pronounced compared with the whole molecule on average. We demonstrate that these induced-fit structural adjustments are comparable in magnitude to conformational selection. Our final model of ubiquitin binding blends conformational selection with the subsequent induced fit and provides a quantitative measure of their respective contributions.
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Tyagi M, Shoemaker BA, Bryant SH, Panchenko AR. Exploring functional roles of multibinding protein interfaces. Protein Sci 2009; 18:1674-83. [PMID: 19591200 DOI: 10.1002/pro.181] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Cellular processes are highly interconnected and many proteins are shared in different pathways. Some of these shared proteins or protein families may interact with diverse partners using the same interface regions; such multibinding proteins are the subject of our study. The main goal of our study is to attempt to decipher the mechanisms of specific molecular recognition of multiple diverse partners by promiscuous protein regions. To address this, we attempt to analyze the physicochemical properties of multibinding interfaces and highlight the major mechanisms of functional switches realized through multibinding. We find that only 5% of protein families in the structure database have multibinding interfaces, and multibinding interfaces do not show any higher sequence conservation compared with the background interface sites. We highlight several important functional mechanisms utilized by multibinding families. (a) Overlap between different functional pathways can be prevented by the switches involving nearby residues of the same interfacial region. (b) Interfaces can be reused in pathways where the substrate should be passed from one protein to another sequentially. (c) The same protein family can develop different specificities toward different binding partners reusing the same interface; and finally, (d) inhibitors can attach to substrate binding sites as substrate mimicry and thereby prevent substrate binding.
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Affiliation(s)
- Manoj Tyagi
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
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