1
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Shor B, Schneidman-Duhovny D. CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2. Nat Methods 2024; 21:477-487. [PMID: 38326495 PMCID: PMC10927564 DOI: 10.1038/s41592-024-02174-0] [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: 05/17/2023] [Accepted: 01/09/2024] [Indexed: 02/09/2024]
Abstract
Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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2
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Shor B, Schneidman-Duhovny D. Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541003. [PMID: 37293053 PMCID: PMC10245790 DOI: 10.1101/2023.05.16.541003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
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3
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Karaca E, Prévost C, Sacquin-Mora S. Modeling the Dynamics of Protein–Protein Interfaces, How and Why? Molecules 2022; 27:molecules27061841. [PMID: 35335203 PMCID: PMC8950966 DOI: 10.3390/molecules27061841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/06/2022] [Accepted: 03/08/2022] [Indexed: 12/07/2022] Open
Abstract
Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.
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Affiliation(s)
- Ezgi Karaca
- Izmir Biomedicine and Genome Center, Izmir 35340, Turkey;
- Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir 35340, Turkey
| | - Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
| | - Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
- Correspondence:
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4
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Gao B, Ji R, Li Z, Su X, Li H, Sun Y, Ji C, Gan J, Li J. Structural analysis and functional study of phosphofructokinase B (PfkB) from Mycobacterium marinum. Biochem Biophys Res Commun 2021; 579:129-135. [PMID: 34597996 DOI: 10.1016/j.bbrc.2021.09.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 09/21/2021] [Indexed: 11/27/2022]
Abstract
Phosphofructokinase B (PfkB) belongs to the ribokinase family, which uses the phosphorylated sugar as substrate, and catalyzes fructose-6-phosphate into fructose-1,6-diphosphate. However, the structural basis of Mycobacterium marinum PfkB is not clear. Here, we found that the PfkB protein was monomeric in solution, which was different from most enzymes in this family. The crystal structure of PfkB protein from M. marinum was solved at a resolution of 2.21 Å. The PfkB structure consists of two domains, a major three-layered α/β/α sandwich-like domain characteristic of the ribokinase-like superfamily, and a second domain composed of four-stranded β sheets. Structural comparison analysis suggested that residues G236, A237, G238, and D239 could be critical for ATP catalysis and substrate binding of PfkB. Our current work provides new insights into understanding the mechanism of the glycolysis in M. marinum.
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Affiliation(s)
- Baocai Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Rui Ji
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Zhengyang Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Xiaoqin Su
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Hongyong Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Yicheng Sun
- MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chaoneng Ji
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Jianhua Gan
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China.
| | - Jixi Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China.
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5
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Prévost C, Sacquin-Mora S. Moving pictures: Reassessing docking experiments with a dynamic view of protein interfaces. Proteins 2021; 89:1315-1323. [PMID: 34038009 DOI: 10.1002/prot.26152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/22/2021] [Accepted: 05/19/2021] [Indexed: 11/06/2022]
Abstract
The modeling of protein assemblies at the atomic level remains a central issue in structural biology, as protein interactions play a key role in numerous cellular processes. This problem is traditionally addressed using docking tools, where the quality of the models is based on their similarity to a single reference experimental structure. However, using a static reference does not take into account the dynamic quality of the protein interface. Here, we used all-atom classical Molecular Dynamics simulations to investigate the stability of the reference interface for three complexes that previously served as targets in the CAPRI competition. For each one of these targets, we also ran MD simulations for ten models that are distributed over the High, Medium and Acceptable accuracy categories. To assess the quality of these models from a dynamic perspective, we set up new criteria which take into account the stability of the reference experimental protein interface. We show that, when the protein interfaces are allowed to evolve along time, the original ranking based on the static CAPRI criteria no longer holds as over 50% of the docking models undergo a category change (which can be either toward a better or a lower accuracy group) when reassessing their quality using dynamic information.
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Affiliation(s)
- Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, Paris, France.,Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, Paris, France
| | - Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, Paris, France.,Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, Paris, France
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6
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Abstract
Macromolecular complexes play a key role in cellular function. Predicting the structure and dynamics of these complexes is one of the key challenges in structural biology. Docking applications have traditionally been used to predict pairwise interactions between proteins. However, few methods exist for modeling multi-protein assemblies. Here we present two methods, CombDock and DockStar, that can predict multi-protein assemblies starting from subunit structural models. CombDock can assemble subunits without any assumptions about the pairwise interactions between subunits, while DockStar relies on the interaction graph or, alternatively, a homology model or a cryo-electron microscopy (EM) density map of the entire complex. We demonstrate the two methods using RNA polymerase II with 12 subunits and TRiC/CCT chaperonin with 16 subunits.
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Affiliation(s)
- Dina Schneidman-Duhovny
- School of Computer Science and Engineering and the Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Haim J Wolfson
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
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7
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Lagarde N, Carbone A, Sacquin-Mora S. Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions. Proteins 2018; 86:723-737. [DOI: 10.1002/prot.25506] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 03/23/2018] [Accepted: 04/07/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Nathalie Lagarde
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
| | - Alessandra Carbone
- Laboratoire de Biologie Computationnelle et Quantitative, CNRS UMR7238, UPMC Univ-Paris 6, Sorbonne Université, 4 place Jussieu; Paris 75005 France
- Institut Universitaire de France; Paris 75005 France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS UPR9080, Institut de Biologie Physico-Chimique, University Paris Diderot, Sorbonne Paris Cité, 13 rue Pierre et Marie Curie; Paris 75005 France
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8
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OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer. Oncotarget 2017; 7:17608-15. [PMID: 26872056 PMCID: PMC4951236 DOI: 10.18632/oncotarget.7305] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 01/29/2016] [Indexed: 11/25/2022] Open
Abstract
High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.
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9
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Kuzu G, Keskin O, Nussinov R, Gursoy A. PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps. Acta Crystallogr D Struct Biol 2016; 72:1137-1148. [PMID: 27710935 PMCID: PMC5053140 DOI: 10.1107/s2059798316013541] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 08/23/2016] [Indexed: 12/29/2022] Open
Abstract
The structures of protein assemblies are important for elucidating cellular processes at the molecular level. Three-dimensional electron microscopy (3DEM) is a powerful method to identify the structures of assemblies, especially those that are challenging to study by crystallography. Here, a new approach, PRISM-EM, is reported to computationally generate plausible structural models using a procedure that combines crystallographic structures and density maps obtained from 3DEM. The predictions are validated against seven available structurally different crystallographic complexes. The models display mean deviations in the backbone of <5 Å. PRISM-EM was further tested on different benchmark sets; the accuracy was evaluated with respect to the structure of the complex, and the correlation with EM density maps and interface predictions were evaluated and compared with those obtained using other methods. PRISM-EM was then used to predict the structure of the ternary complex of the HIV-1 envelope glycoprotein trimer, the ligand CD4 and the neutralizing protein m36.
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Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, 34450 Istanbul, Turkey
- Chemical and Biological Engineering, College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research Inc., National Cancer Institute, Frederick National Laboratory for Cancer Research, 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
| | - Attila Gursoy
- Computer Engineering, Koc University, 34450 Istanbul, Turkey
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10
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Tuncbag N, Gursoy A, Keskin O, Nussinov R. The potential impact of recent developments in three-dimensional quantitative interaction proteomics on structural biology. Expert Rev Proteomics 2016; 13:447-9. [PMID: 27104235 PMCID: PMC4938151 DOI: 10.1080/14789450.2016.1182023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Nurcan Tuncbag
- Middle East Technical University, Graduate School of Informatics, Department of Health Informatics, Ankara, Turkey
| | - Attila Gursoy
- Koc University, Center for Computational Biology and Bioinformatics, Istanbul, Turkey
- Koc University, Computer Engineering, College of Engineering, Istanbul, Turkey
| | - Ozlem Keskin
- Koc University, Center for Computational Biology and Bioinformatics, Istanbul, Turkey
- Koc University, Computer Engineering, College of Engineering, Istanbul, Turkey
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory, National Cancer Institute, Frederick, Maryland, United States
- Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Sackler Institute of Molecular Medicine, Tel Aviv University, Tel Aviv, Israel
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11
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Dietzen M, Kalinina OV, Taškova K, Kneissl B, Hildebrandt AK, Jaenicke E, Decker H, Lengauer T, Hildebrandt A. Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces. Proteins 2015; 83:1887-99. [PMID: 26248608 PMCID: PMC5049452 DOI: 10.1002/prot.24873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 07/20/2015] [Accepted: 07/29/2015] [Indexed: 11/06/2022]
Abstract
Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.
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Affiliation(s)
- Matthias Dietzen
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Olga V Kalinina
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Katerina Taškova
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Institute for Molecular Biology, Johannes Gutenberg University, Ackermannweg 4, Mainz, 55128, Germany
| | - Benny Kneissl
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany.,Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center Penzberg, Nonnenwald 2, Penzberg, 82377, Germany
| | | | - Elmar Jaenicke
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Heinz Decker
- Institute of Molecular Biophysics, Johannes Gutenberg University, Jakob-Welder-Weg 26, Mainz, 55128, Germany
| | - Thomas Lengauer
- Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany
| | - Andreas Hildebrandt
- Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, Mainz, 55128, Germany
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12
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Amir N, Cohen D, Wolfson HJ. DockStar: a novel ILP-based integrative method for structural modeling of multimolecular protein complexes. Bioinformatics 2015; 31:2801-7. [PMID: 25913207 DOI: 10.1093/bioinformatics/btv270] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 04/19/2015] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Atomic resolution modeling of large multimolecular assemblies is a key task in Structural Cell Biology. Experimental techniques can provide atomic resolution structures of single proteins and small complexes, or low resolution data of large multimolecular complexes. RESULTS We present a novel integrative computational modeling method, which integrates both low and high resolution experimental data. The algorithm accepts as input atomic resolution structures of the individual subunits obtained from X-ray, NMR or homology modeling, and interaction data between the subunits obtained from mass spectrometry. The optimal assembly of the individual subunits is formulated as an Integer Linear Programming task. The method was tested on several representative complexes, both in the bound and unbound cases. It placed correctly most of the subunits of multimolecular complexes of up to 16 subunits and significantly outperformed the CombDock and Haddock multimolecular docking methods. AVAILABILITY AND IMPLEMENTATION http://bioinfo3d.cs.tau.ac.il/DockStar CONTACT naamaamir@mail.tau.ac.il or wolfson@tau.ac.il SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Naama Amir
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Dan Cohen
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Haim J Wolfson
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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13
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Nussinov R, Jang H, Tsai CJ. The structural basis for cancer treatment decisions. Oncotarget 2014; 5:7285-302. [PMID: 25277176 PMCID: PMC4202123 DOI: 10.18632/oncotarget.2439] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2014] [Accepted: 09/03/2014] [Indexed: 12/31/2022] Open
Abstract
Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.
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Affiliation(s)
- Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, U.S.A
- Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Hyunbum Jang
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, U.S.A
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, U.S.A
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14
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Konc J, Janežič D. ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites. Nucleic Acids Res 2014; 42:W215-20. [PMID: 24861616 PMCID: PMC4086080 DOI: 10.1093/nar/gku460] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands.
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Affiliation(s)
- Janez Konc
- National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Glagoljaška 8, 6000 Koper, Slovenia
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15
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Guven-Maiorov E, Acuner-Ozbabacan SE, Keskin O, Gursoy A, Nussinov R. Structural pathways of cytokines may illuminate their roles in regulation of cancer development and immunotherapy. Cancers (Basel) 2014; 6:663-83. [PMID: 24670367 PMCID: PMC4074797 DOI: 10.3390/cancers6020663] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Revised: 03/11/2014] [Accepted: 03/12/2014] [Indexed: 01/06/2023] Open
Abstract
Cytokines are messengers between tissues and the immune system. They play essential roles in cancer initiation, promotion, metastasis, and immunotherapy. Structural pathways of cytokine signaling which contain their interactions can help understand their action in the tumor microenvironment. Here, our aim is to provide an overview of the role of cytokines in tumor development from a structural perspective. Atomic details of protein-protein interactions can help in understanding how an upstream signal is transduced; how higher-order oligomerization modes of proteins can influence their function; how mutations, inhibitors or antagonists can change cellular consequences; why the same protein can lead to distinct outcomes, and which alternative parallel pathways can take over. They also help to design drugs/inhibitors against proteins de novo or by mimicking natural antagonists as in the case of interferon-γ. Since the structural database (PDB) is limited, structural pathways are largely built from a series of predicted binary protein-protein interactions. Below, to illustrate how protein-protein interactions can help illuminate roles played by cytokines, we model some cytokine interaction complexes exploiting a powerful algorithm (PRotein Interactions by Structural Matching-PRISM).
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Affiliation(s)
- Emine Guven-Maiorov
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey.
| | - Saliha Ece Acuner-Ozbabacan
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey.
| | - Ozlem Keskin
- 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
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
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