1
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Schwarz D, Georges G, Kelm S, Shi J, Vangone A, Deane CM. Co-evolutionary distance predictions contain flexibility information. Bioinformatics 2021; 38:65-72. [PMID: 34383892 DOI: 10.1093/bioinformatics/btab562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 06/19/2021] [Accepted: 08/10/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue-residue distance predictions to be informative of protein flexibility rather than simply static structure. RESULTS We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dominik Schwarz
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Guy Georges
- Department of Computational Engineering and Data Science, Large Molecule Research, Penzberg 82377, Germany
| | - Sebastian Kelm
- Computer-Aided Drug Design, UCB Pharma, Slough SL1 3WE, UK
| | - Jiye Shi
- Computer-Aided Drug Design, UCB Pharma, Slough SL1 3WE, UK
| | - Anna Vangone
- Department of Computational Engineering and Data Science, Large Molecule Research, Penzberg 82377, Germany
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2
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Barradas-Bautista D, Cao Z, Vangone A, Oliva R, Cavallo L. A random forest classifier for protein-protein docking models. Bioinform Adv 2021; 2:vbab042. [PMID: 36699405 PMCID: PMC9710594 DOI: 10.1093/bioadv/vbab042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/11/2021] [Accepted: 12/06/2021] [Indexed: 01/28/2023]
Abstract
Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein-protein complexes obtained by popular docking software. To this aim, we generated 3 × 10 4 docking models for each of the 230 complexes in the protein-protein benchmark, version 5, using three different docking programs (HADDOCK, FTDock and ZDOCK), for a cumulative set of ≈ 7 × 10 6 docking models. Three different machine learning approaches (Random Forest, Supported Vector Machine and Perceptron) were used to train classifiers with 158 different scoring functions (features). The Random Forest algorithm outperformed the other two algorithms and was selected for further optimization. Using a features selection algorithm, and optimizing the random forest hyperparameters, allowed us to train and validate a random forest classifier, named COnservation Driven Expert System (CoDES). Testing of CoDES on independent datasets, as well as results of its comparative performance with machine learning methods recently developed in the field for the scoring of docking decoys, confirm its state-of-the-art ability to discriminate correct from incorrect decoys both in terms of global parameters and in terms of decoys ranked at the top positions. Supplementary information Supplementary data are available at Bioinformatics Advances online. Software and data availability statement The docking models are available at https://doi.org/10.5281/zenodo.4012018. The programs underlying this article will be shared on request to the corresponding authors.
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Affiliation(s)
- Didier Barradas-Bautista
- Kaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Saudi Arabia,To whom correspondence should be addressed. or or
| | - Zhen Cao
- Kaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Saudi Arabia
| | - Anna Vangone
- Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Munich Large Molecule Research, 82377 Penzberg, Germany
| | - Romina Oliva
- Department of Sciences and Technologies, University Parthenope of Naples, Centro Direzionale Isola C4, I-80143 Naples, Italy,To whom correspondence should be addressed. or or
| | - Luigi Cavallo
- Kaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Saudi Arabia,To whom correspondence should be addressed. or or
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3
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Oliva R, Shaikh AR, Petta A, Vangone A, Cavallo L. D936Y and Other Mutations in the Fusion Core of the SARS-CoV-2 Spike Protein Heptad Repeat 1: Frequency, Geographical Distribution, and Structural Effect. Molecules 2021; 26:molecules26092622. [PMID: 33946306 PMCID: PMC8124767 DOI: 10.3390/molecules26092622] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/08/2021] [Accepted: 04/12/2021] [Indexed: 11/16/2022] Open
Abstract
The crown of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is constituted by its spike (S) glycoprotein. S protein mediates the SARS-CoV-2 entry into the host cells. The “fusion core” of the heptad repeat 1 (HR1) on S plays a crucial role in the virus infectivity, as it is part of a key membrane fusion architecture. While SARS-CoV-2 was becoming a global threat, scientists have been accumulating data on the virus at an impressive pace, both in terms of genomic sequences and of three-dimensional structures. On 15 February 2021, from the SARS-CoV-2 genomic sequences in the GISAID resource, we collected 415,673 complete S protein sequences and identified all the mutations occurring in the HR1 fusion core. This is a 21-residue segment, which, in the post-fusion conformation of the protein, gives many strong interactions with the heptad repeat 2, bringing viral and cellular membranes in proximity for fusion. We investigated the frequency and structural effect of novel mutations accumulated over time in such a crucial region for the virus infectivity. Three mutations were quite frequent, occurring in over 0.1% of the total sequences. These were S929T, D936Y, and S949F, all in the N-terminal half of the HR1 fusion core segment and particularly spread in Europe and USA. The most frequent of them, D936Y, was present in 17% of sequences from Finland and 12% of sequences from Sweden. In the post-fusion conformation of the unmutated S protein, D936 is involved in an inter-monomer salt bridge with R1185. We investigated the effect of the D936Y mutation on the pre-fusion and post-fusion state of the protein by using molecular dynamics, showing how it especially affects the latter one.
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Affiliation(s)
- Romina Oliva
- Department of Sciences and Technologies, University Parthenope of Naples, Centro Direzionale Isola C4, I-80143 Naples, Italy
- Correspondence:
| | - Abdul Rajjak Shaikh
- Kaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; (A.R.S.); (L.C.)
| | - Andrea Petta
- Dipartimento di Informatica ed Applicazioni, University of Salerno, Via Papa Paolo Giovanni II, I-84048 Fisciano, Italy;
| | - Anna Vangone
- Roche Innovation Center Munich, Pharma Research and Early Development, Large Molecule Research, Nonnenwald 2, 82377 Penzberg, Germany;
| | - Luigi Cavallo
- Kaust Catalysis Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia; (A.R.S.); (L.C.)
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4
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Jiménez-García B, Elez K, Koukos PI, Bonvin AM, Vangone A. PRODIGY-crystal: a web-tool for classification of biological interfaces in protein complexes. Bioinformatics 2020; 35:4821-4823. [PMID: 31141126 PMCID: PMC9186318 DOI: 10.1093/bioinformatics/btz437] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/06/2019] [Accepted: 05/25/2019] [Indexed: 01/11/2023] Open
Abstract
Summary Distinguishing biologically relevant interfaces from crystallographic ones in
biological complexes is fundamental in order to associate cellular functions to the
correct macromolecular assemblies. Recently, we described a detailed study reporting the
differences in the type of intermolecular residue–residue contacts between biological
and crystallographic interfaces. Our findings allowed us to develop a fast predictor of
biological interfaces reaching an accuracy of 0.92 and competitive to the current state
of the art. Here we present its web-server implementation, PRODIGY-CRYSTAL, aimed at the
classification of biological and crystallographic interfaces. PRODIGY-CRYSTAL has the
advantage of being fast, accurate and simple. This, together with its user-friendly
interface and user support forum, ensures its broad accessibility. Availability and implementation PRODIGY-CRYSTAL is freely available without registration requirements at https://haddock.science.uu.nl/services/PRODIGY-CRYSTAL.
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Affiliation(s)
- Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3512 JE, The Netherlands
| | - Katarina Elez
- Present address: University of Bologna, Via Selmi 3 40126, Bologna, Italy
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3512 JE, The Netherlands
| | - Alexandre Mjj Bonvin
- To whom correspondence should be addressed. E-mail: or . Present address: Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, Penzberg 82377, Germany
| | - Anna Vangone
- To whom correspondence should be addressed. E-mail: or . Present address: Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, Penzberg 82377, Germany
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5
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Vangone A, Schaarschmidt J, Koukos P, Geng C, Citro N, Trellet ME, Xue LC, Bonvin AMJJ. Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server. Bioinformatics 2020; 35:1585-1587. [PMID: 31051038 DOI: 10.1093/bioinformatics/bty816] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 08/15/2018] [Accepted: 09/19/2018] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the prediction of binding affinity in protein-protein complexes. By using a combination of simple structural properties, such as the residue-contacts made at the interface, PRODIGY has demonstrated a top performance compared with other state-of-the-art predictors in the literature. Here we present an extension of it, named PRODIGY-LIG, aimed at the prediction of affinity in protein-small ligand complexes. The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 protein-ligand complexes during the D3R Grand Challenge 2. PRODIGY-LIG has the advantage of being simple, generic and applicable to any kind of protein-ligand complex. It provides an automatic, fast and user-friendly tool ensuring broad accessibility. AVAILABILITY AND IMPLEMENTATION PRODIGY-LIG is freely available without registration requirements at http://milou.science.uu.nl/services/PRODIGY-LIG.
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Affiliation(s)
- Anna Vangone
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Joerg Schaarschmidt
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Panagiotis Koukos
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Cunliang Geng
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Nevia Citro
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Mikael E Trellet
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Li C Xue
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands
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6
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Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue LC, Honorato RV, Moreira I, Kurkcuoglu Z, Vangone A, Bonvin AMJJ. An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45. Proteins 2019; 88:1029-1036. [PMID: 31886559 DOI: 10.1002/prot.25869] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 12/17/2019] [Accepted: 12/26/2019] [Indexed: 01/18/2023]
Abstract
Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.
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Affiliation(s)
- Panagiotis I Koukos
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands.,Department of Physics, Sapienza University, Rome, Italy
| | - Cunliang Geng
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands.,Multiscale Materials Modelling and Virtual Design, Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Mikael E Trellet
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Adrien S J Melquiond
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Li C Xue
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V Honorato
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Irina Moreira
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands.,CNC-Center for Neuroscience and Cell Biology, Rua Larga, FMUC, Polo I, 1° andar, Universidade de Coimbra, Coimbra, Portugal
| | - Zeynep Kurkcuoglu
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Anna Vangone
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands
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7
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Geng C, Vangone A, Folkers GE, Xue LC, Bonvin AMJJ. iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins 2018; 87:110-119. [PMID: 30417935 PMCID: PMC6587874 DOI: 10.1002/prot.25630] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 10/19/2018] [Accepted: 11/05/2018] [Indexed: 02/06/2023]
Abstract
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning‐based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy‐based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein‐protein complexes. It competes with existing state‐of‐the‐art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.
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Affiliation(s)
- Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.,Roche Pharmaceutical Research and Early Development, Large Molecule Research, Roche Innovation Center Penzberg, Penzberg, Germany
| | - Gert E Folkers
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
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8
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Elez K, Bonvin AMJJ, Vangone A. Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification. BMC Bioinformatics 2018; 19:438. [PMID: 30497368 PMCID: PMC6266931 DOI: 10.1186/s12859-018-2414-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems. RESULTS In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on "pair-properties" of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA). CONCLUSION In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier .
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Affiliation(s)
- Katarina Elez
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
- Present address: University of Bologna, Via Selmi 3, 40126, Bologna, Italy
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
- present address: Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, Penzberg, Germany.
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9
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Calvanese L, D'Auria G, Vangone A, Falcigno L, Oliva R. Structural Basis for Mutations of Human Aquaporins Associated to Genetic Diseases. Int J Mol Sci 2018; 19:E1577. [PMID: 29799470 PMCID: PMC6032259 DOI: 10.3390/ijms19061577] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 05/17/2018] [Accepted: 05/23/2018] [Indexed: 02/06/2023] Open
Abstract
Aquaporins (AQPs) are among the best structural-characterized membrane proteins, fulfilling the role of allowing water flux across cellular membranes. Thus far, 34 single amino acid polymorphisms have been reported in HUMSAVAR for human aquaporins as disease-related. They affect AQP2, AQP5 and AQP8, where they are associated with nephrogenic diabetes insipidus, keratoderma and colorectal cancer, respectively. For half of these mutations, although they are mostly experimentally characterized in their dysfunctional phenotypes, a structural characterization at a molecular level is still missing. In this work, we focus on such mutations and discuss what the structural defects are that they appear to cause. To achieve this aim, we built a 3D molecular model for each mutant and explored the effect of the mutation on all of their structural features. Based on these analyses, we could collect the structural defects of all the pathogenic mutations (here or previously analysed) under few main categories, that we found to nicely correlate with the experimental phenotypes reported for several of the analysed mutants. Some of the structural analyses we present here provide a rationale for previously experimentally observed phenotypes. Furthermore, our comprehensive overview can be used as a reference frame for the interpretation, on a structural basis, of defective phenotypes of other aquaporin pathogenic mutants.
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MESH Headings
- Amino Acid Sequence
- Aquaporin 2/chemistry
- Aquaporin 2/genetics
- Aquaporin 2/metabolism
- Aquaporin 5/chemistry
- Aquaporin 5/genetics
- Aquaporin 5/metabolism
- Aquaporins/chemistry
- Aquaporins/genetics
- Aquaporins/metabolism
- Colorectal Neoplasms/genetics
- Colorectal Neoplasms/metabolism
- Colorectal Neoplasms/pathology
- Databases, Protein
- Diabetes Insipidus, Nephrogenic/genetics
- Diabetes Insipidus, Nephrogenic/metabolism
- Diabetes Insipidus, Nephrogenic/pathology
- Gene Expression
- Genetic Predisposition to Disease
- Genotype
- Humans
- Keratoderma, Palmoplantar/genetics
- Keratoderma, Palmoplantar/metabolism
- Keratoderma, Palmoplantar/pathology
- Models, Molecular
- Mutation
- Phenotype
- Protein Conformation, alpha-Helical
- Protein Conformation, beta-Strand
- Protein Interaction Domains and Motifs
- Protein Multimerization
- Sequence Alignment
- Sequence Homology, Amino Acid
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Affiliation(s)
- Luisa Calvanese
- CIRPeB, University of Naples Federico II, Napoli I-80134, Italy.
| | - Gabriella D'Auria
- CIRPeB, University of Naples Federico II, Napoli I-80134, Italy.
- Department of Pharmacy, University of Naples Federico II, Napoli I-80134, Italy.
- Institute of Biostructures and Bioimaging, CNR, Napoli I-80134, Italy.
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.
| | - Lucia Falcigno
- CIRPeB, University of Naples Federico II, Napoli I-80134, Italy.
- Department of Pharmacy, University of Naples Federico II, Napoli I-80134, Italy.
- Institute of Biostructures and Bioimaging, CNR, Napoli I-80134, Italy.
| | - Romina Oliva
- Department of Sciences and Technologies, University Parthenope of Naples, Napoli I-80143, Italy.
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10
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Kurkcuoglu Z, Koukos PI, Citro N, Trellet ME, Rodrigues JPGLM, Moreira IS, Roel-Touris J, Melquiond ASJ, Geng C, Schaarschmidt J, Xue LC, Vangone A, Bonvin AMJJ. Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2. J Comput Aided Mol Des 2018; 32:175-185. [PMID: 28831657 PMCID: PMC5767195 DOI: 10.1007/s10822-017-0049-y] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 08/18/2017] [Indexed: 10/28/2022]
Abstract
We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.
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Affiliation(s)
- Zeynep Kurkcuoglu
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Nevia Citro
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Mikael E Trellet
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - J P G L M Rodrigues
- James H. Clark Center, Stanford University, 318 Campus Drive, S210, Stanford, CA, 94305, USA
| | - Irina S Moreira
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
- CNC - Center for Neuroscience and Cell Biology, FMUC, Universidade de Coimbra, Rua Larga, Polo I, 1ºandar, 3004-517, Coimbra, Portugal
| | - Jorge Roel-Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Adrien S J Melquiond
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands
| | - A M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands.
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11
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Vangone A, Rodrigues JPGLM, Xue LC, van Zundert GCP, Geng C, Kurkcuoglu Z, Nellen M, Narasimhan S, Karaca E, van Dijk M, Melquiond ASJ, Visscher KM, Trellet M, Kastritis PL, Bonvin AMJJ. Sense and Simplicity in HADDOCK Scoring: Lessons from CASP-CAPRI (page 418). Proteins 2017; 85:1589-1590. [PMID: 28730688 DOI: 10.1002/prot.25339] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- A Vangone
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - J P G L M Rodrigues
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - L C Xue
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - G C P van Zundert
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - C Geng
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - Z Kurkcuoglu
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - M Nellen
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - S Narasimhan
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - E Karaca
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - M van Dijk
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - A S J Melquiond
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - K M Visscher
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - M Trellet
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - P L Kastritis
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
| | - A M J J Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, 3584CH, Utrecht, The Netherlands
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12
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de Bruin RCG, Stam AGM, Vangone A, van Bergen En Henegouwen PMP, Verheul HMW, Sebestyén Z, Kuball J, Bonvin AMJJ, de Gruijl TD, van der Vliet HJ. Correction: Prevention of Vγ9Vδ2 T Cell Activation by a Vγ9Vδ2 TCR Nanobody. J Immunol 2017; 198:3759. [PMID: 28416722 DOI: 10.4049/jimmunol.1700317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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13
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Vangone A, Bonvin AMJJ. PRODIGY: A Contact-based Predictor of Binding Affinity in Protein-protein Complexes. Bio Protoc 2017; 7:e2124. [PMID: 34458447 DOI: 10.21769/bioprotoc.2124] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 11/07/2016] [Accepted: 01/06/2017] [Indexed: 11/02/2022] Open
Abstract
Biomolecular interactions between proteins regulate and control almost every biological process in the cell. Understanding these interactions is therefore a crucial step in the investigation of biological systems and in drug design. Many efforts have been devoted to unraveling principles of protein-protein interactions. Recently, we introduced a simple but robust descriptor of binding affinity based only on structural properties of a protein-protein complex. In Vangone and Bonvin (2015), we demonstrated that the number of interfacial contacts at the interface of a protein-protein complex correlates with the experimental binding affinity. Our findings have led one of the best performing predictor so far reported (Pearson's Correlation r = 0.73; RMSE = 1.89 kcal mol-1). Despite the importance of the topic, there is surprisingly only a limited number of online tools for fast and easy prediction of binding affinity. For this reason, we implemented our predictor into the user-friendly PRODIGY web-server. In this protocol, we explain the use of the PRODIGY web-server to predict the affinity of a protein-protein complex from its three-dimensional structure. The PRODIGY server is freely available at: http://milou.science.uu.nl/services/PRODIGY.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology group, Bijvoet Center for Biomolecular Research, Faculty of Science Chemistry, Utrecht University, Utrecht, the Netherlands
| | - Alexandre M J J Bonvin
- Computational Structural Biology group, Bijvoet Center for Biomolecular Research, Faculty of Science Chemistry, Utrecht University, Utrecht, the Netherlands
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14
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de Bruin RCG, Stam AGM, Vangone A, van Bergen En Henegouwen PMP, Verheul HMW, Sebestyén Z, Kuball J, Bonvin AMJJ, de Gruijl TD, van der Vliet HJ. Prevention of Vγ9Vδ2 T Cell Activation by a Vγ9Vδ2 TCR Nanobody. J Immunol 2016; 198:308-317. [PMID: 27895170 DOI: 10.4049/jimmunol.1600948] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Accepted: 11/02/2016] [Indexed: 01/09/2023]
Abstract
Vγ9Vδ2 T cell activation plays an important role in antitumor and antimicrobial immune responses. However, there are conditions in which Vγ9Vδ2 T cell activation can be considered inappropriate for the host. Patients treated with aminobisphosphonates for hypercalcemia or metastatic bone disease often present with a debilitating acute phase response as a result of Vγ9Vδ2 T cell activation. To date, no agents are available that can clinically inhibit Vγ9Vδ2 T cell activation. In this study, we describe the identification of a single domain Ab fragment directed to the TCR of Vγ9Vδ2 T cells with neutralizing properties. This variable domain of an H chain-only Ab (VHH or nanobody) significantly inhibited both phosphoantigen-dependent and -independent activation of Vγ9Vδ2 T cells and, importantly, strongly reduced the production of inflammatory cytokines upon stimulation with aminobisphosphonate-treated cells. Additionally, in silico modeling suggests that the neutralizing VHH binds the same residues on the Vγ9Vδ2 TCR as the Vγ9Vδ2 T cell Ag-presenting transmembrane protein butyrophilin 3A1, providing information on critical residues involved in this interaction. The neutralizing Vγ9Vδ2 TCR VHH identified in this study might provide a novel approach to inhibit the unintentional Vγ9Vδ2 T cell activation as a consequence of aminobisphosphonate administration.
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Affiliation(s)
- Renée C G de Bruin
- Department of Medical Oncology, VU University Medical Center, 1081 HV Amsterdam, the Netherlands
| | - Anita G M Stam
- Department of Medical Oncology, VU University Medical Center, 1081 HV Amsterdam, the Netherlands
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584 CH Utrecht, the Netherlands
| | | | - Henk M W Verheul
- Department of Medical Oncology, VU University Medical Center, 1081 HV Amsterdam, the Netherlands
| | - Zsolt Sebestyén
- Laboratory of Translational Immunology, Department of Hematology, University Medical Center Utrecht, 3508 GA Utrecht, the Netherlands
| | - Jürgen Kuball
- Laboratory of Translational Immunology, Department of Hematology, University Medical Center Utrecht, 3508 GA Utrecht, the Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584 CH Utrecht, the Netherlands
| | - Tanja D de Gruijl
- Department of Medical Oncology, VU University Medical Center, 1081 HV Amsterdam, the Netherlands
| | - Hans J van der Vliet
- Department of Medical Oncology, VU University Medical Center, 1081 HV Amsterdam, the Netherlands;
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15
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Vangone A, Rodrigues JPGLM, Xue LC, van Zundert GCP, Geng C, Kurkcuoglu Z, Nellen M, Narasimhan S, Karaca E, van Dijk M, Melquiond ASJ, Visscher KM, Trellet M, Kastritis PL, Bonvin AMJJ. Sense and simplicity in HADDOCK scoring: Lessons from CASP-CAPRI round 1. Proteins 2016; 85:417-423. [PMID: 27802573 PMCID: PMC5324763 DOI: 10.1002/prot.25198] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 10/14/2016] [Accepted: 10/25/2016] [Indexed: 12/28/2022]
Abstract
Our information-driven docking approach HADDOCK is a consistent top predictor and scorer since the start of its participation in the CAPRI community-wide experiment. This sustained performance is due, in part, to its ability to integrate experimental data and/or bioinformatics information into the modelling process, and also to the overall robustness of the scoring function used to assess and rank the predictions. In the CASP-CAPRI Round 1 scoring experiment we successfully selected acceptable/medium quality models for 18/14 of the 25 targets - a top-ranking performance among all scorers. Considering that for only 20 targets acceptable models were generated by the community, our effective success rate reaches as high as 90% (18/20). This was achieved using the standard HADDOCK scoring function, which, thirteen years after its original publication, still consists of a simple linear combination of intermolecular van der Waals and Coulomb electrostatics energies and an empirically derived desolvation energy term. Despite its simplicity, this scoring function makes sense from a physico-chemical perspective, encoding key aspects of biomolecular recognition. In addition to its success in the scoring experiment, the HADDOCK server takes the first place in the server prediction category, with 16 successful predictions. Much like our scoring protocol, because of the limited time per target, the predictions relied mainly on either an ab initio center-of-mass and symmetry restrained protocol, or on a template-based approach whenever applicable. These results underline the success of our simple but sensible prediction and scoring scheme. Proteins 2017; 85:417-423. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- A Vangone
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - J P G L M Rodrigues
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - L C Xue
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - G C P van Zundert
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - C Geng
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - Z Kurkcuoglu
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - M Nellen
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - S Narasimhan
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - E Karaca
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - M van Dijk
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - A S J Melquiond
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - K M Visscher
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - M Trellet
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - P L Kastritis
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
| | - A M J J Bonvin
- Department of Chemistry, Computational Structural Biology Group, Faculty of Science, Utrecht University, Padualaan 8, Utrecht, 3584CH, The Netherlands
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16
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Lensink MF, Velankar S, Kryshtafovych A, Huang SY, Schneidman-Duhovny D, Sali A, Segura J, Fernandez-Fuentes N, Viswanath S, Elber R, Grudinin S, Popov P, Neveu E, Lee H, Baek M, Park S, Heo L, Rie Lee G, Seok C, Qin S, Zhou HX, Ritchie DW, Maigret B, Devignes MD, Ghoorah A, Torchala M, Chaleil RAG, Bates PA, Ben-Zeev E, Eisenstein M, Negi SS, Weng Z, Vreven T, Pierce BG, Borrman TM, Yu J, Ochsenbein F, Guerois R, Vangone A, Rodrigues JPGLM, van Zundert G, Nellen M, Xue L, Karaca E, Melquiond ASJ, Visscher K, Kastritis PL, Bonvin AMJJ, Xu X, Qiu L, Yan C, Li J, Ma Z, Cheng J, Zou X, Shen Y, Peterson LX, Kim HR, Roy A, Han X, Esquivel-Rodriguez J, Kihara D, Yu X, Bruce NJ, Fuller JC, Wade RC, Anishchenko I, Kundrotas PJ, Vakser IA, Imai K, Yamada K, Oda T, Nakamura T, Tomii K, Pallara C, Romero-Durana M, Jiménez-García B, Moal IH, Férnandez-Recio J, Joung JY, Kim JY, Joo K, Lee J, Kozakov D, Vajda S, Mottarella S, Hall DR, Beglov D, Mamonov A, Xia B, Bohnuud T, Del Carpio CA, Ichiishi E, Marze N, Kuroda D, Roy Burman SS, Gray JJ, Chermak E, Cavallo L, Oliva R, Tovchigrechko A, Wodak SJ. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins 2016; 84 Suppl 1:323-48. [PMID: 27122118 PMCID: PMC5030136 DOI: 10.1002/prot.25007] [Citation(s) in RCA: 116] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 12/30/2015] [Accepted: 02/02/2016] [Indexed: 12/26/2022]
Abstract
We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Marc F Lensink
- University Lille, CNRS UMR8576 UGSF, Lille, F-59000, France.
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | | | - Shen-You Huang
- Research Support Computing, University of Missouri Bioinformatics Consortium, and Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
- California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, 94158
| | - Joan Segura
- GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), Madrid, 28049, Spain
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, SY233FG, United Kingdom
| | - Shruthi Viswanath
- Department of Computer Science, University of Texas at Austin, Austin, Texas, 78712
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
| | - Ron Elber
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
- Department of Chemistry, University of Texas at Austin, Austin, Texas, 78712
| | - Sergei Grudinin
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Petr Popov
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Emilie Neveu
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | | | - Bernard Maigret
- CNRS, LORIA, Campus Scientifique, BP 239, Vandœuvre-lès-Nancy, 54506, France
| | | | - Anisah Ghoorah
- Department of Computer Science and Engineering, University of Mauritius, Reduit, Mauritius
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Raphaël A G Chaleil
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Paul A Bates
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Efrat Ben-Zeev
- G-INCPM, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Miriam Eisenstein
- Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Surendra S Negi
- Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas, 77555-0857
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Tyler M Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Françoise Ochsenbein
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - João P G L M Rodrigues
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Gydo van Zundert
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Mehdi Nellen
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Li Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Ezgi Karaca
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Adrien S J Melquiond
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Koen Visscher
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Chengfei Yan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
- Department of Biochemistry, University of Missouri, Columbia, Missouri, 65211
| | - Yang Shen
- Toyota Technological Institute at Chicago, 6045 S Kenwood Avenue, Chicago, Illinois, 60637
| | - Lenna X Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Hyung-Rae Kim
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Amit Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, National Institutes of Health, Hamilton, Montano 59840
| | - Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | | | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Department of Computer Science, Purdue University, West Lafayette, IN, USA, 47907
| | - Xiaofeng Yu
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Neil J Bruce
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Jonathan C Fuller
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ivan Anishchenko
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047
| | - Kenichiro Imai
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Kazunori Yamada
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Toshiyuki Oda
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Tsukasa Nakamura
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Kentaro Tomii
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Chiara Pallara
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Miguel Romero-Durana
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Brian Jiménez-García
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Iain H Moal
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Juan Férnandez-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Jong Young Joung
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jong Yun Kim
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Keehyoung Joo
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jooyoung Lee
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- School of Computational Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Scott Mottarella
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - David R Hall
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Artem Mamonov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Carlos A Del Carpio
- Institute of Biological Diversity, International Pacific Institute of Indiana, Bloomington, Indiana, 47401
- Drosophila Genetic Resource Center, Kyoto Institute of Technology, Ukyo-Ku, 616-8354, Japan
| | - Eichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Asushiobara-City, Tochigi Prefecture, 329-2763, Japan
| | - Nicholas Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Edrisse Chermak
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Andrey Tovchigrechko
- J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, Maryland, 20850
| | - Shoshana J Wodak
- Departments of Biochemistry and Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- VIB Structural Biology Research Center, VUB Pleinlaan 2, Brussels, 1050, Belgium.
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17
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Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016; 32:3676-3678. [PMID: 27503228 DOI: 10.1093/bioinformatics/btw514] [Citation(s) in RCA: 444] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 07/17/2016] [Accepted: 07/30/2016] [Indexed: 11/13/2022] Open
Abstract
Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given protein-protein complex. Here we present PROtein binDIng enerGY prediction (PRODIGY), a web server to predict the binding affinity of protein-protein complexes from their 3D structure. The PRODIGY server implements our simple but highly effective predictive model based on intermolecular contacts and properties derived from non-interface surface. AVAILABILITY AND IMPLEMENTATION PRODIGY is freely available at: http://milou.science.uu.nl/services/PRODIGY CONTACT: a.m.j.j.bonvin@uu.nl, a.vangone@uu.nl.
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Affiliation(s)
- Li C Xue
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands
| | - João Pglm Rodrigues
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Panagiotis L Kastritis
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Alexandre Mjj Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science - Department of Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands
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18
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Geng C, Vangone A, Bonvin AMJJ. Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein Eng Des Sel 2016; 29:291-299. [PMID: 27284087 DOI: 10.1093/protein/gzw020] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 05/09/2016] [Indexed: 11/14/2022] Open
Abstract
Reliable prediction of binding affinity changes (ΔΔG) upon mutations in protein complexes relies not only on the performance of computational methods but also on the availability and quality of experimental data. Binding affinity changes can be measured by various experimental methods with different accuracies and limitations. To understand the impact of these on the prediction of binding affinity change, we present the Database of binding Affinity Change Upon Mutation (DACUM), a database of 1872 binding affinity changes upon single-point mutations, a subset of the SKEMPI database (Moal,I.H. and Fernández-Recio,J. Bioinformatics, 2012;28:2600-2607) extended with information on the experimental methods used for ΔΔG measurements. The ΔΔG data were classified into different data sets based on the experimental method used and the position of the mutation (interface and non-interface). We tested the prediction performance of the original HADDOCK score, a newly trained version of it and mutation Cutoff Scanning Matrix (Pires,D.E.V., Ascher,D.B. and Blundell,T.L. Bioinformatics 2014;30:335-342), one of the best reported ΔΔG predictors so far, on these various data sets. Our results demonstrate a strong impact of the experimental methods on the performance of binding affinity change predictors for protein complexes. This underscores the importance of properly considering and carefully choosing experimental methods in the development of novel binding affinity change predictors. The DACUM database is available online at https://github.com/haddocking/DACUM.
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Affiliation(s)
- Cunliang Geng
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
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19
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Calvanese L, D'Auria G, Vangone A, Falcigno L, Oliva R. Analysis of the interface variability in NMR structure ensembles of protein-protein complexes. J Struct Biol 2016; 194:317-24. [PMID: 26968364 DOI: 10.1016/j.jsb.2016.03.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 03/07/2016] [Accepted: 03/08/2016] [Indexed: 01/22/2023]
Abstract
NMR structures consist in ensembles of conformers, all satisfying the experimental restraints, which exhibit a certain degree of structural variability. We analyzed here the interface in NMR ensembles of protein-protein heterodimeric complexes and found it to span a wide range of different conservations. The different exhibited conservations do not simply correlate with the size of the systems/interfaces, and are most probably the result of an interplay between different factors, including the quality of experimental data and the intrinsic complex flexibility. In any case, this information is not to be missed when NMR structures of protein-protein complexes are analyzed; especially considering that, as we also show here, the first NMR conformer is usually not the one which best reflects the overall interface. To quantify the interface conservation and to analyze it, we used an approach originally conceived for the analysis and ranking of ensembles of docking models, which has now been extended to directly deal with NMR ensembles. We propose this approach, based on the conservation of the inter-residue contacts at the interface, both for the analysis of the interface in whole ensembles of NMR complexes and for the possible selection of a single conformer as the best representative of the overall interface. In order to make the analyses automatic and fast, we made the protocol available as a web tool at: https://www.molnac.unisa.it/BioTools/consrank/consrank-nmr.html.
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Affiliation(s)
- Luisa Calvanese
- CIRPeB, University of Naples "Federico II", via Mezzocannone 16, 80134 Naples, Italy; Department of Pharmacy, University of Naples "Federico II", via Mezzocannone 16, 80134 Naples, Italy; Institute of Biostructures and Bioimaging - CNR, via Mezzocannone, 16, 80134 Naples, Italy.
| | - Gabriella D'Auria
- CIRPeB, University of Naples "Federico II", via Mezzocannone 16, 80134 Naples, Italy; Department of Pharmacy, University of Naples "Federico II", via Mezzocannone 16, 80134 Naples, Italy; Institute of Biostructures and Bioimaging - CNR, via Mezzocannone, 16, 80134 Naples, Italy.
| | - Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands.
| | - Lucia Falcigno
- CIRPeB, University of Naples "Federico II", via Mezzocannone 16, 80134 Naples, Italy; Department of Pharmacy, University of Naples "Federico II", via Mezzocannone 16, 80134 Naples, Italy; Institute of Biostructures and Bioimaging - CNR, via Mezzocannone, 16, 80134 Naples, Italy.
| | - Romina Oliva
- Department of Sciences and Technologies, University Parthenope of Naples, Centro Direzionale Isola C4, I-80143 Naples, Italy.
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20
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Vreven T, Moal IH, Vangone A, Pierce BG, Kastritis PL, Torchala M, Chaleil R, Jiménez-García B, Bates PA, Fernandez-Recio J, Bonvin AMJJ, Weng Z. Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. J Mol Biol 2015; 427:3031-41. [PMID: 26231283 PMCID: PMC4677049 DOI: 10.1016/j.jmb.2015.07.016] [Citation(s) in RCA: 248] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 07/17/2015] [Accepted: 07/17/2015] [Indexed: 01/31/2023]
Abstract
We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Iain H Moal
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom
| | - Raphael Chaleil
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom
| | - Brian Jiménez-García
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom.
| | - Juan Fernandez-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain.
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands.
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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21
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Abstract
Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre Mjj Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
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22
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Abstract
Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre MJJ Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
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23
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Chermak E, Petta A, Serra L, Vangone A, Scarano V, Cavallo L, Oliva R. CONSRANK: a server for the analysis, comparison and ranking of docking models based on inter-residue contacts. Bioinformatics 2014; 31:1481-3. [PMID: 25535242 DOI: 10.1093/bioinformatics/btu837] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Accepted: 12/14/2014] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Herein, we present CONSRANK, a web tool for analyzing, comparing and ranking protein-protein and protein-nucleic acid docking models, based on the conservation of inter-residue contacts and its visualization in 2D and 3D interactive contact maps. AVAILABILITY AND IMPLEMENTATION CONSRANK is accessible as a public web tool at https://www.molnac.unisa.it/BioTools/consrank/. CONTACT romina.oliva@uniparthenope.it.
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Affiliation(s)
- Edrisse Chermak
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Dipartimento di Informatica ed Applicazioni, Department of Chemistry and Biology, University of Salerno, Fisciano, SA, Italy and Department of Sciences and Technologies, University "Parthenope" of Naples, Naples, Italy
| | - Andrea Petta
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Dipartimento di Informatica ed Applicazioni, Department of Chemistry and Biology, University of Salerno, Fisciano, SA, Italy and Department of Sciences and Technologies, University "Parthenope" of Naples, Naples, Italy
| | - Luigi Serra
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Dipartimento di Informatica ed Applicazioni, Department of Chemistry and Biology, University of Salerno, Fisciano, SA, Italy and Department of Sciences and Technologies, University "Parthenope" of Naples, Naples, Italy
| | - Anna Vangone
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Dipartimento di Informatica ed Applicazioni, Department of Chemistry and Biology, University of Salerno, Fisciano, SA, Italy and Department of Sciences and Technologies, University "Parthenope" of Naples, Naples, Italy
| | - Vittorio Scarano
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Dipartimento di Informatica ed Applicazioni, Department of Chemistry and Biology, University of Salerno, Fisciano, SA, Italy and Department of Sciences and Technologies, University "Parthenope" of Naples, Naples, Italy
| | - Luigi Cavallo
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Dipartimento di Informatica ed Applicazioni, Department of Chemistry and Biology, University of Salerno, Fisciano, SA, Italy and Department of Sciences and Technologies, University "Parthenope" of Naples, Naples, Italy
| | - Romina Oliva
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Dipartimento di Informatica ed Applicazioni, Department of Chemistry and Biology, University of Salerno, Fisciano, SA, Italy and Department of Sciences and Technologies, University "Parthenope" of Naples, Naples, Italy
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24
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Vangone A, Abdel-Azeim S, Caputo I, Sblattero D, Di Niro R, Cavallo L, Oliva R. Structural basis for the recognition in an idiotype-anti-idiotype antibody complex related to celiac disease. PLoS One 2014; 9:e102839. [PMID: 25076134 PMCID: PMC4116137 DOI: 10.1371/journal.pone.0102839] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2014] [Accepted: 06/21/2014] [Indexed: 11/19/2022] Open
Abstract
Anti-idiotype antibodies have potential therapeutic applications in many fields, including autoimmune diseases. Herein we report the isolation and characterization of AIM2, an anti-idiotype antibody elicited in a mouse model upon expression of the celiac disease-specific autoantibody MB2.8 (directed against the main disease autoantigen type 2 transglutaminase, TG2). To characterize the interaction between the two antibodies, a 3D model of the MB2.8-AIM2 complex has been obtained by molecular docking. Analysis and selection of the different obtained docking solutions was based on the conservation within them of the inter-residue contacts. The selected model is very well representative of the different solutions found and its stability is confirmed by molecular dynamics simulations. Furthermore, the binding mode it adopts is very similar to that observed in most of the experimental structures available for idiotype-anti-idiotype antibody complexes. In the obtained model, AIM2 is directed against the MB2.8 CDR region, especially on its variable light chain. This makes the concurrent formation of the MB2.8-AIM2 complex and of the MB2.8-TG2 complex incompatible, thus explaining the experimentally observed inhibitory effect on the MB2.8 binding to TG2.
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Affiliation(s)
- Anna Vangone
- Department of Chemistry and Biology, University of Salerno, Fisciano, Salerno, Italy
| | - Safwat Abdel-Azeim
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Ivana Caputo
- Department of Chemistry and Biology, University of Salerno, Fisciano, Salerno, Italy
- European Laboratory for the Investigation of Food-Induced Diseases (ELFID), University Federico II, Naples, Italy
| | - Daniele Sblattero
- Department of Health Sciences and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Italy
| | - Roberto Di Niro
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Luigi Cavallo
- Department of Chemistry and Biology, University of Salerno, Fisciano, Salerno, Italy
- Kaust Catalysis Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University “Parthenope” of Naples, Naples, Italy
- * E-mail:
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25
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Abdel-Azeim S, Chermak E, Vangone A, Oliva R, Cavallo L. MDcons: Intermolecular contact maps as a tool to analyze the interface of protein complexes from molecular dynamics trajectories. BMC Bioinformatics 2014; 15 Suppl 5:S1. [PMID: 25077693 PMCID: PMC4095001 DOI: 10.1186/1471-2105-15-s5-s1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background Molecular Dynamics (MD) simulations of protein complexes suffer from the lack of specific tools in the analysis step. Analyses of MD trajectories of protein complexes indeed generally rely on classical measures, such as the RMSD, RMSF and gyration radius, conceived and developed for single macromolecules. As a matter of fact, instead, researchers engaged in simulating the dynamics of a protein complex are mainly interested in characterizing the conservation/variation of its biological interface. Results On these bases, herein we propose a novel approach to the analysis of MD trajectories or other conformational ensembles of protein complexes, MDcons, which uses the conservation of inter-residue contacts at the interface as a measure of the similarity between different snapshots. A "consensus contact map" is also provided, where the conservation of the different contacts is drawn in a grey scale. Finally, the interface area of the complex is monitored during the simulations. To show its utility, we used this novel approach to study two protein-protein complexes with interfaces of comparable size and both dominated by hydrophilic interactions, but having binding affinities at the extremes of the experimental range. MDcons is demonstrated to be extremely useful to analyse the MD trajectories of the investigated complexes, adding important insight into the dynamic behavior of their biological interface. Conclusions MDcons specifically allows the user to highlight and characterize the dynamics of the interface in protein complexes and can thus be used as a complementary tool for the analysis of MD simulations of both experimental and predicted structures of protein complexes.
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Menegatti M, Vangone A, Palla R, Milano G, Cavallo L, Oliva R, De Cristofaro R, Peyvandi F. A recurrent Gly43Asp substitution in coagulation Factor X rigidifies its catalytic pocket and impairs catalytic activity and intracellular trafficking. Thromb Res 2014; 133:481-7. [DOI: 10.1016/j.thromres.2013.12.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Revised: 11/28/2013] [Accepted: 12/16/2013] [Indexed: 11/30/2022]
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Kelm S, Vangone A, Choi Y, Ebejer JP, Shi J, Deane CM. Fragment-based modeling of membrane protein loops: successes, failures, and prospects for the future. Proteins 2013; 82:175-86. [PMID: 23589399 DOI: 10.1002/prot.24299] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2012] [Revised: 02/22/2013] [Accepted: 03/26/2013] [Indexed: 11/12/2022]
Abstract
Membrane proteins (MPs) have become a major focus in structure prediction, due to their medical importance. There is, however, a lack of fast and reliable methods that specialize in the modeling of MP loops. Often methods designed for soluble proteins (SPs) are applied directly to MPs. In this article, we investigate the validity of such an approach in the realm of fragment-based methods. We also examined the differences in membrane and soluble protein loops that might affect accuracy. We test our ability to predict soluble and MP loops with the previously published method FREAD. We show that it is possible to predict accurately the structure of MP loops using a database of MP fragments (0.5-1 Å median root-mean-square deviation). The presence of homologous proteins in the database helps prediction accuracy. However, even when homologues are removed better results are still achieved using fragments of MPs (0.8-1.6 Å) rather than SPs (1-4 Å) to model MP loops. We find that many fragments of SPs have shapes similar to their MP counterparts but have very different sequences; however, they do not appear to differ in their substitution patterns. Our findings may allow further improvements to fragment-based loop modeling algorithms for MPs. The current version of our proof-of-concept loop modeling protocol produces high-accuracy loop models for MPs and is available as a web server at http://medeller.info/fread.
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Affiliation(s)
- Sebastian Kelm
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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Vangone A, Cavallo L, Oliva R. Using a consensus approach based on the conservation of inter-residue contacts to rank CAPRI models. Proteins 2013; 81:2210-20. [PMID: 24115176 DOI: 10.1002/prot.24423] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Revised: 09/04/2013] [Accepted: 09/10/2013] [Indexed: 01/09/2023]
Abstract
Herein we propose the use of a consensus approach, CONSRANK, for ranking CAPRI models. CONSRANK relies on the conservation of inter-residue contacts in the analyzed decoys ensemble. Models are ranked according to their ability to match the most frequently observed contacts. We applied CONSRANK to 19 CAPRI protein-protein targets, covering a wide range of prediction difficulty and involved in a variety of biological functions. CONSRANK results are consistently good, both in terms of native-like (NL) solutions ranked in the top positions and of values of the Area Under the receiver operating characteristic Curve (AUC). For targets having a percentage of NL solutions above 3%, an excellent performance is found, with AUC values approaching 1. For the difficult target T46, having only 3.4% NL solutions, the number of NL solutions in the top 5 and 10 ranked positions is enriched by a factor 30, and the AUC value is as high as 0.997. AUC values below 0.8 are only found for targets featuring a percentage of NL solutions within 1.1%. Remarkably, a false consensus emerges only in one case, T42, which happens to be an artificial protein, whose assembly details remain uncertain, based on controversial experimental data. We also show that CONSRANK still performs very well on a limited number of models, provided that more than 1 NL solution is included in the ensemble, thus extending its applicability to cases where few dozens of models are available.
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Affiliation(s)
- Anna Vangone
- Department of Chemistry and Biology, University of Salerno, Via ponte don Melillo, 84084, Fisciano (SA), Italy
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Oliva R, Vangone A, Cavallo L. Ranking multiple docking solutions based on the conservation of inter-residue contacts. Proteins 2013; 81:1571-84. [PMID: 23609916 DOI: 10.1002/prot.24314] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 03/16/2013] [Accepted: 04/08/2013] [Indexed: 01/11/2023]
Abstract
Molecular docking is the method of choice for investigating the molecular basis of recognition in a large number of functional protein complexes. However, correctly scoring the obtained docking solutions (decoys) to rank native-like (NL) conformations in the top positions is still an open problem. Herein we present CONSRANK, a simple and effective tool to rank multiple docking solutions, which relies on the conservation of inter-residue contacts in the analyzed decoys ensemble. First it calculates a conservation rate for each inter-residue contact, then it ranks decoys according to their ability to match the more frequently observed contacts. We applied CONSRANK to 102 targets from three different benchmarks, RosettaDock, DOCKGROUND, and Critical Assessment of PRedicted Interactions (CAPRI). The method performs consistently well, both in terms of NL solutions ranked in the top positions and of values of the area under the receiver operating characteristic curve. Its ideal application is to solutions coming from different docking programs and procedures, as in the case of CAPRI targets. For all the analyzed CAPRI targets where a comparison is feasible, CONSRANK outperforms the CAPRI scorers. The fraction of NL solutions in the top ten positions in the RosettaDock, DOCKGROUND, and CAPRI benchmarks is enriched on average by a factor of 3.0, 1.9, and 9.9, respectively. Interestingly, CONSRANK is also able to specifically single out the high/medium quality (HMQ) solutions from the docking decoys ensemble: it ranks 46.2 and 70.8% of the total HMQ solutions available for the RosettaDock and CAPRI targets, respectively, within the top 20 positions.
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Affiliation(s)
- Romina Oliva
- Department of Applied Sciences, University "Parthenope" of Naples, Centro Direzionale Isola C4, 80143, Naples, Italy
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Vangone A, Oliva R, Cavallo L. CONS-COCOMAPS: a novel tool to measure and visualize the conservation of inter-residue contacts in multiple docking solutions. BMC Bioinformatics 2012; 13 Suppl 4:S19. [PMID: 22536965 PMCID: PMC3434444 DOI: 10.1186/1471-2105-13-s4-s19] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background The development of accurate protein-protein docking programs is making this kind of simulations an effective tool to predict the 3D structure and the surface of interaction between the molecular partners in macromolecular complexes. However, correctly scoring multiple docking solutions is still an open problem. As a consequence, the accurate and tedious screening of many docking models is usually required in the analysis step. Methods All the programs under CONS-COCOMAPS have been written in python, taking advantage of python libraries such as SciPy and Matplotlib. CONS-COCOMAPS is freely available as a web tool at the URL: http://www.molnac.unisa.it/BioTools/conscocomaps/. Results Here we presented CONS-COCOMAPS, a novel tool to easily measure and visualize the consensus in multiple docking solutions. CONS-COCOMAPS uses the conservation of inter-residue contacts as an estimate of the similarity between different docking solutions. To visualize the conservation, CONS-COCOMAPS uses intermolecular contact maps. Conclusions The application of CONS-COCOMAPS to test-cases taken from recent CAPRI rounds has shown that it is very efficient in highlighting even a very weak consensus that often is biologically meaningful.
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Affiliation(s)
- Anna Vangone
- Department of Applied Sciences, University Parthenope of Naples, Centro Direzionale Isola C4, Naples, 80143, Italy
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Vangone A, Spinelli R, Scarano V, Cavallo L, Oliva R. COCOMAPS: a web application to analyze and visualize contacts at the interface of biomolecular complexes. Bioinformatics 2011; 27:2915-6. [PMID: 21873642 DOI: 10.1093/bioinformatics/btr484] [Citation(s) in RCA: 205] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
SUMMARY Herein we present COCOMAPS, a novel tool for analyzing, visualizing and comparing the interface in protein-protein and protein-nucleic acids complexes. COCOMAPS combines traditional analyses and 3D visualization of the interface with the effectiveness of intermolecular contact maps. AVAILABILITY COCOMAPS is accessible as a public web tool at http://www.molnac.unisa.it/BioTools/cocomaps CONTACT romina.oliva@uniparthenope.it; lcavallo@unisa.it.
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Affiliation(s)
- Anna Vangone
- Department of Chemistry and Biology, University of Salerno, 84084 Fisciano, Italy
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Borg MJ, Marcuccio F, Poerio AM, Vangone A. [Magnetic fields in physical therapy. Experience in orthopedics and traumatology rehabilitation]. Minerva Med 1996; 87:495-7. [PMID: 8992414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The present research is based on the premise that magnetic fields stimulate biological tissues, as many international works assert. They believe in the real aid of this therapeutical treatment in orthopedy and traumatology. The authors work in Rehabilitation Department of a traumatological hospital, so they have studied therapeutical results in ELF magnetotherapy on their patients for as long as six months.
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Affiliation(s)
- M J Borg
- Servizio di Recupero e Rieducazione Funzionale, Ospedale CTO-ASL 1-Napoli
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