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Egbert M, Ghani U, Ashizawa R, Kotelnikov S, Nguyen T, Desta I, Hashemi N, Padhorny D, Kozakov D, Vajda S. Assessing the binding properties of CASP14 targets and models. Proteins 2021; 89:1922-1939. [PMID: 34368994 DOI: 10.1002/prot.26209] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/22/2021] [Accepted: 08/04/2021] [Indexed: 12/27/2022]
Abstract
An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein-protein interactions by docking both the X-ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X-ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein-protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.
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Affiliation(s)
- Megan Egbert
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Thu Nguyen
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nasser Hashemi
- Division of Systems Engineering, Boston University, Boston, Massachusetts, USA
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.,Department of Chemistry, Boston University, Boston, Massachusetts, USA
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2
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Egbert M, Porter KA, Ghani U, Kotelnikov S, Nguyen T, Ashizawa R, Kozakov D, Vajda S. Conservation of binding properties in protein models. Comput Struct Biotechnol J 2021; 19:2549-2566. [PMID: 34025942 PMCID: PMC8114079 DOI: 10.1016/j.csbj.2021.04.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 04/19/2021] [Accepted: 04/22/2021] [Indexed: 01/09/2023] Open
Abstract
We study the models submitted to round 12 of the Critical Assessment of protein Structure Prediction (CASP) experiment to assess how well the binding properties are conserved when the X-ray structures of the target proteins are replaced by their models. To explore small molecule binding we generate distributions of molecular probes - which are fragment-sized organic molecules of varying size, shape, and polarity - around the protein, and count the number of interactions between each residue and the probes, resulting in a vector of interactions we call a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model of the protein, is determined by calculating the correlation coefficient between the two vectors. The resulting correlation coefficients are shown to correlate with global measures of accuracy established in CASP, and the relationship yields an accuracy threshold that has to be reached for meaningful binding surface conservation. The clusters formed by the probe molecules reliably predict binding hot spots and ligand binding sites in both X-ray structures and reasonably accurate models of the target, but ensembles of models may be needed for assessing the availability of proper binding pockets. We explored ligand docking to the few targets that had bound ligands in the X-ray structure. More targets were available to assess the ability of the models to reproduce protein-protein interactions by docking both the X-ray structures and models to their interaction partners in complexes. It was shown that this application is more difficult than finding small ligand binding sites, and the success rates heavily depend on the local structure in the potential interface. In particular, predicted conformations of flexible loops are frequently incorrect in otherwise highly accurate models, and may prevent predicting correct protein-protein interactions.
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Affiliation(s)
- Megan Egbert
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States
| | - Kathryn A. Porter
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, United States
| | - Thu Nguyen
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, United States
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, United States
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, United States
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States
- Department of Chemistry, Boston University, Boston, MA 02215, United States
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3
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Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical assessment of methods of protein structure prediction (CASP)-Round XIII. Proteins 2019; 87:1011-1020. [PMID: 31589781 DOI: 10.1002/prot.25823] [Citation(s) in RCA: 269] [Impact Index Per Article: 53.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/25/2019] [Accepted: 09/27/2019] [Indexed: 12/24/2022]
Abstract
CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically "ab initio" modeling). Progress was driven by the successful application of deep learning techniques to predict inter-residue distances. In turn, these results drove dramatic improvements in three-dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long-standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template-based models. Other areas-model refinement, accuracy estimation, and the structure of protein assemblies-have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants.
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Affiliation(s)
| | - Torsten Schwede
- Biozentrum & SIB Swiss Institute of Bioinformatics, University of Basel, Basel, Switzerland
| | - Maya Topf
- Institute of Structural and Molecular Biology, Birkbeck College, University of London, London, UK
| | | | - John Moult
- Institute for Bioscience and Biotechnology Research, Rockville, Maryland.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
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4
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Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins 2018; 86 Suppl 1:7-15. [PMID: 29082672 PMCID: PMC5897042 DOI: 10.1002/prot.25415] [Citation(s) in RCA: 245] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 10/25/2017] [Accepted: 10/27/2017] [Indexed: 12/24/2022]
Abstract
This article reports the outcome of the 12th round of Critical Assessment of Structure Prediction (CASP12), held in 2016. CASP is a community experiment to determine the state of the art in modeling protein structure from amino acid sequence. Participants are provided sequence information and in turn provide protein structure models and related information. Analysis of the submitted structures by independent assessors provides a comprehensive picture of the capabilities of current methods, and allows progress to be identified. This was again an exciting round of CASP, with significant advances in 4 areas: (i) The use of new methods for predicting three-dimensional contacts led to a two-fold improvement in contact accuracy. (ii) As a consequence, model accuracy for proteins where no template was available improved dramatically. (iii) Models based on a structural template showed overall improvement in accuracy. (iv) Methods for estimating the accuracy of a model continued to improve. CASP continued to develop new areas: (i) Assessing methods for building quaternary structure models, including an expansion of the collaboration between CASP and CAPRI. (ii) Modeling with the aid of experimental data was extended to include SAXS data, as well as again using chemical cross-linking information. (iii) A team of assessors evaluated the suitability of models for a range of applications, including mutation interpretation, analysis of ligand binding properties, and identification of interfaces. This article describes the experiment and summarizes the results. The rest of this special issue of PROTEINS contains papers describing CASP12 results and assessments in more detail.
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Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research and Department of Cell Biology and Molecular Genetics, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Torsten Schwede
- University of Basel, Biozentrum & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Anna Tramontano
- Department of Physics and Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
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Liu T, Ish‐Shalom S, Torng W, Lafita A, Bock C, Mort M, Cooper DN, Bliven S, Capitani G, Mooney SD, Altman RB. Biological and functional relevance of CASP predictions. Proteins 2018; 86 Suppl 1:374-386. [PMID: 28975675 PMCID: PMC5820171 DOI: 10.1002/prot.25396] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 09/12/2017] [Accepted: 10/03/2017] [Indexed: 02/06/2023]
Abstract
Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.
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Affiliation(s)
- Tianyun Liu
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Shirbi Ish‐Shalom
- Biomedical Informatics Training Program, Stanford UniversityStanfordCalifornia
| | - Wen Torng
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Aleix Lafita
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of Biosystems Science and EngineeringETH Zurich4058BaselSwitzerland
| | - Christian Bock
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
- Heidelberg UniversityHeidelbergGermany
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - David N Cooper
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - Spencer Bliven
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- National Center for Biotechnology Information, National Library of MedicineNational Institutes of HealthBethesdaMaryland
| | - Guido Capitani
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of BiologyETH ZurichZurichSwitzerland
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
| | - Russ B. Altman
- Department of BioengineeringStanford UniversityStanfordCalifornia
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Fu DY, Meiler J. Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review. J Chem Inf Model 2018; 58:225-233. [PMID: 29286651 DOI: 10.1021/acs.jcim.7b00418] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.
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Affiliation(s)
- Darwin Y Fu
- Department of Chemistry Vanderbilt University Nashville, Tennessee 37235, United States
| | - Jens Meiler
- Department of Chemistry Vanderbilt University Nashville, Tennessee 37235, United States
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Nealon JO, Philomina LS, McGuffin LJ. Predictive and Experimental Approaches for Elucidating Protein-Protein Interactions and Quaternary Structures. Int J Mol Sci 2017; 18:E2623. [PMID: 29206185 PMCID: PMC5751226 DOI: 10.3390/ijms18122623] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 11/29/2017] [Accepted: 11/30/2017] [Indexed: 11/17/2022] Open
Abstract
The elucidation of protein-protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development.
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Affiliation(s)
- John Oliver Nealon
- School of Biological Sciences, University of Reading, Reading RG6 6AS, UK.
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Moult J, Fidelis K, Kryshtafovych A, Schwede T. A tribute to Anna Tramontano (1957-2017). Proteins 2017; 86 Suppl 1:5-6. [DOI: 10.1002/prot.25406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 10/23/2017] [Indexed: 12/26/2022]
Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research; University of Maryland; Rockville Maryland
| | | | | | - Torsten Schwede
- Biozentrum, University of Basel & SIB Swiss Institute of Bioinformatics; Basel Switzerland
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