For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
35
views
70
references
 Top references
cited by
98
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      80
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Motivation

          Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.

          Results

          We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations, which abolish detectable binding.

          Availability and implementation

          The database is available as supplementary data and at https://life.bsc.es/pid/skempi2/.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

          Related collections

          Most cited references70

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          mCSM: predicting the effects of mutations in proteins using graph-based signatures

          Douglas Pires, David B. Ascher, Tom Blundell (2013)
          Motivation: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein–protein and protein–nucleic acid interactions. Results: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario. Availability and implementation: A web server is available at http://structure.bioc.cam.ac.uk/mcsm. Contact: dpires@dcc.ufmg.br; tom@cryst.bioc.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
          Article 0 comments Cited 218 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2.

            Thom Vreven, Iain Moal, Anna Vangone … (2015)
            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.
            Article 0 comments Cited 143 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations

              Yves Dehouck, Jean Kwasigroch, Marianne Rooman … (2013)
              The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein–protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein–protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.
              Article 0 comments Cited 120 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Ioannis Xenarios: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 01 February 2019
                Publication date (Electronic): 18 July 2018
                Publication date PMC-release: 18 July 2018
                Volume: 35
                Issue: 3
                Pages: 462-469
                Affiliations
                [1 ]Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
                [2 ]Barcelona Supercomputing Center (BSC), Barcelona, Spain
                [3 ]Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, Utrecht, the Netherlands
                [4 ]Institut de Biologia Molecular de Barcelona (IBMB), CSIC, Barcelona, Spain
                [5 ]European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
                Author notes
                To whom correspondence should be addressed. E-mail: moal@ 123456ebi.ac.uk
                Author information
                http://orcid.org/0000-0002-4960-5487
                Article
                Publisher ID: bty635
                DOI: 10.1093/bioinformatics/bty635
                PMC ID: 6361233
                PubMed ID: 30020414
                SO-VID: 7ea4e15d-927e-472c-a06c-d4036866c374
                Copyright © © The Author(s) 2018. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 16 May 2018
                Date revision received : 16 May 2018
                Date accepted : 17 July 2018
                Page count
                Pages: 8
                Funding
                Funded by: European Molecular Biology Laboratory 10.13039/100013060
                Funded by: Biotechnology and Biological Sciences Research Council 10.13039/501100000268
                Funded by: Future Leader Fellowship
                Award ID: BB/N011600/1
                Funded by: Spanish Ministry of Economy and Competitiveness
                Funded by: MINECO 10.13039/501100003329
                Award ID: BIO2016-79930-R
                Funded by: Interreg POCTEFA
                Award ID: EFA086/15
                Funded by: European Commission 10.13039/501100000780
                Award ID: 676566
                Categories
                Subject: Original Papers
                Subject: Structural Bioinformatics

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

                Comments

                Comment on this article

                scite_

                Similar content80

                See all similar

                Cited by96

                See all cited by

                Most referenced authors1,336

                See all reference authors