Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification
Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/10/2/114 |
| _version_ | 1798040348276031488 |
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| author | Katarina Elez Alexandre M. J. J. Bonvin Anna Vangone |
| author_facet | Katarina Elez Alexandre M. J. J. Bonvin Anna Vangone |
| author_sort | Katarina Elez |
| collection | DOAJ |
| description | Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method. |
| first_indexed | 2024-04-11T22:06:22Z |
| format | Article |
| id | doaj.art-b03b65cc9b2b426ba7f66597df4616e2 |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-04-11T22:06:22Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-b03b65cc9b2b426ba7f66597df4616e22022-12-22T04:00:42ZengMDPI AGCrystals2073-43522020-02-0110211410.3390/cryst10020114cryst10020114Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their ClassificationKatarina Elez0Alexandre M. J. J. Bonvin1Anna Vangone2Bijvoet Center for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The NetherlandsBijvoet Center for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The NetherlandsPharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, 82377 Penzberg, GermanyComplexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method.https://www.mdpi.com/2073-4352/10/2/114protein-protein interfacebiological interfacecrystallographic interfaceclassificationwebserverx-ray crystallographyprotein structuremachine learning |
| spellingShingle | Katarina Elez Alexandre M. J. J. Bonvin Anna Vangone Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification Crystals protein-protein interface biological interface crystallographic interface classification webserver x-ray crystallography protein structure machine learning |
| title | Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification |
| title_full | Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification |
| title_fullStr | Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification |
| title_full_unstemmed | Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification |
| title_short | Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification |
| title_sort | biological vs crystallographic protein interfaces an overview of computational approaches for their classification |
| topic | protein-protein interface biological interface crystallographic interface classification webserver x-ray crystallography protein structure machine learning |
| url | https://www.mdpi.com/2073-4352/10/2/114 |
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