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Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification

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Bijvoet Center for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
2
Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, 82377 Penzberg, Germany
*
Authors to whom correspondence should be addressed.
Present address: Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany.
Crystals 2020, 10(2), 114; https://doi.org/10.3390/cryst10020114
Received: 8 January 2020 / Revised: 4 February 2020 / Accepted: 5 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Protein Crystallography)
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. View Full-Text
Keywords: protein-protein interface; biological interface; crystallographic interface; classification; webserver; X-ray crystallography; protein structure; machine learning protein-protein interface; biological interface; crystallographic interface; classification; webserver; X-ray crystallography; protein structure; machine learning
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Elez, K.; Bonvin, A.M.J.J.; Vangone, A. Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification. Crystals 2020, 10, 114.

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