Abstract
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.
Highlights
Proteins are considered the building blocks of cells
The vast majority of high-resolution structures has been obtained by X-ray crystallography, representing the 89% of all the protein structures deposited in the Protein Data Bank (PDB, https://www.rcsb.org), as of October 2019 (Figure 1)
Composition, undergo evolutionary pressure and are thermodynamically more stable. Based on those studies, a number of computational methodologies have been developed in order to crystallographic interfaces are usually considered non-specific and are governed mostly by distinguish those interfaces and are playing a major role in protein interface classification
Summary
Proteins are considered the building blocks of cells. By interacting with each other and with other biomolecules, they control and elicit almost every biological process. The increasing number of experimentally determined 3D structures of protein complexes over biological vs crystallographic interfaces with only crystallographic data is not always trivial and can the years has allowed for a deeper analysis of the physico-chemical characteristics of biological versus be prone to errors. Based on those studies, a number of computational methodologies have been developed in order to crystallographic interfaces are usually considered non-specific and are governed mostly by distinguish those interfaces and are playing a major role in protein interface classification. We give a comprehensive overview of computational methodologies proposed so far have been developed in order to distinguish those interfaces and are playing a major role in to classify protein interfaces as biological or crystallographic ones.
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