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Protein loops with multiple meta‐stable conformations: A challenge for sampling and scoring methods
Author(s) -
Barozet Amélie,
Bianciotto Marc,
Vaisset Marc,
Siméon Thierry,
Minoux Hervé,
Cortés Juan
Publication year - 2021
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.26008
Subject(s) - computer science , context (archaeology) , set (abstract data type) , energy landscape , loop (graph theory) , reliability (semiconductor) , sampling (signal processing) , conformational ensembles , data mining , artificial intelligence , algorithm , molecular dynamics , mathematics , chemistry , biology , computational chemistry , physics , filter (signal processing) , paleontology , biochemistry , power (physics) , combinatorics , quantum mechanics , computer vision , programming language
Abstract Flexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conformations within an ensemble generated by loop sampling techniques remains an open problem. The difficulty is primarily related to the lack of structural data about these flexible regions. With the majority of structural data coming from x‐ray crystallography and ignoring plasticity, the conception and evaluation of loop scoring methods is challenging. In this work, we compare the performance of various scoring methods on a set of eight protein loops that are known to be flexible. The ability of each method to identify and select all of the known conformations is assessed, and the underlying energy landscapes are produced and projected to visualize the qualitative differences obtained when using the methods. Statistical potentials are found to provide considerable reliability despite their being designed to tradeoff accuracy for lower computational cost. On a large pool of loop models, they are capable of filtering out statistically improbable states while retaining those that resemble known (and thus likely) conformations. However, computationally expensive methods are still required for more precise assessment and structural refinement. The results also highlight the importance of employing several scaffolds for the protein, due to the high influence of small structural rearrangements in the rest of the protein over the modeled energy landscape for the loop.