Exploring the high selectivity of 3-D protein structures using distributed memetic algorithms

Inostroza-Ponta, Mario; Dorn, Márcio; Escobar, Iván; Corrêa, Leonardo de Lima; Rosas, Erika; Hidalgo, Nicolás

doi:10.1016/j.jocs.2020.101087

Cited by 3 publications

(2 citation statements)

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“…For example, Dubchak et al 38 found that combining conformational preferences with sequence profiles improved the prediction of protein folding class. Other works also combines conformational preference of amino acid residues with heuristic search methods as a way to reduce the conformational search space of the 3D structure of proteins 39–42 …”

Section: Resultsmentioning

confidence: 99%

“…Other works also combines conformational preference of amino acid residues with heuristic search methods as a way to reduce the conformational search space of the 3D structure of proteins. [39][40][41][42] Thus, our knowledge of conformational angles is one of the guiding pieces of information in understating PSP. It is known that amino acids have limited degrees of freedom due to potential steric restriction and clashes between side chains in different secondary structures.…”

Section: Nias Database For Protein Structure Prediction and Dockingmentioning

confidence: 99%

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NIAS‐Server 2.0: A versatile complementary tool for structural biology studies

et al. 2023

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Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X‐ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.

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Section: Resultsmentioning

confidence: 99%