Titlebook: Computational Peptide Science; Methods and Protocol Thomas Simonson Book 2022 The Editor(s) (if applicable) and The Author(s), under exclus

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Tools for Characterizing Proteins: Circular Variance, Mutual Proximity, Chameleon Sequences, and Suproximity, and a subsequence-based foldability score. These concepts were used in estimating foldability of globular, intrinsically disordered and fold-switching proteins, properties of protein–protein interfaces, quantifying sphericity, helping to improve protein–protein docking scores, and estimat

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Exploring the Peptide Potential of Genomes, the pervasive translation of the resulting RNAs offer to the organisms a vast reservoir of novel peptides. Although the majority of these peptides are anticipated as deleterious or neutral, and thereby expected to be degraded right away or short-lived in evolutionary history, some of them can confe

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,Computational Identification and Design of Complementary β-Strand Sequences,l processes, and some are known to be related to serious diseases such as neurologic disorders and amyloidosis. The self-assembly of ß-sheet peptides also has practical applications in material sciences since they can be building blocks of repeated nanostructures. Therefore, computational algorithms

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Predicting Membrane-Active Peptide Dynamics in Fluidic Lipid Membranes,for infections but also be applied to finding targeted therapies for cancer and other diseases. However, designing biophysical experiments to study molecular interactions between flexible peptides and fluidic lipid membranes has been an ongoing challenge. Recently, with hardware advances, algorithm

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Peptide Dynamics and Metadynamics: Leveraging Enhanced Sampling Molecular Dynamics to Robustly ModeHowever, even with modern computing power, they are limited in the timescales they can sample, which is especially problematic for peptides that are fully or partially disordered. Here, we discuss how the enhanced sampling methods accelerated molecular dynamics (aMD) and metadynamics can be leverage