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Updated: Jun 16, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
Published on: July 16, 2017
Digital nets conformational sampling (DNCS) - an enhanced sampling technique to explore the conformational space of
Abraham Rebairo J1, Sam Paul D2, Stephen Arumainathan1,3
1Department of Nuclear Physics, University of Madras, Chennai, Tamil Nadu, India. stephen_arum@hotmail.com.
Abstract:
We propose digital nets conformational sampling (DNCS) - an enhanced sampling technique to explore the conformational ensembles of peptides, especially intrinsically disordered peptides (IDPs). The DNCS algorithm relies on generating history-dependent samples of dihedral variables using bitwise XOR operations and binary angle measurements (BAM). The algorithm was initially studied using met-enkephalin, a highly elusive neuropeptide. The DNCS method predicted near-native structures and the energy landscape of met-enkephalin was observed to be in direct correlation with earlier studies on the neuropeptide. Clustering analysis revealed that there are only 24 low-lying conformations of the molecule. The DNCS method has then been tested for predicting optimal conformations of 42 oligopeptides of length varying from 3 to 8 residues. The closest-to-native structures of 86% of cases are near-native and 24% of them have a root mean square deviation of less than 1.00 Å with respect to their crystal structures. The results obtained reveal that the DNCS method performs well, that too in less computational time.
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