bioRxivBiophysics
ClustENMD: Efficient sampling of biomolecular conformational space at atomic resolution
Abstract
SummaryEfficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe the new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model (ENM) for deformations along global modes, followed by clustering and short molecular dynamics (MD) simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace. Availability and implementationClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy. Contactburak.kaynak@pitt.edu or doruker@pitt.edu Supplementary informationSupplementary materials comprise method details, figures, table and tutorial.
- assay: protein structure
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Tutorial on quantifying and sampling biomolecular ensembles with ShapeGMM
Subarna Sasmal, Martin McCullagh, Glen M. Hocky·The Journal of Chemical PhysicsPeer-reviewed·Dec 23, 2025
- An enhanced-sampling MD-based protocol for molecular docking
A. Basciu, G. Malloci, … A. V. Vargiu·bioRxiv·Oct 3, 2018
- ConformFlow: scalable normalizing flow for protein conformational ensemble generation
- Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-based Algorithms
A. C. Macke, J. E. Stump, … R. I. Dima·bioRxiv·Sep 13, 2023
- ProTDyn: a foundation Protein language model for Thermodynamics and Dynamics generation
Yikai Liu, Haoyang Zheng, … Guang Lin·bioRxiv·Sep 17, 2025
- k-Nearest Neighbour Adaptive Sampling (kNN-AS), a Simple Tool to Efficiently Explore Conformational Space
Evianne Rovers, Anvith Thudi, … Matthieu Schapira·bioRxiv·Mar 5, 2025