Methods in molecular biologyBioinformaticsPeer-reviewed journal article
Protocols for fast simulations of protein structure flexibility using CABS-flex and SURPASS
Aleksandra Elzbieta Badaczewska-DawidiD, Andrzej KolińskiiD, Sebastian KmiecikiD
Abstract
Conformational flexibility of protein structures can play an important role in protein function. The flexibility is often studied using computational methods, since experimental characterization can be difficult. Depending on protein system size; computational tools may require large computational resources or significant simplifications in the modeled systems to speed-up calculations. In this work, we present the protocols for efficient simulations of flexibility of folded protein structures that use coarse-grained simulation tools of different resolutions: medium, represented by CABS-flex, and low, represented by SUPRASS. We test the protocols using a set of 140 globular proteins and compare the results with structure fluctuations observed in MD simulations, ENM modeling and NMR ensembles. As demonstrated, CABS-flex predictions show high correlation to experimental and MD simulation data, while SURPASS is less accurate but promising in terms of future developments.
- assay: protein structure
- research focus: protein structure prediction
- research focus: benchmarking
- approach: statistical model
- approach: simulation
- Citations
- 26
- FWCI
- 4.07
- Field percentile
- top 5%
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
- PBxplore: A Tool To Analyze Local Protein Structure And Deformability With Protein Blocks
Jonathan Barnoud, Hubert Santuz, … Pierre Poulain·PeerJPeer-reviewed·Nov 20, 2017
- Protein-protein docking with large-scale backbone flexibility using coarse-grained Monte-Carlo simulations
Mateusz Kurciński, Sebastian Kmiecik, … Andrzej Koliński·International Journal of Molecular SciencesPeer-reviewed·Jul 8, 2021
- Deep Learning Structural Ensembles as Proxies for Protein Flexibility
Mehmet Tahir Tunc, Ayten Dizkırıcı, Mustafa Tekpinar·bioRxiv·May 18, 2026
- Fast prediction of protein flexibility
Jure Pražnikar·BioinformaticsPeer-reviewed·Apr 1, 2026
- FF12MC: A revised AMBER forcefield and new protein simulation protocol
Yuan-Ping Pang·bioRxiv·Jul 11, 2016
- Refinement of highly flexible protein structures using simulation-guided spectroscopy
Jennifer M. Hays, Marissa K. Kieber, … Peter M. Kasson·Angewandte Chemie International EditionPeer-reviewed·Nov 5, 2018