Result: The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships.

Title:
The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships.
Authors:
Mailhot O; Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal H3T 1J4, Canada.; Department of Computer Science and Operations Research, Université de Montréal, Montreal H3T 1J4, Canada.; Institute for Research in Immunology and Cancer, Université de Montréal, Montreal H3T 1J4, Canada.; Department of Pharmacology and Physiology, Université de Montréal, Montreal H3T 1J4, Canada., Major F; Department of Computer Science and Operations Research, Université de Montréal, Montreal H3T 1J4, Canada.; Institute for Research in Immunology and Cancer, Université de Montréal, Montreal H3T 1J4, Canada., Najmanovich R; Department of Pharmacology and Physiology, Université de Montréal, Montreal H3T 1J4, Canada.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2023 Apr 03; Vol. 39 (4).
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
Software* , Machine Learning*, Humans
References:
Protein Sci. 2015 Apr;24(4):474-83. (PMID: 25367089)
Nucleic Acids Res. 2011 May;39(10):4007-22. (PMID: 21300639)
PLoS Comput Biol. 2021 Aug 5;17(8):e1009286. (PMID: 34351895)
PLoS Comput Biol. 2022 Dec 14;18(12):e1010777. (PMID: 36516216)
Bioinformatics. 2021 Oct 11;37(19):3369-3371. (PMID: 33742655)
PLoS Comput Biol. 2014 Apr 24;10(4):e1003569. (PMID: 24762569)
Mol Cell. 2015 Oct 1;60(1):131-45. (PMID: 26412306)
Nature. 2008 Mar 6;452(7183):51-5. (PMID: 18322526)
Grant Information:
MOP-93679 Canadian Institutes of Health Research (CIHR)
Entry Date(s):
Date Created: 20230420 Date Completed: 20230428 Latest Revision: 20230508
Update Code:
20250114
PubMed Central ID:
PMC10130421
DOI:
10.1093/bioinformatics/btad180
PMID:
37079725
Database:
MEDLINE

Further Information

The DynaSig-ML ('Dynamical Signatures-Machine Learning') Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of sequence variants. It does so by predicting 3D structural dynamics for every variant using the Elastic Network Contact Model (ENCoM), a sequence-sensitive coarse-grained normal mode analysis model. Dynamical Signatures represent the fluctuation at every position in the biomolecule and are used as features fed into machine learning models of the user's choice. Once trained, these models can be used to predict experimental outcomes for theoretical variants. The whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps are easily parallelized in the case of either large biomolecules or vast amounts of sequence variants. As an example application, we use the DynaSig-ML package to predict the maturation efficiency of human microRNA miR-125a variants from high-throughput enzymatic assays.
Availability and Implementation: DynaSig-ML is open-source software available at https://github.com/gregorpatof/dynasigml_package.
(© The Author(s) 2023. Published by Oxford University Press.)