ArticlePLoS computational biology2016
Ensembler: Enabling High-Throughput Molecular Simulations at the Superfamily Scale.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Heterogeneous folding landscapes and predetermined breaking points within a protein family.Protein science : a publication of the Protein Society · 2024Article
- Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.Journal of chemical theory and computation · 2023Review
- A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.Journal of chemical information and modeling · 2023Article
- SOURSOP: A Python Package for the Analysis of Simulations of Intrinsically Disordered Proteins.Journal of chemical theory and computation · 2023Article
- Basic science methods for the characterization of variants of uncertain significance in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2023Review
- Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations.Journal of chemical information and modeling · 2021Article
- Article
- A deep learning framework to predict binding preference of RNA constituents on protein surface.Nature communications · 2019Article
- Article
- Ensemble Docking in Drug Discovery.Biophysical journal · 2018Review
- MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories.Biophysical journal · 2015Article
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Authors and funding
5 authors.
Funding
Abstract
The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences-from a single sequence to an entire superfamily-and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time-consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics-such as Markov state models (MSMs)-which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine kinase family, using all available kinase catalytic domain structures from any organism as structural templates. Ensembler is free and open source software licensed under the GNU General Public License (GPL) v2. It is compatible with Linux and OS X. The latest release can be installed via the conda package manager, and the latest source can be downloaded from https://github.com/choderalab/ensembler.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.