Dynameomics: Data‐driven methods and models for utilizing large‐scale protein structure repositories for improving fragment‐based loop prediction

Abstract

Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, 25-75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments.

Surviving the deluge of biosimulation data

“…Thus, the first wide initiative to store MD trajectories and use them to extract general characteristics of protein dynamics should be attributed to V. Daggett, who developed and maintained the Dynameomics database and server in 2010. In the latest version of this database accessible to external users (http://www.dynameomics.org/), Dynameomics comprises trajectories of around 800 soluble proteins (127 of which human) carefully selected based on structural diversity and enriched in enzymes and thermophilic proteins . Simulations were performed using 1995‐version of Levitt's force field and an “in house” thermalization and equilibration procedure, followed by microcanonical simulations (NVE) in the presence of explicit solvent, using a local MD code and 10 Å cutoff for nonbonded interactions.…”

“…Dynameomics contains also unfolding trajectories at very high temperature and polymorphic versions of a few proteins used to understand the impact of single nucleotide polymorphisms in protein structure and dynamics. The database has been used by Daggett's group for many purposes, including the study of equilibrium dynamics, unfolding, derivation of fragment properties, rotamer analysis, or analyses of conformational entropy . The server provides access to a set of standard analyses on the dynamics of the proteins.…”

“…In the latest version of this database accessible to external users (http://www.dynameomics.org/), Dynameomics comprises trajectories of around 800 soluble proteins (127 of which human) carefully selected based on structural diversity and enriched in enzymes and thermophilic proteins. 60,61 Simulations were performed using 1995-version of Levitt's force field 62 and an "in house" thermalization and equilibration procedure, followed by microcanonical simulations (NVE) in the presence of explicit solvent, using a local MD code and 10 Å cutoff for nonbonded interactions. Typical simulation length was around 30 ns (with data stored every ps) and several replicas of each protein were collected.…”

“…The database has been used by Daggett's group for many purposes, including the study of equilibrium dynamics, unfolding, derivation of fragment properties, rotamer analysis, or analyses of conformational entropy. 60,61,[63][64][65][66] The server provides access to a set of standard analyses on the dynamics of the proteins. Despite the database was built on trajectories created using force fields that now would be considered outdated, and with time scale that might be short for today's standards, it was crucial in introducing many of the key concepts required for an MD database.…”

A new approach for extracting information from protein dynamics

“…Combined with increased computational power, these advances have led to rapidly increasing numbers of longer MD simulations for larger macromolecular systems 5 . As a result, large datasets of MD trajectories are available from individual research labs 5 and repositories such as MoDEL 6 , Dynameomics 7 , Dryad, NoMaD, and MolSSI 8 .…”