Issue 1, 2020

Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems

Abstract

Molecular dynamics (MD) simulations generate valuable all-atom resolution trajectories of complex systems, but analyzing this high-dimensional data as well as reaching practical timescales, even with powerful supercomputers, remain open problems. As such, many specialized sampling and reaction coordinate construction methods exist that alleviate these problems. However, these methods typically don't work directly on all atomic coordinates, and still require previous knowledge of the important distinguishing features of the system, known as order parameters (OPs). Here we present AMINO, an automated method that generates such OPs by screening through a very large dictionary of OPs, such as all heavy atom contacts in a biomolecule. AMINO uses ideas from information theory to learn OPs that can then serve as an input for designing a reaction coordinate which can then be used in many enhanced sampling methods. Here we outline its key theoretical underpinnings, and apply it to systems of increasing complexity. Our applications include a problem of tremendous pharmaceutical and engineering relevance, namely, calculating the binding affinity of a protein–ligand system when all that is known is the structure of the bound system. Our calculations are performed in a human-free fashion, obtaining very accurate results compared to long unbiased MD simulations on the Anton supercomputer, but in orders of magnitude less computer time. We thus expect AMINO to be useful for the calculation of thermodynamics and kinetics in the study of diverse molecular systems.

Graphical abstract: Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems

Article information

Article type
Paper
Submitted
27 Там. 2019
Accepted
14 Қар. 2019
First published
14 Қар. 2019

Mol. Syst. Des. Eng., 2020,5, 339-348

Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems

P. Ravindra, Z. Smith and P. Tiwary, Mol. Syst. Des. Eng., 2020, 5, 339 DOI: 10.1039/C9ME00115H

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