Effective data-driven collective variables for free energy calculations from metadynamics of paths

PNAS Nexus, vol. 3, pp. pgae159

Abstract

A variety of enhanced sampling (ES) methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets-ideally incorporating information about physical pathways and transition states-which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of ES simulations in trajectory space via the metadynamics of paths algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in ES simulations. We demonstrate our approach with two numerical examples, a 2D model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice.

Authors 5

1. Lukas Müllender Aachen Department of Physics

   Forschungszentrum Jülich · Science for Life Laboratory · KTH Royal Institute of Technology · RWTH Aachen University

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH , 52428 Jülich , Germany

   Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology , SE-171 21 Solna , Sweden

   Department of Physics, RWTH Aachen University , 52062 Aachen , Germany

2. Andrea Rizzi

   Forschungszentrum Jülich · Italian Institute of Technology

   Affiliation as printed

   Atomistic Simulations, Italian Institute of Technology , 16163 Genova , Italy

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH , 52428 Jülich , Germany

3. Michele Parrinello

   Italian Institute of Technology

   Affiliation as printed

   Atomistic Simulations, Italian Institute of Technology , 16163 Genova , Italy

4. Paolo Carloni corresponding Aachen Department of Physics

   Forschungszentrum Jülich · Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH , 52428 Jülich , Germany

   Department of Physics, RWTH Aachen University , 52062 Aachen , Germany

   Universitätsklinikum, RWTH Aachen University , 52062 Aachen , Germany

5. Davide Mandelli corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH , 52428 Jülich , Germany