Enhanced Sampling with Suboptimal Collective Variables: Reconciling Accuracy and Convergence Speed.

We introduce an enhanced sampling algorithm to obtain converged free energy landscapes of molecular rare events, even when the collective variable (CV) used for biasing is not optimal. Our approach samples a time-dependent target distribution by combining the on-the-fly probability enhanced sampling and its exploratory variant, OPES Explore. This promotes more transitions between the relevant metastable states and accelerates the convergence speed of the free energy estimate.

Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol.

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but the agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables to address the sampling problem.

Absolute Binding Free Energies with OneOPES.

The calculation of absolute binding free energies (ABFEs) for protein-ligand systems has long been a challenge. Recently, refined force fields and algorithms have improved the quality of the ABFE calculations. However, achieving the level of accuracy required to inform drug discovery efforts remains difficult.

DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables.

Path-like collective variables (CVs) can be very effective for accurately modeling complex biomolecular processes in molecular dynamics simulations. Recently, we have introduced DeepLNE (deep-locally non-linear-embedding), a machine learning-based path-like CV that provides a progression variable s along the path as a non-linear combination of several descriptors. We have demonstrated the effectiveness of DeepLNE by showing that for simple models such as the Müller-Brown potential and alanine dipeptide, the progression along the path variable closely approximates the ideal reaction coordinate.

Deep learning path-like collective variable for enhanced sampling molecular dynamics.

Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a number of limitations. In this paper, we address these challenges by introducing a new path-like collective variable called the "deep-locally non-linear-embedding," which is inspired by principles of the locally linear embedding technique and is trained on a reactive trajectory.

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces.

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets.

Addressing Suboptimal Poses in Nonequilibrium Alchemical Calculations.

Alchemical transformations can be used to quantitatively estimate absolute binding free energies at a reasonable computational cost. However, most of the approaches currently in use require knowledge of the correct (crystallographic) pose. In this paper, we present a combined Hamiltonian replica exchange nonequilibrium alchemical method that allows us to reliably calculate absolute binding free energies, even when starting from suboptimal initial binding poses.

Cation Chloride Cotransporter NKCC1 Operates through a Rocking-Bundle Mechanism.

The sodium, potassium, and chloride cotransporter 1 (NKCC1) plays a key role in tightly regulating ion shuttling across cell membranes. Lately, its aberrant expression and function have been linked to numerous neurological disorders and cancers, making it a novel and highly promising pharmacological target for therapeutic interventions. A better understanding of how NKCC1 dynamically operates would therefore have broad implications for ongoing efforts toward its exploitation as a therapeutic target through its modulation.

Unexpected Single-Ligand Occupancy and Negative Cooperativity in the SARS-CoV-2 Main Protease.

Many homodimeric enzymes tune their functions by exploiting either negative or positive cooperativity between subunits. In the SARS-CoV-2 Main protease (Mpro) homodimer, the latter has been suggested by symmetry in most of the 500 reported protease/ligand complex structures solved by macromolecular crystallography (MX). Here we apply the latter to both covalent and noncovalent ligands in complex with Mpro.

OneOPES, a Combined Enhanced Sampling Method to Rule Them All.

Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems.

Rare Event Kinetics from Adaptive Bias Enhanced Sampling.

We introduce a novel enhanced sampling approach named on-the-fly probability enhanced sampling (OPES) flooding for calculating the kinetics of rare events from atomistic molecular dynamics simulation. This method is derived from the OPES approach [Invernizzi and Parrinello, 7, 2731-2736], which has been recently developed for calculating converged free energy surfaces for complex systems. In this paper, we describe the theoretical details of the OPES flooding technique and demonstrate the application on three systems of increasing complexity: barrier crossing in a two-dimensional double-well potential, conformational transition in the alanine dipeptide in the gas phase, and the folding and unfolding of the chignolin polypeptide in an aqueous environment.

Water regulates the residence time of Benzamidine in Trypsin.

The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system.

Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques.

Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs.

Water-Triggered, Irreversible Conformational Change of SARS-CoV-2 Main Protease on Passing from the Solid State to Aqueous Solution.

The main protease from SARS-CoV-2 is a homodimer. Yet, a recent 0.1-ms-long molecular dynamics simulation performed by D.

The role of water in host-guest interaction.

One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape.

Data-Driven Collective Variables for Enhanced Sampling.

Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network.

Path integral molecular dynamics for bosons.

Trapped bosons exhibit fundamental physical phenomena and are at the core of emerging quantum technologies. We present a method for simulating bosons using path integral molecular dynamics. The main difficulty in performing such simulations is enumerating all ring-polymer configurations, which arise due to permutations of identical particles.

Blind Search for Complex Chemical Pathways Using Harmonic Linear Discriminant Analysis.

Disentangling the mechanistic details of a chemical reaction pathway is a hard problem that often requires a considerable amount of chemical intuition and a component of luck. Experiments struggle in observing short-life metastable intermediates, while computer simulations often rely upon a good initial guess. In this work, we propose a method that, from the simulations of a reactant and a product state, searches for reaction mechanisms connecting the two by exploring the configuration space through metadynamics, a well-known enhanced molecular dynamics method.

The Onset of Dehydrogenation in Solid Ammonia Borane: An Ab Initio Metadynamics Study.

The discovery of effective hydrogen storage materials is fundamental for the progress of a clean energy economy. Ammonia borane (H BNH , AB) has attracted great interest as a promising candidate but the reaction path that leads from its solid phase to hydrogen release is not yet fully understood. To address the need for insights in the atomistic details of such a complex solid state process, in this work we use ab-initio molecular dynamics and metadynamics to study the early stages of AB dehydrogenation.

Inelastic electron injection in a water chain.

Irradiation of biological matter triggers a cascade of secondary particles that interact with their surroundings, resulting in damage. Low-energy electrons are one of the main secondary species and electron-phonon interaction plays a fundamental role in their dynamics. We have developed a method to capture the electron-phonon inelastic energy exchange in real time and have used it to inject electrons into a simple system that models a biological environment, a water chain.