An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package.

Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques.

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions.

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA).

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information.

HF trimer: 12D fully coupled quantum calculations of HF-stretch excited intramolecular and intermolecular vibrational states using contracted bases of intramolecular and intermolecular eigenstates.

We present the computational methodology, which for the first time allows rigorous twelve-dimensional (12D) quantum calculations of the coupled intramolecular and intermolecular vibrational states of hydrogen-bonded trimers of flexible diatomic molecules. Its starting point is the approach that we introduced recently for fully coupled 9D quantum calculations of the intermolecular vibrational states of noncovalently bound trimers comprised of diatomics treated as rigid. In this paper, it is extended to include the intramolecular stretching coordinates of the three diatomic monomers.

Efficient analytical gradients of property-based diabatic states: Geometry optimizations for localized holes.

We present efficient analytical gradients of property-based diabatic states and couplings using a Lagrangian formalism. Unlike previous formulations, the method achieves a computational scaling that is independent of the number of adiabatic states used to construct the diabats. The approach is generalizable to other property-based diabatization schemes and electronic structure methods as long as analytical energy gradients are available and integral derivatives with the property operator can be formed.

Perspective: Reference-Potential Methods for the Study of Thermodynamic Properties in Chemical Processes: Theory, Applications, and Pitfalls.

In silico investigations of enzymatic reactions and chemical reactions in condensed phases often suffer from formidable computational costs due to a large number of degrees of freedom and enormous important volume in phase space. Usually, accuracy must be compromised to trade for efficiency by lowering the reliability of the Hamiltonians employed or reducing the sampling time. Reference-potential methods (RPMs) offer an alternative approach to reaching high accuracy of simulation without much loss of efficiency.

Development of multiscale ultra-coarse-grained models for the SARS-CoV-2 virion from cryo-electron microscopy data.

The global spread of the new coronavirus COVID-19 has seriously affected human health and has caused a large number of deaths. Using molecular dynamics (MD) simulations to study the microscopic dynamic behavior of the virion provides an important means to study the pathogenic mechanism. In this work, we develop an ultra-coarse-grained (UCG) model of the SARS-CoV-2 virion from the authentic cryo-electron microscopy data, which enables MD simulation of the entire virion within microseconds.

Revealing the Sequence Characteristics and Molecular Mechanisms of ACE Inhibitory Peptides by Comprehensive Characterization of 160,000 Tetrapeptides.

Chronic diseases, such as hypertension, cause great harm to human health. Conventional drugs have promising therapeutic effects, but also cause significant side effects. Food-sourced angiotensin-converting enzyme (ACE) inhibitory peptides are an excellent therapeutic alternative to pharmaceuticals, as they have fewer side effects.

Geometric Deep Learning for Molecular Crystal Structure Prediction.

We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based learning and the availability of large molecular crystal data sets, we train models for density prediction and stability ranking which are accurate, fast to evaluate, and applicable to molecules of widely varying size and composition. Our density prediction model, MolXtalNet-D, achieves state-of-the-art performance, with lower than 2% mean absolute error on a large and diverse test data set.

Binding of berberine derivates to G-quadruplex: insight from a computational study.

Human telomerase exhibits significant activity in cancer cells relative to normal cells, which contributes to the immortal proliferation of cancer cells. To counter this, the stabilization of G-quadruplexes formed in the guanine-rich sequence of the cancer cell chromosome has emerged as a promising avenue for anti-cancer therapy. Berberine (BER), an alkaloid that is derived from traditional Chinese medicines, has shown potential for stabilizing G-quadruplexes.

Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-Guest Binding: III. Force-Field Comparison, Three-Trajectory Realization and Further Dielectric Augmentation.

Host-guest binding, despite the relatively simple structural and chemical features of individual components, still poses a challenge in computational modelling. The extreme underperformance of standard end-point methods in host-guest binding makes them practically useless. In the current work, we explore a potentially promising modification of the three-trajectory realization.

Hydroxide Diffusion in Functionalized Cylindrical Nanopores as Idealized Models of Anion Exchange Membrane Environments: An Ab Initio Molecular Dynamics Study.

Anion exchange membranes (AEMs) have attracted significant interest for their applications in fuel cells and other electrochemical devices in recent years. Understanding water distributions and hydroxide transport mechanisms within AEMs is critical to improving their performance as concerns hydroxide conductivity. Recently, nanoconfined environments have been used to mimic AEM environments.

MolTaut: A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution.

Fast and proper treatment of the tautomeric states for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomeric states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic p.

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.

Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen , , 2021, , 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.

Molecular Modelling of Ionic Liquids: Situations When Charge Scaling Seems Insufficient.

Charge scaling as an effective solution to the experiment-computation disagreement in molecular modelling of ionic liquids (ILs) could bring the computational results close to the experimental reference for various thermodynamic properties. According to the large-scale benchmark calculations of mass density, solvation, and water-ILs transfer-free energies in our series of papers, the charge-scaling factor of 0.8 serves as a near-optimal option generally applicable to most ILs, although a system-dependent parameter adjustment could be attempted for further improved performance.

Multitask Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties.

The past few years have witnessed significant advances in developing machine learning methods for molecular energetics predictions, including calculated electronic energies with high-level quantum mechanical methods and experimental properties, such as solvation free energy and logP. Typically, task-specific machine learning models are developed for distinct prediction tasks. In this work, we present a multitask deep ensemble model, sPhysNet-MT-ens5, which can simultaneously and accurately predict electronic energies of molecules in gas, water, and octanol phases, as well as transfer free energies at both calculated and experimental levels.

Rational Design of a Highly Specific Prolyl Endopeptidase To Activate the Antihypertensive Effect of Peptides.

Enzymatic hydrolysis of food-derived proteins to produce bioactive peptides could activate food functions such as antihypertension. However, the diversity of enzymatic hydrolysis products can reduce bioactive peptides' efficacy. Highly specific proteases can homogenize the hydrolysis products to reduce the production of impotent peptides.

A Green's Function Approach for Determining Surface Induced Broadening and Shifting of Molecular Energy Levels.

Upon adsorption of a molecule onto a surface, the molecular energy levels (MELs) broaden and change their alignment. This phenomenon directly affects electron transfer across the interface and is, therefore, a fundamental observable that influences electrochemical device performance. Here, we propose a rigorous parameter-free framework, built upon the theoretical construct of Green's functions, for studying the interface between a molecule and a bulk surface and its effect on MELs.

Design Two Novel Tetrahydroquinoline Derivatives against Anticancer Target LSD1 with 3D-QSAR Model and Molecular Simulation.

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure-activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q = 0.

CovBinderInPDB: A Structure-Based Covalent Binder Database.

Covalent inhibition has emerged as a promising orthogonal approach for drug discovery, despite the significant challenge in achieving target specificity. To facilitate the structure-based rational design of target-specific covalent modulators, we developed an integrated computational protocol to curate covalent binders from the RCSB Protein Data Bank (PDB). Starting from the macromolecular crystallographic information files (mmCIF) in the PDB archive, covalent bond records, which indicate the side chain modification of amino acid residue by a covalent binder, were collected and cleaned.

An efficient method to predict protein thermostability in alanine mutation.

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔ) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory.

Machine learning the Hohenberg-Kohn map for molecular excited states.

The Hohenberg-Kohn theorem of density-functional theory establishes the existence of a bijection between the ground-state electron density and the external potential of a many-body system. This guarantees a one-to-one map from the electron density to all observables of interest including electronic excited-state energies. Time-Dependent Density-Functional Theory (TDDFT) provides one framework to resolve this map; however, the approximations inherent in practical TDDFT calculations, together with their computational expense, motivate finding a cheaper, more direct map for electronic excitations.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: II. regression and dielectric constant.

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g.