CREST—A program for the exploration of low-energy molecular chemical space

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(11)

Published: March 21, 2024

Conformer–rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Phys. 22, 7169 (2020)] as driver calculations at extended tight-binding level (xTB), it offers a variety molecular- metadynamics simulations, geometry optimization, structure analysis capabilities. Implemented algorithms include procedures conformational sampling, explicit solvation studies, calculation absolute entropy, identification protonation deprotonation sites. Calculations are set up to run concurrently, providing single-node parallelization. CREST designed require minimal user input comes with implementation GFNn-xTB Hamiltonians GFN-FF force-field. Furthermore, interfaces any quantum chemistry force-field software can easily be created. In this article, we present recent developments code show selection applications most important features program. An novelty refactored backend, which provides significant speed-up small or medium-sized drug molecules allows more sophisticated setups, example, mechanics/molecular mechanics minimum energy crossing point calculations.

Language: Английский

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Quick-and-Easy Validation of Protein–Ligand Binding Models Using Fragment-Based Semiempirical Quantum Chemistry

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Electronic structure calculations in enzymes converge very slowly with respect to the size of model region that is described using quantum mechanics (QM), requiring hundreds atoms obtain converged results and exhibiting substantial sensitivity (at least smaller models) which amino acids are included QM region. As such, there considerable interest developing automated procedures construct a based on well-defined criteria. However, testing such burdensome due cost large-scale electronic calculations. Here, we show semiempirical methods can be used as alternatives density functional theory (DFT) assess convergence sequences models generated by various protocols. The these tests reduced even further means many-body expansion. We use this approach examine (with size) protein–ligand binding energies. Fragment-based afford well-converged interaction energies tiny fraction required for DFT Two-body interactions between ligand single-residue acid fragments low-cost way "QM-informed" enzyme size, furnishing an automatable active-site model-building procedure. This provides streamlined, user-friendly constructing binding-site requires neither priori information nor manual adjustments. Extension thermochemical should straightforward.

Language: Английский

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Quantum Computing for Molecular Biology**

ChemBioChem, Journal Year: 2023, Volume and Issue: 24(13)

Published: May 8, 2023

Molecular biology and biochemistry interpret microscopic processes in the living world terms of molecular structures their interactions, which are quantum mechanical by very nature. Whereas theoretical foundations these interactions well established, computational solution relevant equations is hard. However, much function can be understood classical mechanics, where electrons nuclei have been mapped onto effective surrogate potentials that model interaction atoms or even larger entities. The simple mathematical structure offers huge advantages; however, this comes at cost all correlations rigorous many-particle nature omitted. In work, we discuss how computation may advance practical usefulness offering advantages for simulations biomolecules. We not only typical problems electronic biomolecules context, but also consider dominating (such as protein folding drug design) data-driven approaches bioinformatics degree to they might become amenable simulation computation.

Language: Английский

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A Vision for the Future of Multiscale Modeling

ACS Physical Chemistry Au, Journal Year: 2024, Volume and Issue: 4(3), P. 202 - 225

Published: March 4, 2024

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic photophysical processes require ab initio treatments be properly described, but breadth length time scales involved makes it practically unfeasible. A way address these issues is couple theories algorithms working at different by dividing system into domains treated levels approximation, ranging quantum mechanics classical molecular dynamics, even including continuum electrodynamics. This approach known as multiscale modeling its use 60 years has led remarkable results. Considering rapid theory, algorithm design, computing power, we believe will massively grow a dominant research methodology forthcoming years. Hereby describe main approaches developed within realm, highlighting their achievements current drawbacks, eventually proposing plausible direction for future developments considering also emergence new techniques such machine learning computing. We then discuss how advanced methods could exploited critical scientific challenges, focusing on simulation complex light-harvesting processes, photosynthesis. While doing so, suggest cutting-edge paradigm consisting performing simultaneous calculations allowing various domains, with appropriate accuracy, move extend while they interact each other. Although this vision very ambitious, quick development lead both massive improvements widespread techniques, resulting and, eventually, our society.

Language: Английский

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emle-engine: A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics Simulations

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(11), P. 4514 - 4522

Published: May 28, 2024

We present in this work the emle-engine package (https://github.com/chemle/emle-engine)─the implementation of a new machine learning embedding scheme for hybrid potential/molecular-mechanics (ML/MM) dynamics simulations. The is based on an that uses physics-based model electronic density and induction with handful tunable parameters derived from vacuo properties subsystem to be embedded. This completely independent potential requires only positions atoms partial charges molecular mechanics environment. These characteristics allow employed existing QM/MM software. demonstrate implemented electrostatic (named EMLE) stable enhanced sampling Through calculation free energy surfaces alanine dipeptide water two different ML options three models, we test performance EMLE. When compared reference DFT/MM surface, EMLE clearly superior MM one fixed charges. configurational dependence inclusion introduced by leads systematic reduction average error surface when embedding. By enabling usage practical ML/MM simulations, will make it possible accurately systems processes feature significant variations charge distribution and/or interacting

Language: Английский

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Eliminating Imaginary Vibrational Frequencies in Quantum-Chemical Cluster Models of Enzymatic Active Sites

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(9), P. 3912 - 3922

Published: April 22, 2024

In constructing finite models of enzyme active sites for quantum-chemical calculations, atoms at the periphery model must be constrained to prevent unphysical rearrangements during geometry relaxation. A simple fixed-atom or "coordinate-lock" approach is commonly employed but leads undesirable artifacts in form small imaginary frequencies. These preclude evaluation finite-temperature free-energy corrections, limiting thermochemical calculations enthalpies only. Full-dimensional vibrational frequency are possible by replacing constraints with harmonic confining potentials. Here, we compare that an alternative strategy which contributions Hessian simply omitted. While latter does eliminate frequencies, it tends underestimate both zero-point energy and entropy while introducing artificial rigidity. Harmonic potentials frequencies provide a flexible means construct active-site can used unconstrained relaxations, affording better convergence reaction energies barrier heights respect size, as compared constraints.

Language: Английский

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Multilevel Framework for Analysis of Protein Folding Involving Disulfide Bond Formation

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: 128(13), P. 3145 - 3156

Published: March 21, 2024

In this study, a three-layered multicenter ONIOM approach is implemented to characterize the naive folding pathway of bovine pancreatic trypsin inhibitor (BPTI). Each layer represents distinct level theory, where initial layer, encompassing entire protein, modeled by general all-atom force-field GFN-FF. An intermediate electronic structure consisting three fragments introduced with state-of-the-art semiempirical tight-binding method GFN2-xTB. Higher accuracy, specifically addressing breaking and formation disulfide bonds, achieved at innermost using composite DFT r2SCAN-3c. Our analysis sheds light on structural stability BPTI, particularly significance interlinking bonds. The accuracy efficiency QM/SQM/MM are benchmarked oxidative cystine. For relative stabilities investigated through calculation free energy contributions for selected intermediates, focusing impact bond. results highlight intricate trade-off between computational cost, demonstrating that provides well-balanced comprehensive solution describe effects in biomolecular systems. We conclude multiscale landscape exploration robust methodology study intriguing biological targets.

Language: Английский

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Machine learning accelerated photodynamics simulations

Chemical Physics Reviews, Journal Year: 2023, Volume and Issue: 4(3)

Published: Sept. 1, 2023

Machine learning (ML) continues to revolutionize computational chemistry for accelerating predictions and simulations by training on experimental or accurate but expensive quantum mechanical (QM) calculations. Photodynamics require hundreds of trajectories coupled with multiconfigurational QM calculations excited-state potential energies surfaces that contribute the prohibitive cost at long timescales complex organic molecules. ML accelerates photodynamics combining nonadiabatic an model trained high-fidelity energies, forces, non-adiabatic couplings. This approach has provided time-dependent molecular structural information understanding photochemical reaction mechanisms reactions in vacuum environments (i.e., explicit solvation). review focuses fundamentals techniques. We, then, discuss strategies balance adequate data generating these data. Finally, we demonstrate power applying ML-photodynamics understand origin reactivities selectivities reactions, such as cis–trans isomerization, [2 + 2]-cycloaddition, 4π-electrostatic ring-closing, hydrogen roaming mechanism.

Language: Английский

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Accurate Simulation for 2D Lubricating Materials in Realistic Environments: From Classical to Quantum Mechanical Methods

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(37)

Published: April 24, 2024

2D materials such as graphene, MoS

Language: Английский

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QMMM 2023: A program for combined quantum mechanical and molecular mechanical modeling and simulations

Computer Physics Communications, Journal Year: 2023, Volume and Issue: 295, P. 108987 - 108987

Published: Oct. 27, 2023

Language: Английский

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