Reactive molecular dynamics: From small molecules to proteins

Locating Transition States for Biomolecular Dynamics via Invertible Dimensionality Reduction

Locating the transition states (TS) for the conformational changes of biomacromolecules is among the major tasks of biomolecular simulations, as they are the bottlenecks of motion encoding key mechanistic insights. However, identifying the short-lived TSs from (even abundant) simulation data has been a long-standing challenge due to the high dimensionality of the molecules. Gentlest ascent dynamics (GAD) is an effective approach that searches for saddle points but only within spaces of small number of (typically <20) dimensions. Such a restriction of GAD may in principle be relieved by dimensionality reduction (DR) that reduces the high-dimensional configurational space of the molecules to a low-dimensional manifold. However, the vast majority of DR algorithms are built to focus on only high-density regions and have therefore distorted the TS regions, disabling a subsequent GAD search. The recently introduced reaction coordinate flows (RCF) is among the few exceptions. As RCF learns an invertible mapping between the configurational space and the reduced RC space through a loss function incorporating both density and transition pair information, it shall be able to preserve kinetics and therefore TS during DR. GAD can then be readily applied to locate the TS candidates in the RCF-learned RC space, which can be validated rigorously through reverse RCF mapping and committor analysis in the original space. Here, we demonstrate the effectiveness of this RCF-GAD integration through alanine dipeptide and the ground-to-excited transition of the T4 lysozyme L99A variant (T4L-L99A) in explicit solvents. For alanine dipeptide, GAD managed to identify three TSs with the RCF reduction to four RCs, but only two TSs with a reduction to two RCs, due to the merging of two low density stable states in the 2RC representation, indicating the necessity of a priori evaluation of the number of intrinsic dimensions for RCF. For T4L-L99A, the TSs located by GAD in a 4RC RCF reduction successfully resembled those found previously via automated path searching, demonstrating the feasibility of our approach for realistic biomolecular systems.

Machine Learning of Reactive Potentials

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

Pinobanksin from peony seed husk: A flavonoid with the potential to inhibit the proliferation of SH-SY5Y

Pinobanksin, as one of the flavonoids, has powerful biological activities but has been under-recognized. In this study, we optimized the extraction method of phragmites from peony seed shells by using organic solvent extraction. The yield of PSMS was 10.54 ± 0.13% under the conditions of ethanol volume fraction 70%, extraction temperature 70°C, material-liquid ratio 1:25 g/mL, and extraction time 60 min; the optimized PSMS could be effectively separated in S-8 macroporous resin coupled with C18. The relative content of PSMS was increased from 0.42% in PSMS to 92.53% after C18 purification; the antioxidant activity test revealed that pinobanksin could exert antioxidant ability by binding catalase (CAT) enzyme. Second, it was found that pinobanksin could effectively inhibit the proliferation of SH-SY5Y cells, mainly by binding to BCL2-associated X (BAX), B-cell lymphoma-2 (BCL-2), and cyclin-dependent Kinase 4/6 (CDK4/6) to produce more hydrogen bonds to inhibit their activities. This study confirms the medicinal potential of pinobanksin and provides the basis for the proper understanding of pinobanksin and the development of related products.

ATP-Bound State of the Uncoupling Protein 1 (UCP1) from Molecular Simulations

The uncoupling protein 1 (UCP1) dissipates the transmembrane (TM) proton gradient in the inner mitochondrial membrane (IMM) by leaking protons across the membrane and producing heat in the process. Such a nonshivering production of heat in the brown adipose tissue can combat obesity-related diseases. UCP1-associated proton leak is activated by free fatty acids and inhibited by purine nucleotides. The mechanism of proton leak and the binding sites of the activators (fatty acids) remain unknown, while the binding site of the inhibitors (nucleotides) was described recently. Using molecular dynamics simulations, we generated a conformational ensemble of UCP1. Using metadynamics-based free energy calculations, we obtained the most likely ATP-bound conformation of UCP1. Our conformational ensemble provides a molecular basis for a breadth of prior biochemical data available for UCP1. Based on the simulations, we make the following testable predictions about the mechanisms of activation of proton leak and proton leak inhibition by ATP: (1) R277 plays the dual role of stabilizing ATP at the binding site for inhibition and acting as a proton surrogate for D28 in the absence of a proton during proton transport, (2) the binding of ATP to UCP1 is mediated by residues R84, R92, R183, and S88, (3) R92 shuttles ATP from the E191-R92 gate in the intermembrane space to the nucleotide binding site and serves to increase ATP affinity, (4) ATP can inhibit proton leak by controlling the ionization states of matrix facing lysine residues such as K269 and K56, and (5) fatty acids can bind to UCP1 from the IMM either via the cavity between TM1 and TM2 or between TM5 and TM6. Our simulations set the platform for future investigations into the proton transport and inhibition mechanisms of UCP1.

Protein and Peptide-Based Nanotechnology for Enhancing Stability, Bioactivity, and Delivery of Anthocyanins

Anthocyanin, a unique natural polyphenol, is abundant in plants and widely utilized in biomedicine, cosmetics, and the food industry due to its excellent antioxidant, anticancer, antiaging, antimicrobial, and anti-inflammatory properties. However, the degradation of anthocyanin in an extreme environment, such as alkali pH, high temperatures, and metal ions, limits its physiochemical stabilities and bioavailabilities. Encapsulation and combining anthocyanin with biomaterials could efficiently stabilize anthocyanin for protection. Promisingly, natural or artificially designed proteins and peptides with favorable stabilities, excellent biocapacity, and wide sources are potential candidates to stabilize anthocyanin. This review focuses on recent progress, strategies, and perspectives on protein and peptide for anthocyanin functionalization and delivery, i.e., formulation technologies, physicochemical stability enhancement, cellular uptake, bioavailabilities, and biological activities development. Interestingly, due to the simplicity and diversity of peptide structure, the interaction mechanisms between peptide and anthocyanin could be illustrated. This work sheds light on the mechanism of protein/peptide-anthocyanin nanoparticle construction and expands on potential applications of anthocyanin in nutrition and biomedicine.

Computational insight into stability-enhanced systems of anthocyanin with protein/peptide

Anthocyanins, which belong to the flavonoid group, are commonly found in the organs of plants native to South and Central America. However, these pigments are unstable under conditions of varying pH, heat, etc., which limits their potential applications. One method for preserving the stability of anthocyanins is through encapsulation using proteins or peptides. Nevertheless, the complex and diverse structure of these molecules, as well as the limitation of experimental technologies, have hindered a comprehensive understanding of the encapsulation processes and the mechanisms by which stability is enhanced. To address these challenges, computational methods, such as molecular docking and molecular dynamics simulation have been used to study the binding affinity and dynamics of interactions between proteins/peptides and anthocyanins. This review summarizes the mechanisms of interaction between these systems, based on computational approaches, and highlights the role of proteins and peptides in the stability enhancement of anthocyanins. It also discusses the current limitations of these methods and suggests possible solutions.

Emerging Trends of Computational Chemistry and Molecular Modeling in Froth Flotation: A Review

Froth flotation is the most versatile process in mineral beneficiation, extensively used to concentrate a wide range of minerals. This process comprises mixtures of more or less liberated minerals, water, air, and various chemical reagents, involving a series of intermingled multiphase physical and chemical phenomena in the aqueous environment. Today's main challenge facing the froth flotation process is to gain atomic-level insights into the properties of its inherent phenomena governing the process performance. While it is often challenging to determine these phenomena via trial-and-error experimentations, molecular modeling approaches not only elicit a deeper understanding of froth flotation but can also assist experimental studies in saving time and budget. Thanks to the rapid development of computer science and advances in high-performance computing (HPC) infrastructures, theoretical/computational chemistry has now matured enough to successfully and gainfully apply to tackle the challenges of complex systems. In mineral processing, however, advanced applications of computational chemistry are increasingly gaining ground and demonstrating merit in addressing these challenges. Accordingly, this contribution aims to encourage mineral scientists, especially those interested in rational reagent design, to become familiarized with the necessary concepts of molecular modeling and to apply similar strategies when studying and tailoring properties at the molecular level. This review also strives to deliver the state-of-the-art integration and application of molecular modeling in froth flotation studies to assist either active researchers in this field to disclose new directions for future research or newcomers to the field to initiate innovative works.

Reactive Martini: Chemical Reactions in Coarse-Grained Molecular Dynamics Simulations

Chemical reactions are ubiquitous in both materials and the biophysical sciences. While coarse-grained (CG) molecular dynamics simulations are often needed to study the spatiotemporal scales present in these fields, chemical reactivity has not been explored thoroughly in CG models. In this work, a new approach to model chemical reactivity is presented for the widely used Martini CG Martini model. Employing tabulated potentials with a single extra particle for the angle dependence, the model provides a generic framework for capturing bonded topology changes using nonbonded interactions. As a first example application, the reactive model is used to study the macrocycle formation of benzene-1,3-dithiol molecules through the formation of disulfide bonds. We show that starting from monomers, macrocycles with sizes in agreement with experimental results are obtained using reactive Martini. Overall, our reactive Martini framework is general and can be easily extended to other systems. All of the required scripts and tutorials to explain its use are provided online.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.

Simulating realistic membrane shapes

Biological membranes exhibit diversity in their shapes and complexity in chemical compositions that are linked to many cellular functions. These two central features of biomembranes have been the subject of numerous simulation studies, using a diverse range of computational techniques. Currently, the field is able to capture this complexity at increasing levels of realism and connect the microscopic view on protein-lipid interactions to cellular morphologies at the level of entire organelles. Here we highlight recent advances in this topic, identify current bottlenecks, and sketch possible ways ahead.

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.

Gaining insights into combustion processes is challenging due to the complex reactions involved. The present work proposes a neural network potential model trained to ab initio data that enables to simulate the combustion of methane by predicting reactants, products and reaction intermediates.

Accuracy of Molecular Simulation-Based Predictions of koff Values: A Metadynamics Study

The k_off values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established method-infrequent metadynamics, based on the AMBER force field-to compute the k_off of the ligand iperoxo (in clinical use) targeting the muscarinic receptor M₂. The ligand charges are calculated by either (i) the Amber standard procedure or (ii) B3LYP-DFT. The calculations using (i) turn out not to provide a reasonable estimation of the transition-state free energy. Those using (ii) differ from experiment by 2 orders of magnitude. On the basis of B3LYP DFT QM/MM simulations, we suggest that the observed discrepancy in (ii) arises, at least in part, from the lack of electronic polarization and/or charge transfer in biomolecular force fields. These issues might be present in other systems, such as DNA-protein complexes.

Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA)—A Review

The unified reaction valley approach (URVA) differs from mainstream mechanistic studies, as it describes a chemical reaction via the reaction path and the surrounding reaction valley on the potential energy surface from the van der Waals region to the transition state and far out into the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting molecules is registered by a change in their normal vibrational modes and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction with curvature minima reflecting minimal change and curvature maxima, the location of important chemical events such as bond breaking/forming, charge polarization and transfer, rehybridization, etc. A unique decomposition of the path curvature into internal coordinate components provides comprehensive insights into the origins of the chemical changes taking place. After presenting the theoretical background of URVA, we discuss its application to four diverse catalytic processes: (i) the Rh catalyzed methanol carbonylation—the Monsanto process; (ii) the Sharpless epoxidation of allylic alcohols—transition to heterogenous catalysis; (iii) Au(I) assisted [3,3]-sigmatropic rearrangement of allyl acetate; and (iv) the Bacillus subtilis chorismate mutase catalyzed Claisen rearrangement—and show how URVA leads to a new protocol for fine-tuning of existing catalysts and the design of new efficient and eco-friendly catalysts. At the end of this article the pURVA software is introduced. The overall goal of this article is to introduce to the chemical community a new protocol for fine-tuning existing catalytic reactions while aiding in the design of modern and environmentally friendly catalysts.

Multistate Reactive Molecular Dynamics Simulations of Proton Diffusion in Water Clusters and in the Bulk

The molecular mechanics with proton transfer (MMPT) force field is combined with multistate adiabatic reactive molecular dynamics (MS-ARMD) to describe proton transport in the condensed phase. Parametrization for small protonated water clusters based on electronic structure calculations at the MP2/6-311+G(2d,2p) level of theory and refinement by comparing with infrared spectra for a protonated water tetramer yields a force field which faithfully describes the minimum energy structures of small protonated water clusters. In protonated water clusters up to (H₂O)₁₀₀H⁺, the proton hopping rate is around 100 hops/ns. This rate converges for 21 ≤ n ≤ 31, and no further speedup in bulk water is found. This indicates that bulklike behavior requires the solvation of a Zundel motif by ∼25 water molecules, which corresponds to the second solvation sphere. For smaller cluster sizes, the number of available states (i.e., the number of proton acceptors) is too small and slows down proton-transfer rates. The cluster simulations confirm that the excess proton is typically located on the surface. The free-energy surface as a function of the weights of the two lowest states and a configurational parameter suggests that the "special pair" plays a central role in rapid proton transport. The barriers between this minimum-energy structure and the Zundel and Eigen minima are sufficiently low (∼1 kcal/mol, consistent with recent experiments and commensurate with a hopping rate of ∼100/ns or 1 every 10 ps), leading to a highly dynamic environment. These findings are also consistent with recent experiments which find that Zundel-type hydration geometries are prevalent in bulk water.