Steered Molecular Dynamics

Elucidating the Molecular Basis of 14-3-3 Interaction with α-Synuclein: Insights from Molecular Dynamics Simulations and the Design of a Novel Protein-Protein Interaction Inhibitor

Parkinson's disease is a widespread age-related neurodegenerative disorder characterized by the loss of dopaminergic neurons in the midbrain along with the appearance of protein aggregates, termed as "Lewy bodies" in the surviving neuronal cells. The components of Lewy bodies include proteins such as α-synuclein, 14-3-3, Parkin, and LRRK2, along with other cellular organelles, which, in their native state, perform a plethora of vital biological functions within the human biome. Formation of these aggregates renders these components inactive, thereby interfering with homeostasis. In this regard, the current study attempts to investigate the complexation behavior of all human-based 14-3-3 isoforms with α-synuclein via a combination of classical and enhanced sampling techniques and thereby determine the causality of these protein-protein interactions. The study indicated that upon complexation, the aggregation propensity of both 14-3-3 and α-synuclein increases, and this increment is propelled by the interfacial residues on either protein. Furthermore, mutagenesis studies revealed that Lys214 of 14-3-3 (henceforth termed K214A) is crucial for the formation of this binary complex. Principal component analysis combined with clustering studies unveiled the stability of these complexes in terms of their conformational distribution across the entire MD trajectory. For K214A, these clustered states were sparsely located, thereby making the transitions between them slightly difficult. Dynamic cross-correlation maps (DCCM) revealed the role of residues in the range 80-130 of 14-3-3 having a potential allosteric role in driving this complexation process. Finally, a novel peptide-based supramolecular inhibitor was designed, which exhibited higher proficiency in limiting the 14-3-3/α-synuclein interaction compared to the previous inhibitor model. It was also revealed that the presence of this inhibitor induces structural rigidity in α-synuclein, making changes in its conformations extremely difficult, as observed through Umbrella Sampling studies. Based on available information, the current study provides an insight into the molecular-level understanding of protein-protein interactions underlying Parkinson's disease and adds on to the methods of devising novel therapeutic approaches to treat the same.

Computing the committor with the committor to study the transition state ensemble

The study of the kinetic bottlenecks that hinder the rare transitions between long-lived metastable states is a major challenge in atomistic simulations. Here we propose a method to explore the transition state ensemble, which is the distribution of configurations that the system passes through as it translocates from one metastable basin to another. We base our method on the committor function and the variational principle that it obeys. We find its minimum through a self-consistent procedure that starts from information limited to the initial and final states. Right from the start, our procedure allows the sampling of very many transition state configurations. With the help of the variational principle, we perform a detailed analysis of the transition state ensemble, ranking quantitatively the degrees of freedom mostly involved in the transition and enabling a systematic approach for the interpretation of simulation results and the construction of efficient physics-informed collective variables.

Acetylcholinesterase inhibitory activities of some flavonoids from the root bark of Pinus krempfii Lecomte: in vitro and in silico study

From the root bark of Pinus krempfii Lecomte, four flavonoids were isolated and evaluated for their inhibitory activities against AChE and BChE enzymes in vitro and in silico. Tectochrysin (1) was found to inhibit AChE with an IC50 value of 33.69 ± 2.80 μM. The docking study results also showed agreement with the in vitro test results. All four compounds also showed the best binding affinity for the AChE enzyme, characterised by binding energy (ΔG) values as low as -8.1 to -9.3 kcal/mol, in which, the compound tectochrysin had the best binding affinity for the AChE protein with a ΔG value of -9.329 kcal/mol. Tectochrysin (1) was also bound to the amino acid Phe295 of AChE with a bond length of 2.8 Å, similar to the control dihydrotanshinone-I. Galangin (2) also showed its in vitro inhibitory activity against BChE with an IC50 value of 82.21 ± 2.70 μM. In silico, it also had the best binding energy value of -9.072 kcal/mol with BChE and formed hydrogen bonds with the His438 (2.85 Å) residues of BChE like the positive control (tacrine). The steered molecular dynamics (SMD) simulation results of these two complexes revealed a mechanistic insight that the protein-ligand complexes showed stable trajectories throughout the 20 and 150 ns simulations. Moreover, the drug likeliness suggested that both flavonoids (1 and 2) were expected to be drug-like and have an LD50 toxicity level of 5. This study has contributed new results for drug discovery and the development of substances with neuroprotective effects, especially for the treatment of Alzheimer's disease.Communicated by Ramaswamy H. Sarma.

Mechanical Stability and Unfolding Pathways of Parallel Tetrameric G-Quadruplexes Probed by Pulling Simulations

Guanine quadruplex (GQ) is a noncanonical nucleic acid structure formed by guanine-rich DNA and RNA sequences. Folding of GQs is a complex process, where several aspects remain elusive, despite being important for understanding structure formation and biological functions of GQs. Pulling experiments are a common tool for acquiring insights into the folding landscape of GQs. Herein, we applied a computational pulling strategy─steered molecular dynamics (SMD) simulations─in combination with standard molecular dynamics (MD) simulations to explore the unfolding landscapes of tetrameric parallel GQs. We identified anisotropic properties of elastic conformational changes, unfolding transitions, and GQ mechanical stabilities. Using a special set of structural parameters, we found that the vertical component of pulling force (perpendicular to the average G-quartet plane) plays a significant role in disrupting GQ structures and weakening their mechanical stabilities. We demonstrated that the magnitude of the vertical force component depends on the pulling anchor positions and the number of G-quartets. Typical unfolding transitions for tetrameric parallel GQs involve base unzipping, opening of the G-stem, strand slippage, and rotation to cross-like structures. The unzipping was detected as the first and dominant unfolding event, and it usually started at the 3'-end. Furthermore, results from both SMD and standard MD simulations indicate that partial spiral conformations serve as a transient ensemble during the (un)folding of GQs.

Understanding the Reversible Binding of a Multichain Protein-Protein Complex through Free-Energy Calculations

We demonstrate that the binding affinity of a multichain protein-protein complex, insulin dimer, can be accurately predicted using a streamlined route of standard binding free-energy calculations. We find that chains A and C, which do not interact directly during binding, stabilize the insulin monomer structures and reduce the binding affinity of the two monomers, therefore enabling their reversible association. Notably, we confirm that although classical methods can estimate the binding affinity of the insulin dimer, conventional molecular dynamics, enhanced sampling algorithms, and classical geometrical routes of binding free-energy calculations may not fully capture certain aspects of the role played by the noninteracting chains in the binding dynamics. Therefore, this study not only elucidates the role of noninteracting chains in the reversible binding of the insulin dimer but also offers a methodological guide for investigating the reversible binding of multichain protein-protein complexes utilizing streamlined free-energy calculations.

Molecular Mechanisms Underlying Medium-Chain Free Fatty Acid-Regulated Activity of the Phospholipase PlaF from Pseudomonas aeruginosa

PlaF is a membrane-bound phospholipase A1 from Pseudomonas aeruginosa that is involved in remodeling membrane glycerophospholipids (GPLs) and modulating virulence-associated signaling and metabolic pathways. Previously, we identified the role of medium-chain free fatty acids (FFAs) in inhibiting PlaF activity and promoting homodimerization, yet the underlying molecular mechanism remained elusive. Here, we used unbiased and biased molecular dynamics simulations and free energy computations to assess how PlaF interacts with FFAs localized in the water milieu surrounding the bilayer or within the bilayer and how these interactions regulate PlaF activity. Medium-chain FFAs localized in the upper bilayer leaflet can stabilize inactive dimeric PlaF, likely through interactions with charged surface residues, as has been experimentally validated. Potential of mean force (PMF) computations indicate that membrane-bound FFAs may facilitate the activation of monomeric PlaF by lowering the activation barrier for changing into a tilted, active configuration. We estimated that the coupled equilibria of PlaF monomerization-dimerization and tilting at the physiological concentration of PlaF lead to the majority of PlaF forming inactive dimers when in a cell membrane loaded with decanoic acid (C10). This is in agreement with a suggested in vivo product feedback loop and gas chromatography-mass spectrometry profiling results, indicating that PlaF catalyzes the release of C10 from P. aeruginosa membranes. Additionally, we found that C10 in the water milieu can access the catalytic site of active monomeric PlaF, contributing to the competitive component of C10-mediated PlaF inhibition. Our study provides mechanistic insights into how medium-chain FFAs may regulate the activity of PlaF, a potential bacterial drug target.

Double charge flips of polyamide membrane by ionic liquid-decoupled bulk and interfacial diffusion for on-demand nanofiltration

Fine design of surface charge properties of polyamide membranes is crucial for selective ionic and molecular sieving. Traditional membranes face limitations due to their inherent negative charge and limited charge modification range. Herein, we report a facile ionic liquid-decoupled bulk/interfacial diffusion strategy to elaborate the double charge flips of polyamide membranes, enabling on-demand transformation from inherently negative to highly positive and near-neutral charges. The key to these flips lies in the meticulous utilization of ionic liquid that decouples intertwined bulk/interfacial diffusion, enhancing interfacial while inhibiting bulk diffusion. These charge-tunable polyamide membranes can be customized for impressive separation performance, for example, profound Cl-/SO42- selectivity above 470 in sulfate recovery, ultrahigh Li+/Mg2+ selectivity up to 68 in lithium extraction, and effective divalent ion removal in pharmaceutical purification, surpassing many reported polyamide nanofiltration membranes. This advancement adds a new dimension to in the design of advanced polymer membranes via interfacial polymerization.

Exploration of novel hydroxamate zinc binding group inhibitors against HDAC-1-3 enzymes by AI-based virtual screening: atomistic insights from steered molecular dynamics

Overexpression of histone deacetylase (HDAC) enzymes is linked to a wide variety of illnesses, including malignancies and neurological disorders, which makes HDAC inhibitors potentially therapeutic. However, most HDAC inhibitors lack subclass or isoform selectivity, which can be dangerous. Featuring both enhanced selectivity and toxicity profiles, slow-binding HDAC inhibitors offer promising treatment options for a variety of disorders. Diseases like cardiac, neurodegenerative disorders and diabetes are mainly associated with the HDAC1, HDAC2 and HDAC3 enzymes. The AI-based virtual screening tool PyRMD is implemented to identify the potential inhibitors from ∼2 million compounds. Based on the IC50 values, the top 10 compounds were selected for molecular docking. From the docking and ADMET study, the top-ranked three compounds were selected for molecular dynamics (MD) simulations. Further, to get more insights into the binding/unbinding mechanism of the ligand, we have employed the steered molecular dynamics (SMD) simulations. This study assists in developing Amber force field parameters for the HDAC1, HDAC2 and HDAC3 proteins and sheds light on the discovery of a potent drug. Our study suggests that hydroxamic acid derivative (i.e. referred to as Comp-1, CHEMBL600072) is the potential inhibitor for the series of HDAC-related diseases.Communicated by Ramaswamy H. Sarma.

Highly parallelizable path sampling with minimal rejections using asynchronous replica exchange and infinite swaps

Capturing rare yet pivotal events poses a significant challenge for molecular simulations. Path sampling provides a unique approach to tackle this issue without altering the potential energy landscape or dynamics, enabling recovery of both thermodynamic and kinetic information. However, despite its exponential acceleration compared to standard molecular dynamics, generating numerous trajectories can still require a long time. By harnessing our recent algorithmic innovations-particularly subtrajectory moves with high acceptance, coupled with asynchronous replica exchange featuring infinite swaps-we establish a highly parallelizable and rapidly converging path sampling protocol, compatible with diverse high-performance computing architectures. We demonstrate our approach on the liquid-vapor phase transition in superheated water, the unfolding of the chignolin protein, and water dissociation. The latter, performed at the ab initio level, achieves comparable statistical accuracy within days, in contrast to a previous study requiring over a year.

Classifying Protein-Protein Binding Affinity with Free-Energy Calculations and Machine Learning Approaches

Understanding the intricate phenomenon of neuronal wiring in the brain is of great interest in neuroscience. In the fruit fly, Drosophila melanogaster, the Dpr-DIP interactome has been identified to play an important role in this process. However, experimental data suggest that a merely limited subset of complexes, essentially 57 out of a total of 231, exhibit strong binding affinity. In this work, we sought to identify the residue-level molecular basis underlying the difference in binding affinity using a state-of-the-art methodology consisting of standard binding free-energy calculations with a geometrical route and machine learning (ML) techniques. We determined the binding affinity for two complexes using statistical mechanics simulations, achieving an excellent reproduction of the experimental data. Moreover, we predicted the binding free energy for two additional low-affinity complexes, devoid of experimental estimation, while simultaneously identifying key residues for the binding. Furthermore, through the use of ML algorithms, linear discriminant analysis, and random forest, we achieved remarkable accuracy, as high as 0.99, in discerning between strong (cognate) and weak (noncognate) binders. The presented ML approach encompasses easily transferable input features, enabling its broad application to any interactome while facilitating the identification of pivotal residues critical for binding interactions. The predictive power of the generated model was probed on similar protein families from 13 diverse species. Our ML model exhibited commendable performance on these additional data sets, showcasing its reliability and robustness across the species barrier.

Ligand Phase Separation-Promoted, "Squeezing-Out" Mode Explaining the Mechanism and Implications of Neutral Nanoparticles That Escaped from Lysosomes

Neutral nanomaterials functionalized with PEG or similar molecules have been popularly employed as nanomedicines. Compared to positive counterparts that are capable of harnessing the well-known proton sponge effect to facilitate their escape from lysosomes, it is yet unclear how neutral substances got their entry into the cytosol. In this study, by taking PEGylated, neutral Au nanospheres as an example, we systematically investigated their time-dependent translocation postuptake. Specifically, we harnessed dissipative particle dynamics simulations to uncover how nanospheres bypass lysosomal entrapment, wherein a mechanism termed as "squeezing-out" mode was discovered. We next conducted a comprehensive investigation on how nanomaterials implicate lysosomes in terms of integrity and functionality. By using single-molecule imaging, specific preservation of PEG-terminated with targeting moieties in lysosomes supports the "squeezing-out" mode as the mechanism underlying the lysosomal escape of nanomaterials. All evidence points out that such a process is benign to lysosomes, wherein the escape of nanomaterials proceeds at the expense of targeting moieties loss. Furthermore, we proved that by fine-tuning of the efficacy of nanomaterials escaping from lysosomes, modulation of distinct pathways and metabolic machinery can be achieved readily, thereby offering us a simple and robust tool to implicate cells.

Molecular basis and cellular functions of vinculin-actin directional catch bonding

The ability of cells and tissues to respond differentially to mechanical forces applied in distinct directions is mediated by the ability of load-bearing proteins to preferentially maintain physical linkages in certain directions. However, the molecular basis and biological consequences of directional force-sensitive binding remain unclear. Vinculin (Vcn) is a load-bearing linker protein that exhibits directional catch bonding due to interactions between the Vcn tail domain (Vt) and filamentous (F)-actin. We developed a computational approach to predict Vcn residues involved in directional catch bonding and produced a set of associated Vcn variants with unaltered Vt structure, actin binding, or phospholipid interactions. Incorporation of the variants did not affect Vcn activation but reduced Vcn loading and altered exchange dynamics, consistent with the loss of directional catch bonding. Expression of Vcn variants perturbed the coordination of subcellular structures and cell migration, establishing key cellular functions for Vcn directional catch bonding.

Interactive molecular dynamics in virtual reality for modelling materials and catalysts

Interactive molecular dynamics simulation in virtual reality (iMD-VR) is emerging as a promising technique in molecular science. Here, we demonstrate its use in a range of fifteen applications in materials science and heterogeneous catalysis. In this work, the iMD-VR package Narupa is used with the MD package, DL_POLY [1]. We show how iMD-VR can be used to: (i) investigate the mechanism of lithium fast ion conduction by directing the formation of defects showing that vacancy transport is favoured over interstitialcy mechanisms, and (ii) guide a molecule through a zeolite pore to explore diffusion within zeolites, examining in detail the motion of methyl n-hexanoate in H-ZSM-5 zeolite and identifying bottlenecks restricting diffusion. iMD-VR allows users to manipulate these systems intuitively, to drive changes in them and observe the resulting changes in structure and dynamics. We make these simulations available, as a resource for both teaching and research. All simulation files, with videos, can be found online (https://doi.org/10.5281/zenodo.8252314) and are provided as open-source material.

Thermal titration molecular dynamics (TTMD): shedding light on the stability of RNA-small molecule complexes

Ribonucleic acids are gradually becoming relevant players among putative drug targets, thanks to the increasing amount of structural data exploitable for the rational design of selective and potent binders that can modulate their activity. Mainly, this information allows employing different computational techniques for predicting how well would a ribonucleic-targeting agent fit within the active site of its target macromolecule. Due to some intrinsic peculiarities of complexes involving nucleic acids, such as structural plasticity, surface charge distribution, and solvent-mediated interactions, the application of routinely adopted methodologies like molecular docking is challenged by scoring inaccuracies, while more physically rigorous methods such as molecular dynamics require long simulation times which hamper their conformational sampling capabilities. In the present work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of unbinding kinetics, to characterize RNA-ligand complexes. In this article, we explored its applicability as a post-docking refinement tool on RNA in complex with small molecules, highlighting the capability of this method to identify the native binding mode among a set of decoys across various pharmaceutically relevant test cases.

Molecular insights into the temperature and pressure dependence of mechanical behavior and dynamics of Na-montmorillonite clay

Sodium montmorillonite (Na-MMT) clay mineral is a common type of swelling clay that has potential applications for nuclear waste storage at high temperatures and pressures. However, there is a limited understanding of the mechanical properties, local molecular stiffness, and dynamic heterogeneity of this material at elevated temperatures and pressures. To address this, we employ all-atomistic (AA) molecular dynamics (MD) simulation to investigate the tensile behavior of Na-MMT clay over a wide temperature range (500 K to 1700 K) and pressures (200 atm to 100 000 atm). The results show that increasing the temperature significantly reduces the tensile modulus, strength, and failure strain, while pressure has a minor effect compared to temperature, as seen in the normalized pressure-temperature plot. Mean-square displacement (MSD) analysis reveals increased molecular stiffness with increasing pressure and decreasing temperature, indicating suppressed atomic mobility. Our simulations indicate temperature-dependent dynamical heterogeneity in the Na-MMT model, supported by experimental studies and quantified local molecular stiffness distribution. These findings enhance our understanding of the tensile response and dynamical heterogeneity of Na-MMT clay under extreme conditions, aiding the development of clay minerals for engineering applications such as nuclear waste storage and shale gas extraction.

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics

Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines a neural network potential (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by ∼5 times and achieve a combined sampling of 1 μs for each complex, marking the longest simulations ever reported for this class of simulations.

Effect of Spacer Length Modification of the Cationic Side Chain on the Energetics of Antimicrobial Peptide Binding to Membrane-Mimetic Bilayers

Understanding the mechanism of action of the antimicrobial peptide (AMP) in terms of its structure and energetics is the key to designing new potent and selective AMPs. Recently, we reported a membranolytic 14-residue-long lysine-rich cationic antimicrobial peptide (LL-14: NH3+-LKWLKKLLKWLKKL-CONH2) against Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus, which is limited by cytotoxicity and expected to undergo facile protease degradation. Aliphatic side-chain-length modification of the cationic amino-acid residues (Lys and Arg) is a popular strategy for designing protease-resistant AMPs. However, the effect of the peptide side-chain length modifications on the membrane binding affinity and its relation to the atomic structure remain an unsolved problem. We report computer simulations that quantitatively calculated the difference in peptide binding affinity to membrane-mimetic-bilayer models (bacterial: 1-palmitoyl-2-oleoyl-phosphatidylethanolamine (POPE)/1-palmitoyl-2-oleoyl-phosphatidylglycerol (POPG) bilayer and mammalian: 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) bilayer) upon decreasing or increasing the spacer length of the cationic lysine residues of LL-14 (as well as their arginine analogues). We show that the peptide/bilayer interaction energetics varies drastically in response to spacer length modification. The strength of peptide discrimination depends strongly on the nature of the bilayer (bacterial or mammalian mimetic model). An increase in the lysine spacer length by one carbon (i.e., homolysine analogue of LL-14) is weakly/strongly disfavored by the bacterial/mammalian-membrane-mimetic bilayer. Recently, we have demonstrated an excellent correlation between the antimicrobial activity of the membranolytic cationic peptides and their binding affinity to membrane-mimetic-bilayer models. Thus, the homolysine analogue of LL-14 is a promising noncytotoxic AMP with conserved activity. On the other hand, homoarginine analogue (arginine spacer length increment by a single carbon) was preferred by both the bacteria and the mammalian mimetic bilayers and displayed the strongest affinity for the former among the peptides studied in this work. Thus, the promising most potent homoarginine analogue is likely to be cytotoxic. Shortening the Lys/Arg side chain to a three-carbon spacer (Dab/Agb) improves the binding affinity to bacterial and mammalian-membrane-mimetic bilayers. Arginine and arginine-derivative peptides exhibited stronger binding affinity to the bilayers relative to the lysine analogue. The results provide a plausible explanation to the previous experimental observations, viz., superior antimicrobial activity of the arginine peptides relative to Lys peptides and the improvement of antimicrobial activity upon substitution of Lys with Dab in the cationic peptides. The simulations revealed that the small change in the peptide hydrophobicity by Lys/Arg spacer length modification could drastically alter the energetics of peptide/bilayer binding by fine-tuning the electrostatic interactions. The energetics underlying the peptide selectivity by simple membrane-mimetic bilayer models may be beneficial for designing new selective and protease-resistant AMPs.

Machine learning coarse-grained potentials of protein thermodynamics

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

Kinetics from Metadynamics: Principles, Applications, and Outlook

Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.

Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease

Alzheimer disease (AD) is a leading cause of dementia in elderly patients who continue to live between 3 and 11 years of diagnosis. A steep rise in AD incidents is observed in the elderly population in East-Asian countries. The disease progresses through several changes, including memory loss, behavioural issues, and cognitive impairment. The etiology of AD is hard to determine because of its complex nature. The whole exome sequences of late-onset AD (LOAD) patients of Korean origin are investigated to identify rare genetic variants that may influence the complex disorder. Computational annotation was performed to assess the function of candidate variants in LOAD. The in silico pathogenicity prediction tools such as SIFT, Polyphen-2, Mutation Taster, CADD, LRT, PROVEAN, DANN, VEST3, fathmm-MKL, GERP +  + , SiPhy, phastCons, and phyloP identified around 17 genes harbouring deleterious variants. The variants in the ALDH3A2 and RAD54B genes were pathogenic, while in 15 other genes were predicted to be variants of unknown significance. These variants can be potential risk candidates contributing to AD. In silico computational techniques such as molecular docking, molecular dynamic simulation and steered molecular dynamics were carried out to understand the structural insights of RAD54B with ATP. The simulation of mutant (T459N) RAD54B with ATP revealed reduced binding strength of ATP at its binding site. In addition, lower binding free energy was observed when compared to the wild-type RAD54B. Our study shows that the identified uncommon variants are linked to AD and could be probable predisposing genetic factors of LOAD.

Hydration layer structure modulates superlubrication by trivalent La3+ electrolytes

Water-based lubricants provide lubrication of rubbing surfaces in many technical, biological, and physiological applications. The structure of hydrated ion layers adsorbed on solid surfaces that determine the lubricating properties of aqueous lubricants is thought to be invariable in hydration lubrication. However, we prove that the ion surface coverage dictates the roughness of the hydration layer and its lubricating properties, especially under subnanometer confinement. We characterize different hydration layer structures on surfaces lubricated by aqueous trivalent electrolytes. Two superlubrication regimes are observed with friction coefficients of 10^-4 and 10^-3, depending on the structure and thickness of the hydration layer. Each regime exhibits a distinct energy dissipation pathway and a different dependence to the hydration layer structure. Our analysis supports the idea of an intimate relationship between the dynamic structure of a boundary lubricant film and its tribological properties and offers a framework to study such relationship at the molecular level.

Tackling Hysteresis in Conformational Sampling: How to Be Forgetful with MEMENTO

The structure of proteins has long been recognized to hold the key to understanding and engineering their function, and rapid advances in structural biology and protein structure prediction are now supplying researchers with an ever-increasing wealth of structural information. Most of the time, however, structures can only be determined in free energy minima, one at a time. While conformational flexibility may thus be inferred from static end-state structures, their interconversion mechanisms─a central ambition of structural biology─are often beyond the scope of direct experimentation. Given the dynamical nature of the processes in question, many studies have attempted to explore conformational transitions using molecular dynamics (MD). However, ensuring proper convergence and reversibility in the predicted transitions is extremely challenging. In particular, a commonly used technique to map out a path from a starting to a target conformation called steered MD (SMD) can suffer from starting-state dependence (hysteresis) when combined with techniques such as umbrella sampling (US) to compute the free energy profile of a transition. Here, we study this problem in detail on conformational changes of increasing complexity. We also present a new, history-independent approach that we term "MEMENTO" (Morphing End states by Modelling Ensembles with iNdependent TOpologies) to generate paths that alleviate hysteresis in the construction of conformational free energy profiles. MEMENTO utilizes template-based structure modelling to restore physically reasonable protein conformations based on coordinate interpolation (morphing) as an ensemble of plausible intermediates, from which a smooth path is picked. We compare SMD and MEMENTO on well-characterized test cases (the toy peptide deca-alanine and the enzyme adenylate kinase) before discussing its use in more complicated systems (the kinase P38α and the bacterial leucine transporter LeuT). Our work shows that for all but the simplest systems SMD paths should not in general be used to seed umbrella sampling or related techniques, unless the paths are validated by consistent results from biased runs in opposite directions. MEMENTO, on the other hand, performs well as a flexible tool to generate intermediate structures for umbrella sampling. We also demonstrate that extended end-state sampling combined with MEMENTO can aid the discovery of collective variables on a case-by-case basis.

Dissociation Rate Calculation via Constant-Force Steered Molecular Dynamics Simulation

Steered molecular dynamics (SMD) simulations are used to study molecular dissociation events by applying a harmonic force to the molecules and pulling them at a constant velocity. Instead of constant-velocity pulling, we use a constant force: the constant-force SMD (CF-SMD) simulation. The CF-SMD simulation employs a constant force to reduce the activation barrier of molecular dissociation, thereby enhancing the dissociation event. Here, we present the capability of the CF-SMD simulation to estimate the dissociation time at equilibrium. We performed all-atom CF-SMD simulations for NaCl and protein-ligand systems, producing dissociation time at various forces. We extrapolated these values to the dissociation rate without a constant force using Bell's model or the Dudko-Hummer-Szabo model. We demonstrate that the CF-SMD simulations with the models predicted the dissociation time in equilibrium. A CF-SMD simulation is a powerful tool for estimating the dissociation rate in a direct and computationally efficient manner.

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

Computational analysis of protein-ligand interaction by targeting a cell cycle restrainer

BACKGROUND AND OBJECTIVE

The cyclin-dependent kinases 4/6 (CDK4/6) are among the most crucial controllers of the cell cycle, and their abnormal activity may induce uncontrolled cell multiplication, leading to cancers. The FDA currently approved three CDK4/6 inhibitors, however, they are associated with a variety of side effects. Thus it is required to design/develop novel potent and safe CDK4/6 inhibitors.

METHODS

In the present work, we furnished an integrated in-silico approach followed by steered molecular dynamics (SMD) simulations to identify molecules that can be developed into novel CDK4/6 inhibitors.

RESULTS

Out of thirty-two 3-methyleneisoindolin-1-one molecules we selected top three M18, M24, and M32 molecules as potential drug candidates based on their respective interaction energies. According to the robust 250 ns MD simulations and thermodynamic free energy, M24 was the best molecule in comparison to palbociclib. In SMD, M24 required ∼205.587 kJ/mol/nm external pulling force, while palbociclib needed ∼160.97 kJ/mol/nm to dissociate from the binding pocket of the CDK4.

CONCLUSIONS

The high pulling force required for M24 dissociation from the binding site denotes stronger binding with CDK4. Therefore, M24 offers the possibility of a critical starting structure in developing effective CDK4 inhibitors.

The Mechanism of the Channel Opening in Channelrhodopsin-2: A Molecular Dynamics Simulation

Channelrhodopsin-2 (ChR2) has been one of the most important objects in the study of optogenetics. The retinal chromophore molecule absorbs photons and undergoes an isomerization reaction, which triggers the photocycle, resulting in a series of conformational changes. In this study, a series of intermediate structures (including D470, P500, P390-early, P390-late, and P520 states) of ChR2 in the photocycle were modeled, and molecular dynamics (MD) simulations were performed to elucidate the mechanism of ion channel opening of ChR2. The maximum absorption wavelength of these intermediates calculated by time-dependent density function theory (TD-DFT) is in general agreement with the experimental values, the distribution of water density gradually increases in the process of photocycle, and the radius of the ion channel is larger than 6 Å. All these results indicate that our structural models of the intermediates are reasonable. The evolution of protonation state of E90 during the photocycle is explained. E90 will deprotonate when the P390-early transforms into P390-late, in which the two conformations of P390-early and P390-late obtained from the simulations are consistent with the experimental descriptions. To validate the conductive P520 state, the potential mean force (PMF) of Na+ ions passing through the P520 intermediate was calculated by using steered molecular dynamics (SMD) simulation combined with umbrella sampling. The result shows that the Na+ ions passing through the channel with a very low energy barrier, especially in the central gate, is almost barrierless. This indicates that the channel is open in the P520 state.

In silico study to predict potential precursors of human dipeptidyl peptidase-IV inhibitors from hazelnut

Chinese hazelnut was chosen to become a probable precursor of biological active peptides via computer simulations in this article. There were a large number of bioactive peptides in Chinese hazelnut sequences according to analytical results from the BIOPEP database. The most prominent of these was the inhibitory peptide for dipeptidyl peptidase-IV (DPP-IV; EC 3.4.14.5), which can be used to treat type 2 diabetes, so the theoretical method to obtain DPP-IV inhibitory peptides by hydrolysis with a single or combination of enzymes was studied. Cytotoxicity analysis performed by ToxinPred showed that all of the DPP-IV inhibitory peptides generated from protein hydrolysis were not cytotoxic. Structural interaction fingerprint analysis revealed that Asp663 and Phe357 may be important residues for ligand binding. In order to further understand the inhibitory mechanism of peptide, VR with lowest half maximum inhibitory concentration (IC50) and IPI (inhibitors have been reported) were selected as ligand of DPP-IV to perform steered molecular dynamics simulations and PMF calculations. The results showed that P1 is the preferred (un)binding tunnel for the inhibitors obtained. Our findings help in the development of new DPP-IV inhibitors which were derived from common food.Communicated by Ramaswamy H. Sarma.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Computational Exploration and Characterization of Potential Calcium Sensitizing Mutations in Cardiac Troponin C

Calcium-dependent heart muscle contraction is regulated by the cardiac troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). cNTnC contains one calcium binding site (site II), and altered calcium binding in this site has been studied for decades. It has been previously shown that cNTnC mutants, which increase calcium sensitization may have therapeutic benefits, such as restoring cardiac muscle contractility and functionality post-myocardial infarction events. Here, we computationally characterized eight mutations for their potential effects on calcium binding affinity in site II of cNTnC. We utilized two distinct methods to estimate calcium binding: adaptive steered molecular dynamics (ASMD) and thermodynamic integration (TI). We observed a sensitizing trend for all mutations based on the employed ASMD methodology. The TI results showed excellent agreement with experimentally known calcium binding affinities in wild-type cNTnC. Based on the TI results, five mutants were predicted to increase calcium sensitivity in site II. This study presents an interesting comparison of the two computational methods, which have both been shown to be valuable tools in characterizing the impacts of calcium sensitivity in mutant cNTnC systems.

A comprehensive picture of roughness evolution in organic crystalline growth: the role of molecular aspect ratio

Exploiting the capabilities of organic semiconductors for applications ranging from light-emitting diodes to photovoltaics to lasers relies on the creation of ordered, smooth layers for optimal charge carrier mobilities and exciton diffusion. This, in turn, creates a demand for organic small molecules that can form smooth thin film crystals via homoepitaxy. We have studied a set of small-molecule organic semiconductors that serve as templates for homoepitaxy. The surface roughness of these materials is measured as a function of adlayer film thickness from which the growth exponent (β) is extracted. Notably, we find that three-dimensional molecules that have low molecular aspect ratios (AR) tend to remain smooth as thickness increases (small β). This is in contrast to planar or rod-like molecules with high AR that quickly roughen (large β). Molecular dynamics simulations find that the Ehrlich-Schwöbel barrier (E_ES) alone is unable to fully explain this trend. We further investigated the mobility of ad-molecules on the crystalline surface to categorize their diffusion behaviors and the effects of aggregation to account for the different degrees of roughness that we observed. Our results suggest that low AR molecules have low molecular mobility and moderate E_ES which creates a downward funneling effect leading to smooth crystal growth.

Atomistic Mismatch Defines Energy-Structure Relationships during Oriented Attachment of Nanoparticles

Oriented attachment is an important crystal growth pathway in nature and has been extensively exploited to develop hierarchically structured crystalline materials. Atomistic mismatch in the crystal structure of two particles in the solvent-separated state creates forces that drive particle motions enabling solvent expulsion and coalescence, but the relative magnitudes of the energy barriers for approach, rotation, and translation are not well-known. Here we use classical molecular simulations to calculate the potential of mean force for these three different motions for basal surface encounters of gibbsite nanoplatelets separated by one water layer. In all cases, the highest energy barrier is associated with removing this last water layer to enable jump to contact, even when coaligned. Mutual rotation is more probable than sliding motion, which are both much more probable than jump to contact. This work provides the first comparison on an equal footing of the energy-structure relationships for multiple alignment paths between solvent-separated particles in bulk aqueous solution.

When the Dust Has Settled: Calculation of Binding Affinities from First Principles for SARS-CoV-2 Variants with Quantitative Accuracy

Accurate determination of binding free energy is pivotal for the study of many biological processes and has been applied in a number of theoretical investigations to compare the affinity of severe acute respiratory syndrome coronavirus 2 variants toward the host cell. Diversity of these variants challenges the development of effective general therapies, their transmissibility relying either on an increased affinity toward their dedicated human receptor, the angiotensin-converting enzyme 2 (ACE2), or on escaping the immune response. Now that robust structural data are available, we have determined with utmost accuracy the standard binding free energy of the receptor-binding domain to the most widespread variants, namely, Alpha, Beta, Delta, and Omicron BA.2, as well as the wild type (WT) in complex either with ACE2 or with antibodies, namely, S2E12 and H11-D4, using a rigorous theoretical framework that combines molecular dynamics and potential-of-mean-force calculations. Our results show that an appropriate starting structure is crucial to ensure appropriate reproduction of the binding affinity, allowing the variants to be compared. They also emphasize the necessity to apply the relevant methodology, bereft of any shortcut, to account for all the contributions to the standard binding free energy. Our estimates of the binding affinities support the view that while the Alpha and Beta variants lean on an increased affinity toward the host cell, the Delta and Omicron BA.2 variants choose immune escape. Moreover, the S2E12 antibody, already known to be active against the WT (Starr et al., 2021; Mlcochova et al., 2021), proved to be equally effective against the Delta variant. In stark contrast, H11-D4 retains a low affinity toward the WT compared to that of ACE2 for the latter. Assuming robust structural information, the methodology employed herein successfully addresses the challenging protein-protein binding problem in the context of coronavirus disease 2019 while offering promising perspectives for predictive studies of ever-emerging variants.

Effect of a monovalent salt on the energetics of an antimicrobial-peptide: micelle dissociation

Antimicrobial peptides (AMPs) are promising antimicrobial and therapeutic agents. Recently, we synthesized a cationic 14 residue AMP (LL-14: LKWLKKLLKWLKKL), which showed high broad-spectrum antimicrobial activity. However, the antimicrobial activity of LL-14 was compromised in the presence of NaCl. Salt sensitivity of antimicrobial potency is one of the fundamental limitations of AMP therapeutics. Thus, understanding the thermodynamics of AMP binding to simple membrane-mimetic systems and the effect of NaCl that contributes to their stability is crucial for designing promising AMPs against microbial infection. In this work, we reported computational analysis of LL-14 binding to SDS micelles (the simplest bacterial membrane mimic) at various NaCl concentrations (0.0%, 0.5%, 1.0% w/v). The thermodynamics of LL-14 dissociation from the SDS micelles was estimated by employing steered molecular dynamics (SMD) simulation followed by umbrella sampling. The results indicated that the increase in NaCl concentration systematically disfavoured the LL-14:SDS binding, primarily by stabilizing the dissociative state (i.e., free LL-14 and free micelles in water). We proposed a kinetic scheme in which the salt-induced selective stabilization of the dissociative state increased the activation barrier for the peptide:micelle binding event resulting in reduced affinity. Center-of-mass pulling indicated that the interactions involving the N-terminal of the LL-14 (residues 1-6) and SDS micelle were crucial for the stability of the LL-14:SDS complex, and LL-14 underwent a conformational change (helix → unstructured) before dissociating from the SDS micelle. The observed structural features from the peptide:micelle dissociation pathway corroborate our previous simulations as well as circular dichroism (CD), and fluorescence experiments.

Microscopic Insight into the Structure-Processing-Property Relationships of Core-Shell Structured Dialcohol Cellulose Nanoparticles

In the quest to develop sustainable and environmentally friendly materials, cellulose is a promising alternative to synthetic polymers. However, native cellulose, in contrast to many synthetic polymers, cannot be melt-processed with traditional techniques because, upon heating, it degrades before it melts. One way to improve the thermoplasticity of cellulose, in the form of cellulose fibers, is through chemical modification, for example, to dialcohol cellulose fibers. To better understand the importance of molecular interactions during melt processing of such modified fibers, we undertook a molecular dynamics study of dialcohol cellulose nanocrystals with different degrees of modification. We investigated the structure of the nanocrystals as well as their interactions with a neighboring nanocrystal during mechanical shearing, Our simulations showed that the stress, interfacial stiffness, hydrogen-bond network, and cellulose conformations during shearing are highly dependent on the degree of modification, water layers between the crystals, and temperature. The melt processing of dialcohol cellulose with different degrees of modification and/or water content in the samples was investigated experimentally by fiber extrusion with water used as a plasticizer. The melt processing was easier when increasing the degree of modification and/or water content in the samples, which was in agreement with the conclusions derived from the molecular modeling. The measured friction between the two crystals after the modification of native cellulose to dialcohol cellulose, in some cases, halved (compared to native cellulose) and is also reduced with increasing temperature. Our results demonstrate that molecular modeling of modified nanocellulose fibers can provide fundamental information on the structure-property relationships of these materials and thus is valuable for the development of new cellulose-based biomaterials.

Free Energy Landscapes, Diffusion Coefficients, and Kinetic Rates from Transition Paths

We address the problem of constructing accurate mathematical models of the dynamics of complex systems projected on a collective variable. To this aim we introduce a conceptually simple yet effective algorithm for estimating the parameters of Langevin and Fokker-Planck equations from a set of short, possibly out-of-equilibrium molecular dynamics trajectories, obtained for instance from transition path sampling or as relaxation from high free-energy configurations. The approach maximizes the model likelihood based on any explicit expression of the short-time propagator, hence it can be applied to different evolution equations. We demonstrate the numerical efficiency and robustness of the algorithm on model systems, and we apply it to reconstruct the projected dynamics of pairs of C₆₀ and C₂₄₀ fullerene molecules in explicit water. Our methodology allows reconstructing the accurate thermodynamics and kinetics of activated processes, namely free energy landscapes, diffusion coefficients, and kinetic rates. Compared to existing enhanced sampling methods, we directly exploit short unbiased trajectories at a competitive computational cost.

Hazardous Shortcuts in Standard Binding Free Energy Calculations

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

The emerging potential of interactive virtual reality in drug discovery

INTRODUCTION

The potential of virtual reality (VR) to contribute to drug design and development has been recognized for many years. A recent advance is to use VR not only to visualize and interact with molecules, but also to interact with molecular dynamics simulations 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful for flexible docking and examining binding processes and conformational changes.

AREAS COVERED

The authors use the term 'interactive VR' to refer to software where interactivity is an inherent part of the user VR experience e.g. in making structural modifications or interacting with a physically rigorous molecular dynamics (MD) simulation, as opposed to using VR controllers to rotate and translate the molecule for enhanced visualization. Here, they describe these methods and their application to problems relevant to drug discovery, highlighting the possibilities that they offer in this arena.

EXPERT OPINION

The ease of viewing and manipulating molecular structures and dynamics, using accessible VR hardware, and the ability to modify structures on the fly (e.g. adding or deleting atoms) - and for groups of researchers to work together in the same virtual environment - makes modern interactive VR a valuable tool to add to the armory of drug design and development methods.

A molecular dynamics investigation of N-glycosylation effects on T-cell receptor kinetics

The binding interaction between the T-cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) is modulated by several factors (known and unknown), however, investigations into effects of glycosylation are limited. A fully glycosylated computational model of the TCR bound to the pMHC is developed to investigate the effects of glycosylation on dissociation kinetics from the pMHC. Here, we examine the effects of N-glycosylation on TCR-pMHC bond strength using steered molecular dynamic simulations. N-glycosylation is a post-translational modification that adds sugar moieties to molecules and can modulate the activity of several immune molecules. Using a TCR-pMHC pair found in melanoma as a case study, our study demonstrates that N-glycosylation of the TCR-pMHC alters the proteins' conformation; increases the bond lifetime; and increases the number of hydrogen bonds and Lennard-Jones Contacts involved in the TCR-pMHC bond. We find that weak glycan-protein or glycan-glycan interactions impact the equilibrated structure of the TCR and pMHC leading to an increase in the overall bond strength of the TCR-pMHC complex including the duration and energetic strength under constant load. These results indicate that N-glycosylation plays an important role in the TCR-pMHC bond and should be considered in future computational and experimental studies.Communicated by Ramaswamy H. Sarma.

Physicochemical Insights on Terahertz Wave Diminished Side Effects of Drugs from Slow Dissociation

Dopamine D2 receptors (D2Rs) are one of the most intensely investigated and well-established drug targets for neuropsychiatric disorders. Selective D2R antagonists have been developed as efficacious antipsychotic drugs. Nevertheless, the potent drugs with necessarily high affinity are prone to slow dissociation, which invokes a plethora of severe side effects such as extrapyramidal symptoms, substantial weight gain, associated diabetes, etc. This has become a major barrier in treating psychiatric patients. In this work, we propose a physical method, terahertz wave modulation, to promote the dissociation of high-affinity antipsychotics and thus diminish the side effects. We have proven that a 4.0 THz wave could reduce the affinity by 71% between the D2R and a risperidone ligand and meanwhile expand the exit via conformation modulation, which promises an accelerated dissociation of risperidone. In addition, it is estimated that the enhancement of the dissociation rate due to lowered binding by terahertz irritation could constitute up to 8 orders of magnitude, which is fairly impressive and resembles the enzyme's catalysis. Also, acceleration of the dissociation rate could be adjusted by the irritation strength. This work elaborates the terahertz wave-modulated noncovalent interactions critical in cell signaling pathways. Most importantly, it demonstrates the feasibility of terahertz technologies intervening in receptor-ligand complex regulated diseases such as neurodegenerative disorders, metabolic diseases, etc.

Exploring the Binding Mechanism of a Supramolecular Tweezer CLR01 to 14-3-3σ Protein via Well-Tempered Metadynamics

Using supramolecules for protein function regulation is an effective strategy in chemical biology and drug discovery. However, due to the presence of multiple binding sites on protein surfaces, protein function regulation via selective binding of supramolecules is challenging. Recently, the functions of 14-3-3 proteins, which play an important role in regulating intracellular signaling pathways via protein–protein interactions, have been modulated using a supramolecular tweezer, CLR01. However, the binding mechanisms of the tweezer molecule to 14-3-3 proteins are still unclear, which has hindered the development of novel supramolecules targeting the 14-3-3 proteins. Herein, the binding mechanisms of the tweezer to the lysine residues on 14-3-3σ (an isoform in 14-3-3 protein family) were explored by well-tempered metadynamics. The results indicated that the inclusion complex formed between the protein and supramolecule is affected by both kinetic and thermodynamic factors. In particular, simulations confirmed that K214 could form a strong binding complex with the tweezer; the binding free energy was calculated to be −10.5 kcal·mol⁻¹ with an association barrier height of 3.7 kcal·mol⁻¹. In addition, several other lysine residues on 14-3-3σ were identified as being well-recognized by the tweezer, which agrees with experimental results, although only K214/tweezer was co-crystallized. Additionally, the binding mechanisms of the tweezer to all lysine residues were analyzed by exploring the representative conformations during the formation of the inclusion complex. This could be helpful for the development of new inhibitors based on tweezers with more functions against 14-3-3 proteins via modifications of CLR01. We also believe that the proposed computational strategies can be extended to understand the binding mechanism of multi-binding sites proteins with supramolecules and will, thus, be useful toward drug design.

Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution

Steered molecular dynamics (SMD) simulation is a powerful method in computer-aided drug design as it can be used to access the relative binding affinity with high precision but with low computational cost. The success of SMD depends on the choice of the direction along which the ligand is pulled from the receptor-binding site. In most simulations, the unidirectional pathway was used, but in some cases, this choice resulted in the ligand colliding with the complex surface of the exit tunnel. To overcome this difficulty, several variants of SMD with multidirectional pulling have been proposed, but they are not completely devoid of disadvantages. Here, we have proposed to determine the direction of pulling with a simple scoring function that minimizes the receptor–ligand interaction, and an optimization algorithm called differential evolution is used for energy minimization. The effectiveness of our protocol was demonstrated by finding expulsion pathways of Huperzine A and camphor from the binding site of Torpedo California acetylcholinesterase and P450cam proteins, respectively, and comparing them with the previous results obtained using memetic sampling and random acceleration molecular dynamics. In addition, by applying this protocol to a set of ligands bound with LSD1 (lysine specific demethylase 1), we obtained a much higher correlation between the work of pulling force and experimental data on the inhibition constant IC50 compared to that obtained using the unidirectional approach based on minimal steric hindrance.

A Coarse-Grained Model for the Mechanical Behavior of Na-Montmorillonite Clay

Sodium montmorillonite (Na-MMT) is one of the most commonly found swelling clay minerals with diverse engineering and technological applications. The nanomechanical properties of this mineral have been extensively investigated computationally utilizing molecular dynamics (MD) simulations to portray the molecular-level changes at different environmental conditions. As the environmentally found Na-MMT clays are generally sized within hundreds of nanometers, all-atomistic (AA) MD simulations of clays within such size range are particularly challenging due to computational inefficiency. Informed from atomistic modeling, a coarse-grained (CG) modeling technique can be employed to overcome the spatiotemporal limitation. The current study presents a modeling strategy to develop a computationally efficient model of Na-MMT clay with a typical size over ≃100 nm by shrinking the atomistic platelet thickness and reducing the number of center-layer atoms. Using the "strain-energy conservation" approach, the force field parameters for the CG model are obtained and the developed CG model can well preserve in-plane tension, shear, and bending behaviors of atomistic counterparts. Remarkably, the CG tactoid model of Na-MMT, a hierarchical multilayer structure, can reproduce the interlayer shear and adhesion as well as d-spacing among the clay sheets as of atomistic one to a good approximation while gaining significantly improved computational speed. Our study demonstrates the efficacy of the CG modeling framework, paving the way for the bottom-up multiscale prediction of mechanical behaviors of clay and related minerals.

Ultrahigh strength, modulus, and conductivity of graphitic fibers by macromolecular coalescence

Theoretical considerations suggest that the strength of carbon nanotube (CNT) fibers be exceptional; however, their mechanical performance values are much lower than the theoretical values. To achieve macroscopic fibers with ultrahigh performance, we developed a method to form multidimensional nanostructures by coalescence of individual nanotubes. The highly aligned wet-spun fibers of single- or double-walled nanotube bundles were graphitized to induce nanotube collapse and multi-inner walled structures. These advanced nanostructures formed a network of interconnected, close-packed graphitic domains. Their near-perfect alignment and high longitudinal crystallinity that increased the shear strength between CNTs while retaining notable flexibility. The resulting fibers have an exceptional combination of high tensile strength (6.57 GPa), modulus (629 GPa), thermal conductivity (482 W/m·K), and electrical conductivity (2.2 MS/m), thereby overcoming the limits associated with conventional synthetic fibers.

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on Density Functional Theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what are the most prevalent non-covalent interactions occurring in a solute-Cl$^-$-THF mixture. The simulations in explicit solvent highlight competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution.

In the footsteps of an inhibitor unbinding from the active site of human carbonic anhydrase II

The crystal structure of human carbonic anhydrase (HCA) II bound to an inhibitor molecule, 6-hydroxy-2-thioxocoumarin (FC5), shows FC5 to be located in a hydrophobic pocket at the active site. The present work employs classical molecular dynamics (MD) simulation to follow the FC5 molecule for 1 μs as it unbinds from its binding location, adopts the path of substrate/product diffusion (path 1) to leave the active site at around 75 ns. It is then found to undergo repeated binding and unbinding at different locations on the surface of the enzyme in water. Several transient excursions through different regions of the enzyme are also observed prior to its exit from the active site. These transient paths are combined with functionally relevant cavities/channels to enlist five additional pathways (path 2-6). Pathways 1-6 are subsequently explored using steered MD and umbrella sampling simulations. A free energy barrier of 0.969 kcal mol^-1 is encountered along path 1, while barriers in the range of 0.57-2.84 kcal mol^-1 are obtained along paths 2, 4 and 5. We also analyze in detail the interaction between FC5 and the enzyme along each path as the former leaves the active site of HCA II. Our results indicate path 1 to be the major exit pathway for FC5, although competing contributions may also come from the paths 2, 4 and 5.Communicated by Ramaswamy H. Sarma.