Molecular Dynamics Trajectories Research Articles

Determination of the Dipole Moment Variation Upon Excitation in the Chromophore of Green Fluorescent Protein From Molecular Dynamic Trajectories with QM/MM Potentials Using Machine Learning Methods

Quantum and molecular mechanics (QM/MM) potentials are used to calculate molecular dynamics trajectories for the EYFP protein of the green fluorescent protein family. Machine learning models are constructed to establish the relationship between the geometric parameters of the chromophore in the frame of its trajectory and the properties of its electronic excitation. It is shown that it is not enough to use only bridging bonds between the phenyl and imidazolidone fragments of the chromophore as a geometric parameter, and at least two more neighboring bonds must be added to the model. The proposed models allow determination of the dipole moment variation upon excitation with an average error of 0.11 a.u.

PIM-1L Kinase Binds to and Inactivates SRPK1: A Biochemical and Molecular Dynamics Study.

SR/RS dipeptide repeats vary in both length and position, and are phosphorylated by SR protein kinases (SRPKs). PIM-1L, the long isoform of PIM-1 kinase, the splicing of which has been implicated in acute myeloid leukemia, contains a domain that consists largely of repeating SR/RS and SH/HS dipeptides (SR/SH-rich). In order to extend our knowledge on the specificity and cellular functions of SRPK1, here we investigate whether PIM-1L could act as substrate of SRPK1 by a combination of biochemical and computational approaches. Our biochemical data showed that the SR/SH-rich domain of PIM-1L was able to associate with SRPK1, yet it could not act as a substrate but, instead, inactivated the kinase. In line with our biochemical data, molecular modeling followed by a microsecond-scale all-atom molecular dynamics (MD) simulation suggests that the SR/SH-rich domain acts as a pseudo-docking peptide that binds to the same acidic docking-groove used in other SRPK1 interactions and induces inactive SRPK1 conformations. Comparative community network analysis of the MD trajectories, unraveled the dynamic architecture of apo SRPK1 and notable alterations of allosteric communications upon PIM-1L peptide binding. This analysis also allowed us to identify key SRPK1 residues, including unique ones, with a pivotal role in mediating allosteric signal propagation within the kinase core. Interestingly, most of the identified amino acids correspond to cancer-associated amino acid changes, validating our results. In total, this work provides insights not only on the details of SRPK1 inhibition by the PIM-1L SR/SH-domain, but also contributes to an in-depth understanding of SRPK1 regulation.

Molecular Docking Assessment of Limonoids from Cameroonian Entandrophragma Species as Potential Inhibitors of Anopheles gambiae Acetylcholinesterase (AChE)

Malaria remains one of the great killers in tropical regions of the world due to the transmission of the Plasmodium parasite by the bites of the female mosquito Anopheles. The resistance of this species to synthetic insecticides contributes to an increase in the incidence of malaria and therefore necessitates the development of new potent and eco-friendly insecticides. In this study, twelve previously reported limonoids from four Entandrophragma species collected in Cameroon have been computationally evaluated for their Anopheles gambiae AChE inhibitory activity. The docking procedure was carried out through Molecular Operating Environment 2019.01 (MOE), while the UCSF Chimera program was used to model the docking results based on interactions between proteins and ligands, and molecular dynamics trajectories were analyzed using the GROMACS 2021.1 tool. Entandrophragmin and encandollens B and C with docking scores ranging from −6.45 to −7.28 kcal/mol were the most promising hits compared to the reference azadirachtin (−6.22 kcal/mol) and were further evaluated for their mechanism of action. Subsequent evaluation classified encandollen C as the best candidate for the development of new potent eco-friendly insecticides based on its lower average RMSD and RMSF and its compactness over a 150 ns duration with acetylcholinesterase.

Phenol Photostatic Spectra and Quantum‐Classical Photodynamic Deprotonation

ABSTRACTThe spectral‐luminescence properties and photochemical conversions of phenol were analyzed for an isolated molecule as well as in water solvents in a continuum implicit model and explicit atomistic surroundings. This involved employing cut‐edge hybrid quantum‐classical methodologies to generate static optical spectra and the excited dissipative crossing potential energy curves. A combination of electronic excitations, gradient calculations, and embedding electrostatic potential fitting charges on quantum‐classical molecular dynamic propagation trajectories provided statistically averaged absorption spectra. The mixed‐reference spin‐flip multiconfigurational linear response method based on reference triplet preprocessed in the time‐dependent density‐functional theory was utilized to determine conical intersections between the lowest excited and ground states, as well as two‐stage transitions from the second excitation to the ground state. Non‐adiabatic quantum‐classical molecular dynamics defined photodissipative trajectories of excited states, their lifetimes, and crossing points through trajectory surface hopping together with the mixed‐reference spin‐flip and embedding electrostatic potential fitting approaches. Dyson orbitals of the extended Koopmans' theorem were applied to reveal the nature of molecular states at conical intersections and key points on photodynamic trajectories. Potential hydroxyl group cleavage predicted with conical intersections searching turns to “swift” OH deprotonation through |π→⟩ transition along photodynamic propagations in contrast with “long” processes leading to benzene ring deformation with stable OH bond.

Molecular Dynamics (MD) Simulations Provide Insights into the Activation Mechanisms of 5-HT2A Receptors

Recent breakthroughs in the determination of atomic resolution 3-D cryo-electron microscopy structures of membrane proteins present an unprecedented opportunity for drug discovery. Structure-based drug discovery utilizing in silico methods enables the study of dynamic connectivity of stable conformations induced by the drug in achieving its effect. With the ever-expanding computational power, simulations of this type reveal protein dynamics in the nano-, micro-, and even millisecond time scales. In the present study, aiming to characterize the protein dynamics of the 5HT2A receptor stimulated by ligands (agonist/antagonist), we performed 1 µs MD simulations on 5HT2A/DOI (agonist), 5HT2A/GSK215083 (antagonist), and 5HT2A (APO, no ligand) systems. The crystal structure of 5HT2A/zotepine (antagonist) (PDB: 6A94) was used to set up the simulation systems in a lipid bilayer environment. We found the monitoring of the ionic lock residue pair (R3.50-E6.30) of 5HT2A in MD simulations to be a good approximation of the effects of agonists (ionic lock breakage) or antagonists (ionic lock formation) on receptor activation. We further performed analyses of the MD trajectories, including Principal Component Analysis (PCA), hydrogen bond, salt bridge, and hydrophobic interaction network analyses, and correlation between residues to identify key elements of receptor activation. Our results suggest that in order to trigger receptor activation, DOI must interact with 5HT2A through residues V5.39, G5.42, S5.43, and S5.46 on TM5, inducing significant conformational changes in the backbone angles of G5.42 and S5.43. DOI also interacted with residues W6.48 (toggle switch) and F6.51 on TM6, causing major conformational shifts in the backbone angles of F6.44 and V6.45. These structural changes were transmitted to the intracellular ends of TM5, TM6, and ICL3, resulting in the breaking of the ionic lock and subsequent G protein activation. The studies could be helpful in future design of selective agonists/antagonists for various serotonin receptors (5HT1A, 5HT2A, 5HT2B, 5HT2C, and 5HT7) involved in detrimental disorders, such as addiction and schizophrenia.

Two riboswitch classes that share a common ligand-binding fold show major differences in the ability to accommodate mutations.

Riboswitches are structured RNAs that sense small molecules to control expression. Prequeuosine1 (preQ1)-sensing riboswitches comprise three classes (I, II and III) that adopt distinct folds. Despite this difference, class II and III riboswitches each use 10 identical nucleotides to bind the preQ1 metabolite. Previous class II studies showed high sensitivity to binding-pocket mutations, which reduced preQ1 affinity and impaired function. Here, we introduced four equivalent mutations into a class III riboswitch, which maintained remarkably tight preQ1 binding.Co-crystal structures of each class III mutant showed compensatory interactions that preserve the fold. Chemical modification analysis revealed localized RNA flexibility changes for each mutant, but molecular dynamics (MD) simulations suggested that each mutation was not overtly destabilizing. Although impaired, class III mutants retained tangible gene-regulatory activity in bacteria compared to equivalent preQ1-II variants; mutations in the preQ1-pocket floor were tolerated better than wall mutations. Principal component analysis of MD trajectories suggested that the most functionally deleterious wall mutation samples different motions compared to wildtype. Overall, the results reveal that formation of compensatory interactions depends on the context of mutations within the overall fold and that functionally deleterious mutations can alter long-range correlated motions that link the riboswitch binding pocket with distal gene-regulatory sequences.

SeqDance: A Protein Language Model for Representing Protein Dynamic Properties.

Proteins perform their functions by folding amino acid sequences into dynamic structural ensembles. Despite the important role of protein dynamics, their complexity and the absence of efficient representation methods have limited their integration into studies on protein function and mutation fitness, especially in deep learning applications. To address this, we present SeqDance, a protein language model designed to learn representation of protein dynamic properties directly from sequence alone. SeqDance is pre-trained on dynamic biophysical properties derived from over 30,400 molecular dynamics trajectories and 28,600 normal mode analyses. Our results show that SeqDance effectively captures local dynamic interactions, co-movement patterns, and global conformational features, even for proteins lacking homologs in the pre-training set. Additionally, we showed that SeqDance enhances the prediction of protein fitness landscapes, disorder-to-order transition binding regions, and phase-separating proteins. By learning dynamic properties from sequence, SeqDance complements conventional evolution- and static structure-based methods, offering new insights into protein behavior and function.

Rare-Event Sampling using a Reinforcement Learning-Based Weighted Ensemble Method.

Despite the power of path sampling strategies in enabling simulations of rare events, such strategies have not reached their full potential. A common challenge that remains is the identification of a progress coordinate that captures the slow relevant motions of a rare event. Here we have developed a weighted ensemble (WE) path sampling strategy that exploits reinforcement learning to automatically identify an effective progress coordinate among a set of potential coordinates during a simulation. We apply our WE strategy with reinforcement learning to three benchmark systems: (i) an egg carton-shaped toy potential, (ii) an S-shaped toy potential, and (iii) a dimer of the HIV-1 capsid protein (C-terminal domain). To enable rapid testing of the latter system at the atomic level, we employed discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model that was based on extensive conventional simulations. Our results demonstrate that using concepts from reinforcement learning with a weighted ensemble of trajectories automatically identifies relevant progress coordinates among multiple candidates at a given time during a simulation. Due to the rigorous weighting of trajectories, the simulations maintain rigorous kinetics.

Application of the flexible and polarisable hydration ion model to study Th(IV) aqueous solutions

Th(IV) hydration in aqueous solution is studied by means of molecular dynamics simulations based on a polarisable Th(IV)- H 2 O interaction potential developed in the framework of the hydrated ion concept. Molecular Dynamics (MD) simulations provide as the only in-solution representative motif the ennea-aqua ion, [ Th ( H 2 O ) 9 ] 4 + , with first shell water molecules at an average distance of 2.47 Å from the ion, in accordance with most of the experimental estimates. A well-defined second hydration shell is also identified, hosting ca. 19 solvent molecules at 4.65 Å. Non-structural aspects of the Th(IV) hydration phenomenon, such hydration enthalpy and ion diffusion are well reproduced by the simulation results. The analysis of the O-Th-O angle distribution suggests that the capped square antiprism arrangement prevails over the trigonal tricapped prism. The well-balanced definition of the ion–water and water–water interactions in such a demanding ion neighbourhood is confirmed by the EXAFS and XANES spectroscopies. The theoretical spectra, computed from the MD trajectory, are in fine agreement with some of the experimentally recorded. The sensitivity of the XAS spectroscopy to structural changes confirms how the presented potential manages in a proper way the response of the water molecules to the highly polarising electric field generated by the tetravalent ion.

Computational insights into dispiro-oxindolopyrrolizidines as aldose reductase inhibitors: Molecular docking, ADME analysis, molecular dynamic simulation, and MM-PBSA analysis

Chronic diabetes problems arise from the activation of aldose reductase (AR), the primary enzyme of the polyol pathway, under hyperglycemic circumstances. The spirooxindole ring system comes out to be a fascinating class of naturally occurring compounds with pharmaceutical interest. In this work, we have chosen three dispirooxindolopyrrolizidines (DSOIP-H, DSOIP-Me, and DSOIP-OMe) to investigate their ability to inhibit AR. Through molecular docking studies, the order of binding energy was calculated as DSOIP-H (-8.23 kcal/mol) < DSOIP-Me (-8.58 kcal/mol) < DSOIP-OMe (-8.72 kcal/mol). The three ligands showed effective binding with AR's catalytic active site through hydrophobic interactions. The ADME analysis revealed valuable insights into the ligands' pharmacokinetic properties. Through protein-ligand interactions, DSOIP-OMe consistently interacted with the amino acid residues of the AR enzyme's active site areas, as demonstrated by the molecular dynamic (MD) trajectory analysis. The exceptional inhibitory efficacy of DSOIP-OMe has been established based on its binding energy values of -92.69 kJ/mol as determined by MM-PBSA. The PHE122 residue of AR is recognized as a crucial residue that made a substantial contribution to the binding energy of MM-PBSA through hydrophobic interactions.

Dy-SheHeRASADe: A representation of the [formula omitted] sheet dynamics through surface descriptors

Molecular dynamics (MD) simulations are important tools for studying the dynamic motions of macromolecules at the atomic level. With the increasing capabilities of high performance computing, MD simulations are becoming more widely used. This allows molecular modelers to simulate the molecular behavior of large molecular architectures for much longer trajectories. Appropriate visualization of MD trajectories is becoming essential to provide an immediate and intuitive understanding of a molecule’s dynamics and function. In this study, we implement a novel 3D graphical representation, Dynamical Sheets Helper for RepresentAtion of SurfAce Descriptors (Dy-SheHeRASADe), to visualize the β sheet secondary structures of proteins in the context of molecular dynamics. Dy-SheHeRASADe is developed in UnityMol, an open source molecular viewer and prototyping platform. We considered β sheet fluctuations and hydrogen bond formation during molecular dynamics simulations to characterize the parts of β sheets with large motions or with labile bonds. We propose two visualization modes based on a surface representation of the β sheets calculated according to the positions of the α carbons and the hydrogen bonds between the β strands. The volumetric mode, in which this surface is enclosed in a semi-transparent volume that represents the fluctuation zone of the sheet during dynamics. The heatmap mode, in which the surface is colored according to the amplitude values of the α carbons. In addition, we quantify the β sheet fluctuations by displaying the values of the largest and smallest movements of the β sheets, the surface area of the sheets, and the number of hydrogen bonds.

Overcoming methicillin resistance by methicillin-resistant Staphylococcus aureus: Computational evaluation of napthyridine and oxadiazoles compounds for potential dual inhibition of PBP-2a and FemA proteins

Abstract The treatment of the various infections caused by Staphylococcus aureus has become challenging due to the evolving resistance against current therapeutics. In this study, the potentials of napthyridine and oxadiazole derivatives to serve as dual inhibitors of penicillin-binding protein 2a (PBP-2a) and FemA protein, which are crucial to resistance to methicillin-based drugs by S. aureus, were evaluated using molecular modeling techniques. Seventy-two compounds were subjected to molecular docking against the proteins, and the hit compounds were subjected to drug-likeness evaluation and in silico pharmacokinetics prediction. The compounds with good safety profiles were subjected to a 250-ns molecular dynamics (MD) simulation and other relevant analyses based on the MD trajectories. Five hit compounds were selected based on their high affinity for the targets as evidenced by their docking scores ranging from −8.6 to −10.1 kcal/mol for PBP-2a and −9.6 to −9.9 kcal/mol for FemA. These compounds also passed Lipinski’s rule of five evaluation with no violation and possessed high human intestinal absorption potential, showcasing their potential as orally administered therapeutic agents. However, three of the compounds were potential mutagens. MD simulation revealed that the final two compounds maintained stable interactions with the target proteins over 250 ns, with minimal deviations and fluctuations. Hydrogen bond stability and energy decomposition analysis further confirmed the strong binding affinity of the hit compounds compared to the control drug, methicillin. Conclusively, the compounds with the CID “135964525” and “44130718” are worthy of further experimental validation in the development of potential inhibitors of PBP-2a and FemA.

Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation.

Graph neural networks (GNNs) have emerged as powerful tools for quantum chemical property prediction, leveraging the inherent graph structure of molecular systems. GNNs depend on an edge-to-node aggregation mechanism for combining edge representations into node representations. Unfortunately, existing learnable edge-to-node aggregation methods substantially increase the number of parameters and, thus, the computational cost relative to simple sum aggregation. Worse, as we report here, they often fail to improve predictive accuracy. We therefore propose a novel learnable edge-to-node aggregation mechanism that aims to improve the accuracy and parameter efficiency of GNNs in predicting molecular properties. The new mechanism, called "patch aggregation", is inspired by the Multi-Head Attention and Mixture of Experts machine learning techniques. We have incorporated the patch aggregation method into the specialized, state-of-the-art GNN models SchNet, DimeNet++, SphereNet, TensorNet, and VisNet and show that patch aggregation consistently outperforms existing learnable and nonlearnable aggregation techniques (sum, multilayer perceptron, softmax, and set transformer aggregation) in the prediction of molecular properties such as QM9 thermodynamic properties and MD17 molecular dynamics trajectory energies and forces. We also find that patch aggregation not only improves prediction accuracy but also is parameter-efficient, making it an attractive option for practical applications for which computational resources are limited. Further, we show that Patch aggregation can be applied across different GNN models. Overall, Patch aggregation is a powerful edge-to-node aggregation mechanism that improves the accuracy of molecular property predictions by GNNs.

Phytobioinformatics screening of ayurvedic plants for potential α-glucosidase inhibitors in diabetes management

The enzyme α-glucosidase in the small intestine regulates blood glucose levels and stimulates the hydrolysis of oligosaccharides and polysaccharides, increasing glucose levels in the body. Inhibiting this enzyme slows glucose digestion and absorption and as a result post-prandial blood glucose levels remain low, causing decreased insulin demand. Here, we investigated the ayurvedic antidiabetic plants and virtually screened an in-house library of 478 phytochemicals of these plants against the human α-glucosidase. We identified 11 secondary metabolites, including palmitic acid α-monoglyceride, (+)-(2 R)-6-propionyloxyethyl-4′,5,7-trihydroxyisoflavanone, Abruquinone E, and Aurantiamide Acetate, among others, showed stronger interactions with the receptor than the native ligand N-acetyl cysteine. Surprisingly, except one, all of these metabolites were from Abrus precatorius L. [Fabaceae] affirming its ethnopharmacological use against diabetes. The stability of the interactions between the ligands and receptor protein was evaluated through Molecular Dynamic (MD) simulation trajectories including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), H bonds, β-factor analysis, and binding energy calculation through MM/GBSA method. The efficacy of top metabolites in inhibiting α-glucosidase is depicted in pharmacophore analysis. A comprehensive pharmacokinetics analysis confirmed the druggability, safety, and efficiency of top drug candidates. Additionally, we predicted the interactions of these top metabolites within the biological system. The medicinal properties described in this study will help develop active drug candidates for therapeutic purposes. Further experiments are recommended to prove the effectiveness of these metabolites in inhibiting the α-glucosidase enzyme for exploring their potential in the treatment of diabetes.

Unveiling Putative Excited State and Transmission of Binding Information in the Fluoride Riboswitch.

Riboswitches regulate downstream gene expression by binding to specific small molecules or ions with multiple mechanisms to transfer the binding information. In the case of the fluoride riboswitch, the transcription termination signal is conveyed through a transient excited state (ES). In this work, we performed conventional molecular dynamics (MD) simulations, totaling 180 μs, to obtain the ES structure and investigate the mechanism underlying information transmission in Mg2+/F- binding within the fluoride riboswitch aptamer. The Mg2+/F- binding pocket exhibits various conformations in its apo form. A series of ES structures were extracted from the MD trajectories of the apo form. The dynamics of the Mg2+/F- binding pocket influenced key pair A40-U48 in ES structures. The pathway connecting the binding pocket to the pair involves interactions between the phosphate groups of U7 and G8 and the nucleobases of G8-C47-U48. Our work presents a structural ensemble of the ES and elucidates a pathway for transferring Mg2+/F- binding information, thereby facilitating the understanding of how the holo-like apo state achieves transcriptional repression.

Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning.

We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much more sampling of chemical space and more data on noncovalent interactions. We train a set of potential energy functions called Nutmeg on it. They are based on the TensorNet architecture. They use a novel mechanism to improve performance on charged and polar molecules, injecting precomputed partial charges into the model to provide a reference for the large-scale charge distribution. Evaluation of the new models shows that they do an excellent job of reproducing energy differences between conformations even on highly charged molecules or ones that are significantly larger than the molecules in the training set. They also produce stable molecular dynamics trajectories and are fast enough to be useful for routine simulation of small molecules.

Unveiling Carbon Cluster Coating in Graphene CVD on MgO: Combining Machine Learning Force field and DFT Modeling.

In this study, we investigate the behavior of carbon clusters (Cn, where n ranges from 16 to 26) supported on the surface of MgO. We consider the impact of doping with common impurities (such as Si, Mn, Ca, Fe, and Al) that are typically found in ores. Our approach combines density functional theory calculations with machine learning force field molecular dynamics simulations. It is found that the C21 cluster, featuring a core-shell structure composed of three pentagons isolated by three hexagons, demonstrates exceptional stability on the MgO surface and behaves as an "enhanced binding agent" on MgO-doped surfaces. The molecular dynamics trajectories reveal that the stable C21 coating on the MgO surface exhibits less mobility compared to other sizes Cn clusters and the flexible graphene layer on MgO. Furthermore, this stability persists even at temperatures up to 1100K. The analysis of the electron localization function and potential function of Cn on MgO reveals the high localization electron density between the central carbon of the C21 ring and the MgO surface. This work proposes that the C21 island serves as a superstable and less mobile precursor coating on MgO surfaces. This explanation sheds light on the experimental defects observed in graphene products, which can be attributed to the reduced mobility of carbon islands on a substrate that remains frozen and unchanged.

Na Vacancy-Driven Phase Transformation and Fast Ion Conduction in W-Doped Na3SbS4 from Machine Learning Force Fields.

Solid-state sodium batteries require effective electrolytes that conduct at room temperature. The Na3PnCh4 (Pn = P, Sb; Ch = S, Se) family has been studied for their high Na ion conductivity. The population of Na vacancies, which mediate ion diffusion in these materials, can be enhanced through aliovalent doping on the pnictogen site. To probe the microscopic role of extrinsic doping and its impact on diffusion and phase stability, we trained a machine learning force field for Na3-x W x Sb1-x S4 based on an equivariant graph neural network. Analysis of large-scale molecular dynamics trajectories shows that an increased Na vacancy population stabilizes the global cubic phase at lower temperatures with enhanced Na ion diffusion and that the explicit role of the substitutional W dopants is limited. In the global cubic phase, we observe large and long-lived deviations of atoms from the averaged symmetry, echoing recent experimental suggestions. Evidence of correlated Na ion diffusion is also presented that underpins the suggested superionic nature of these materials.

Exploring Chemical Space to Identify Partial Binders Against hMPV Nucleocapsid Protein.

Human metapneumovirus (hMPV) has gained prominence in recent times as the predominant etiological agent of acute respiratory tract infections. This virus targets children, the elderly, and individuals with compromised immune systems. Given the protracted duration of hMPV transmission, it is probable that the majority of children will have acquired the virus by the age of 5. In individuals with compromised immune systems, recurrence of hMPV infection is possible. As hMPV matures, it remains latent from the time of acquisition. The genome of hMPV encompasses a pivotal protein referred to as the nucleocapsid protein (N). This protein assumes the form of a left-handed helical nucleocapsid, enveloping the viral RNA genome. The primary function of this structure is to protect nucleases, rendering it a potentially promising target for therapeutic advancements. The present study employs a methodology that involves structure-based virtual screening, followed by molecular dynamics simulation at a 250-ns time scale, to identify potential natural molecules or their derivatives from the ZINC Database. These molecules are investigated for their binding properties against the hMPV nucleoprotein. Based on an evaluation of the docking score, binding site interaction, and molecular dynamics studies, it has been found that two naturally occurring molecules, namely M1 (ZINC85629735) and M3 (ZINC85569125), have shown notable docking scores of -9.6and -10.7 kcal/mol, acceptable RMSD, RMSF, Rg, and soon calculated from molecular dynamics trajectory associated with MMGBSA binding energy of -81.94 and -99.63 kcal/mol, respectively. These molecules have shown the highest binding affinity towardnucleocapsid protein and demonstrated promising attributes as potential binders against hMPV.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.