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The Kirsten Rat Sarcoma (KRAS) G12D mutant protein is a primary driver of pancreatic ductal adenocarcinoma, necessitating the identification of targeted drug molecules. Repurposing of drugs quickly finds new uses, speeding treatment development. This study employs microsecond molecular dynamics simulations to unveil the binding mechanisms of the FDA‐approved MEK inhibitor trametinib with KRASG12D, providing insights for potential drug repurposing. The binding of trametinib was compared with clinical trial drug MRTX1133, which demonstrates exceptional activity against KRASG12D, for better understanding of interaction mechanism of trametinib with KRASG12D. The resulting stable MRTX1133‐KRASG12D complex reduces root mean square deviation (RMSD) values, in Switch I and II domains, highlighting its potential for inhibiting KRASG12D. MRTX1133's robust interaction with Tyr64 and disruption of Tyr96‐Tyr71‐Arg68 network showcase its ability to mitigate the effects of the G12D mutation. In contrast, trametinib employs a distinctive binding mechanism involving P‐loop, Switch I and II residues. Extended simulations to 1 μs reveal sustained network interactions with Tyr32, Thr58, and GDP, suggesting a role of trametinib in maintaining KRASG12D in an inactive state and impede the further cell signaling. The decomposition binding free energy values illustrate amino acids' contributions to binding energy, elucidating ligand–protein interactions and molecular stability. The machine learning approach reveals that van der Waals interactions among the residues play vital role in complex stability and the potential amino acids involved in drug–receptor interactions of each complex. These details provide a molecular‐level understanding of drug binding mechanisms, offering essential knowledge for further drug repurposing and potential drug discovery. [ABSTRACT FROM AUTHOR]

Relative solubilities, i.e. whether a given molecule is more soluble in one solvent compared to others, is a critical parameter for pharmaceutical and agricultural formulation development and chemical synthesis, material science, and environmental chemistry. In silico predictions of this crucial variable can help reducing experiments, waste of solvents and synthesis optimization. In this study, we evaluate the performance of different physics-based methods for predicting relative solubilities. Our assessment involves quantum mechanics-based COSMO-RS and molecular dynamics-based free energy methods using OPLS4, the open-source OpenFF Sage, and GAFF force fields, spanning over 200 solvent–solute combinations. Our investigation highlights the important role of compound multimerization, an effect which must be accounted for to obtain accurate relative solubility predictions. The performance landscape of these methods is varied, with significant differences in precision depending on both the method used and the solute considered, thereby offering an improved understanding of the predictive power of physics-based methods in chemical research. [ABSTRACT FROM AUTHOR]

This article provides an overview of various methods for calculating free energy in the interactions between biomolecules and nanomaterials. The authors discuss the advantages and limitations of these methods and their applicability to different systems. The article aims to guide future research in this field and emphasizes the need for further improvements in force fields, software, and the use of machine learning. The document also includes a list of references to scientific articles related to drug discovery, computational methods, and the calculation of free energy in molecular systems. These articles provide valuable insights and techniques for researchers in these fields. Additionally, there are references to two specific articles that discuss the use of machine learning in drug discovery and the computational characterization of membrane proteins as potential targets for anticancer treatments. [Extracted from the article]

As a critical step in advancing the simulation of photosynthetic complexes, we present the Martini 3 coarse-grained (CG) models of key cofactors associated with light harvesting (LHCII) proteins and the photosystem II (PSII) core complex. Our work focuses on the parametrization of beta-carotene, plastoquinone/quinol, violaxanthin, lutein, neoxanthin, chlorophyll A, chlorophyll B, and heme. We derived the CG parameters to match the all-atom reference simulations, while structural and thermodynamic properties of the cofactors were compared to experimental values when available. To further assess the reliability of the parameterization, we tested the behavior of these cofactors within their physiological environments, specifically in a lipid bilayer and bound to photosynthetic complexes. The results demonstrate that our CG models maintain the essential features required for realistic simulations. This work lays the groundwork for detailed simulations of the PSII-LHCII super-complex, providing a robust parameter set for future studies. [ABSTRACT FROM AUTHOR]

Estimating free energy differences, an important problem in computational drug discovery and in a wide range of other application areas, commonly involves a computationally intensive process of sampling a family of high-dimensional probability distributions and a procedure for computing estimates based on those samples. The variance of the free energy estimate of interest typically depends strongly on how the total computational resources available for sampling are divided among the distributions, but determining an efficient allocation is difficult without sampling the distributions. Here we introduce the Times Square sampling algorithm, a novel on-the-fly estimation method that dynamically allocates resources in such a way as to significantly accelerate the estimation of free energies and other observables, while providing rigorous convergence guarantees for the estimators. We also show that it is possible, surprisingly, for on-the-fly free energy estimation to achieve lower asymptotic variance than the maximum-likelihood estimator MBAR, raising the prospect that on-the-fly estimation could reduce variance in a variety of other statistical applications. for this article are available online. [ABSTRACT FROM AUTHOR]

In a protein, nearby titratable sites can be coupled: the (de)protonation of one may affect the other. The degree of this interaction depends on several factors and can influence the measured pKa. Here, we derive a formalism based on double free energy differences (ΔΔG) for quantifying the individual site pKa values of coupled residues. As ΔΔG values can be obtained by means of alchemical free energy calculations, the presented approach allows for a convenient estimation of coupled residue pKas in practice. We demonstrate that our approach and a previously proposed microscopic pKa formalism, can be combined with alchemical free energy calculations to resolve pH‐dependent protein pKa values. Toy models and both, regular and constant‐pH molecular dynamics simulations, alongside experimental data, are used to validate this approach. Our results highlight the insights gleaned when coupling and microstate probabilities are analyzed and suggest extensions to more complex enzymatic contexts. Furthermore, we find that naïvely computed pKa values that ignore coupling, can be significantly improved when coupling is accounted for, in some cases reducing the error by half. In short, alchemical free energy methods can resolve the pKa values of both uncoupled and coupled residues. [ABSTRACT FROM AUTHOR]

AutoChem is a software package consisting of two modules. The first module is a virtual chemical reactor that can generate reaction products starting from inserted reactants and reactions. The process can be iterated, calculating reactions of the products obtained from the previous steps. To avoid a combinatorial explosion of products, constraints can be inserted. AutoChem module can generate 3D structures of the products obtained as well as a list of all the reactions occurred (chemical network).This can be useful for the calculation of reaction free energies by using computational chemistry programs like Gaussian and ORCA. Usually, the computational step involves the geometry optimization of all products followed by the calculation of vibrational frequencies of the optimized structures, to assess if a local minimum is reached. Finally, when products free energies are obtained, the calculation of the reactions free energies can be done.The second module of Autochem, called check, helps to perform the latter step, launching the jobs, collecting products free energies, restarting false minima and calculating the reactions free energies. This module is intended as a general use even outside AutoChem and might be used to perform a large number of free energy calculations with a little effort from the user. This permits to have results at the level of theory needed. AutoChem might also be used for teaching organic chemistry and basic cheminformatics dealing with SMILES and SMARTS by inserting reactants and reactions and analyzing the products obtained.

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

The sulfur compounds in diesel fuel produce harmful environmental pollutants during combustion. Hydrodesulfurization (HDS) is the most common technique to reduce the sulfur content of diesel fuel but cannot effectively remove aromatic sulfur compounds to produce ultra-low-sulfur diesel. Ionic liquids (ILs) show potential as alternative solvents for extractive desulfurization to be implemented after a conventional HDS process, but the mechanism is not well understood. This work focuses on using a combination of molecular simulation free energy calculations and detailed structural analysis to better understand the molecular-level interactions between dibenzothiophene and seven common imidazolium-based ILs. The free energy calculations suggest that the ILs interact differently with thiophene and dibenzothiophene. No specific interactions were observed between the anion and dibenzothiophene; varying the anion showed no remarkable differences in the observed interactions. It was determined that interactions between dibenzothiophene and the cation were more significant; π-π stacking between the imidazole ring and thiophene ring plus favorable electrostatic interactions between the alkyl chain and benzene rings were observed. The primary goal of this work is to use molecular simulations to complement current experimental research to identify suitable ILs for potential extractive desulfurization applications.

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.

Multisite λ‐dynamics (MSλD) is a novel method for the calculation of relative free energies of binding for ligands to their targeted receptors. It can be readily used to examine a large number of molecules with multiple functional groups at multiple sites around a common core. This makes MSλD a powerful tool in structure‐based drug design. In the present study, MSλD is applied to calculate the relative binding free energies of 1296 inhibitors to the testis specific serine kinase 1B (TSSK1B), a validated target for male contraception. For this system, MSλD requires significantly fewer computational resources compared to traditional free energy methods like free energy perturbation or thermodynamic integration. From MSλD simulations, we examined whether modifications of a ligand at two different sites are coupled or not. Based on our calculations, we established a quantitative structure–activity relationship (QSAR) for this set of molecules and identified a site in the ligand where further modification, such as adding more polar groups, may lead to increased binding affinity. [ABSTRACT FROM AUTHOR]

FAK (focal adhesin kinase), a tyrosine kinase, plays an imperative role in cell–cell communication, particularly in cell signaling systems. It is a multi-functional signaling protein, which integrates and transduces signals into cancer cells through growth factor receptors or integrin and its interaction with Paxillin (PAX). The molecular processes by which FAK promotes the development and progression of cancer have progressively established the possible relationship between FAK-PAX complex in many types of cancer. The interaction of FAX and PAX is very important in breast cancer and thus acts as an essential biomarker for drugs, vaccines or peptide inhibitor designing. In this regard, computational approaches, particularly peptide designing to target the binding interface of the interacting partners, would greatly assist the design of peptide inhibitors against various cancer. Accordingly, in this present study, we screened 236 experimentally validated anti-breast cancer peptides using computational drugs repositioning approach to design peptides targeting the FAK-PAX complex. Using protein-peptide docking the binding site for the HP1 was confirmed and a total of 236 anti-breast cancer peptides were screened. Among the 236, only 12 peptides reported a docking score better than the control. From these 12, Magainin with the docking score − 103.8 ± 10.3 kcal/mol, NRC-07 with the docking score − 100.8 ± 16.5 kcal/mol, and Indolicidin with the docking score − 101.7 ± 3.9 kcal/mol, peptides potentially inhibit the FAX-PAX binding. Calculation of protein's motion and FEL revealed the binding and inhibitory behavior. Moreover, binding free energy (MM/GBSA) confirmed that Magainin exhibited the total binding energy − 53.28 kcal/mol, NRC-07 possessed the TBE − 44.16 kcal/mol, and Indolicidin reported the TBE of − 40.48 kcal/mol, thus explaining the inhibitory potential of these peptides. In conclusion, these peptides exhibit strong inhibitory potential and could abrogate the FAK-PAX complex in in vitro models and thus may relieve the burden of breast cancer. [ABSTRACT FROM AUTHOR]

The development of potent non-nucleoside inhibitors (NNIs) could be an alternate strategy to combating infectious bovine viral diarrhea virus (BVDV), other than the traditional vaccination. RNA-dependent RNA polymerase (RdRp) is an essential enzyme for viral replication; therefore, it is one of the primary targets for countermeasures against infectious diseases. The reported NNIs, belonging to the classes of quinolines (2h: imidazo[4,5-g]quinolines and 5m: pyrido[2,3-g] quinoxalines), displayed activity in cell-based and enzyme-based assays. Nevertheless, the RdRp binding site and microscopic mechanistic action are still elusive, and can be explored at a molecular level. Here, we employed a varied computational arsenal, including conventional and accelerated methods, to identify quinoline compounds' most likely binding sites. Our study revealed A392 and I261 as the mutations that can render RdRp resistant against quinoline compounds. In particular, for ligand 2h, mutation of A392E is the most probable mutation. The loop L1 and linker of the fingertip is recognized as a pivotal structural determinant for the stability and escape of quinoline compounds. Overall, this work demonstrates that the quinoline inhibitors bind at the template entrance channel, which is governed by conformational dynamics of interactions with loops and linker residues, and reveals structural and mechanistic insights into inhibition phenomena, for the discovery of improved antivirals. [ABSTRACT FROM AUTHOR]

Aedes aegypti (A. aegypti) is the principal vector for several diseases and despite having synthetic repellents to curb its feeding and transmission, they pose a threat to humans upon chronic use. Therefore, this study explores natural alternative thymol, via both structural and molecular binding properties against whole proteome targets of A. using a multi-target approach. Properties of thymol were studied using ProTox-II to determine the toxicity class. A preliminary screening of the proteome of A. aegypti was performed using existing microarray data analysis, conserved domain studies and protein modelling to narrow down the target categories. RaptorX standalone high-computing server was utilised for 309 protein structure modelling. Molecular docking was performed for 20 shortlisted protein categories against thymol, and top three docked complexes were simulated at 100 ns. Results showed that thymol belonged to class 4 low-toxicity, and molecular docking and 100 ns simulations in dynamic environment revealed stable complexes of thymol with glutathione-S-transferase (GST), octopamine receptor and glutamate-gated chloride channels (GGCC). Free energy binding via molecular mechanics revealed thymol with GST and GGCC to be stable. Our multi-target study presents insights into the molecular binding events that take place when thymol binds to newly identified A. aegypti targets. [ABSTRACT FROM AUTHOR]

The physical aggregation of polycyclic aromatic compounds (PACs) is a key step in soot inception. In this work, we set out to elucidate which molecular properties of PACs influence the physical growth process and develop a machine learning framework to quantitatively relate these features to the propensity of PACs to physically dimerize. To this end, we identify a pool of compounds with a diverse range of properties and create a dataset of PAC monomers along with their calculated free energies of dimerization, obtained via molecular dynamics simulations enhanced by well-tempered Metadynamics. We then demonstrate that a machine learning model based on the least absolute shrinkage and selection operator (Lasso) is able to quantitatively learn how molecular features contribute to physical aggregation and predict the free energy of dimerization for new pairs of molecules. Results show that our model is able to accurately determine the stability of dimers obtained from both homo- and hetero-molecular dimerization cases. Our approach provides also a data driven method to determine the molecular features most important to predicting the dimer stability. Indeed, we identified size, shape, oxygenation, and presence of rotatable bonds as the most influential characteristics of PACs that contribute to physical dimerization. This work highlights the molecular complexity of the PAC monomers that must be accounted for in order to accurately represent physical aggregation. We anticipate that our approach is key to modeling soot inception as it allows for the efficient prediction of dimerization propensity from easily calculable molecular features. [ABSTRACT FROM AUTHOR]

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease‐related variants that can benefit from analyses with structure‐based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high‐throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high‐throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication‐ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction. [ABSTRACT FROM AUTHOR]

Claudins (Cldns) define a family of transmembrane proteins that are the major determinants of the tight junction integrity and tissue selectivity. They promote the formation of either barriers or ion-selective channels at the interface between two facing cells, across the paracellular space. Multiple Cldn subunits form complexes that include cis- (intracellular) interactions along the membrane of a single cell and trans- (intercellular) interactions across adjacent cells. The first description of Cldn assemblies was provided by electron microscopy, while electrophysiology, mutagenesis and cell biology experiments addressed the functional role of different Cldn homologs. However, the investigation of the molecular details of Cldn subunits and complexes are hampered by the lack of experimental native structures, currently limited to Cldn15. The recent implementation of computer-based techniques greatly contributed to the elucidation of Cldn properties. Molecular dynamics simulations and docking calculations were extensively used to refine the first Cldn multimeric model postulated from the crystal structure of Cldn15, and contributed to the introduction of a novel, alternative, arrangement. While both these multimeric assemblies were found to account for the physiological properties of some family members, they gave conflicting results for others. In this review, we illustrate the major findings on Cldn-based systems that were achieved by using state-of-the-art computational methodologies. The information provided by these results could be useful to improve the characterization of the Cldn properties and help the design of new efficient strategies to control the paracellular transport of drugs or other molecules.

The M protein of the novel coronavirus 2019 (SARS-CoV-2) is the major structural component of the viral envelope and is also the minimum requirement for virus particle budding. M proteins generally exist as dimers. In virus assembly, they are the main driving force for envelope formation through lateral interactions and interactions with other viral structural proteins that play a central role. We built 100 candidate models and finally analyzed the six most convincing structural features of the SARS-CoV-2 M protein dimer based on long-timescale molecular dynamics (MD) simulations, multiple free energy analyses (potential mean force (PMF) and molecular mechanics Poisson-Boltzmann surface area (MMPBSA)) and principal component analysis (PCA) to obtain the most reasonable structure. The dimer stability was found to depend on the Leu-Ile zipper motif and aromatic amino acids in the transmembrane domain (TMD). Furthermore, the C-terminal domain (CTD) effects were relatively small. These results highlight a model in which there is sufficient binding affinity between the TMDs of M proteins to form dimers through the residues at the interface of the three transmembrane helices (TMHs). This study aims to help find more effective inhibitors of SARS-CoV-2 M dimers and to develop vaccines based on structural information.

At central nervous system synapses, agonist binding to postsynaptic ionotropic glutamate receptors (iGluRs) results in signaling between neurons. N-Methyl-D-aspartic acid (NMDA) receptors are a unique family of iGluRs that activate in response to the concurrent binding of glutamate and glycine. Here, we investigate the process of agonist binding to the GluN2A (glutamate binding) and GluN1 (glycine binding) NMDA receptor subtypes using long-timescale unbiased molecular dynamics simulations. We find that positively charged residues on the surface of the GluN2A ligand-binding domain (LBD) assist glutamate binding via a "guided-diffusion" mechanism, similar in fashion to glutamate binding to the GluA2 LBD of AMPA receptors. Glutamate can also bind in an inverted orientation. Glycine, on the other hand, binds to the GluN1 LBD via an "unguided-diffusion" mechanism, whereby glycine finds its binding site primarily by random thermal fluctuations. Free energy calculations quantify the glutamate- and glycine-binding processes.

The type III secretion system (T3SS) is an important molecular machinery in gram‐negative bacteria Shigella flexneri as it provides ways for translocating virulence factors from the bacteria into host cells, eventually leading to severe disease symptoms such as bacillary dysentery. Due to the rising concerns of antibiotics resistance in bactericidal strategy, the anti‐virulence strategy that primarily targets the T3SS components becomes an attractive alternative. MxiM, the secretin pilot protein of Shigella flexneri, binds the secretin MxiD and facilitates the formation of the secretin ring in outer membrane in T3SS assembly. MxiM harbors a large hydrophobic pocket that has been shown to be important in MxiM‐MxiD interaction. In this work, I examined the ligand binding property of MxiM by performing molecular dynamics (MD) simulations of the association between MxiM and a series of hydrophobic ligands, with simulation time amounted to 30 μs. MD simulations successfully captured spontaneous ligand binding events in 153 of the 300 trajectories. The ligand binding can be categorized into two types: a fast type, in which the ligand binds quickly into the hydrophobic pocket and a slow type, in which the ligand forms an encounter complex with the protein before binding into the hydrophobic pocket. Using the MxiM‐ligand binding poses captured in MD simulations, I additionally performed umbrella‐sampling MD simulations with total simulation time amounted to 63 μs to obtain protein–ligand binding free energies. The relationship between the ligand binding free energy and ligand size appears to be nonlinear and exhibits an exponential decay pattern. In summary, I performed computational characterization of MxiM‐hydrophobic ligand binding capabilities and properties, which may provide valuable insights into designing anti‐bacterial medicine against antibiotics resistance in Shigella flexneri. [ABSTRACT FROM AUTHOR]

The von Willebrand disease (vWD) is the most common hereditary bleeding disorder caused by defects of the von Willebrand Factor (vWF), a large extracellular protein in charge of adhering platelets to sites of vascular lesions. vWF performs this essential homeostatic task via specific protein–protein interactions between the vWF A1 domain and the platelet receptor, the glycoprotein Ib alpha (GPIBα). The two naturally occurring vWF A1 domain mutations G1324A and G1324S, near the GPIBα binding site, induce a dramatic decrease in platelet adhesion, resulting in a bleeding disorder classified as type 2M vWD. However, the reason for the drastic phenotypic response induced by these two supposedly minor modifications remains unclear. We addressed this question using a combination of equilibrium‐molecular dynamics (MD) and nonequilibrium MD‐based free energy simulations. Our data confirms that both mutations maintain the highly stable Rossmann fold of the vWF A1 domain. G1324A and G1324S mutations hardly changed the per‐residue flexibility of the A1 domain but induced a global conformational change affecting the region near the binding site to GPIBα. Furthermore, we observed two significant changes in the vWF A1 domain upon mutation, the global redistribution of the internal mechanical stress and the increased thermodynamic stability of the A1 domain. These observations are consistent with previously reported mutations increasing the melting temperature. Overall, our results support the idea of thermodynamic conformational restriction of A1—before the binding to GPIBα—as a crucial factor determining the loss‐of‐function of the G1324A(S) vWD mutants. [ABSTRACT FROM AUTHOR]

Claudins (Cldns) form a large family of protein homologs that are essential for the assembly of paracellular tight junctions (TJs), where they form channels or barriers with tissue‐specific selectivity for permeants. In contrast to several family members whose physiological role has been identified, the function of claudin 4 (Cldn4) remains elusive, despite experimental evidence suggesting that it can form anion‐selective TJ channels in the renal epithelium. Computational approaches have recently been employed to elucidate the molecular basis of Cldns' function, and hence could help in clarifying the role of Cldn4. In this work, we use structural modeling and all‐atom molecular dynamics simulations to transfer two previously introduced structural models of Cldn‐based paracellular complexes to Cldn4 to reproduce a paracellular anion channel. Free energy calculations for ionic transport through the pores allow us to establish the thermodynamic properties driving the ion‐selectivity of the structures. While one model shows a cavity permeable to chloride and repulsive to cations, the other forms barrier to the passage of all the major physiological ions. Furthermore, our results confirm the charge selectivity role of the residue Lys65 in the first extracellular loop of the protein, rationalizing Cldn4 control of paracellular permeability. [ABSTRACT FROM AUTHOR]

The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development. [ABSTRACT FROM AUTHOR]

The structural scaffold of epithelial and endothelial tight junctions (TJs) comprises multimeric strands of claudin (Cldn) proteins that anchor adjacent cells and control the paracellular flux of water and solutes. Based on the permeability properties they confer to the TJs, Cldns are classified as channel- or barrier-forming. For instance, Cldn10b, expressed in kidneys, lungs, and other tissues, displays high permeability for cations and low permeability for water. Along with its high sequence similarity to the cation- and water-permeable TJ protein Cldn15, this makes Cldn10b a valuable test case for investigating the molecular determinants of paracellular transport. In lack of high-resolution experimental information on TJ architectures, here we use molecular dynamics simulations to determine whether atomistic models recapitulate the differences in ion and water transport between of Cldn10b and Cldn15. Our data, based on extensive standard simulations and free energy calculations, reveal that Cldn10b models form cation-permeable pores narrower than Cldn15, which, together with the stable coordination of Na + ions to acidic pore-lining residues (E153, D36, D56), limit the passage of water molecules. By providing a mechanism driving a peculiar case of paracellular transport, these results provide a structural basis for the specific permeability properties of Cldn subtypes that define their physiological role., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

MTAN is an attainable therapeutic target for H. pylori because it may minimize virulence production, limit resistance, and impair quorum sensing without affecting gut flora. Here, 457 compounds with anti-H. pylori activity were methodically analyzed, revealing a diverse array of chemical classes and unique compounds. Molecular docking studies identified three potential compounds with high binding affinities, Dehydrocostus lactone, keramamine B, and ZINC00013531409, each having binding affinity of -7.9, -9.2, and -8.3 kcal/mol, respectively. Molecular dynamics simulations of the ZINC00013531409-MTAN interactions in comparison with Apo-MTAN demonstrated stability and interactions of 300 ns, with key residues involved in protein-ligand binding illuminated. Analysis of hydrogen bonds (Ile52, Met174, and Arg194) and secondary structure variations further elucidated the binding interactions and conformational changes within the complex. Binding free energy calculations shed light on the energetics and interactions governing the complex formation of the ZINC00013531409-MTAN complex. PCA elucidated the dominant modes of motion, along with FEL revealed the energetically favorable states and then DCCM shed light on the correlated motions between residues. Overall, this study offers a detailed computational evaluation of ZINC00013531409 with anti-H. pylori activity, highlighting toxicity profile, conformational stability, and binding interactions, providing a foundation for further drug development efforts toward bacterial resistance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Protein-protein and protein-peptide interactions (PPI and PPepI) belong to a similar category of interactions, yet seemingly subtle differences exist among them. To characterize differences between protein-protein (PP) and protein-peptide (PPep) interactions, we have focussed on two important classes of residues-hotspot and anchor residues. Using implicit solvation-based free energy calculations, a very large-scale alanine scanning has been performed on benchmark datasets, consisting of over 5700 interface residues. The differences in the two categories are more pronounced, if the data were divided into three distinct types, namely - weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG, ∼2-10 kcal/mol), moderate hotspots (ΔΔG, ∼10-20 kcal/mol) and strong hotspots (ΔΔG ≥ ∼20 kcal/mol). The analysis suggests that for PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate hotspot and charged residues (principally Arg) are overwhelmingly present in the strong hotspot. On the other hand, in the PPepI dataset, the distribution shifts from predominantly hydrophobic and polar (in the weak type) to almost similar preference for polar, hydrophobic and charged residues (in moderate type) and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. The preferred anchor residues in both categories are Arg, Tyr and Leu, possessing bulky side chain and which also strike a delicate balance between side chain flexibility and rigidity. The present knowledge should aid in effective design of biologics, by augmentation or disruption of PPIs with peptides or peptidomimetics.Communicated by Ramaswamy H. Sarma.

While Relative Binding Free Energy (RBFE) calculations have become a mainstay in lead optimization programs, the computational expense of performing these calculations has limited their broader application. Active learning (AL), a machine learning method used to direct a search iteratively, has explored larger chemical libraries using RBFE calculations. While AL has been successfully applied, there has not been a systematic study of the impact of parameter settings on the performance of AL. To address this gap, we have generated an exhaustive dataset of RBFE calculations on 10,000 congeneric molecules. We used this dataset to explore the impact of several AL design choices, including the number of molecules sampled at each iteration, the method used to select an initial sample, the method used to build a machine learning model, and the acquisition function that defines the balance between exploration and exploitation in the search. Our studies demonstrated that the performance of AL is largely insensitive to the specific machine learning method and acquisition functions used. In our studies, the most significant factor impacting performance was the number of molecules sampled at each iteration where selecting too few molecules hurts performance. Under the best conditions, we were able to identify 75% of the 100 top scoring molecules by sampling only 6% of the dataset. We hope that the dataset of 10K molecules will provide the basis for future studies exploring additional AL strategies. The source code and supporting data for the work are available at https://github.com/google-research/google-research/tree/master/al_for_fep.

The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.

FMS-like tyrosine kinase 3 (FLT3) serves as an important drug target for acute myeloid leukemia (AML), and gene mutations of FLT3 have been closely associated with AML patients with an incidence rate of ~ 30%. However, the mechanism of the clinically relevant F691L gatekeeper mutation conferred resistance to the drug gilteritinib remained poorly understood. In this study, multiple microsecond molecular dynamics (MD) simulations, end-point free energy calculations, and dynamic correlated and network analyses were performed to investigate the molecular basis of gilteritinib resistance to the FLT3-F691L mutation. The simulations revealed that the resistant mutation largely induced the conformational changes of the activation loop (A-loop), the phosphate-binding loop, and the helix αC of the FLT3 protein. The binding abilities of the gilteritinib to the wild-type and the F691L mutant were different through the binding free energy prediction. The simulation results further indicated that the driving force to determine the binding affinity of gilteritinib was derived from the differences in the energy terms of electrostatic and van der Waals interactions. Moreover, the per-residue free energy decomposition suggested that the four residues (Phe803, Gly831, Leu832, and Ala833) located at the A-loop of FLT3 had a significant impact on the binding affinity of gilteritinib to the F691L mutant. This study may provide useful information for the design of novel FLT3 inhibitors specially targeting the F691L gatekeeper mutant. [ABSTRACT FROM AUTHOR]

We describe the theory of the so‐called common‐core/serial‐atom‐insertion (CC/SAI) approach to compute alchemical free energy differences and its practical implementation in a Python package called Transformato. CC/SAI is not tied to a specific biomolecular simulation program and does not rely on special purpose code for alchemical transformations. To calculate the alchemical free energy difference between several small molecules, the physical end‐states are mutated into a suitable common core. Since this only requires turning off interactions, the setup of intermediate states is straightforward to automate. Transformato currently supports CHARMM and OpenMM as back ends to carry out the necessary molecular dynamics simulations, as well as post‐processing calculations. We validate the method by computing a series of relative solvation free energy differences. [ABSTRACT FROM AUTHOR]

Temperature‐accelerated sliced sampling (TASS) is an enhanced sampling method for achieving accelerated and controlled exploration of high‐dimensional free energy landscapes in molecular dynamics simulations. With the aid of umbrella bias potentials, the TASS method realizes a controlled exploration and divide‐and‐conquer strategy for computing high‐dimensional free energy surfaces. In TASS, diffusion of the system in the collective variable (CV) space is enhanced with the help of metadynamics bias and elevated‐temperature of the auxiliary degrees of freedom (DOF) that are coupled to the CVs. Usually, a low‐dimensional metadynamics bias is applied in TASS. In order to further improve the performance of TASS, we propose here to use a highdimensional metadynamics bias, in the same form as in a parallel bias metadynamics scheme. Here, a modified reweighting scheme, in combination with artificial neural network is used for computing unbiased probability distribution of CVs and projections of high‐dimensional free energy surfaces. We first validate the accuracy and efficiency of our method in computing the four‐dimensional free energy landscape for alanine tripeptide in vacuo. Subsequently, we employ the approach to calculate the eight‐dimensional free energy landscape of alanine pentapeptide in vacuo. Finally, the method is applied to a more realistic problem wherein we compute the broad four‐dimensional free energy surface corresponding to the deacylation of a drug molecule which is covalently complexed with a β‐lactamase enzyme. We demonstrate that using parallel bias in TASS improves the efficiency of exploration of high‐dimensional free energy landscapes. [ABSTRACT FROM AUTHOR]

Inhibition of the papain-like protease (PLpro) of SARS-CoV-2 has been demonstrated to be a successful target to prevent the spreading of the coronavirus in the infected body. In this regard, covalent inhibitors, such as the recently proposed VIR251 ligand, can irreversibly inactivate PLpro by forming a covalent bond with a specific residue of the catalytic site (Cys111), through a Michael addition reaction. An inhibition mechanism can therefore be proposed, including four steps: (i) ligand entry into the protease pocket; (ii) Cys111 deprotonation of the thiol group by a Brønsted–Lowry base; (iii) Cys111-S− addition to the ligand; and (iv) proton transfer from the protonated base to the covalently bound ligand. Evaluating the energetics and PLpro conformational changes at each of these steps could aid the design of more efficient and selective covalent inhibitors. For this aim, we have studied by means of MD simulations and QM/MM calculations the whole mechanism. Regarding the first step, we show that the inhibitor entry in the PLpro pocket is thermodynamically favorable only when considering the neutral Cys111, that is, prior to the Cys111 deprotonation. For the second step, MD simulations revealed that His272 would deprotonate Cys111 after overcoming an energy barrier of ca. 32 kcal/mol (at the QM/MM level), but implying a decrease of the inhibitor stability inside the protease pocket. This information points to a reversible Cys111 deprotonation, whose equilibrium is largely shifted toward the neutral Cys111 form. Although thermodynamically disfavored, if Cys111 is deprotonated in close proximity to the vinylic carbon of the ligand, then covalent binding takes place in an irreversible way (third step) to form the enolate intermediate. Finally, due to Cys111-S− negative charge redistribution over the bound ligand, proton transfer from the initially protonated His272 is favored, finally leading to an irreversibly modified Cys111 and a restored His272. These results elucidate the selectivity of Cys111 to enable formation of a covalent bond, even if a weak proton acceptor is available, as His272. [ABSTRACT FROM AUTHOR]

The earliest metazoan ancestors of humans include the ctenophore Mnemiopsis leidyi The genome of this comb jelly encodes homologs of vertebrate ionotropic glutamate receptors (iGluRs) that are distantly related to glycine-activated NMDA receptors and that bind glycine with unusually high affinity. Using ligand-binding domain (LBD) mutants for electrophysiological analysis, we demonstrate that perturbing a ctenophore-specific interdomain Arg-Glu salt bridge that is notably absent from vertebrate AMPA, kainate, and NMDA iGluRs greatly increases the rate of recovery from desensitization, while biochemical analysis reveals a large decrease in affinity for glycine. X-ray crystallographic analysis details rearrangements in the binding pocket stemming from the mutations, and molecular dynamics simulations suggest that the interdomain salt bridge acts as a steric barrier regulating ligand binding and that the free energy required to access open conformations in the glycine-bound LBD is largely responsible for differences in ligand affinity among the LBD variants.

Many problems in the physical sciences, machine learning, and statistical inference necessitate sampling from a high-dimensional, multimodal probability distribution. Markov Chain Monte Carlo (MCMC) algorithms, the ubiquitous tool for this task, typically rely on random local updates to propagate configurations of a given system in a way that ensures that generated configurations will be distributed according to a target probability distribution asymptotically. In high-dimensional settings with multiple relevant metastable basins, local approaches require either immense computational effort or intricately designed importance sampling strategies to capture information about, for example, the relative populations of such basins. Here, we analyze an adaptive MCMC, which augments MCMC sampling with nonlocal transition kernels parameterized with generative models known as normalizing flows. We focus on a setting where there are no preexisting data, as is commonly the case for problems in which MCMC is used. Our method uses 1) an MCMC strategy that blends local moves obtained from any standard transition kernel with those from a generative model to accelerate the sampling and 2) the data generated this way to adapt the generative model and improve its efficacy in the MCMC algorithm. We provide a theoretical analysis of the convergence properties of this algorithm and investigate numerically its efficiency, in particular in terms of its propensity to equilibrate fast between metastable modes whose rough lo- cation is known a priori but respective probability weight is not. We show that our algorithm can sample effectively across large free energy barriers, providing dramatic accelerations relative to traditional MCMC algorithms. [ABSTRACT FROM AUTHOR]

The emergence of recent SARS-CoV-2 has become a global health issue. This single-stranded positive-sense RNA virus is continuously spreading with increasing morbidities and mortalities. The proteome of this virus contains four structural and sixteen nonstructural proteins that ensure the replication of the virus in the host cell. However, the role of phosphoprotein (N) in RNA recognition, replicating, transcribing the viral genome, and modulating the host immune response is indispensable. Recently, the NMR structure of the N-terminal domain of the Nucleocapsid Phosphoprotein has been reported, but its precise structural mechanism of how the ssRNA interacts with it is not reported yet. Therefore, here, we have used an integrated computational pipeline to identify the key residues, which play an essential role in RNA recognition. We generated multiple variants by using an alanine scanning strategy and performed an extensive simulation for each system to signify the role of each interfacial residue. Our analyses suggest that residues T57A, H59A, S105A, R107A, F171A, and Y172A significantly affected the dynamics and binding of RNA. Furthermore, per-residue energy decomposition analysis suggests that residues T57, H59, S105 and R107 are the key hotspots for drug discovery. Thus, these residues may be useful as potential pharmacophores in drug designing.

The development of potent non-nucleoside inhibitors (NNIs) could be an alternate strategy to combating infectious bovine viral diarrhea virus (BVDV), other than the traditional vaccination. RNA-dependent RNA polymerase (RdRp) is an essential enzyme for viral replication; therefore, it is one of the primary targets for countermeasures against infectious diseases. The reported NNIs, belonging to the classes of quinolines (2h: imidazo[4,5-g]quinolines and 5m: pyrido[2,3-g] quinoxalines), displayed activity in cell-based and enzyme-based assays. Nevertheless, the RdRp binding site and microscopic mechanistic action are still elusive, and can be explored at a molecular level. Here, we employed a varied computational arsenal, including conventional and accelerated methods, to identify quinoline compounds’ most likely binding sites. Our study revealed A392 and I261 as the mutations that can render RdRp resistant against quinoline compounds. In particular, for ligand 2h, mutation of A392E is the most probable mutation. The loop L1 and linker of the fingertip is recognized as a pivotal structural determinant for the stability and escape of quinoline compounds. Overall, this work demonstrates that the quinoline inhibitors bind at the template entrance channel, which is governed by conformational dynamics of interactions with loops and linker residues, and reveals structural and mechanistic insights into inhibition phenomena, for the discovery of improved antivirals.

Methods that combine collective variable (CV) based enhanced sampling and global tempering approaches are used in speeding‐up the conformational sampling and free energy calculation of large and soft systems with a plethora of energy minima. In this paper, a new method of this kind is proposed in which the well‐sliced metadynamics approach (WSMTD) is united with replica exchange with solute tempering (REST2) method. WSMTD employs a divide‐and‐conquer strategy wherein high‐dimensional slices of a free energy surface are independently sampled and combined. The method enables one to accomplish a controlled exploration of the CV‐space with a restraining bias as in umbrella sampling, and enhance‐sampling of one or more orthogonal CVs using a metadynamics like bias. The new hybrid method proposed here enables boosting the sampling of more slow degrees of freedom in WSMTD simulations, without the need to specify associated CVs, through a replica exchange scheme within the framework of REST2. The high‐dimensional slices of the probability distributions of CVs computed from the united WSMTD and REST2 simulations are subsequently combined using the weighted histogram analysis method to obtain the free energy surface. We show that the new method proposed here is accurate, improves the conformational sampling, and achieves quick convergence in free energy estimates. We demonstrate this by computing the conformational free energy landscapes of solvated alanine tripeptide and Trp‐cage mini protein in explicit water. [ABSTRACT FROM AUTHOR]

Temperature accelerated sliced sampling (TASS) is an efficient method to compute high dimensional free energy landscapes. The original TASS method employs the weighted histogram analysis method (WHAM) which is an iterative post‐processing to reweight and stitch high dimensional probability distributions in sliced windows that are obtained in the presence of restraining biases. The WHAM necessitates that TASS windows lie close to each other for proper overlap of distributions and span the collective variable space of interest. On the other hand, increase in number of TASS windows implies more number of simulations, and thus it affects the efficiency of the method. To overcome this problem, we propose herein a new mean‐force (MF) based reweighting scheme called TASS‐MF, which enables accurate computation with a fewer number of windows devoid of the WHAM post‐processing. Application of the technique is demonstrated for alanine di‐ and tripeptides in vacuo to compute their two‐ and four‐dimensional free energy landscapes, the latter of which is formidable in conventional umbrella sampling and metadynamics. The landscapes are computed within a kcal mol−1 accuracy, ensuring a safe usage for broad applications in computational chemistry. [ABSTRACT FROM AUTHOR]

Tris(hydroxymethyl)aminomethane (Tris) has been used as the buffer in bioactivity testing for over two decades and has become a standard choice for the scientific community. While it is believed to be non-interacting, the extent of its interactions with titanium oxide surfaces has not been systematically studied. Here, we use experimental (zeta potential measurements) and computational (molecular dynamics) approaches to evaluate the interaction of Tris with a rutile surface and how it affects the adsorption of other molecules relevant in biomedical in vitro testing. We show that the interaction of Tris with the rutile surface is strong and significantly affects the interaction of other organic residues with the surface. These strong interactions are compounded by the Tris concentration in the in vitro testing protocol, which is much higher compared to other components. Our findings indicate that the kinetics observed in in vitro tests will be strongly influenced by the presence of Tris as a buffering agent when compared to the natural CO2 buffer in blood. These results reveal that considering the so-far neglected active role of Tris in in vitro testing is critically needed and that in vitro protocols using CO2 partial pressure as the buffering agent should yield more reliable results.

With over 150 heritable mutations identified as disease‐causative, superoxide dismutase 1 (SOD1) has been a main target of amyotrophic lateral sclerosis (ALS) research and therapeutic efforts. However, recent evidence has suggested that neither loss of function nor protein aggregation is responsible for promoting neurotoxicity. Furthermore, there is no clear pattern to the nature or the location of these mutations that could suggest a molecular mechanism behind SOD1‐linked ALS. Here, we utilize reliable and accurate computational techniques to predict the perturbations of 10 such mutations to the free energy changes of SOD1 as it matures from apo monomer to metallated dimer. We find that the free energy perturbations caused by these mutations strongly depend on maturational progress, indicating the need for state‐specific therapeutic targeting. We also find that many mutations exhibit similar patterns of perturbation to native and non‐native maturation, indicating strong thermodynamic coupling between the dynamics at various sites of maturation within SOD1. These results suggest the presence of an allosteric network in SOD1 which is vulnerable to disruption by these mutations. Analysis of these perturbations may contribute to uncovering a unifying molecular mechanism which explains SOD1‐linked ALS and help to guide future therapeutic efforts. [ABSTRACT FROM AUTHOR]

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica‐exchange GaMD (rex‐GaMD) and replica‐exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the Ligand GaMD (LiGaMD) and Peptide GaMD (Pep‐GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small‐molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein–protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design. This article is categorized under:Structure and Mechanism > Computational Biochemistry and BiophysicsMolecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo MethodsMolecular and Statistical Mechanics > Free Energy Methods [ABSTRACT FROM AUTHOR]

In this paper, we review recent advances in the Continuous Fractional Component Monte Carlo (CFCMC) methodology and present a historic overview of the most important developments that have led to this method. The CFCMC method has gained attention for Monte Carlo simulations of adsorption at high loading, and phase and reaction equilibria of dense systems. It has recently been extended to reactive systems. The main features of the CFCMC method are: (1) Increased molecule exchange efficiency between different phases in single and multicomponent (reactive) systems, which improves the efficiency and accuracy of phase equilibria simulations at high densities; (2) Direct calculation of the chemical potential from a single simulation; (3) Direct calculation of partial molar properties from a single simulation. The developed simulation techniques are incorporated in the open-source molecular simulation software Brick-CFCMC. [ABSTRACT FROM AUTHOR]

This work provides a curated database of experimental and calculated hydration free energies for small neutral molecules in water, along with molecular structures, input files, references, and annotations. We call this the Free Solvation Database, or FreeSolv. Experimental values were taken from prior literature and will continue to be curated, with updated experimental references and data added as they become available. Calculated values are based on alchemical free energy calculations using molecular dynamics simulations. These used the GAFF small molecule force field in TIP3P water with AM1-BCC charges. Values were calculated with the GROMACS simulation package, with full details given in references cited within the database itself. This database builds in part on a previous, 504-molecule database containing similar information. However, additional curation of both experimental data and calculated values has been done here, and the total number of molecules is now up to 643. Additional information is now included in the database, such as SMILES strings, PubChem compound IDs, accurate reference DOIs, and others. One version of the database is provided in the Supporting Information of this article, but as ongoing updates are envisioned, the database is now versioned and hosted online. In addition to providing the database, this work describes its construction process. The database is available free-of-charge via http://www.escholarship.org/uc/item/6sd403pz .