Recent Developments and Applications of the CHARMM force fields

Origin of Enantioselectivity in Engineered Cytochrome c-Catalyzed Carbon-Radical FePP Hydrolysis Revealed Using QM/MM (ABEEM Polarizable Force Field) and MD Simulations

The origin of highly efficient asymmetric aminohydroxylation of styrene catalyzed by engineered cytochrome c is investigated by the developed Atom-Bond Electronegativity Equalization Method polarizable force field (ABEEM PFF), which is a combined outcome of electronic and steric effects. Model molecules were used to establish the charge parameters of the ABEEM PFF, for which the bond-stretching and angle-bending parameters were obtained by using a combination of modified Seminario and scan methods. The interactions between carbon-radical Fe-porphyrin (FePP) and waters are simulated by molecular dynamics, which shows a clear preference for the pre-R over the pre-S. This preference is attributed to the hydrogen-bond between the mutated 100S and 101P residues as well as van der Waals interactions, enforcing a specific conformation of the carbon-radical FePP complex within the binding pocket. Meanwhile, the hydrogen-bond between water and the nitrogen atom in the active intermediate dictates the stereochemical outcome. Quantum mechanics/molecular mechanics (QM/MM (ABEEM PFF)) and free-energy perturbation calculations elucidate that the 3RTS is characterized by sandwich-like structure among adjacent amino acid residues, which exhibits greater stability than crowed arrangement in 3STS and enables the R enantiomer to form more favorably. Thus, this study provides mechanistic insight into the catalytic reaction of hemoproteins.

New enantioenriched β-indolyl ketones as aromatase inhibitors: Unraveling heme-ligand interactions by MD simulation and MMPBSA analysis

A series of enantioenriched β-indolyl ketones as aromatase inhibitors (AI) is synthesized through the Michael-type Friedel-Crafts alkylation of indole. A highly efficient bifunctionalized amino catalyst is developed to access structurally diverse β-indolyl ketones in high yields (up to 91%) and excellent enantioselectivity (enantiomeric ratio up to 98:2). All the synthesized compounds demonstrated promising aromatase inhibitory potential, where ortho-substituted analogs (3c and 3e) were found most active with IC50 values of 0.68 and 0.90 µM, respectively. Both of these compounds exhibited significant cytotoxicity (IC50 = 0.34 and 0.37 µM) against the MCF-7 breast cancer cell line in the (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide) assay. Molecular docking studies of the synthesized compounds demonstrate favorable binding interactions with the estrogens controlling CYP19A1 (3EQM) and metabolizing CYP3A4 (5VCC) enzymes. Molecular dynamic (MD) simulation analysis revealed the essentiality of heme-ligand interactions to build a stable protein-ligand complex. An average root mean square deviation of 0.35 nm observed during a 100-ns MD simulation and binding free energy in the range of -190 to -227 kJ/mol calculated by g_mmpbsa analysis authenticated the stability of the 3c-3EQM complex. ADMET and drug-likeness parameters supported the suitability of these indole derivatives as the drug lead to develop potent inhibitors for estrogen-dependent breast cancer.

Prediction and Control in DNA Nanotechnology

DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.

Theophylline as a quorum sensing and biofilm inhibitor in Pseudomonas aeruginosa and Chromobacterium violaceum

Quorum sensing (QS) is pivotal in coordinating virulence factors and biofilm formation in various pathogenic bacteria, making it a prime target for disrupting bacterial communication. Pseudomonas aeruginosa is a member of the "ESKAPE" group of bacterial pathogens known for their association with antimicrobial resistance and biofilm formation. The current antibiotic arsenal falls short of addressing biofilm-related infections effectively, highlighting the urgent need for novel therapeutic agents. In this study, we explored the anti-QS and anti-biofilm properties of theophylline against two significant pathogens, Chromobacterium violaceum and P. aeruginosa. The production of violacein, pyocyanin, rhamnolipid, and protease was carried out, along with the evaluation of biofilm formation through methods including crystal violet staining, triphenyl tetrazolium chloride assay, and fluorescence microscopy. Furthermore, computational analyses were conducted to predict the targets of theophylline in the QS pathways of P. aeruginosa and C. violaceum. Our study demonstrated that theophylline effectively inhibits QS activity and biofilm formation in C. violaceum and P. aeruginosa. In P. aeruginosa, theophylline inhibited the production of key virulence factors, including pyocyanin, rhamnolipid, protease, and biofilm formation. The computational analyses suggest that theophylline exhibits robust binding affinity to CviR in C. violaceum and RhlR in P. aeruginosa, key participants in the QS-mediated biofilm pathways. Furthermore, theophylline also displays promising interactions with LasR and QscR in P. aeruginosa. Our study highlights theophylline as a versatile anti-QS agent and offers a promising avenue for future research to develop novel therapeutic strategies against biofilm-associated infections.

Mechanistic Insights into the Adsorption of Monoclonal Antibodies at the Water/Vapor Interface

Monoclonal antibodies (mAbs) are active components of therapeutic formulations that interact with the water-vapor interface during manufacturing, storage, and administration. Surface adsorption has been demonstrated to mediate antibody aggregation, which leads to a loss of therapeutic efficacy. Controlling mAb adsorption at interfaces requires a deep understanding of the microscopic processes that lead to adsorption and identification of the protein regions that drive mAb surface activity. Here, we report all-atom molecular dynamics (MD) simulations of the adsorption behavior of a full IgG1-type antibody at the water/vapor interface. We demonstrate that small local changes in the protein structure play a crucial role in promoting adsorption. Also, interfacial adsorption triggers structural changes in the antibody, potentially contributing to the further enhancement of surface activity. Moreover, we identify key amino acid sequences that determine the adsorption of antibodies at the water-air interface and outline strategies to control the surface activity of these important therapeutic proteins.

Identification of a hub gene VCL for atherosclerotic plaques and discovery of potential therapeutic targets by molecular docking

BACKGROUND

Atherosclerosis (AS) is a pathology factor for cardiovascular diseases and instability of atherosclerotic plaques contributes to acute coronary events. This study identified a hub gene VCL for atherosclerotic plaques and discovered its potential therapeutic targets for atherosclerotic plaques.

METHODS

Differential expressed genes (DEGs) were screened between unstable and stable plaques from GSE120521 dataset and then used for construction of a protein-protein interactions (PPI) network. Through topological analysis, hub genes were identified within this PPI network, followed by construction of a diagnostic model. GSE41571 dataset was utilized to validate the diagnostic model. A key hub gene was identified and its association with immune characteristics and pathways were further investigated. Molecular docking and molecular dynamics (MD) simulation were employed to discover potential therapeutic targets.

RESULTS

According to the PPI network, 3 tightly connected protein clusters were found. Topological analysis identified the top 5 hub genes, Vinculin (VCL), Dystrophin (DMD), Actin alpha 2 (ACTA2), Filamin A (FLNA), and transgelin (TAGLN). Among these hub genes, VCL had the highest diagnostic value. VCL was selected for further analysis and we found that VCL was negatively correlated with immune score and AS-related inflammatory pathways. Next, we identified 408 genes that were highly correlated with VCL and determined potential drug candidates. The results from molecular docking and MD simulation showed compound DB07117 combined with VCL protein stably, the binding energy is -7.7 kcal/mol, indicating that compound DB07117 was a potential inhibitor of VCL protein.

CONCLUSION

This study identified VCL as a key gene for atherosclerotic plaques and provides a potential therapeutic target of VCL for the treatment of atherosclerotic plaques.

Two Receptor Binding Strategy of SARS-CoV-2 Is Mediated by Both the N-Terminal and Receptor-Binding Spike Domain

It is not well understood why severe acute respiratory syndrome (SARS)-CoV-2 spreads much faster than other β-coronaviruses such as SARS-CoV and Middle East respiratory syndrome (MERS)-CoV. In a previous publication, we predicted the binding of the N-terminal domain (NTD) of SARS-CoV-2 spike to sialic acids (SAs). Here, we experimentally validate this interaction and present simulations that reveal a second possible interaction between SAs and the spike protein via a binding site located in the receptor-binding domain (RBD). The predictions from molecular-dynamics simulations and the previously-published 2D-Zernike binding-site recognition approach were validated through flow-induced dispersion analysis (FIDA)─which reveals the capability of the SARS-CoV-2 spike to bind to SA-containing (glyco)lipid vesicles, and flow-cytometry measurements─which show that spike binding is strongly decreased upon inhibition of SA expression on the membranes of angiotensin converting enzyme-2 (ACE2)-expressing HEK cells. Our analyses reveal that the SA binding of the NTD and RBD strongly enhances the infection-inducing ACE2 binding. Altogether, our work provides in silico, in vitro, and cellular evidence that the SARS-CoV-2 virus utilizes a two-receptor (SA and ACE2) strategy. This allows the SARS-CoV-2 spike to use SA moieties on the cell membrane as a binding anchor, which increases the residence time of the virus on the cell surface and aids in the binding of the main receptor, ACE2, via 2D diffusion.

CLIP-Seq analysis enables the design of protective ribosomal RNA bait oligonucleotides against C9ORF72 ALS/FTD poly-GR pathophysiology

Amyotrophic lateral sclerosis and frontotemporal dementia patients with a hexanucleotide repeat expansion in C9ORF72 (C9-HRE) accumulate poly-GR and poly-PR aggregates. The pathogenicity of these arginine-rich dipeptide repeats (R-DPRs) is thought to be driven by their propensity to bind low-complexity domains of multivalent proteins. However, the ability of R-DPRs to bind native RNA and the significance of this interaction remain unclear. Here, we used computational and experimental approaches to characterize the physicochemical properties of R-DPRs and their interaction with RNA. We find that poly-GR predominantly binds ribosomal RNA (rRNA) in cells and exhibits an interaction that is predicted to be energetically stronger than that for associated ribosomal proteins. Critically, modified rRNA "bait" oligonucleotides restore poly-GR-associated ribosomal deficits and ameliorate poly-GR toxicity in patient neurons and Drosophila models. Our work strengthens the hypothesis that ribosomal function is impaired by R-DPRs, highlights a role for direct rRNA binding in mediating ribosomal dysfunction, and presents a strategy for protecting against C9-HRE pathophysiological mechanisms.

Antibiofilm potential of gallic acid against Klebsiella pneumoniae and Enterobacter hormaechei: in-vitro and in-silico analysis

Biofilm refers to a community of microorganisms that adhere to a substrate and play a crucial role in microbial pathogenesis and developing infections associated with medical devices. Enterobacter hormaechei and Klebsiella pneumoniae are classified as significant nosocomial pathogens within the ESKAPE category and cause diverse infections. In addition to their reputation as prolific biofilm formers, these pathogens are increasingly becoming drug-resistant and pose a substantial threat to the healthcare setting. Due to the inherent resistance of biofilms to conventional therapies, novel strategies are imperative for effectively controlling E. hormaechei and K. pneumoniae biofilms. This study aimed to assess the anti-biofilm activity of gallic acid (GA) against E. hormaechei and K. pneumoniae. The results of biofilm quantification assays demonstrated that GA exhibited significant antibiofilm activity against E. hormaechei and K. pneumoniae at concentrations of 4 mg mL-1, 2 mg mL-1, 1 mg mL-1, and 0.5 mg mL-1. Similarly, GA exhibited a dose-dependent reduction in violacein production, a QS-regulated purple pigment, indicating its ability to suppress violacein production and disrupt QS mechanisms in Chromobacterium violaceum. Additionally, computational tools were utilized to identify the potential target involved in the biofilm formation pathway. The computational analysis further indicated the strong binding affinity of GA to essential biofilm regulators, MrkH and LuxS, suggesting its potential in targeting the c-di-GMP and quorum sensing (QS) pathways to hinder biofilm formation in K. pneumoniae. These compelling findings strongly advocate GA as a promising drug candidate against biofilm-associated infections caused by E. hormaechei and K. pneumoniae.

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.

Investigation of Macrocyclic mTOR Modulators of Rapamycin Binding Site via Pharmacoinformatics Approaches

The PI3K/Akt/mTOR is an essential intracellular signaling pathway in which the serine/threonine mTOR kinase portrays a major role in cell growth, proliferation and survival. The mTOR kinase is frequently dysregulated in a broad spectrum of cancers, thus making it a potential target. Rapamycin and its analogs (rapalogs) allosterically inhibit mTOR, thereby dodging the deleterious effects prompted by ATP-competitive mTOR inhibitors. However, the available mTOR allosteric site inhibitors exhibit low oral bioavailability and suboptimal solubility. Bearing in mind this narrow therapeutic window of the current allosteric mTOR inhibitors, an in silico study was designed in search of new macrocyclic inhibitors. The macrocycles from the ChemBridge database (12,677 molecules) were filtered for their drug-likeness properties and the procured compounds were subjected for molecular docking within the binding cleft between FKBP25 and FRB domains of mTOR. The docking analysis resulted with 15 macrocycles displaying higher scores than the selective mTOR allosteric site inhibitor, DL001. The docked complexes were refined by subsequent molecular dynamics simulations for a period of 100 ns. Successive binding free energy computation revealed a total of 7 macrocyclic compounds (HITS) demonstrating better binding affinity than DL001, towards mTOR. The consequent assessment of pharmacokinetic properties resulted in HITS with similar or better properties than the selective inhibitor, DL001. The HITS from this investigation could act as effective mTOR allosteric site inhibitors and serve as macrocyclic scaffolds for developing compounds targeting the dysregulated mTOR.

Novel Lennard-Jones Parameters for Cysteine and Selenocysteine in the AMBER Force Field

Cysteine is a common amino acid with a thiol group that plays a pivotal role in a variety of scenarios in redox biochemistry. In contrast, selenocysteine, the 21st amino acid, is only present in 25 human proteins. Classical force-field parameters for cysteine and selenocysteine are still scarce. In this context, we present a methodology to obtain Lennard-Jones parameters for cysteine and selenocysteine in different physiologically relevant oxidation and protonation states. The new force field parameters obtained in this work are available at https://github.com/MALBECC/AMBER-parameters-database. The parameters were adjusted to reproduce water radial distribution functions obtained by density functional theory ab initio molecular dynamics. We validated the results by evaluating the impact of the choice of parameters on the structure and dynamics in classical molecular dynamics simulations of representative proteins containing catalytic cysteine/selenocysteine residues. There are significant changes in protein structure and dynamics depending on the parameters choice, specifically affecting the residues close to the catalytic sites.

Differentiating interactions of antimicrobials with Gram-negative and Gram-positive bacterial cell walls using molecular dynamics simulations

Developing molecular models to capture the complex physicochemical architecture of the bacterial cell wall and to study the interaction with antibacterial molecules is an important aspect of assessing and developing novel antimicrobial molecules. We carried out molecular dynamics simulations using an atomistic model of peptidoglycan to represent the architecture for Gram-positive S. aureus. The model is developed to capture various structural features of the Staphylococcal cell wall, such as the peptide orientation, area per disaccharide, glycan length distribution, cross-linking, and pore size. A comparison of the cell wall density and electrostatic potentials is made with a previously developed cell wall model of Gram-negative bacteria, E. coli, and properties for both single and multilayered structures of the Staphylococcal cell wall are studied. We investigated the interactions of the antimicrobial peptide melittin with peptidoglycan structures. The depth of melittin binding to peptidoglycan is more pronounced in E. coli than in S. aureus, and consequently, melittin has greater contacts with glycan units of E. coli. Contacts of melittin with the amino acids of peptidoglycan are comparable across both the strains, and the D-Ala residues, which are sites for transpeptidation, show enhanced interactions with melittin. A low energetic barrier is observed for translocation of a naturally occurring antimicrobial thymol with the four-layered peptidoglycan model. The molecular model developed for Gram-positive peptidoglycan allows us to compare and contrast the cell wall penetrating properties with Gram-negative strains and assess for the first time binding and translocation of antimicrobial molecules for Gram-positive cell walls.

Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.

Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

Comparison of the United- and All-Atom Representations of (Halo)alkanes Based on Two Condensed-Phase Force Fields Optimized against the Same Experimental Data Set

The level of accuracy that can be achieved by a force field is influenced by choices made in the interaction-function representation and in the relevant simulation parameters. These choices, referred to here as functional-form variants (FFVs), include for example the model resolution, the charge-derivation procedure, the van der Waals combination rules, the cutoff distance, and the treatment of the long-range interactions. Ideally, assessing the effect of a given FFV on the intrinsic accuracy of the force-field representation requires that only the specific FFV is changed and that this change is performed at an optimal level of parametrization, a requirement that may prove extremely challenging to achieve in practice. Here, we present a first attempt at such a comparison for one specific FFV, namely the choice of a united-atom (UA) versus an all-atom (AA) resolution in a force field for saturated acyclic (halo)alkanes. Two force-field versions (UA vs AA) are optimized in an automated way using the CombiFF approach against 961 experimental values for the pure-liquid densities ρ_liq and vaporization enthalpies ΔH_vap of 591 compounds. For the AA force field, the torsional and third-neighbor Lennard-Jones parameters are also refined based on quantum-mechanical rotational-energy profiles. The comparison between the UA and AA resolutions is also extended to properties that have not been included as parameterization targets, namely the surface-tension coefficient γ, the isothermal compressibility κ_T, the isobaric thermal-expansion coefficient α_P, the isobaric heat capacity c_P, the static relative dielectric permittivity ϵ, the self-diffusion coefficient D, the shear viscosity η, the hydration free energy ΔG_wat, and the free energy of solvation ΔG_che in cyclohexane. For the target properties ρ_liq and ΔH_vap, the UA and AA resolutions reach very similar levels of accuracy after optimization. For the nine other properties, the AA representation leads to more accurate results in terms of η; comparably accurate results in terms of γ, κ_T, α_P, ϵ, D, and ΔG_che; and less accurate results in terms of c_P and ΔG_wat. This work also represents a first step toward the calibration of a GROMOS-compatible force field at the AA resolution.

In Silico Evaluation of Hexamethylene Amiloride Derivatives as Potential Luminal Inhibitors of SARS-CoV-2 E Protein

The coronavirus E proteins are small membrane proteins found in the virus envelope of alpha and beta coronaviruses that have a high degree of overlap in their biochemical and functional properties despite minor sequence variations. The SARS-CoV-2 E is a 75-amino acid transmembrane protein capable of acting as an ion channel when assembled in a pentameric fashion. Various studies have found that hexamethylene amiloride (HMA) can inhibit the ion channel activity of the E protein in bilayers and also inhibit viral replication in cultured cells. Here, we use the available structural data in conjunction with homology modelling to build a comprehensive model of the E protein to assess potential binding sites and molecular interactions of HMA derivatives. Furthermore, we employed an iterative cycle of molecular modelling, extensive docking simulations, molecular dynamics and leveraging steered molecular dynamics to better understand the pore characteristics and quantify the affinity of the bound ligands. Results from this work highlight the potential of acylguanidines as blockers of the E protein and guide the development of subsequent small molecule inhibitors.

Extension of the CHARMM Classical Drude Polarizable Force Field to N- and O-Linked Glycopeptides and Glycoproteins

Molecular dynamic simulations are an effective tool to study complex molecular systems and are contingent upon the availability of an accurate and reliable molecular mechanics force field. The Drude polarizable force field, which allows for the explicit treatment of electronic polarization in a computationally efficient fashion, has been shown to reproduce experimental properties that were difficult or impossible to reproduce with the CHARMM additive force field, including peptide folding cooperativity, RNA hairpin structures, and DNA base flipping. Glycoproteins are essential components of glycoconjugate vaccines, antibodies, and many pharmaceutically important molecules, and an accurate polarizable force field that includes compatibility between the protein and carbohydrate aspect of the force field is essential to study these types of systems. In this work, we present an extension of the Drude polarizable force field to glycoproteins, including both N- and O-linked species. Parameter optimization focused on the dihedral terms using a reweighting protocol targeting NMR solution J-coupling data for model glycopeptides. Validation of the model include eight model glycopeptides and four glycoproteins with multiple N- and O-linked glycosylations. The new glycoprotein carbohydrate force field can be used in conjunction with the remainder of Drude polarizable force field through a variety of MD simulation programs including GROMACS, OPENMM, NAMD, and CHARMM and may be accessed through the Drude Prepper module in the CHARMM-GUI.

The Ability of Chlorophyll to Trap Carcinogen Aflatoxin B1: A Theoretical Approach

The coordination of one and two aflatoxin B1 (AFB1, a potent carcinogen) molecules with chlorophyll a (chl a) was studied at a theoretical level. Calculations were performed using the M06-2X method in conjunction with the 6-311G(d,p) basis set, in both gas and water phases. The molecular electrostatic potential map shows the chemical activity of various sites of the AFB1 and chl a molecules. The energy difference between molecular orbitals of AFB1 and chl a allowed for the establishment of an intermolecular interaction. A charge transfer from AFB1 to the central cation of chl a was shown. The energies of the optimized structures for chl a show two configurations, unfolded and folded, with a difference of 15.41 kcal/mol. Chl a appeared axially coordinated to the plane (α-down or β-up) of the porphyrin moiety, either with the oxygen atom of the ketonic group, or with the oxygen atom of the lactone moiety of AFB1. The complexes of maximum stability were chl a 1-α-E-AFB1 and chl a 2-β-E-AFB1, at −36.4 and −39.2 kcal/mol, respectively. Additionally, with two AFB1 molecules were chl a 1-D-2AFB1 and chl a 2-E-2AFB1, at −60.0 and −64.8 kcal/mol, respectively. Finally, biosorbents containing chlorophyll could improve AFB1 adsorption.

Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase

HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.

Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase

Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods

Owing to several mutations, the oncogene Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) is activated in the majority of cancers, and targeting it has been pharmacologically challenging. In this study, using an in silico approach comprised of pharmacophore modeling, molecular docking, and molecular dynamics simulations, potential KRAS G12D inhibitors were investigated. A ligand-based common feature pharmacophore model was generated to identify the framework necessary for effective KRAS inhibition. The chemical features in the selected pharmacophore model comprised two hydrogen bond donors, one hydrogen bond acceptor, two aromatic rings and one hydrophobic feature. This model was used for screening in excess of 214,000 compounds from InterBioScreen (IBS) and ZINC databases. Eighteen compounds from the IBS and ten from the ZINC database mapped onto the pharmacophore model and were subjected to molecular docking. Molecular docking results highlighted a higher affinity of four hit compounds towards KRAS G12D in comparison to the reference inhibitor, BI-2852. Sequential molecular dynamics (MD) simulation studies revealed all four hit compounds them possess higher KRAS G12D binding free energy and demonstrate stable polar interaction with key residues. Further, Principal Component Analysis (PCA) analysis of the hit compounds in complex with KRAS G12D also indicated stability. Overall, the research undertaken provides strong support for further in vitro testing of these newly identified KRAS G12D inhibitors, particularly Hit1 and Hit2.

CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field

Explicit treatment of electronic polarizability in empirical force fields (FFs) represents an extension over a traditional additive or pairwise FF and provides a more realistic model of the variations in electronic structure in condensed phase, macromolecular simulations. To facilitate utilization of the polarizable FF based on the classical Drude oscillator model, Drude Prepper has been developed in CHARMM-GUI. Drude Prepper ingests additive CHARMM protein structures file (PSF) and pre-equilibrated coordinates in CHARMM, PDB, or NAMD format, from which the molecular components of the system are identified. These include all residues and patches connecting those residues along with water, ions, and other solute molecules. This information is then used to construct the Drude FF-based PSF using molecular generation capabilities in CHARMM, followed by minimization and equilibration. In addition, inputs are generated for molecular dynamics (MD) simulations using CHARMM, GROMACS, NAMD, and OpenMM. Validation of the Drude Prepper protocol and inputs is performed through conversion and MD simulations of various heterogeneous systems that include proteins, nucleic acids, lipids, polysaccharides, and atomic ions using the aforementioned simulation packages. Stable simulations are obtained in all studied systems, including 5 μs simulation of ubiquitin, verifying the integrity of the generated Drude PSFs. In addition, the ability of the Drude FF to model variations in electronic structure is shown through dipole moment analysis in selected systems. The capabilities and availability of Drude Prepper in CHARMM-GUI is anticipated to greatly facilitate the application of the Drude FF to a range of condensed phase, macromolecular systems.

A molecular view of plasticization of polyvinyl alcohol

Although macromolecules such as polymers are in widespread industrial use, pure formulations rarely have precisely the properties new applications demand. Pure polymer is often too brittle and inflexible, necessitating plasticizers to soften or toughen films and bulk polymer materials. In practice, new formulations are developed by extensive trial-and-error methods, as no general molecular explanations exist for the mechanism of plasticization to aid in determining the optimal structure and concentration of plasticizers. Here, through atomistic molecular simulations augmented with advanced sampling techniques, we develop an atomic-level picture of the processes in plasticization by directly calculating free energies that govern the interaction between polymers and small-molecule plasticizers. This work focuses on the influence of two common plasticizer molecules-glycerol and sorbitol-interacting with polyvinyl alcohol (PVA), a frequently used component of polymer films. In particular, we focus on conformational and hydrogen bond structure changes induced in globules of PVA by the plasticizer molecules, with the hypothesis that hydrogen bonding plays a role in the incorporation of these plasticizers into PVA and, thus, in the observed mechanical properties. While we focus on nanoscopic systems, we observe distinct preferences in the conformational free energy that can be connected to the performance of polymer materials at laboratory and industrial scales. This work presents a new molecular perspective from which effective plasticizers can be developed and presents a firm basis from which important analyses of plasticization in complex chemical environments relevant to industry may be developed.

Investigation of Marine-Derived Natural Products as Raf Kinase Inhibitory Protein (RKIP)-Binding Ligands

Raf kinase inhibitory protein (RKIP) is an essential regulator of the Ras/Raf-1/MEK/ERK signaling cascade and functions by directly interacting with the Raf-1 kinase. The abnormal expression of RKIP is linked with numerous diseases including cancers, Alzheimer's and diabetic nephropathy. Interestingly, RKIP also plays an indispensable role as a tumor suppressor, thus making it an attractive therapeutic target. To date, only a few small molecules have been reported to modulate the activity of RKIP, and there is a need to explore additional scaffolds. In order to achieve this objective, a pharmacophore model was generated that explores the features of locostatin, the most potent RKIP modulator. Correspondingly, the developed model was subjected to screening, and the mapped compounds from Marine Natural Products (MNP) library were retrieved. The mapped MNPs after ensuing drug-likeness filtration were escalated for molecular docking, where locostatin was regarded as a reference. The MNPs exhibiting higher docking scores than locostatin were considered for molecular dynamics simulations, and their binding affinity towards RKIP was computed via MM/PBSA. A total of five molecules revealed significantly better binding free energy scores than compared to locostatin and, therefore, were reckoned as hits. The hits from the present in silico investigation could act as potent RKIP modulators and disrupt interactions of RKIP with its binding proteins. Furthermore, the identification of potent modulators from marine natural habitat can act as a future drug-discovery source.

Recent advances in design and applications of biomimetic self-assembled peptide hydrogels for hard tissue regeneration

Abstract

The development of natural biomaterials applied for hard tissue repair and regeneration is of great importance, especially in societies with a large elderly population. Self-assembled peptide hydrogels are a new generation of biomaterials that provide excellent biocompatibility, tunable mechanical stability, injectability, trigger capability, lack of immunogenic reactions, and the ability to load cells and active pharmaceutical agents for tissue regeneration. Peptide-based hydrogels are ideal templates for the deposition of hydroxyapatite crystals, which can mimic the extracellular matrix. Thus, peptide-based hydrogels enhance hard tissue repair and regeneration compared to conventional methods. This review presents three major self-assembled peptide hydrogels with potential application for bone and dental tissue regeneration, including ionic self-complementary peptides, amphiphilic (surfactant-like) peptides, and triple-helix (collagen-like) peptides. Special attention is given to the main bioactive peptides, the role and importance of self-assembled peptide hydrogels, and a brief overview on molecular simulation of self-assembled peptide hydrogels applied for bone and dental tissue engineering and regeneration.

Graphic abstract

Computational drug repurposing study of antiviral drugs against main protease, RNA polymerase, and spike proteins of SARS-CoV-2 using molecular docking method

OBJECTIVES

The new Coronavirus (SARS-CoV-2) created a pandemic in the world in late 2019 and early 2020. Unfortunately, despite the increasing prevalence of the disease, there is no effective drug for the treatment. A computational drug repurposing study would be an appropriate and rapid way to provide an effective drug in the treatment of the coronavirus disease of 2019 (COVID-19) pandemic. In this study, the inhibitory potential of more than 50 antiviral drugs on three important proteins of SARS-CoV-2, was investigated using the molecular docking method.

METHODS

By literature review, three important proteins, including main protease, RNA-dependent RNA polymerase (RdRp), and spike, were selected as the drug targets. The three-dimensional (3D) structure of protease, spike, and RdRp proteins was obtained from the Protein Data Bank. Proteins were energy minimized. More than 50 antiviral drugs were considered as candidates for protein inhibition, and their 3D structure was obtained from Drug Bank. Molecular docking settings were defined using Autodock 4.2 software and the algorithm was executed.

RESULTS

Based on the estimated binding energy of docking and hydrogen bond analysis and the position of drug binding, five drugs including, indinavir, lopinavir, saquinavir, nelfinavir, and remdesivir, had the highest inhibitory potential for all three proteins.

CONCLUSIONS

According to the results, among the mentioned drugs, saquinavir and lopinavir showed the highest inhibitory potential for all three proteins compared to the other drugs. This study suggests that saquinavir and lopinavir could be included in the laboratory phase studies as a two-drug treatment for SARS-CoV-2 inhibition.

Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The site identification by ligand competitive saturation (SILCS) methodology is based on functional group affinity patterns in the form of free energy maps that may be used to compute protein-ligand binding poses and affinities. Presented are results obtained from the SILCS methodology for a set of eight target proteins as reported originally in Wang et al. (J. Am. Chem. Soc., 2015, 137, 2695-2703) using free energy perturbation (FEP) methods in conjunction with enhanced sampling and cycle closure corrections. These eight targets have been subsequently studied by many other authors to compare the efficacy of their method while comparing with the outcomes of Wang et al. In this work, we present results for a total of 407 ligands on the eight targets and include specific analysis on the subset of 199 ligands considered previously. Using the SILCS methodology we can achieve an average accuracy of up to 77% and 74% when considering the eight targets with their 199 and 407 ligands, respectively, for rank-ordering ligand affinities as calculated by the percent correct metric. This accuracy increases to 82% and 80%, respectively, when the SILCS atomic free energy contributions are optimized using a Bayesian Markov-chain Monte Carlo approach. We also report other metrics including Pearson's correlation coefficient, Pearlman's predictive index, mean unsigned error, and root mean square error for both sets of ligands. The results obtained for the 199 ligands are compared with the outcomes of Wang et al. and other published works. Overall, the SILCS methodology yields similar or better-quality predictions without a priori need for known ligand orientations in terms of the different metrics when compared to current FEP approaches with significant computational savings while additionally offering quantitative estimates of individual atomic contributions to binding free energies. These results further validate the SILCS methodology as an accurate, computationally efficient tool to support lead optimization and drug discovery.

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.

Computer-Aided Drug Discovery Identifies Alkaloid Inhibitors of Parkinson's Disease Associated Protein, Prolyl Oligopeptidase

Parkinson's disease is a common neurodegenerative disorder marked by the accumulation of the protein alpha synuclein. Studies have indicated the role of prolyl oligopeptidase (POP), a serine protease, in alpha synuclein accumulation. Therefore, POP emerges as an attractive medicinal target. Traditionally, most of the early medicines have been plant-based owing to their ready availability and negligible side effects. Alkaloids owing to their neurotransmitter modulatory, anti-amyloid, anti-oxidant, and anti-inflammatory activities have shown potential in neurodegenerative disease. In this work, we computationally evaluated alkaloid class of phytochemicals for their therapeutic efficacy against POP. Alkaloids were retrieved from the publically available database, Chemical Entities of Biological Interest (ChEBI), and screened for their drug likeness (Lipinski's rule of 5) and absorption, distribution, metabolism, and excretion, and toxicity (ADMET) in Discovery Studio by ensuring parameters suitable for a central nervous system disease such as blood-brain barrier (BBB) level set to ≤2, absorption level set to 0 and solubility level permitted set to 2, 3, or 4. Next, molecular docking was performed to learn about the affinity of the filtered alkaloids with the POP. Subsequently, molecular dynamic simulations were conducted to assess the reliability and stability of the alkaloid-protein complex. Our study identified metergoline, pipercallosine, celacinnine, lobeline, cystodytin G, lycoperine A, hookerianamide J, and martefragin A as putative lead compounds against POP. Among these, metergoline, pipercallosine, hookerianamide J, and lobeline showed the most promising results. These compounds demonstrated better or equivalent molecular docking scores in comparison to three POP inhibitors that had reached clinical trials, i.e., Z-321, S-17092, and JTP-4819. MD simulations indicated that these compounds remained intact at the active site while adhering to the binding mode and interaction patterns as that of the reported inhibitors. The research conducted here, therefore, provides evidence for conducting in vitro POP inhibitory studies of these newly identified plant-based POP inhibitors.

Identification of Flavonoids as Putative ROS-1 Kinase Inhibitors Using Pharmacophore Modeling for NSCLC Therapeutics

Non-small cell lung cancer (NSCLC) is a lethal non-immunogenic malignancy and proto-oncogene ROS-1 tyrosine kinase is one of its clinically relevant oncogenic markers. The ROS-1 inhibitor, crizotinib, demonstrated resistance due to the Gly2032Arg mutation. To curtail this resistance, researchers developed lorlatinib against the mutated kinase. In the present study, a receptor-ligand pharmacophore model exploiting the key features of lorlatinib binding with ROS-1 was exploited to identify inhibitors against the wild-type (WT) and the mutant (MT) kinase domain. The developed model was utilized to virtually screen the TimTec flavonoids database and the retrieved drug-like hits were subjected for docking with the WT and MT ROS-1 kinase. A total of 10 flavonoids displayed higher docking scores than lorlatinib. Subsequent molecular dynamics simulations of the acquired flavonoids with WT and MT ROS-1 revealed no steric clashes with the Arg2032 (MT ROS-1). The binding free energy calculations computed via molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) demonstrated one flavonoid (Hit) with better energy than lorlatinib in binding with WT and MT ROS-1. The Hit compound was observed to bind in the ROS-1 selectivity pocket comprised of residues from the β-3 sheet and DFG-motif. The identified Hit from this investigation could act as a potent WT and MT ROS-1 inhibitor.

Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic target. To attain this objective, an in silico investigation was designed, employing a pharmacophore modeling approach. A structure-based pharmacophore (SBP) model exploiting the key features of a selective mTOR inhibitor, Torkinib directed at the ATP-binding pocket was generated. A Marine Natural Products (MNP) library was screened using SBP model as a query. The retrieved compounds after consequent drug-likeness filtration were subjected to molecular docking with mTOR, thus revealing four MNPs with better scores than Torkinib. Successive refinement via molecular dynamics simulations demonstrated that the hits formed crucial interactions with key residues of the pocket. Furthermore, the four identified hits exhibited good binding free energy scores through MM-PBSA calculations and the subsequent in silico toxicity assessments displayed three hits deemed essentially non-carcinogenic and non-mutagenic. The hits presented in this investigation could act as potent ATP-competitive mTOR inhibitors, representing a platform for the future discovery of drugs from marine natural origin.

1-(3-Tert-Butylphenyl)-2,2,2-Trifluoroethanone as a Potent Transition-State Analogue Slow-Binding Inhibitor of Human Acetylcholinesterase: Kinetic, MD and QM/MM Studies

Kinetic studies and molecular modeling of human acetylcholinesterase (AChE) inhibition by a fluorinated acetophenone derivative, 1-(3-tert-butylphenyl)-2,2,2-trifluoroethanone (TFK), were performed. Fast reversible inhibition of AChE by TFK is of competitive type with K_i = 5.15 nM. However, steady state of inhibition is reached slowly. Kinetic analysis showed that TFK is a slow-binding inhibitor (SBI) of type B with K_i* = 0.53 nM. Reversible binding of TFK provides a long residence time, τ = 20 min, on AChE. After binding, TFK acylates the active serine, forming an hemiketal. Then, disruption of hemiketal (deacylation) is slow. AChE recovers full activity in approximately 40 min. Molecular docking and MD simulations depicted the different steps. It was shown that TFK binds first to the peripheral anionic site. Then, subsequent slow induced-fit step enlarged the gorge, allowing tight adjustment into the catalytic active site. Modeling of interactions between TFK and AChE active site by QM/MM showed that the “isomerization” step of enzyme-inhibitor complex leads to a complex similar to substrate tetrahedral intermediate, a so-called “transition state analog”, followed by a labile covalent intermediate. SBIs of AChE show prolonged pharmacological efficacy. Thus, this fluoroalkylketone intended for neuroimaging, could be of interest in palliative therapy of Alzheimer’s disease and protection of central AChE against organophosphorus compounds.

Balanced polarizable Drude force field parameters for molecular anions: phosphates, sulfates, sulfamates, and oxides

Polarizable force fields are emerging as a more accurate alternative to additive force fields in terms of modeling and simulations of a variety of chemicals including biomolecules. Explicit treatment of induced polarization in charged species such as phosphates and sulfates offers the potential for achieving an improved atomistic understanding of the physical forces driving their interactions with their environments. To help achieve this, in this study we present balanced Drude polarizable force field parameters for molecular ions including phosphates, sulfates, sulfamates, and oxides. Better balance was primarily achieved in the relative values of minimum interaction energies and distances of the anionic model compounds with water at the Drude and quantum mechanical (QM) model chemistries. Parametrization involved reoptimizing available parameters as well as extending the force field to new molecules with the goal of achieving self-consistency with respect to the Lennard-Jones and electrostatic parameters targeting QM and experimental hydration free energies. The resulting force field parameters achieve consistent treatment across the studied anions, facilitating more balanced simulations of biomolecules and small organic molecules in the context of the classical Drude polarizable force field. Graphical abstract.

Molecular dynamics simulations in photosynthesis

Photosynthesis is regulated by a dynamic interplay between proteins, enzymes, pigments, lipids, and cofactors that takes place on a large spatio-temporal scale. Molecular dynamics (MD) simulations provide a powerful toolkit to investigate dynamical processes in (bio)molecular ensembles from the (sub)picosecond to the (sub)millisecond regime and from the Å to hundreds of nm length scale. Therefore, MD is well suited to address a variety of questions arising in the field of photosynthesis research. In this review, we provide an introduction to the basic concepts of MD simulations, at atomistic and coarse-grained level of resolution. Furthermore, we discuss applications of MD simulations to model photosynthetic systems of different sizes and complexity and their connection to experimental observables. Finally, we provide a brief glance on which methods provide opportunities to capture phenomena beyond the applicability of classical MD.

Impact of electronic polarizability on protein-functional group interactions

Interactions of proteins with functional groups are key to their biological functions, making it essential that they be accurately modeled. To investigate the impact of the inclusion of explicit treatment of electronic polarizability in force fields on protein-functional group interactions, the additive CHARMM and Drude polarizable force field are compared in the context of the Site-Identification by Ligand Competitive Saturation (SILCS) simulation methodology from which functional group interaction patterns with five proteins for which experimental binding affinities of multiple ligands are available, were obtained. The explicit treatment of polarizability produces significant differences in the functional group interactions in the ligand binding sites including overall enhanced binding of functional groups to the proteins. This is associated with variations of the dipole moments of solutes representative of functional groups in the binding sites relative to aqueous solution with higher dipole moments systematically occurring in the latter, though exceptions occur with positively charged methylammonium. Such variation indicates the complex, heterogeneous nature of the electronic environments of ligand binding sites and emphasizes the inherent limitation of fixed charged, additive force fields for modeling ligand-protein interactions. These effects yield more defined orientation of the functional groups in the binding pockets and a small, but systematic improvement in the ability of the SILCS method to predict the binding orientation and relative affinities of ligands to their target proteins. Overall, these results indicate that the physical model associated with the explicit treatment of polarizability along with the presence of lone pairs in a force field leads to changes in the nature of the interactions of functional groups with proteins versus that occurring with additive force fields, suggesting the utility of polarizable force fields in obtaining a more realistic understanding of protein-ligand interactions.

How to learn from inconsistencies: Integrating molecular simulations with experimental data

Molecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modeling tool to interpret experimental measurements, and to use experimental data to refine our biophysical models. Thus, explicit integration and synergy between molecular simulations and experiments is fundamental for furthering our understanding of biological processes. This is especially true in the case where discrepancies between measured and simulated observables emerge. In this chapter, we provide an overview of some of the core ideas behind methods that were developed to improve the consistency between experimental information and numerical predictions. We distinguish between situations where experiments are used to refine our understanding and models of specific systems, and situations where experiments are used more generally to refine transferable models. We discuss different philosophies and attempt to unify them in a single framework. Until now, such integration between experiments and simulations have mostly been applied to equilibrium data, and we discuss more recent developments aimed to analyze time-dependent or time-resolved data.

Oversized ubiquinones as molecular probes for structural dynamics of the ubiquinone reaction site in mitochondrial respiratory complex I

NADH-quinone oxidoreductase (complex I) couples electron transfer from NADH to quinone with proton translocation across the membrane. Quinone reduction is a key step for energy transmission from the site of quinone reduction to the remotely located proton-pumping machinery of the enzyme. Although structural biology studies have proposed the existence of a long and narrow quinone-access channel, the physiological relevance of this channel remains debatable. We investigated here whether complex I in bovine heart submitochondrial particles (SMPs) can catalytically reduce a series of oversized ubiquinones (OS-UQs), which are highly unlikely to transit the narrow channel because their side chain includes a bulky "block" that is ∼13 Å across. We found that some OS-UQs function as efficient electron acceptors from complex I, accepting electrons with an efficiency comparable with ubiquinone-2. The catalytic reduction and proton translocation coupled with this reduction were completely inhibited by different quinone-site inhibitors, indicating that the reduction of OS-UQs takes place at the physiological reaction site for ubiquinone. Notably, the proton-translocating efficiencies of OS-UQs significantly varied depending on their side-chain structures, suggesting that the reaction characteristics of OS-UQs affect the predicted structural changes of the quinone reaction site required for triggering proton translocation. These results are difficult to reconcile with the current channel model; rather, the access path for ubiquinone may be open to allow OS-UQs to access the reaction site. Nevertheless, contrary to the observations in SMPs, OS-UQs were not catalytically reduced by isolated complex I reconstituted into liposomes. We discuss possible reasons for these contradictory results.

Dual action of amitriptyline on NMDA receptors: enhancement of Ca-dependent desensitization and trapping channel block

Although the tricyclic antidepressant amitriptyline (ATL) is widely used in the clinic, the mechanism underlying its high therapeutic efficacy against neuropathic pain remains unclear. NMDA receptors (NMDARs) represent a target for ATL and are involved in sensitization of neuropathic pain. Here we describe two actions of ATL on NMDARs: 1) enhancement of Ca²⁺-dependent desensitization and 2) trapping channel block. Inhibition of NMDARs by ATL was found to be dependent upon external Ca²⁺ concentration ([Ca²⁺]) in a voltage-independent manner, with an IC₅₀ of 0.72 μM in 4 mM [Ca²⁺]. The ATL IC₅₀ value increased exponentially with decreasing [Ca²⁺], with an e-fold change observed per 0.69 mM decrease in [Ca²⁺]. Loading neurons with BAPTA abolished Ca²⁺-dependent inhibition, suggesting that Ca²⁺ affects NMDARs from the cytosol. Since there is one known Ca²⁺-dependent process in gating of NMDARs, we conclude that ATL most likely promotes Ca²⁺-dependent desensitization. We also found ATL to be a trapping open-channel blocker of NMDARs with an IC₅₀ of 220 µM at 0 mV. An e-fold change in ATL IC₅₀ was observed to occur with a voltage shift of 50 mV in 0.25 mM [Ca²⁺]. Thus, we disclose here a robust dependence of ATL potency on extracellular [Ca²⁺], and demonstrate that ATL bound in the NMDAR pore can be trapped by closure of the channel.

Planarity and out-of-plane vibrational modes of tryptophan and tyrosine in biomolecular modeling

Tryptophan and tyrosine are amino acids that play significant roles in the folding processes of proteins at water-membrane interfaces because of their amphipathic heteroaromatic rings. Employing appropriate heteroaromatic molecular structures is essential for obtaining accurate dynamics and predictive capabilities in molecular simulations of these amino acids. In this study, molecular dynamics simulations that applied the most recent version of the CHARMM36 force field were conducted on aqueous solutions of tryptophan and of tyrosine. Geometric analysis and dynamics quantified how aromatic rings deviated from planar structures and exhibited out-of-plane fluctuations. Radial distribution functions showed possible biological significance because the extent of ring planarity slightly affected local water concentrations near aromatic rings. Instantaneous all-atom normal mode analysis (NMA) and Fourier transformation of time autocorrelation functions of out-of-plane displacements were applied to study out-of-plane vibrations of atoms in these rings. The NMA started with minimum energy configurations and then averaged over fluctuations in aqueous solution. The frequencies and frequency patterns that were obtained for tryptophan and tyrosine with CHARMM36 differed from literature reports of Raman spectra, infrared spectra, and frequencies calculated using quantum mechanics, with some out-of-plane modes found at higher frequencies. Effects of imposing improper torsion potentials and changing torsion angle force constants were investigated for all atoms in the rings of tryptophan and tyrosine. Results show that these coarse force field variations only affect planarity and out-of-plane vibrations of atoms within the rings, and not other vibrations. Although increasing improper torsion force constants reduced deviations from aromatic ring planarity significantly, it increased out-of-plane mode frequencies. Reducing torsion angle force constants (with and without improper torsions) shifted modes to lower frequencies. A combination of decreasing most torsion angle force constants for ring atoms in both amino acids and including improper torsion forces attained frequencies and frequency patterns for out-of-plane normal modes that were more similar to the literature spectra. These force field variations decreased the extents of out-of-plane vibrations within the heteroaromatic rings of tryptophan, especially around the nitrogen atom in the ring, but not within the heteroaromatic ring of tyrosine. Conclusions were unaffected by the peptide endgroup, water, or simulation ensemble.

Molecular Dynamics Simulations of Ionic Liquids and Electrolytes Using Polarizable Force Fields

Many applications in chemistry, biology, and energy storage/conversion research rely on molecular simulations to provide fundamental insight into structural and transport properties of materials with high ionic concentrations. Whether the system is comprised entirely of ions, like ionic liquids, or is a mixture of a polar solvent with a salt, e.g., liquid electrolytes for battery applications, the presence of ions in these materials results in strong local electric fields polarizing solvent molecules and large ions. To predict properties of such systems from molecular simulations often requires either explicit or mean-field inclusion of the influence of polarization on electrostatic interactions. In this manuscript, we review the pros and cons of different treatments of polarization ranging from the mean-field approaches to the most popular explicit polarization models in molecular dynamics simulations of ionic materials. For each method, we discuss their advantages and disadvantages and emphasize key assumptions as well as their adjustable parameters. Strategies for the development of polarizable models are presented with a specific focus on extracting atomic polarizabilities. Finally, we compare simulations using polarizable and nonpolarizable models for several classes of ionic systems, discussing the underlying physics that each approach includes or ignores, implications for implementation and computational efficiency, and the accuracy of properties predicted by these methods compared to experiments.