Molecular dynamics: survey of methods for simulating the activity of proteins

Modeling One-Electron Oxidation Potentials and Hole Delocalization in Double-Stranded DNA by Multilayer and Dynamic Approaches

The number of innovative applications for DNA nowadays is growing quickly. Its use as a nanowire or electrochemical biosensor leads to the need for a deep understanding of the charge-transfer process along the strand, as well as its redox properties. These features are computationally simulated and analyzed in detail throughout this work by combining molecular dynamics, multilayer schemes, and the Marcus theory. One-electron oxidation potential and hole delocalization have been analyzed for six DNA double strands that cover all possible binary combinations of nucleotides. The results have revealed that the one-electron oxidation potential decreases with respect to the single-stranded DNA, giving evidence that the greater rigidity of a double helix induces an increase in the capacity of storing the positive charge generated upon oxidation. In addition, the hole is mainly stored in nucleobases with large reducer character, i.e., purines, especially when those are arranged in a stacked configuration in the same strand. From the computational point of view, the sampling needed to describe biological systems implies a significant computational cost. Here, we show that a small number of representative conformations generated by clustering analysis provides accurate results when compared with those obtained from sampling, reducing considerably the computational cost.

Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains

The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.

Structural Flexibility and Disassembly Kinetics of Single Ferritin Molecules Using Optical Nanotweezers

Ferritin, a spherical protein shell assembled from 24 subunits, functions as an efficient iron storage and release system through its channels. Understanding how various chemicals affect the structural behavior of ferritin is crucial for unravelling the origins of iron-related diseases in living organisms including humans. In particular, the influence of chemicals on ferritin's dynamics and iron release is barely explored at the single-protein level. Here, by employing optical nanotweezers using double-nanohole (DNH) structures, we examined the effect of ascorbic acid (reducing reagent) and pH on individual ferritin's conformational dynamics. The dynamics of ferritin increased as the concentration of ascorbic acid approached saturation. At pH 2.0, ferritin exhibited significant structural fluctuations and eventually underwent a stepwise disassembly into fragments. This work demonstrated the disassembly pathway and kinetics of a single ferritin molecule in solution. We identified four critical fragments during its disassembly pathway, which are 22-mer, 12-mer, tetramer, and dimer subunits. Moreover, we present single-molecule evidence of the cooperative disassembly of ferritin. Interrogating ferritin's structural change in response to different chemicals holds importance for understanding their roles in iron metabolism, hence facilitating further development of medical treatments for its associated diseases.

Discovery of Novel N-Phenyltriazinone Derivatives Containing Oxime Ether or Oxime Ester Moieties as Promising Protoporphyrinogen IX Oxidase Inhibitors

Protoporphyrinogen IX oxidase (PPO, EC 1.3.3.4) is one of the most important targets for the discovery of green herbicides. In order to find novel PPO inhibitors with a higher herbicidal activity, a series of novel N-phenyltriazinone derivatives containing oxime ether and oxime ester groups were designed and synthesized based on the strategy of pharmacophore and scaffold hopping. Bioassay results revealed that some compounds showed herbicidal activities; especially, compound B16 exhibited broad-spectrum and excellent 100% herbicidal effects to Echinochloa crusgalli, Digitaria sanguinalis, Setaria faberii, Abutilon juncea, Amaranthus retroflexus, and Portulaca oleracea at a concentration of 37.5 g a.i./ha, which were comparable to trifludimoxazin. Nicotiana tabacum PPO (NtPPO) enzyme inhibitory assay indicated that B16 showed an excellent enzyme inhibitory activity with a value of 32.14 nM, which was similar to that of trifludimoxazin (31.33 nM). Meanwhile, compound B16 revealed more safety for crops (rice, maize, wheat, peanut, soybean, and cotton) than trifludimoxazin at a dose of 150 g a.i./ha. Moreover, molecular docking and molecular dynamics simulation further showed that B16 has a very strong and stable binding to NtPPO. It indicated that B16 can be used as a potential PPO inhibitor and herbicide candidate for application in the field.

On the use OF 1H-NMR chemical shifts and thermodynamic data for the prediction of the predominant conformation of organic molecules in solution: the example of the flavonoid rutin

Conformational analyses of organic compounds in solution still represent a challenge to be overcome. The traditional methodology uses the relative energies of the conformations to decide which one is most likely to exist in the experimental sample. The goal of this work was to deepen the approach of conformational analysis of flavonoid rutin (a well-known antioxidant agent) in DMSO solution. The methodology we used in this paper involves expanding the sample configuration space to a total of 44 possible geometries, using Molecular Dynamics (MD) simulations, which accesses structures that would hardly be considered with our chemical perception, followed by DFT geometry optimizations using the ωB97X-D/6-31G(d,p) - PCM level of theory. Spectroscopic and thermodynamic analyses were done, by calculating the relative energies and nuclear magnetic resonance (1H-NMR) chemical shifts, comparing the theoretical and experimental 1H-NMR spectra (DMSO-d 6) and evaluating Mean Absolute Error (MAE). The essence of this procedure lies in searching for patterns, like those found in traditional DNA tests common in healthcare. Here, the theoretical spectrum plays the role of the analyzed human sample, while the experimental spectrum acts as the reference standard. In solution, it is natural for the solute to dynamically alter its geometry, going through various conformations (simulated here by MD). However, our DFT/PCM results show that a structure named 32 with torsion angles ϕ 1 and ϕ 2 manually rotated by approx. 20° showed the best theoretical-experimental agreement of 1H-NMR spectra (in DMSO-d 6). Relative energies benchmarking involving 16 DFT functionals revealed that the ωB97X-D is very adequate for estimating energies of organic compounds with dispersion of charge (MAE < 1.0 kcal mol-1, using ab initio post-Hartree-Fock MP2 method as reference). To describe the stability of the conformations, calculations of Natural Bonding Orbitals (NBO) were made, aiming to reveal possible intramolecular hydrogen bonds that stabilize the structures. Since van der Waals (vdW) interactions are difficult to be identified by NBO donations, the Reduced Density Gradient (RDG) were calculated, which provides 2D plots and 3D surfaces that describe Non-Covalent Interactions (NCI). These data allowed us to analyze the effect of dispersion interactions on the relative stability of the rutin conformations. Our results strongly indicate that a combination of DFT (ωB97X-D)-PCM relative energies and NMR spectroscopic criterion is a more efficient strategy in conformational analysis of organic compounds in solution.

The Unique Role of the Second Coordination Sphere to Unlock and Control Catalysis in Nonheme Fe(II)/2-Oxoglutarate Histone Demethylase KDM2A

Nonheme Fe(II) and 2-oxoglutarate (2OG)-dependent histone lysine demethylases 2A (KDM2A) catalyze the demethylation of the mono- or dimethylated lysine 36 residue in the histone H3 peptide (H3K36me1/me2), which plays a crucial role in epigenetic regulation and can be involved in many cancers. Although the overall catalytic mechanism of KDMs has been studied, how KDM2 catalysis takes place in contrast to other KDMs remains unknown. Understanding such differences is vital for enzyme redesign and can help in enzyme-selective drug design. Herein, we employed molecular dynamics (MD) and combined quantum mechanics/molecular mechanics (QM/MM) to explore the complete catalytic mechanism of KDM2A, including dioxygen diffusion and binding, dioxygen activation, and substrate oxidation. Our study demonstrates that the catalysis of KDM2A is controlled by the conformational change of the second coordination sphere (SCS), specifically by a change in the orientation of Y222, which unlocks the 2OG rearrangement from off-line to in-line mode. The study demonstrates that the variant Y222A makes the 2OG rearrangement more favorable. Furthermore, the study reveals that it is the size of H3K36me3 that prevents the 2OG rearrangement, thus rendering the enzyme inactivity with trimethylated lysine. Calculations show that the SCS and long-range interacting residues that stabilize the HAT transition state in KDM2A differ from those in KDM4A, KDM7B, and KDM6A, thus providing the basics for the enzyme-selective redesign and modulation of KDM2A without influencing other KDMs.

Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections

Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruction. Recently, deep neural networks, e.g., CryoDRGN, have demonstrated conformational and compositional heterogeneity of complexes. However, the lack of ground-truth conformations poses a challenge to assess the performance of heterogeneity analysis methods. In this work, variational autoencoders (VAE) with three types of deep generative priors were learned for latent variable inference and heterogeneous 3D reconstruction via Bayesian inference. More specifically, VAEs with "Variational Mixture of Posteriors" priors (VampPrior-SPR), non-parametric exemplar-based priors (ExemplarPrior-SPR) and priors from latent score-based generative models (LSGM-SPR) were quantitatively compared with CryoDRGN. We built four simulated datasets composed of hypothetical continuous conformation or discrete states of the hERG K + channel. Empirical and quantitative comparisons of inferred latent representations were performed with affine-transformation-based metrics. These models with more informative priors gave better regularized, interpretable factorized latent representations with better conserved pairwise distances, less deformed latent distributions and lower within-cluster variances. They were also tested on experimental datasets to resolve compositional and conformational heterogeneity (50S ribosome assembly, cowpea chlorotic mottle virus, and pre-catalytic spliceosome) with comparable high resolution. Codes and data are available: https://github.com/benjamin3344/DGP-SPR.

100 Years of the Lennard-Jones Potential

It is now 100 years since Lennard-Jones published his first paper introducing the now famous potential that bears his name. It is therefore timely to reflect on the many achievements, as well as the limitations, of this potential in the theory of atomic and molecular interactions, where applications range from descriptions of intermolecular forces to molecules, clusters, and condensed matter.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

Discovery of a new Bcl-2 inhibitor through synthesis, anticancer activity, docking and MD simulations

A database of 300 compounds was virtually screened and docked against Bcl-2 protein; the stability of the best-formed complex was evaluated through Molecular dynamics, the top ten compounds with the best in-silico complexation affinities were synthesized, and their In-vitro cytotoxic activity was examined. Thiazolidinone (4e) and isoxazoline (4a-d) were evaluated in-silico. For further evaluation and examination, we designed and synthesized from naturally occurring (R)-carvone and characterized it via spectroscopic analysis, as well as tested for their anticancer activities towards human cancer cell lines such as HT-1080 (fibrosarcome cancer), MCF-7 and MDA-MB-231 (breast cancer) and A-549 (lung cancer) by using MTT method with Doxorubicin as standard drug. Among them, compound 4d showed the most promising anticancer activity against HT-1080, A-549, MCF-7, and MDA-MB-231 cell lines with IC50 values of 15.59 ± 3.21 µM; 18.32 ± 2.73 µM; 17.28 ± 0.33 µM and 19.27 ± 2.73 µM respectively.Communicated by Ramaswamy H. Sarma.

Identification of potential anti-mucor agents by targeting endothelial cell receptor glucose-regulated protein-78 using in silico approach

Mucormycosis is a fungal infection of the sinuses, brain and lungs that is the cause of approximately 50% mortality rate despite the available first-line therapy. Glucose-Regulated Protein 78 (GRP78) is already reported to be a novel host receptor that mediates invasion and damage of human endothelial cells by Rhizopus oryzae and Rhizopus delemar, the most common etiologic species of Mucorales. The expression of GRP78 is also regulated by the levels of iron and glucose in the blood. There are several antifungal drugs in the market but they pose a serious side effect to the vital organs of the body. Therefore, there is an immediate need to discover effective drug molecules having increased efficacy with no side effects. With the help of various computational tools, the current study was attempted to determine potential antimucor agents against GRP78. The receptor molecule GRP78 was screened against 8820 known drugs deposited in DrugBank library using high-throughput virtual screening method. Total top 10 compounds were selected based on the binding energies greater than the reference co-crystal molecule. Furthermore, molecular dynamic (MD) simulations using AMBER were performed to calculate the stability of the top-ranked compounds in the active site of GRP78. After extensive computational studies, we propose that two compounds (CID439153 and CID5289104) have inhibitory potency against mucormycosis and can serve as potential drugs that can form the basis of treating mucormycosis disease.Communicated by Ramaswamy H. Sarma.

Construction of a gene signature associated with anoikis to evaluate the prognosis and immune infiltration in patients with colorectal cancer

Background

Colorectal cancer (CRC) is characterized by a high metastasis rate, leading to poor prognosis and increased mortality. Anoikis, a physiological process, serves as a crucial barrier against metastasis. The objective of this research is to construct a prognostic model for CRC based on genes associated with anoikis.

Methods

The study involved differential analysis and univariate Cox analysis of anoikis-related genes (ARGs), resulting in the selection of 47 genes closely associated with prognosis. Subsequently, unsupervised k-means clustering analysis was conducted on all patients to identify distinct clusters. Survival analysis, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) analysis were performed on the different clusters to investigate associations within the clusters. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were utilized to assess metabolic pathway enrichment between the identified clusters. Furthermore, single-sample GSEA (ssGSEA) was applied to explore variations in immune infiltration. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk model based on ten signatures, which enabled the grouping of all samples according to their risk scores. The prognostic value of the model was validated using receiver operating characteristic (ROC) curves, area under the curve (AUC) calculations, and survival curves. Additionally, the expression of candidate genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR).

Results

Forty-seven survival-related ARGs were screened out. Somatic mutation analysis showed that these genes revealed a high mutation rate. Based on their expression, two clusters were identified. Cluster B patients exhibited a shortened overall survival and higher immune infiltration. A risk scoring model including ten genes was subsequently developed, which exhibited excellent prognostic predictive ability for CRC, as evidenced by the survival curve, ROC curve, and AUC curve. In addition, a nomogram was developed for predicting 3- and 5-year survival probabilities. The qRT-PCR results indicated the dissimilarities among the ten signatures in the tumor tissues and adjacent tissues of patients with CRC were fundamentally consistent with the analytical findings.

Conclusions

This study comprehensively evaluated the prognostic significance of ARGs in CRC. It identified two distinct anoikis-related clusters and examined their respective immune microenvironments. Furthermore, an ARGs signature was developed to effectively predict the prognosis of CRC, thereby establishing a solid foundation for investigating the clinical prognostic role of anoikis in CRC.

Structural, Spectroscopic, and Computational Insights from Canavanine-Bound and Two Catalytically Compromised Variants of the Ethylene-Forming Enzyme

The ethylene-forming enzyme (EFE) is an Fe(II), 2-oxoglutarate (2OG), and l-arginine (l-Arg)-dependent oxygenase that either forms ethylene and three CO2/bicarbonate from 2OG or couples the decarboxylation of 2OG to C5 hydroxylation of l-Arg. l-Arg binds with C5 toward the metal center, causing 2OG to change from monodentate to chelate metal interaction and OD1 to OD2 switch of D191 metal coordination. We applied anaerobic UV-visible spectroscopy, X-ray crystallography, and computational approaches to three EFE systems with high-resolution structures. The ineffective l-Arg analogue l-canavanine binds to the EFE with O5 pointing away from the metal center while promoting chelate formation by 2OG but fails to switch the D191 metal coordination from OD1 to OD2. Substituting alanine for R171 that interacts with 2OG and l-Arg inactivates the protein, prevents metal chelation by 2OG, and weakens l-Arg binding. The R171A EFE had electron density at the 2OG binding site that was identified by mass spectrometry as benzoic acid. The substitution by alanine of Y306 in the EFE, a residue 12 Å away from the catalytic metal center, generates an interior cavity that leads to multiple local and distal structural changes that reduce l-Arg binding and significantly reduce the enzyme activity. Flexibility analyses revealed correlated and anticorrelated motions in each system, with important distinctions from the wild-type enzyme. In combination, the results are congruent with the currently proposed enzyme mechanism, reinforce the importance of metal coordination by OD2 of D191, and highlight the importance of the second coordination sphere and longer range interactions in promoting EFE activity.

Hierarchical Framework for Predicting Entropies in Bottom-Up Coarse-Grained Models

The thermodynamic entropy of coarse-grained (CG) models stands as one of the most important properties for quantifying the missing information during the CG process and for establishing transferable (or extendible) CG interactions. However, performing additional CG simulations on top of model construction often leads to significant additional computational overhead. In this work, we propose a simple hierarchical framework for predicting the thermodynamic entropies of various molecular CG systems. Our approach employs a decomposition of the CG interactions, enabling the estimation of the CG partition function and thermodynamic properties a priori. Starting from the ideal gas description, we leverage classical perturbation theory to systematically incorporate simple yet essential interactions, ranging from the hard sphere model to the generalized van der Waals model. Additionally, we propose an alternative approach based on multiparticle correlation functions, allowing for systematic improvements through higher-order correlations. Numerical applications to molecular liquids validate the high fidelity of our approach, and our computational protocols demonstrate that a reduced model with simple energetics can reasonably estimate the thermodynamic entropy of CG models without performing any CG simulations. Overall, our findings present a systematic framework for estimating not only the entropy but also other thermodynamic properties of CG models, relying solely on information from the reference system.

Taste-Active Peptides from Triple-Enzymatically Hydrolyzed Straw Mushroom Proteins Enhance Salty Taste: An Elucidation of Their Effect on the T1R1/T1R3 Taste Receptor via Molecular Docking

The objective of our study was to analyze and identify enzymatic peptides from straw mushrooms that can enhance salty taste with the aim of developing saltiness enhancement peptides to reduce salt intake and promote dietary health. We isolated taste-related peptides from the straw mushroom extract using ultrafiltration and identified them using UPLC-Q-TOF-MS/MS. The study found that the ultrafiltration fraction (500-2000 Da) of straw mushroom peptides had a saltiness enhancement effect, as revealed via subsequent E-tongue and sensory analyses. The ultrafiltration fractions (500-2000 Da) were found to contain 220 peptides, which were identified through UPLC-Q-TOF-MS/MS analysis. The interaction of these peptides with the T1R1/T1R3 receptor was also assessed. The investigation highlighted the significant involvement of Asp223, Gln243, Leu232, Asp251, and Pro254 in binding peptides from triple-enzymatically hydrolyzed straw mushrooms to T1R1/T1R3. Based on the binding energy and active site analysis, three peptides were selected for synthesis: DFNALPFK (-9.2 kcal/mol), YNEDNGIVK (-8.8 kcal/mol), and VPGGQEIKDR (-8.9 kcal/mol). Importantly, 3.2 mmol of VPGGQEIKDR increased the saltiness level of a 0.05% NaCl solution to that of a 0.15% NaCl solution. Additionally, the addition of 0.8 mmol of YNEDNGIVK to a 0.05% NaCl solution resulted in the same level of saltiness as a 0.1% NaCl solution.

In silico screening of selective ATP mimicking inhibitors targeting the Plasmodium falciparum Grp94

Plasmodium falciparum parasites export more than 400 proteins to remodel the host cell environment and increase its chances of surviving and reproducing. The endoplasmic reticulum (ER) plays a central role in protein export by facilitating protein sorting and folding. The ER resident member of the Hsp90 family, glucose-regulated protein 94 (Grp94), is a molecular chaperone that facilitates the proper folding of client proteins in the ER lumen. In P. falciparum, Grp94 (PfGrp94) is essential for parasite survival, rendering it a promising anti-malarial drug target. Despite this, its druggability has not been fully explored. Consequently, this study sought to identify small molecule inhibitors targeting the PfGrp94. Potential small molecule inhibitors of PfGrp94 were designed and screened using in silico studies. Molecular docking studies indicate that two novel compounds, Compound S and Compound Z selectively bind to PfGrp94 over its human homologues. Comparatively, Compound Z had a higher affinity for PfGrp94 than Compound S. Further interrogation of the inhibitor binding using molecular dynamics (MD) analysis confirmed that Compound Z formed stable binding poses within the ATP-binding pocket of the PfGrp94 N-terminal domain (NTD) during the 250 ns simulation run. PfGrp94 interacted with Compound Z through hydrogen bonding and hydrophobic interactions with residues Asp 148, Asn 106, Gly 152, Ile 151 and Lys 113. Based on the findings of this study, Compound Z could serve as a competitive and selective inhibitor of PfGrp94 and may be useful as a starting point for the development of a potential drug for malaria.Communicated by Ramaswamy H. Sarma.

Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data

Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.

Exploration of potential hit compounds targeting 1-deoxy-d-xylulose 5-phosphate reductoisomerase (IspC) from Acinetobacter baumannii: an in silico investigation

The emergence of carbapenem-resistant Acinetobacter baumannii, a highly concerning bacterial species designated as a Priority 1: Critical pathogen by the WHO, has become a formidable global threat. In this study, we utilised computational methods to explore the potent molecules capable of inhibiting the IspC enzyme, which plays a crucial role in the methylerythritol 4-phosphate (MEP) biosynthetic pathway. Employing high-throughput virtual screening of small molecules from the Enamine library, we focused on the highly conserved substrate binding site of the DXR target protein, resulting in the identification of 1000 potential compounds. Among these compounds, we selected the top two candidates (Z2615855584 and Z2206320703) based on Lipinski's rule of Five and ADMET filters, along with FR900098, a known IspC inhibitor, and DXP, the substrate of IspC, for molecular dynamics (MD) simulations. The MD simulation trajectories revealed remarkable structural and thermodynamic stability, as well as strong binding affinity, for all the IspC-ligand complexes. Furthermore, binding free energy calculations based on MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) methodology demonstrated significant interactions between the selected ligand molecules and IspC. Taking into consideration all the aforementioned criteria, we suggest Z2206320703 as the potent lead candidate against IspC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03923-w.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets' stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (μ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO-LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening and molecular dynamics approaches to identify new inhibitors of Staphylococcus aureus sortase A

Staphylococcus aureus is a prevalent Gram-positive bacteria leading cause of a wide range of human pathologies. Moreover, antibiotic résistance of pathogenesis bacteria is one of the worldwide health problems. In Gram-positive bacteria, the enzyme of SrtA, is responsible for the anchoring of surface-exposed proteins to the cell wall peptidoglycan. Because of its critical role in Gram-positive bacterial pathogenesis, SrtA is an attractive target for anti-virulence during drug development. To date, some SrtA inhibitors have been discovered most of them being derived from flavonoid compounds, like Myricetin. In order to provide potential hit molecules against SrtA for clinical use, we obtained a total of 293 compounds by performing in silico shape-based screening of compound libraries against Myristin as a reference structure. Employing molecular docking and scoring functions, the top 3 compounds Apigenin, Efloxate, and Compound 8261032 were screened by comparing their docking scores with Myricetin. Furthermore, MD simulations and MM-PBSA binding energy calculation studies revealed that only Compound 8261032 strongly binds to the catalytic core of the SrtA enzyme than Myricetin, and stable behavior was consistently observed in the docking complex. Compound 8261032 showed a good number of hydrogen bonds with SrtA and higher MM-PBSA binding energy when compared to all three molecules. Also, it makes strength interactions with Arg139 and His62, which are critical for SrtA biological activity. This study showed that the development of this inhibitor could be a fundamental strategy against resistant bacteria, but further studies in vitro are needed to confirm this claim.Communicated by Ramaswamy H. Sarma.

Virtual screening and molecular dynamics investigations using natural compounds against autotaxin for the treatment of chronic pain

Chronic pain is a common and debilitating condition with a huge social and economic burden worldwide. Currently, available drugs in clinics are not adequately effective and possess a variety of severe side effects leading to treatment withdrawal and poor quality of life. Recent findings highlight the potential role of autotaxin (ATX) as a promising novel target for chronic pain management, extending beyond its previously established involvement in arthritis and other neurological disorders, such as Alzheimer's disease. In the present study, we used a virtual screening strategy by targeting ATX against commercially available natural compounds (enamine- phenotypic screening library) to identify the potential inhibitors for the treatment of chronic pain. After initial identification using molecular docking based virtual screening, molecular mechanics (MM/GBSA), ADMET profiling and molecular dynamics simulation were performed to verify top hits. The computational screening resulted in the identification of fifteen top scoring structurally diverse hits that have free energy of binding (ΔG) values in the range of -25.792 (for compound Enamine_1850) to -74.722 Kcal/mol (for compound Enamine_1687). Moreover, the top-scoring hits have favourable ADME properties as calculated using in-silico algorithms. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about amino acid residues involved in binding. This study led to the identification of potential autotaxin inhibitors with favourable pharmacokinetic properties. Identified hits may further be investigated for their safety and efficacy potential using in-vitro and in-vivo models of chronic pain.Communicated by Ramaswamy H. Sarma.

Biased Signaling in Mutated Variants of β2-Adrenergic Receptor: Insights from Molecular Dynamics Simulations

The molecular basis of receptor bias in G protein-coupled receptors (GPCRs) caused by mutations that preferentially activate specific intracellular transducers over others remains poorly understood. Two experimentally identified biased variants of β2-adrenergic receptors (β2AR), a prototypical GPCR, are a triple mutant (T68F, Y132A, and Y219A) and a single mutant (Y219A); the former bias the receptor toward the β-arrestin pathway by disfavoring G protein engagement, while the latter induces G protein signaling explicitly due to selection against GPCR kinases (GRKs) that phosphorylate the receptor as a prerequisite of β-arrestin binding. Though rigorous characterizations have revealed functional implications of these mutations, the atomistic origin of the observed transducer selectivity is not clear. In this study, we investigated the allosteric mechanism of receptor bias in β2AR using microseconds of all-atom Gaussian accelerated molecular dynamics (GaMD) simulations. Our observations reveal distinct rearrangements in transmembrane helices, intracellular loop 3, and critical residues R1313.50 and Y3267.53 in the conserved motifs D(E)RY and NPxxY for the mutant receptors, leading to their specific transducer interactions. Moreover, partial dissociation of G protein from the receptor core is observed in the simulations of the triple mutant in contrast to the single mutant and wild-type receptor. The reorganization of allosteric communications from the extracellular agonist BI-167107 to the intracellular receptor-transducer interfaces drives the conformational rearrangements responsible for receptor bias in the single and triple mutants. The molecular insights into receptor bias of β2AR presented here could improve the understanding of biased signaling in GPCRs, potentially opening new avenues for designing novel therapeutics with fewer side-effects and superior efficacy.

Mapping the epitope of PD-L1 to the paratope of the antibody durvalumab using molecular dynamics simulation

OBJECTIVES

Durvalumab, a human monoclonal antibody that stops PD-L1 from attaching itself to CD80 and PD-1, was approved by the Food and Drug Administration for use in cancer therapy. An essential stage in antibody optimization is mapping paratope residues to epitope residues. In this study, our earlier computer-aided method based on molecular dynamics (MD) simulations was used to observe the paratope residues on durvalumab and their companions on PD-L1.

METHODS

The durvalumab/PD-L1 complex model was obtained from the Protein Data Bank and used in a rectangular box for solvation. On durvalumab, the paratope residues and their companions on PD-L1 were identified using MD simulations. The interface residues were ranked on the basis of their contributions to the binding of durvalumab and PD-L1 by assessing the stability of hydrogen bonds and salt bridges. This assessment was conducted using free and guided MD simulations.

RESULTS

Seventeen residues, including ASP26, GLU58, GLU60, ASP61, ARG113, ARG125, and THR127 on PD-L1 and H31ARG, H52LYS, H53GLN, H57GLU, H99GLU, H103PHE, H113ARG, L28ARG, L31SER, and L92TYR on durvalumab, were expected to be necessary for the binding of durvalumab to PD-L1. ASP26, ARG113, and ARG125 on PD-L1 were essential for its binding to PD-1. Eight residues (GLU60, ASP61, and THR127 on PD-L1 and L31SER, H99GLU, H53GLU, H31ARG, and H113ARG on durvalumab) were newly found, and two residues (LYS124 on PD-L1 and L94SER on durvalumab) proven nonessential for complexation, compared to the findings from the examined crystal structure.

CONCLUSIONS

The antithrombotic antibody of durvalumab's paratope may be effectively mapped to the PD-L1 epitope using the existing computer method. This information will help optimize durvalumab.

Discovery of Novel Inhibitors of BRD4 for Treating Prostate Cancer: A Comprehensive Case Study for Considering Water Networks in Virtual Screening and Drug Design

Androgen receptor (AR) is the primary target for treating prostate cancer (PCa), which inevitably progresses due to drug-resistant mutations. Bromodomain-containing protein 4 (BRD4) has been a new potential drug target for PCa treatment. Herein, we report the rational design and discovery of novel BRD4 inhibitors through computer-aided drug design (CADD), and a hit compound SQ-1 (IC50 = 676 nM) was identified by structure-based virtual screening (SBVS) with the conserved water network. To optimize the structure of SQ-1, the free energy landscape was constructed, and the binding mechanism was explored by characterizing the water profile and the dissociation mechanism. Finally, the compound SQ-17 with improved inhibitory activity (IC50 < 100 nM) was discovered, which showed potent antiproliferative activity against LNCaP. These data highlighted a successful attempt to identify and optimize a small molecule by comprehensive CADD application and provided essential clues for developing novel therapeutics for PCa treatment.

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.

Ionome mapping and amino acid metabolome profiling of Phaseolus vulgaris L. seeds imbibed with computationally informed phytoengineered copper sulphide nanoparticles

This study reports the effects of a computationally informed and avocado-seed mediated Phyto engineered CuS nanoparticles as fertilizing agent on the ionome and amino acid metabolome of Pinto bean seeds using both bench top and ion beam analytical techniques. Physico-chemical analysis of the Phyto engineered nanoparticles with scanning-electron microscopy, transmission electron microscopy, X-ray diffraction, and Fourier Transform Infrared Spectroscopy confirmed the presence of CuS nanoparticles. Molecular dynamics simulations to investigate the interaction of some active phytocompounds in avocado seeds that act as reducing agents with the nano-digenite further showed that 4-hydroxybenzoic acid had a higher affinity for interacting with the nanoparticle's surface than other active compounds. Seeds treated with the digenite nanoparticles exhibited a unique ionome distribution pattern as determined with external beam proton-induced X-ray emission, with hotspots of Cu and S appearing in the hilum and micropyle area that indicated a possible uptake mechanism via the seed coat. The nano-digenite also triggered a plant stress response by slightly altering seed amino acid metabolism. Ultimately, the nano-digenite may have important implications as a seed protective or nutritive agent as advised by its unique distribution pattern and effect on amino acid metabolism.

Identification of mitogen-activated protein kinase 7 inhibitors from natural products: Combined virtual screening and dynamic simulation studies

Mitogen-activated protein kinase 7 (MAPK7) is a serine/threonine protein kinase that belongs to the MAPK family and plays a vital role in various cellular processes such as cell proliferation, differentiation, gene transcription, apoptosis, metabolism, and cell survival. The elevated expression of MAPK7 has been associated with the onset and progression of multiple aggressive tumors in humans, underscoring the potential of targeting MAPK7 pathways in therapeutic research. This pursuit holds promise for the advancement of anticancer drug development by developing potential MAPK7 inhibitors. To look for potential MAPK7 inhibitors, we exploited structure-based virtual screening of natural products from the ZINC database. First, the Lipinski rule of five criteria was used to filter a large library of ~90,000 natural compounds, followed by ADMET and pan-assay interference compounds (PAINS) filters. Then, top hits were chosen based on their strong binding affinity as determined by molecular docking. Further, interaction analysis was performed to find effective and specific compounds that can precisely bind to the binding pocket of MAPK7. Consequently, two compounds, ZINC12296700 and ZINC02123081, exhibited significant binding affinity and demonstrated excellent drug-like properties. All-atom molecular dynamics simulations for 200 ns confirmed the stability of MAPK7-ZINC12296700 and MAPK7-ZINC02123081 docked complexes. According to the molecular mechanics Poisson-Boltzmann surface area investigation, the binding affinities of both complexes were considerable. Overall, the result suggests that ZINC12296700 and ZINC02123081 might be used as promising leads to develop novel MAPK7 inhibitors. Since these compounds would interfere with the kinase activity of MAPK7, therefore, may be implemented to control cell growth and proliferation in cancer after required validations.

Structural and computational studies of HIV-1 RNA

Viruses remain a global threat to animals, plants, and humans. The type 1 human immunodeficiency virus (HIV-1) is a member of the retrovirus family and carries an RNA genome, which is reverse transcribed into viral DNA and further integrated into the host-cell DNA for viral replication and proliferation. The RNA structures from the HIV-1 genome provide valuable insights into the mechanisms underlying the viral replication cycle. Moreover, these structures serve as models for designing novel therapeutic approaches. Here, we review structural data on RNA from the HIV-1 genome as well as computational studies based on these structural data. The review is organized according to the type of structured RNA element which contributes to different steps in the viral replication cycle. This is followed by an overview of the HIV-1 transactivation response element (TAR) RNA as a model system for understanding dynamics and interactions in the viral RNA systems. The review concludes with a description of computational studies, highlighting the impact of biomolecular simulations in elucidating the mechanistic details of various steps in the HIV-1's replication cycle.

Effect of temperature on hepatitis a virus and exploration of binding mode mechanism of phytochemicals from tinospora cordifolia: an insight into molecular docking, MM/GBSA, and molecular dynamics simulation study

The hepatitis A virus (HAV), which causes hepatitis A, is a contagious liver ailment. The infections are not specifically treated by any medications. Therefore, the development of less harmful, more effective and cost-effective antiviral agents are necessary. The present work highlighted the in-silico activity of phytocompounds from tinospora cordifolia against HAV. The binding interaction of HAV with the phytocompounds was analyzed through molecular docking. Molecular docking revealed that chasmanthin, malabarolide, menispermacide, tinosporaside, and tinosporinone compounds bind with HAV more efficiently than other compounds. Further evaluation using 100 ns molecular dynamics simulation, MM/GBSA and free energy landscape indicated that all phytocompounds studied here were found to be most promising drug candidate against hepatitis A virus. Our computational study will encourage promoting in further investigation for in vitro and in vivo clinical trials.Communicated by Ramaswamy H. Sarma.

Molecular Free Energies, Rates, and Mechanisms from Data-Efficient Path Sampling Simulations

Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of complex molecular events. However, these simulations can rarely sample the required time scales in practice. Transition path sampling overcomes this limitation by collecting unbiased trajectories and capturing the relevant events. Moreover, the integration of machine learning can boost the sampling while simultaneously learning a quantitative representation of the mechanism. Still, the resulting trajectories are by construction non-Boltzmann-distributed, preventing the calculation of free energies and rates. We developed an algorithm to approximate the equilibrium path ensemble from machine-learning-guided path sampling data. At the same time, our algorithm provides efficient sampling, mechanism, free energy, and rates of rare molecular events at a very moderate computational cost. We tested the method on the folding of the mini-protein chignolin. Our algorithm is straightforward and data-efficient, opening the door to applications in many challenging molecular systems.

Attempting Well-Tempered Funnel Metadynamics Simulations for the Evaluation of the Binding Kinetics of Methionine Aminopeptidase-II Inhibitors

Understanding the unbinding kinetics of protein-ligand complexes is considered a significant approach for the design of ligands with desired specificity and safety. In recent years, enhanced sampling methods have emerged as effective tools for studying the unbinding kinetics of protein-ligand complexes at the atomistic level. MetAP-II is a target for the treatment of cancer for which not a single effective drug is available yet. The identification of the dissociation rate of ligands from the complexes often serves as a better predictor for in vivo efficacy than the ligands' binding affinity. Here, funnel-based restraint well-tempered metadynamics simulations were applied to predict the residence time of two ligands bound to MetAP-II, along with the ligand association and dissociation mechanism involving the identification of the binding hotspot during ligand egress. The ligand-egressing route revealed by metadynamics simulations also correlated with the identified pathways from the CAVER analysis and by the enhanced sampling simulation using PLUMED. Ligand 1 formed a strong H-bond interaction with GLU364 estimating a higher residence time of 28.22 ± 5.29 ns in contrast to ligand 2 with a residence time of 19.05 ± 3.58 ns, which easily dissociated from the binding pocket of MetAP-II. The results obtained from the simulations were consistent to reveal ligand 1 being superior to ligand 2; however, the experimental data related to residence time were close for both ligands, and no kinetic data were available for ligand 2. The current study could be considered the first attempt to apply an enhanced sampling method for the evaluation of the binding kinetics and thermodynamics of two different classes of ligands to a binuclear metalloprotein.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

K-Means Clustering Coarse-Graining (KMC-CG): A Next Generation Methodology for Determining Optimal Coarse-Grained Mappings of Large Biomolecules

Coarse-grained (CG) molecular dynamics (MD) has become a method of choice for simulating various large scale biomolecular processes; therefore, the systematic definition of the CG mappings for biomolecules remains an important topic. Appropriate CG mappings can significantly enhance the representability of a CG model and improve its ability to capture critical features of large biomolecules. In this work, we present a systematic and more generalized method called K-means clustering coarse-graining (KMC-CG), which builds on the earlier approach of essential dynamics coarse-graining (ED-CG). KMC-CG removes the sequence-dependent constraints of ED-CG, allowing it to explore a more extensive space and thus enabling the discovery of more physically optimal CG mappings. Furthermore, the implementation of the K-means clustering algorithm can variationally optimize the CG mapping with efficiency and stability. This new method is tested in three cases: ATP-bound G-actin, the HIV-1 CA pentamer, and the Arp2/3 complex. In these examples, the CG models generated by KMC-CG are seen to better capture the structural, dynamic, and functional domains. KMC-CG therefore provides a robust and consistent approach to generating CG models of large biomolecules that can then be more accurately parametrized by either bottom-up or top-down CG force fields.

Combined NMR and molecular dynamics conformational filter identifies unambiguously dynamic ensembles of Dengue protease NS2B/NS3pro

The dengue protease NS2B/NS3pro has been reported to adopt either an 'open' or a 'closed' conformation. We have developed a conformational filter that combines NMR with MD simulations to identify conformational ensembles that dominate in solution. Experimental values derived from relaxation parameters for the backbone and methyl side chains were compared with the corresponding back-calculated relaxation parameters of different conformational ensembles obtained from free MD simulations. Our results demonstrate a high prevalence for the 'closed' conformational ensemble while the 'open' conformation is absent, indicating that the latter conformation is most probably due to crystal contacts. Conversely, conformational ensembles in which the positioning of the co-factor NS2B results in a 'partially' open conformation, previously described in both MD simulations and X-ray studies, were identified by our conformational filter. Altogether, we believe that our approach allows for unambiguous identification of true conformational ensembles, an essential step for reliable drug discovery.

One-Electron Oxidation Potentials and Hole Delocalization in Heterogeneous Single-Stranded DNA

The study of DNA processes is essential to understand not only its intrinsic biological functions but also its role in many innovative applications. The use of DNA as a nanowire or electrochemical biosensor leads to the need for a deep investigation of the charge transfer process along the strand as well as of the redox properties. In this contribution, the one-electron oxidation potential and the charge delocalization of the hole formed after oxidation are computationally investigated for different heterogeneous single-stranded DNA strands. We have established a two-step protocol: (i) molecular dynamics simulations in the frame of quantum mechanics/molecular mechanics (QM/MM) were performed to sample the conformational space; (ii) energetic properties were then obtained within a QM1/QM2/continuum approach in combination with the Marcus theory over an ensemble of selected geometries. The results reveal that the one-electron oxidation potential in the heterogeneous strands can be seen as a linear combination of that property within the homogeneous strands. In addition, the hole delocalization between different nucleobases is, in general, small, supporting the conclusion of a hopping mechanism for charge transport along the strands. However, charge delocalization becomes more important, and so does the tunneling mechanism contribution, when the reducing power of the nucleobases forming the strand is similar. Moreover, charge delocalization is slightly enhanced when there is a correlation between pairs of some of the interbase coordinates of the strand: twist/shift, twist/slide, shift/slide, and rise/tilt. However, the internal structure of the strand is not the predominant factor for hole delocalization but the specific sequence of nucleotides that compose the strand.

Structural Insights into Dopamine Receptor-Ligand Interactions: From Agonists to Antagonists

This study explores the intricacies of dopamine receptor-ligand interactions, focusing on the D1R and D5R subtypes. Using molecular modeling techniques, we investigate the binding of the pan-agonist rotigotine, revealing a universal binding mode at the orthosteric binding pocket (OBP). Additionally, we analyze the stability of antagonist-receptor complexes with SKF83566 and SCH23390. By examining the impact of specific mutations on ligand-receptor interactions through computational simulations and thermostability assays, we gain insights into binding stability. Our research also delves into the structural and energetic aspects of antagonist binding to D1R and D5R in their inactive states. These findings enhance our understanding of dopamine receptor pharmacology and hold promise for drug development in central nervous system disorders, opening doors to future research and innovation in this field.

Binding affinity predictions with hybrid quantum-classical convolutional neural networks

Central in drug design is the identification of biomolecules that uniquely and robustly bind to a target protein, while minimizing their interactions with others. Accordingly, precise binding affinity prediction, enabling the accurate selection of suitable candidates from an extensive pool of potential compounds, can greatly reduce the expenses associated to practical experimental protocols. In this respect, recent advances revealed that deep learning methods show superior performance compared to other traditional computational methods, especially with the advent of large datasets. These methods, however, are complex and very time-intensive, thus representing an important clear bottleneck for their development and practical application. In this context, the emerging realm of quantum machine learning holds promise for enhancing numerous classical machine learning algorithms. In this work, we take one step forward and present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical counterpart while still maintaining optimal performance in the predictions. Additionally, this results in a significant cost and time savings of up to 40% in the training stage, which means a substantial speed-up of the drug design process.

Effect of the Macromolecular Crowding Agents on the Structure and Function of Human Arginase-I, a Therapeutically Important Enzyme

Macromolecular crowding has been known to influence the structure and function of many enzymes through excluded volume effects and/or soft interactions. Here, we employed two synthetic macromolecular crowders, Dextrans and poly(ethylene glycol)s (PEGs) with varying molecular masses, to examine how they affected the structure and function of a therapeutically important enzyme, human arginase-I that catalyzes the conversion of l-arginine to l-ornithine and urea. Except at greater concentrations of Dextran 200, Dextrans were observed to slightly reduce the enzymatic activity, indicating that they exert their influence mainly through the excluded volume effects. Similar outcomes were seen with PEGs, with the exception of PEG 1000, where the activity decreased with increasing PEG concentrations, showing the maximum effect at a 20 g/L concentration. This finding suggests that the enzyme function is reduced by the soft interactions of this macromolecule with the enzyme, supported by the binding measurement. Secondary and local tertiary structures and thermodynamic stability were also affected, suggesting that PEG 1000 has an impact on the protein's structure. Furthermore, molecular dynamics simulation studies suggest that the catalytic pocket is disturbed, presumably by the unwinding of neighboring helix 9. As a result, the positioning of nearby Glu277 is altered, which prevents His141 and Glu277 from making contact. This hampers the proton transfer from the catalytic His141 to the intermediate species to form ornithine, a crucial step for the substrate hydrolysis reaction by this arginase. Overall, the knowledge gained from this study might be helpful for understanding how different enzymes work in a crowded/cellular environment.

Structural, Functional, and Mutational Studies of a Potent Subtilisin Inhibitor from Budgett's Frog, Lepidobatrachus laevis

Subtilases play a significant role in microbial pathogen infections by degrading the host proteins. Subtilisin inhibitors are crucial in fighting against these harmful microorganisms. LL-TIL, from skin secretions of Lepidobatrachus laevis, is a cysteine-rich peptide belonging to the I8 family of inhibitors. Protease inhibitory assays demonstrated that LL-TIL acts as a slow-tight binding inhibitor of subtilisin Carlsberg and proteinase K with inhibition constants of 91 pM and 2.4 nM, respectively. The solution structures of LL-TIL and a mutant peptide reveal that they adopt a typical TIL-type fold with a canonical conformation of a reactive site loop (RSL). The structure of the LL-TIL-subtilisin complex and molecular dynamics (MD) simulations provided an in-depth view of the structural basis of inhibition. NMR relaxation data and molecular dynamics simulations indicated a rigid conformation of RSL, which does not alter significantly upon subtilisin binding. The energy calculation for subtilisin inhibition predicted Ile31 as the highest contributor to the binding energy, which was confirmed experimentally by site-directed mutagenesis. A chimeric mutant of LL-TIL broadened the inhibitory profile and attenuated subtilisin inhibition by 2 orders of magnitude. These results provide a template to engineer more specific and potent TIL-type subtilisin inhibitors.

Identification of potential flavonoid compounds as antibacterial therapeutics against Klebsiella pneumoniae infection using structure-based virtual screening and molecular dynamics simulation

Klebsiella pneumoniae, which is among the top three pathogens on WHO's priority list, is one of the gram-negative bacteria that doctors and researchers around the world have fought for decades. Capsular polysaccharide (CPS) protein is extensively recognized as an important K. pneumoniae virulence factor. Thus, CPS has become the most characterized target for the discovery of novel drug candidates. The ineffectiveness of currently existing antibiotics urges the search for potent antimicrobial compounds. Flavonoids are a group of plant metabolites that have antibacterial potential and can enhance the present medications to elicit improved results against diverse diseases without adverse reactions. Henceforth, the present study aims to illustrate the inhibitory potential of flavonoids with varying pharmacological properties, targeting the CPS protein of K. pneumoniae by in silico approaches. The flavonoid compounds (n = 169) were retrieved from the PubChem database and screened using the structure-based virtual screening approach. Compounds with the highest binding score were estimated through their pharmacokinetic effects by ADMET descriptors. Finally, four potential inhibitors with PubChem CID: (4301534, 5213, 5481948, and 637080) were selected after molecular docking and drug-likeness analysis. All four lead compounds were employed for the MDS analysis of a 100 ns time period. Various studies were undertaken to assess the stability of the protein-ligand complexes. The binding free energy was computed using MM-PBSA, and the outcomes indicated that the molecules are having stable interactions with the binding site of the target protein. The results revealed that all four compounds can be employed as potential therapeutics against K. pneumoniae.

Modeling the Homologous Recombination Process: Methods, Successes and Challenges

Homologous recombination (HR) is a fundamental process common to all species. HR aims to faithfully repair DNA double strand breaks. HR involves the formation of nucleoprotein filaments on DNA single strands (ssDNA) resected from the break. The nucleoprotein filaments search for homologous regions in the genome and promote strand exchange with the ssDNA homologous region in an unbroken copy of the genome. HR has been the object of intensive studies for decades. Because multi-scale dynamics is a fundamental aspect of this process, studying HR is highly challenging, both experimentally and using computational approaches. Nevertheless, knowledge has built up over the years and has recently progressed at an accelerated pace, borne by increasingly focused investigations using new techniques such as single molecule approaches. Linking this knowledge to the atomic structure of the nucleoprotein filament systems and the succession of unstable, transient intermediate steps that takes place during the HR process remains a challenge; modeling retains a very strong role in bridging the gap between structures that are stable enough to be observed and in exploring transition paths between these structures. However, working on ever-changing long filament systems submitted to kinetic processes is full of pitfalls. This review presents the modeling tools that are used in such studies, their possibilities and limitations, and reviews the advances in the knowledge of the HR process that have been obtained through modeling. Notably, we will emphasize how cooperative behavior in the HR nucleoprotein filament enables modeling to produce reliable information.

In-silico and in-vitro study reveals ziprasidone as a potential aromatase inhibitor against breast carcinoma

Aromatase enzyme plays a fundamental role in the development of estrogen receptors, and due to this functionality, the enzyme has gained significant attention as a therapeutic for reproductive disorders and cancer diseases. The currently employed aromatase inhibitors have severe side effects whereas our novel aromatase inhibitor is more selective and less toxic, therefore has greater potential to be developed as a drug. The research framework of this study is to identify a potent inhibitor for the aromatase target by profiling molecular descriptors of the ligand and to find a functional pocket in the target by docking and MD simulations. For assessing cellular and metabolic activities as indicators of cell viability and cytotoxicity, in-vitro studies were performed by using the colorimetric MTT assay. Aromatase activities were determined by a fluorometric method. Cell morphology was assessed by phase-contrast light microscopy. Flow cytometry and Annexin V-FITC/PI staining assay determined cell cycle distribution and apoptosis. This study reports that CHEMBL708 (Ziprasidone) is the most promising compound that showed excellent aromatase inhibitory activity. By using better drug design methods and experimental studies, our study identified a novel compound that could be effective as a high-potential drug candidate against aromatase enzyme. We conclude that the compound ziprasidone effectively blocks the cell cycle at the G1-S phase and induces cancer cell death. Further, in-vivo studies are vital for developing ziprasidone as an anticancer agent. Lastly, our research outcomes based on the results of the in-silico experiments may pave the way for identifying effective drug candidates for therapeutic use in breast cancer.

Catalysis by KDM6 Histone Demethylases - A Synergy between the Non-Heme Iron(II) Center, Second Coordination Sphere, and Long-Range Interactions

KDM6A (UTX) and KDM6B (JMJD3) are human non-heme Fe(II) and 2-oxoglutarate (2OG) dependent JmjC oxygenases that catalyze the demethylation of trimethylated lysine 27 in the N-terminal tail of histone H3, a post-translational modification that regulates transcription. A Combined Quantum Mechanics/ Molecular Mechanics (QM/MM) and Molecular Dynamics (MD) study on the catalytic mechanism of KDM6A/B reveals that the transition state for the rate-limiting hydrogen atom transfer (HAT) reaction in KDM6A catalysis is stabilized by polar (Asn217) and aromatic (Trp369)/non-polar (Pro274) residues in contrast to KDM4, KDM6B and KDM7 demethylases where charged residues (Glu, Arg, Asp) are involved. KDM6A employs both σ- and π-electron transfer pathways for HAT, whereas KDM6B employs the σ-electron pathway. Differences in hydrogen bonding of the Fe-chelating Glu252(KDM6B) contribute to the lower energy barriers in KDM6B vs. KDM6A. The study reveals a dependence of the activation barrier of the rebound hydroxylation on the Fe-O-C angle in the transition state of KDM6A. Anti-correlation of the Zn-binding domain with the active site residues is a key factor distinguishing KDM6A/B from KDM7/4s. The results reveal the importance of communication between the Fe center, second coordination sphere, and long-range interactions in catalysis by KDMs and, by implication, other 2OG oxygenases.

Pharmacoinformatics studies of coenzyme Q10 and potassium polyacrylate on angiotensin-converting enzyme associated with hypertension

Coenzyme Q10's (CoQ10) favorable impact on cardiovascular diseases risk factors like hypertension and atherosclerosis is linked to the antioxidant action of CoQ10 in these conditions. This study showed the possible effects of CoQ10, potassium polyacrylate (PCK), and valsartan, a reference drug, on the angiotensin-converting enzyme (ACE), a crucial component of the renin-angiotensin system. The Glide tool on Maestro 11.1 was used to calculate the respective binding affinity and binding energy of these compounds towards ACE. The Schrödinger suite was used to run molecular dynamic simulations for 100 ns. The pkCSM tool was used to forecast the pharmacokinetic characteristics and toxicological effects. The SwissADME server was used to estimate the drug-like properties of these compounds. Based on their corresponding scoring values and the negative values of the binding free energies, molecular docking analysis of CoQ10 and PCK revealed that both exhibited favorable binding affinities towards the ACE, with CoQ10 having the highest binding scores. The results showed that both CoQ10 and PCK and the reference drug, valsartan, have some amino acids in common (at the pocket site of ACE) as the key residues for binding to ACE. Both CoQ10 and PCK demonstrated drug-like qualities and were not harmful, according to the predicted pharmacokinetics and toxicology studies. The results of this study suggest that because of its inhibitory interactions with ACE, CoQ10 in particular could be useful in regulating and reducing hypertension.Communicated by Ramaswamy H. Sarma.

SGNet: Sequence-Based Convolution and Ligand Graph Network for Protein Binding Affinity Prediction

Protein-ligand binding can play an important role in many fields. It is of great importance to accurately predict the binding affinity between molecules by computational methods. Most computational binding affinity methods require molecular structures. However, there are still a large number of protein molecules with known amino acid sequences whose structures have not yet been solved. To address this issue, this paper proposes a sequence-based convolution and ligand graph network, called SGNet, to fuse the molecular graph information and the amino acid sequence information. This method integrates Conjoint Triad (CT) encoding of amino acid sequence and one-dimensional convolutional neural network module to extract protein molecules, develops graph attention network to extract molecular features of ligand, and then fuses the two feature sets to predict the binding affinity between molecules from the fully connected layer. As a result, SGNet achieves good prediction performance on both KIKD and IC50 data sets, with prediction error RMSEs of 1.287 and 1.58, and correlation Pearson Rs of 0.687 and 0.592, respectively. Comparative experimental results under the same conditions showed that SGNet outperformed Kdeep and GraphDTA in predicting binding affinities between protein-ligand molecules.

Learning Correlations between Internal Coordinates to Improve 3D Cartesian Coordinates for Proteins

We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, which allows reconstruction of protein structures better than using fixed bond lengths and bond angles or a static library method that relies on backbone torsion angles and residue types in a local environment. The method is able to be used for structure validation, as we show that the agreement between Int2Cart-predicted bond geometries and those from an AlphaFold 2 model can be used to estimate model quality. Additionally, by using Int2Cart to reconstruct an IDP ensemble, we are able to decrease the clash rate during modeling. The Int2Cart algorithm has been implemented as a publicly accessible python package at https://github.com/THGLab/int2cart.

In-silico and in-vitro assessment of the antibiofilm potential of azo dye, carmoisine against Pseudomonas aeruginosa

Biofilm is a community of microorganisms attached to the substrate and plays a significant role in microbial pathogenesis and medical device-related infection. Pseudomonas aeruginosa (PA) is a highly infectious gram-negative opportunistic biofilm-forming bacterium with high antibiotic resistance. Several reports underscore the antimicrobial activity of natural and synthetic food coloring agents, including carmoisine, turmeric dye, red amaranth dye, and phloxine B. However, their ability to suppress the PA biofilm is not clearly understood. Carmoisine is a red-colored synthetic azo dye containing naphthalene subunits and sulfonic groups and is widely used as a food coloring agent. This study investigated the antibiofilm potential and possible mechanism of biofilm inhibition by carmoisine against PA. Computational studies through molecular docking revealed that carmoisine strongly binds to QS regulator LasR (-12.7) and relatively less strongly but significantly with WspR (-6.9). Further analysis of the docked LasR-carmoisine complex using 100 ns MD simulation (Desmond, Schrödinger) validated the bonding strength and stability. Crystal violet assay, triphenyl tetrazolium chloride salt assay, and confocal microscopic studies were adopted for biofilm quantification, and the results indicated the dose-dependent antibiofilm activity of carmoisine against PA. We hypothesise that the carmoisine-mediated reduction of biofilm in PA is due to its interaction with LasR and interference with the QS system. The computational and biochemical analysis of another compound, 1,2-naphthoquinone-4-sulphonic acid, reiterated the role of the naphthalene ring in biofilm inhibition. Hence, this work will pave the way for the future discovery of antibiofilm drugs based on naphthalene ring-based lead compounds.Communicated by Ramaswamy H. Sarma.

Soft corrugated channel with synergistic exclusive discrimination gating for CO2 recognition in gas mixture

Developing artificial porous systems with high molecular recognition performance is critical but very challenging to achieve selective uptake of a particular component from a mixture of many similar species, regardless of the size and affinity of these competing species. A porous platform that integrates multiple recognition mechanisms working cooperatively for highly efficient guest identification is desired. Here, we designed a flexible porous coordination polymer (PCP) and realised a corrugated channel system that cooperatively responds to only target gas molecules by taking advantage of its stereochemical shape, location of binding sites, and structural softness. The binding sites and structural deformation act synergistically, exhibiting exclusive discrimination gating (EDG) effect for selective gate-opening adsorption of CO₂ over nine similar gas molecules, including N₂, CH₄, CO, O₂, H₂, Ar, C₂H₆, and even higher-affinity gases such as C₂H₂ and C₂H₄. Combining in-situ crystallographic experiments with theoretical studies, it is clear that this unparalleled ability to decipher the CO₂ molecule is achieved through the coordination of framework dynamics, guest diffusion, and interaction energetics. Furthermore, the gas co-adsorption and breakthrough separation performance render the obtained PCP an efficient adsorbent for CO₂ capture from various gas mixtures.

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.