Force fields for protein simulations

Comparison of the function of two novel human dopamine D2 receptor variants identifies a likely mechanism for their pathogenicity

Two recently discovered DRD2 mutations, c.634A > T, p.Ile212Phe and c.1121T > G, p.Met374Arg, cause hyperkinetic movement disorders that have overlapping features but apparently differ in severity. The two known carriers of the Met374Arg variant had early childhood disease onset and more severe motor, cognitive, and neuropsychiatric deficits than any known carriers of the Ile212Phe variant, whose symptoms were first apparent in adolescence. Here, we evaluated if differences in the function of the two variants in cultured cells could explain differing pathogenicity. Both variants were expressed less abundantly than the wild type receptor and exhibited loss of agonist-induced arrestin binding, but differences in expression and arrestin binding between the variants were minor. Basal and agonist-induced activation of heterotrimeric Gi/o/z proteins, however, showed clear differences; agonists were generally more potent at Met374Arg than at the Ile212Phe or wild type variants. Furthermore, all Gα subtypes tested were constitutively activated more by Met374Arg than by Ile212Phe. Met374Arg produced greater constitutive inhibition of cyclic AMP accumulation than Ile212Phe or the wild type D2 receptor. Met374Arg and Ile212Phe were more sensitive to thermal inactivation than the wild type D2 receptor, as reported for other constitutively active receptors, but Ile212Phe was affected more than Met374Arg. Additional pharmacological characterization suggested that the mutations differentially affect the shape of the agonist binding pocket and the potency of dopamine, norepinephrine, and tyramine. Molecular dynamics simulations provided a structural rationale for enhanced constitutive activation and agonist potency. Enhanced constitutive and agonist-induced G protein-mediated signaling likely contributes to the pathogenicity of these novel variants.

Diffusion Models in De Novo Drug Design

Diffusion models have emerged as powerful tools for molecular generation, particularly in the context of 3D molecular structures. Inspired by nonequilibrium statistical physics, these models can generate 3D molecular structures with specific properties or requirements crucial to drug discovery. Diffusion models were particularly successful at learning the complex probability distributions of 3D molecular geometries and their corresponding chemical and physical properties through forward and reverse diffusion processes. This review focuses on the technical implementation of diffusion models tailored for 3D molecular generation. It compares the performance, evaluation methods, and implementation details of various diffusion models used for molecular generation tasks. We cover strategies for atom and bond representation, architectures of reverse diffusion denoising networks, and challenges associated with generating stable 3D molecular structures. This review also explores the applications of diffusion models in de novo drug design and related areas of computational chemistry, such as structure-based drug design, including target-specific molecular generation, molecular docking, and molecular dynamics of protein-ligand complexes. We also cover conditional generation on physical properties, conformation generation, and fragment-based drug design. By summarizing the state-of-the-art diffusion models for 3D molecular generation, this review sheds light on their role in advancing drug discovery and their current limitations.

SuFEx Chemistry Enables Covalent Assembly of a 280-kDa 18-Subunit Pore-Forming Complex

Proximity-enhanced chemical cross-linking is an invaluable tool for probing protein-protein interactions and enhancing the potency of potential peptide and protein drugs. Here, we extend this approach to covalently stabilize large macromolecular assemblies. We used SuFEx chemistry to covalently stabilize an 18-subunit pore-forming complex, CsgG:CsgF, consisting of nine CsgG membrane protein subunits that noncovalently associate with nine CsgF peptides. Derivatives of the CsgG:CsgF pore have been used for DNA sequencing, which places high demands on the structural stability and homogeneity of the complex. To increase the robustness of the pore, we designed and synthesized derivatives of CsgF-bearing sulfonyl fluorides, which react with CsgG in very high yield to form a covalently stabilized CsgG:CsgF complex. The resulting pores formed highly homogeneous channels when added to artificial membranes. The high yield and rapid reaction rate of the SuFEx reaction prompted molecular dynamics simulations, which revealed that the SO2F groups in the initially formed complex are poised for nucleophilic reaction with a targeted Tyr. These results demonstrate the utility of SuFEx chemistry to structurally stabilize very large (here, 280 kDa) assemblies.

Exploration of lipid bilayer mechanical properties using molecular dynamics simulation

Important biological structures known for their exceptional mechanical qualities, lipid bilayers are essential to many cellular functions. Fluidity, elasticity, permeability, stiffness, tensile strength, compressibility, shear viscosity, line tension, and curvature elasticity are some of the fundamental characteristics affecting their behavior. The purpose of this review is to examine these characteristics in more detail by molecular dynamics simulation, elucidating their importance and the elements that lead to their appearance in lipid bilayers. Comprehending the mechanical characteristics of lipid bilayers is critical for creating medications, drug delivery systems, and biomaterials that interact with biological membranes because it allows one to understand how these materials respond to different stresses and deformations. The influence of mechanical characteristics on important lipid bilayer properties is examined in this review. The mechanical properties of lipid bilayers were clarified through the use of molecular dynamics simulation analysis techniques, including bilayer thickness, stress-strain analysis, lipid bilayer area compressibility, membrane bending rigidity, and time- or ensemble-averaged the area per lipid evaluation. We explain the significance of molecular dynamics simulation analysis methods, providing important new information about the stability and dynamic behavior of the bilayer. In the end, we hope to use molecular dynamics simulation to provide a comprehensive understanding of the mechanical properties and behavior of lipid bilayers, laying the groundwork for further studies and applications. Taken together, careful investigation of these mechanical aspects deepens our understanding of the adaptive capacities and functional roles of lipid bilayers in biological environments.

Combining network pharmacology, machine learning, molecular docking and molecular dynamic to explore the mechanism of Chufeng Qingpi decoction in treating schistosomiasis

Background

Although the Chufeng Qingpi Decoction (CQD) has demonstrated clinical effectiveness in the treatment of schistosomiasis, the precise active components and the underlying mechanisms of its therapeutic action remain elusive. To achieve a profound comprehension, we incorporate network pharmacology, bioinformatics analysis, molecular docking, and molecular dynamics simulations as investigative methodologies within our research framework.

Method

Utilizing TCMSP and UniProt, we identified formula components and targets. Cytoscape 3.10.0 was used to construct an herb-target interaction network. Genecards, DisGeNET, and OMIM databases were examined for disease-related objectives. A Venn diagram identified the intersection of compound and disease targets. Using Draw Venn, overlapping targets populated STRING for PPI network. CytoNCA identified schistosomiasis treatment targets. GO & KEGG enrichment analysis followed High-scoring genes in PPI were analyzed by LASSO, RF, SVM-RFE. Molecular docking & simulations investigated target-compound interactions.

Result

The component's target network encompassed 379 nodes, 1629 edges, highlighting compounds such as wogonin, kaempferol, luteolin, and quercetin. Amongst the proteins within the PPI network, PTGS2, TNF, TGFB1, BCL2, TP53, IL10, JUN, MMP2, IL1B, and MYC stood out as the most prevalent entities. GO and KEGG revealed that mainly involved the responses to UV, positive regulation of cell migration and motility. The signal pathways encompassed Pathways in cancer, Lipid and atherosclerosis, Fluid shear stress and atherosclerosis, as well as the AGE-RAGE. Bioinformatics analysis indicated TP53 was the core gene. Ultimately, the molecular docking revealed that wogonin, kaempferol, luteolin, and quercetin each exhibited significant affinity in their respective interactions with TP53. Notably, kaempferol exhibited the lowest binding energy, indicating a highly stable interaction with TP53. Lastly, we validated the stability of the binding interaction between the four small molecules and the TP53 through molecular dynamics simulations. The molecular dynamics simulation further validated the strongest binding between TP53 and kaempferol. In essence, our research groundbreaking in its nature elucidates for the first time the underlying molecular mechanism of CQD in the therapeutic management of schistosomiasis, thereby providing valuable insights and guidance for the treatment of this disease.

Conclusion

This study uncovered the efficacious components and underlying molecular mechanisms of the Chufeng Qingpi Decoction in the management of schistosomiasis, thereby offering valuable insights for future fundamental research endeavors.

Unlocking precision in aptamer engineering: a case study of the thrombin binding aptamer illustrates why modification size, quantity, and position matter

The thrombin binding aptamer (TBA) is a prototypical platform used to understand the impact of chemically-modified nucleotides on aptamer stability and target affinity. To provide structural insight into the experimentally-observed effects of modification size, location, and number on aptamer performance, long time-scale molecular dynamics (MD) simulations were performed on multiple binding orientations of TBA-thrombin complexes that contain a large, flexible tryptophan thymine derivative (T-W) or a truncated analogue (T-K). Depending on modification position, T-W alters aptamer-target binding orientations, fine-tunes aptamer-target interactions, strengthens networks of nucleic acid-protein contacts, and/or induces target conformational changes to enhance binding. The proximity and 5'-to-3' directionality of nucleic acid structural motifs also play integral roles in the behavior of the modifications. Modification size can differentially influence target binding by promoting more than one aptamer-target binding pose. Multiple modifications can synergistically strengthen aptamer-target binding by generating novel nucleic acid-protein structural motifs that are unobtainable for single modifications. By studying a diverse set of modified aptamers, our work uncovers design principles that must be considered in the future development of aptamers containing chemically-modified nucleotides for applications in medicine and biotechnology, highlighting the value of computational studies in nucleic acids research.

Quantitative study on a simple electrochemical dsDNA-pregabalin biosensor; multi-spectroscopic, molecular docking and modelling studies

Pregabalin (PGB) is a γ-aminobutyric acid (GABA) alkylated analog prescribed to treat neuropathic pain, fibromyalgia, and postherpetic neuralgia. Using analytical, spectroscopic methods and molecular docking and molecular dynamics (MD) simulations, a detailed experimental and theoretical investigation was conducted into the binding process and interactions between PGB and double-stranded fish sperm deoxyribonucleic acid (dsDNA). It was evident from the collected experimental results that PGB binds with ds-DNA. PGB attaches to dsDNA via minor groove binding, as demonstrated by the results of electrochemical studies, UV-Vis absorption spectroscopy, and replacement study with ethidium bromide and Hoechst-32588. PGB's binding constant (Kb) with dsDNA, as determined by the Benesi-Hildebrand plot, is 2.41×104 ± 0.30 at 298 K. The fluorescence investigation indicates that PGB and dsDNA have a binding stoichiometry (n) of 1.21 ± 0.09. Molecular docking simulations were used in the research to computational determination of the interactions between PGB and dsDNA. The findings demonstrated that minor groove binding was the mechanism by which PGB interacted with dsDNA. Based on the electrochemically responsive PGB-dsDNA biosensor, we developed a technique for low-concentration detection of PGB utilizing differential pulse voltammetry (DPV). The voltammetric analysis of the peak current decrease in the deoxyadenosine oxidation signals resulting from the association between PGB and dsDNA enabled a sensitive estimation of PGB in pH 4.80 acetate buffer. The deoxyguanosine oxidation signals exhibited a linear relationship between 2 and 16 μM PGB. The values for the limit of detection (LOD) and limit of quantitation (LOQ) were 0.57 μM and 1.91 μM, respectively.

Selagibenzophenone B and Its Derivatives: , a Dual Topoisomerase I/II Inhibitor Identified through In Vitro and In Silico Analyses

The development of multitargeted drugs represents an innovative approach to cancer treatment, aiming to enhance drug effectiveness while minimizing side effects. Herein, we sought to elucidate the inhibitory effect of selagibenzophenone B derivatives on the survival of cancer cells and dual topoisomerase I/II enzyme activity. Results demonstrated that among the compounds, SelB-1 selectively inhibited the proliferation and migration of prostate cancer cells while exhibiting minimal effects on healthy cells. Furthermore, SelB-1 showed a dual inhibitory effect on topoisomerases. Computational analyses mirrored the results from enzyme inhibition assays, demonstrating the compound's strong binding affinity to the catalytic sites of the topoisomerases. To our surprise, SelB-1 did not induce apoptosis in prostate cancer cells; instead, it induced autophagic gene expression and lipid peroxidation while reducing GSH levels, which might be associated with ferroptotic death mechanisms. To summarize, the findings suggest that SelB-1 possesses the potential to serve as a dual topoisomerase inhibitor and can be further developed as a promising candidate for prostate cancer treatment.

Monte Carlo simulations for free energies of hydration: Past to present

A summary of the development of Monte Carlo statistical mechanics simulations for the computation of free energies of hydration of organic molecules is followed by presentation of results with the latest version of the optimized potentials for liquid simulations-all atom force field and the TIP4P water model. Scaling of the Lennard-Jones interactions between water, oxygen, and carbon atoms by a factor of 1.25 is found to improve the accuracy of free energies of hydration for 50 prototypical organic molecules from a mean unsigned error of 1.0-1.2 to 0.4 kcal/mol.

Deciphering the Mystery in p300 Taz2-p53 TAD2 Recognition

Intrinsically disordered proteins (IDPs) engage in various fundamental biological activities, and their behavior is of particular importance for a better understanding of the verbose but well-organized signal transduction in cells. IDPs exhibit uniquely paradoxical features with low affinity but simultaneously high specificity in recognizing their binding targets. The transcription factor p53 plays a crucial role in cancer suppression, carrying out some of its biological functions using its disordered regions, such as N-terminal transactivation domain 2 (TAD2). Exploration of the binding and unbinding processes between proteins is challenging, and the inherently disordered properties of these regions further complicate the issue. Computer simulations are a powerful tool to complement the experiments to fill gaps to explore the binding/unbinding processes between proteins. Here, we investigated the binding mechanism between p300 Taz2 and p53 TAD2 through extensive molecular dynamics (MD) simulations using the physics-based UNited RESidue (UNRES) force field with additional Go̅-like potentials. Distance restraints extracted from the NMR-resolved structures were imposed on intermolecular residue pairs to accelerate binding simulations, in which Taz2 was immobilized in a native-like conformation and disordered TAD2 was fully free. Starting from six structures with TAD2 placed at different positions around Taz2, we observed a metastable intermediate state in which the middle helical segment of TAD2 is anchored in the binding pocket, highlighting the significance of the TAD2 helix in directing protein recognition. Physics-based binding simulations show that successful binding is achieved after a series of stages, including (1) protein collisions to initiate the formation of encounter complexes, (2) partial attachment of TAD2, and finally (3) full attachment of TAD2 to the correct binding pocket of Taz2. Furthermore, machine-learning-based PathDetect-SOM was used to identify two binding pathways, the encounter complexes, and the intermediate states.

Development of human lactate dehydrogenase a inhibitors: high-throughput screening, molecular dynamics simulation and enzyme activity assay

Lactate dehydrogenase A (LDHA) is highly expressed in many tumor cells and promotes the conversion of pyruvate to lactic acid in the glucose pathway, providing energy and synthetic precursors for rapid proliferation of tumor cells. Therefore, inhibition of LDHA has become a widely concerned tumor treatment strategy. However, the research and development of highly efficient and low toxic LDHA small molecule inhibitors still faces challenges. To discover potential inhibitors against LDHA, virtual screening based on molecular docking techniques was performed from Specs database of more than 260,000 compounds and Chemdiv-smart database of more than 1,000 compounds. Through molecular dynamics (MD) simulation studies, we identified 12 potential LDHA inhibitors, all of which can stably bind to human LDHA protein and form multiple interactions with its active central residues. In order to verify the inhibitory activities of these compounds, we established an enzyme activity assay system and measured their inhibitory effects on recombinant human LDHA. The results showed that Compound 6 could inhibit the catalytic effect of LDHA on pyruvate in a dose-dependent manner with an EC50 value of 14.54 ± 0.83 µM. Further in vitro experiments showed that Compound 6 could significantly inhibit the proliferation of various tumor cell lines such as pancreatic cancer cells and lung cancer cells, reduce intracellular lactic acid content and increase intracellular reactive oxygen species (ROS) level. In summary, through virtual screening and in vitro validation, we found that Compound 6 is a small molecule inhibitor for LDHA, providing a good lead compound for the research and development of LDHA related targeted anti-tumor drugs.

An Atomistic View on the Mechanism of Diatom Peptide-Guided Biomimetic Silica Formation

Deciphering nature's remarkable way of encoding functions in its biominerals holds the potential to enable the rational development of nature-inspired materials with tailored properties. However, the complex processes that convert solution-state precursors into solid biomaterials remain largely unknown. In this study, an unconventional approach is presented to characterize these precursors for the diatom-derived peptides R5 and synthetic Silaffin-1A1 (synSil-1A1). These molecules can form defined supramolecular assemblies in solution, which act as templates for solid silica structures. Using a tailored structural biology toolbox, the structure-function relationships of these self-assemblies are unveiled. NMR-derived constraints are employed to enable a recently developed fractal-cluster formalism and then reveal the architecture of the peptide assemblies in atomistic detail. Finally, by monitoring the self-assembly activities during silica formation at simultaneous high temporal and residue resolution using real-time spectroscopy, the mechanism is elucidated underlying template-driven silica formation. Thus, it is demonstrated how to exercise morphology control over bioinorganic solids by manipulating the template architectures. It is found that the morphology of the templates is translated into the shape of bioinorganic particles via a mechanism that includes silica nucleation on the solution-state complexes' surfaces followed by complete surface coating and particle precipitation.

Computational screening and MM/GBSA-based MD simulation studies reveal the high binding potential of FDA-approved drugs against Cutibacterium acnes sialidase

Cutibacterium acnes is an opportunistic pathogen linked with acne vulgaris, affecting 80-90% of teenagers globally. On the leukocyte (WBCs) cell surface, the cell wall anchored sialidase in C. acnes virulence factor, catalysing the sialoconjugates into sialic acids and nutrients for C. acnes resulting in human skin inflammation. The clinical use of antibiotics for acne treatments has severe adverse effects, including microbial dysbiosis and resistance. Therefore, identifying inhibitors for primary virulence factors (Sialidase) was done using molecular docking of 1030 FDA-approved drugs. Initially, based on binding energies (ΔG), Naloxone (ZINC000000389747), Fenoldopam (ZINC000022116608), Labetalol (ZINC000000403010) and Thalitone (ZINC000000057255) were identified that showed high binding energies as -10.2, -10.1, -9.9 and -9.8 kcal/mol, respectively. In 2D analysis, these drugs also showed considerable structural conformer of hydrogen and hydrophobic interactions. Further, a 100 ns MD simulation study found the lowest deviation and fluctuations with various intermolecular interactions to stabilise the complexes. Out of 4, the Naloxone molecule showed robust, steady, and stable RMSD 0.23 ± 0.18 nm. Further, MMGBSA analysis supports MD results and found strong binding energy (ΔG) -29.71 ± 4.97 kcal/mol. In Comparative studies with Neu5Ac2en (native substrate) revealed naloxone has a higher affinity for sialidase. The PCA analysis showed that Naloxone and Thalitone were actively located on the active site, and other compounds were flickered. Our extensive computational and statistical report demonstrates that these FDA drugs can be validated as potential sialidase inhibitors.Communicated by Ramaswamy H. Sarma.

Characterization of structure of peptidyl-tRNA hydrolase from Enterococcus faecium and its inhibition by a pyrrolinone compound

In bacteria, peptidyl-tRNA hydrolase (Pth, E.C. 3.1.1.29) is a ubiquitous and essential enzyme for preventing the accumulation of peptidyl-tRNA and sequestration of tRNA. Pth is an esterase that cleaves the ester bond between peptide and tRNA. Here, we present the crystal structure of Pth from Enterococcus faecium (EfPth) at a resolution of 1.92 Å. The two molecules in the asymmetric unit differ in the orientation of sidechain of N66, a conserved residue of the catalytic site. Enzymatic hydrolysis of substrate α-N-BODIPY-lysyl-tRNALys (BLT) by EfPth was characterized by Michaelis-Menten parameters KM 163.5 nM and Vmax 1.9 nM/s. Compounds having pyrrolinone scaffold were tested for inhibition of Pth and one compound, 1040-C, was found to have IC50 of 180 nM. Antimicrobial activity profiling was done for 1040-C. It exhibited equipotent activity against drug-susceptible and resistant S. aureus (MRSA and VRSA) and Enterococcus (VSE and VRE) with MICs 2-8 μg/mL. 1040-C synergized with gentamicin and the combination was effective against the gentamicin resistant S. aureus strain NRS-119. 1040-C was found to reduce biofilm mass of S. aureus to an extent similar to Vancomycin. In a murine model of infection, 1040-C was able to reduce bacterial load to an extent comparable to Vancomycin.

Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems

This study introduces a novel method named multiple parameter replica exchange Gaussian accelerated molecular dynamics (MP-Rex-GaMD), building on the Gaussian accelerated molecular dynamics (GaMD) algorithm. GaMD enhances sampling and retrieves free energy information for biomolecular systems by adding a harmonic boost potential to smooth the potential energy surface without the need for predefined reaction coordinates. Our innovative approach advances the acceleration power and energetic reweighting accuracy of GaMD by incorporating a replica exchange algorithm that enables the exchange of multiple parameters, including the GaMD boost parameters of force constant and energy threshold, as well as temperature. Applying MP-Rex-GaMD to the three model systems of dialanine, chignolin, and HIV protease, we demonstrate its superior capability over conventional molecular dynamics and GaMD simulations in exploring protein conformations and effectively navigating various biomolecular states across energy barriers. MP-Rex-GaMD allows users to accurately map free energy landscapes through energetic reweighting, capturing the ensemble of biomolecular states from low-energy conformations to rare high-energy transitions within practical computational time scales.

Gadolinium-Based NMR Spin Relaxation Measurements of Near-Surface Electrostatic Potentials of Biomolecules

NMR spectroscopy is an important tool for the measurement of the electrostatic properties of biomolecules. To this point, paramagnetic relaxation enhancements (PREs) of 1H nuclei arising from nitroxide cosolutes in biomolecular solutions have been used to measure effective near-surface electrostatic potentials (ϕENS) of proteins and nucleic acids. Here, we present a gadolinium (Gd)-based NMR method, exploiting Gd chelates with different net charges, for measuring ϕENS values and demonstrate its utility through applications to a number of biomolecular systems. The use of Gd-based cosolutes offers several advantages over nitroxides for ϕENS measurements. First, unlike nitroxide compounds, Gd chelates enable electrostatic potential measurements on oxidation-sensitive proteins that require reducing agents. Second, the large electron spin quantum number of Gd (7/2) results in notably larger PREs for Gd chelates when used at the same concentrations as nitroxide radicals. Thus, it is possible to measure ϕENS values exclusively from + and - charged compounds even for highly charged biomolecules, avoiding the use of neutral cosolutes that, as we further establish here, limits the accuracy of the measured electrostatic potentials. In addition, the smaller concentrations of cosolutes required minimize potential binding to sites on macromolecules. Fourth, the closer proximity of the paramagnetic center and charged groups within Gd chelates, in comparison to the corresponding nitroxide compounds, enables more accurate predictions of ϕENS potentials for cross-validation of the experimental results. The Gd-based method described here, thus, broadens the applicability of studies of biomolecular electrostatics using solution NMR spectroscopy.

Importance of Polarizable Embedding for Computing Optical Rotation: The Case of Camphor in Ethanol

Solvation effects on optical rotation are notoriously challenging to model for computational chemistry, as the specific rotatory power of a molecule can vary wildly going from apolar to polar or even protic solvents. To address such a problem, we present a polarizable embedding implementation of an electric and magnetic response property based on density functional theory and the AMOEBA polarizable force field, and apply such an implementation to the study of the optical rotation of camphor in ethanol. By comparing a continuum model, and electrostatic and polarizable embedding QM/MM models, we observe that accounting for the environment's polarization gives rise to not only a different quantitative prediction, in very good agreement with experiments for the QM/AMOEBA model, but also to a very different qualitative picture, with the values of the optical rotation computed along a classical molecular dynamics trajectory with electrostatic embedding being statistically uncorrelated to the ones obtained with the polarizable description.

Many-Body MYP2 Force-Field: Toward the Crystal Growth Modeling of Hybrid Perovskites

Hybrid perovskites are well-known for their optoelectronic and photovoltaic properties. Molecular dynamics simulations allow the study of these soft and ionic crystals by including dynamical effects (e.g., molecular rotations, octahedra tilting, ionic diffusion and hysteresis), yet the high computational cost restricts the use of accurate ab initio forces for bulk or small atomic systems. Hence, great interest exists in the development of classical force-fields for hybrid perovskites of low and linear scaling computational cost, via both empirical methods and machine-learning. This work aims at extending the transferability of our MYP0 model, which has been successfully tailored to methylammonium lead iodide (MAPI) and applied to the study of molecular rotations, vibrations, diffusion of defects, and many other properties. The extended model, named MYP2, improves the description of inorganic or hybrid fragments and the processes of crystal formation while preserving a good description of bulk properties. More importantly, it allows for the direct simulation of the crystal growth of cubic MAPI from deposition of PbI and MAI precursors on the surfaces. Our findings pave the way toward classical force-fields able to model the microstructure evolution of hybrid perovskites and the crystalline synthesis from deposition in vacuo.

The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling

Force fields are a key component of physics-based molecular modeling, describing the energies and forces in a molecular system as a function of the positions of the atoms and molecules involved. Here, we provide a review and scientific status report on the work of the Open Force Field (OpenFF) Initiative, which focuses on the science, infrastructure and data required to build the next generation of biomolecular force fields. We introduce the OpenFF Initiative and the related OpenFF Consortium, describe its approach to force field development and software, and discuss accomplishments to date as well as future plans. OpenFF releases both software and data under open and permissive licensing agreements to enable rapid application, validation, extension, and modification of its force fields and software tools. We discuss lessons learned to date in this new approach to force field development. We also highlight ways that other force field researchers can get involved, as well as some recent successes of outside researchers taking advantage of OpenFF tools and data.

Calcium Binding Mechanism in TAT Rhodopsin

TAT rhodopsin binds Ca2+ near the Schiff base region, which accompanies deprotonation of the Schiff base. This paper reports the Ca2+-free and Ca2+-bound structures of TAT rhodopsin by molecular dynamics (MD) simulation launched from AlphaFold structures. In the Ca2+-bound TAT rhodopsin, Ca2+ is directly coordinated by eight oxygen atoms, four oxygens of the side chains of E54 and D227, and four oxygens of water molecules. E54 is not involved in the hydrogen-bonding network of the Ca2+-free TAT rhodopsin, while flipping motion of E54 allows Ca2+ binding to TAT rhodopsin with deformation of helices observed by FTIR spectroscopy. The hydrogen-bonding network plays a crucial role in maintaining the Ca2+ binding, as mutations of E54, Y55, R79, Y200, E220, and D227 abolished the binding. Only T82V exhibited the Ca2+ binding like the wild type among the mutants in this study. The molecular mechanism of Ca2+ binding is discussed based on the present computational and experimental analysis.

Optimizing Force Fields with Experimental Data Using Ensemble Reweighting and Potential Contrasting

Despite force field improvements over the past decades, we still encounter situations where simulation results disagree with experiments due to force field inaccuracies. Such situations provide opportunities to improve force fields. In this study, we introduce a novel framework for optimizing force fields using experimental data. The unique feature of this framework is that it aims to optimize force fields to match experiments while minimizing the perturbation made to the original force field. To achieve this, we combine ensemble reweighting techniques with the potential contrasting method. Ensemble reweighting is used to reweight an ensemble of conformations generated using an existing force field to match experimental data while minimizing the perturbation to the original ensemble. Potential contrasting is then utilized to optimize force field parameters to reproduce the reweighted ensemble. We demonstrate the framework's effectiveness by optimizing a coarse-grained force field for intrinsically disordered proteins using experimental radius of gyration data.

DNA methylation analysis in patients with neurodevelopmental disorders improves variant interpretation and reveals complexity

Analysis of genomic DNA methylation by generating epigenetic signature profiles (episignatures) is increasingly being implemented in genetic diagnosis. Here we report our experience using episignature analysis to resolve both uncomplicated and complex cases of neurodevelopmental disorders (NDDs). We analyzed 97 NDDs divided into (1) a validation cohort of 59 patients with likely pathogenic/pathogenic variants characterized by a known episignature and (2) a test cohort of 38 patients harboring variants of unknown significance or unidentified variants. The expected episignature was obtained in most cases with likely pathogenic/pathogenic variants (53/59 [90%]), a revealing exception being the overlapping profile of two SMARCB1 pathogenic variants with ARID1A/B:c.6200, confirmed by the overlapping clinical features. In the test cohort, five cases showed the expected episignature, including (1) novel pathogenic variants in ARID1B and BRWD3; (2) a deletion in ATRX causing MRXFH1 X-linked mental retardation; and (3) confirmed the clinical diagnosis of Cornelia de Lange (CdL) syndrome in mutation-negative CdL patients. Episignatures analysis of the in BAF complex components revealed novel functional protein interactions and common episignatures affecting homologous residues in highly conserved paralogous proteins (SMARCA2 M856V and SMARCA4 M866V). Finally, we also found sex-dependent episignatures in X-linked disorders. Implementation of episignature profiling is still in its early days, but with increasing utilization comes increasing awareness of the capacity of this methodology to help resolve the complex challenges of genetic diagnoses.

Identifying plant-derived antiviral alkaloids as dual inhibitors of SARS-CoV-2 main protease and spike glycoprotein through computational screening

COVID-19 is currently considered the ninth-deadliest pandemic, spreading through direct or indirect contact with infected individuals. It has imposed a consistent strain on both the financial and healthcare resources of many countries. To address this challenge, there is a pressing need for the development of new potential therapeutic agents for the treatment of this disease. To identify potential antiviral agents as novel dual inhibitors of SARS-CoV-2, we retrieved 404 alkaloids from 12 selected medicinal antiviral plants and virtually screened them against the renowned catalytic sites and favorable interacting residues of two essential proteins of SARS-CoV-2, namely, the main protease and spike glycoprotein. Based on docking scores, 12 metabolites with dual inhibitory potential were subjected to drug-likeness, bioactivity scores, and drug-like ability analyses. These analyses included the ligand-receptor stability and interactions at the potential active sites of target proteins, which were analyzed and confirmed through molecular dynamic simulations of the three lead metabolites. We also conducted a detailed binding free energy analysis of pivotal SARS-CoV-2 protein inhibitors using molecular mechanics techniques to reveal their interaction dynamics and stability. Overall, our results demonstrated that 12 alkaloids, namely, adouetine Y, evodiamide C, ergosine, hayatinine, (+)-homoaromoline, isatithioetherin C, N,alpha-L-rhamnopyranosyl vincosamide, pelosine, reserpine, toddalidimerine, toddayanis, and zanthocadinanine, are shortlisted as metabolites based on their interactions with target proteins. All 12 lead metabolites exhibited a higher unbound fraction and therefore greater distribution compared with the standards. Particularly, adouetine Y demonstrated high docking scores but exhibited a nonspontaneous binding profile. In contrast, ergosine and evodiamide C showed favorable binding interactions and superior stability in molecular dynamics simulations. Ergosine demonstrated exceptional performance in several key pharmaceutical metrics. Pharmacokinetic evaluations revealed that ergosine exhibited pronounced bioactivity, good absorption, and optimal bioavailability. Additionally, it was predicted not to cause skin sensitivity and was found to be non-hepatotoxic. Importantly, ergosine and evodiamide C emerged as superior drug candidates for dual inhibition of SARS-CoV-2 due to their strong binding affinity and drug-like ability, comparable to known inhibitors like N3 and molnupiravir. This study is limited by its in silico nature and demands the need for future in vitro and in vivo studies to confirm these findings.

MiMiC: A high-performance framework for multiscale molecular dynamics simulations

MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient and flexible, we adopt an interoperable approach based on a multiple-program multiple-data (MPMD) paradigm, serving as an intermediary responsible for fast data exchange and interactions between the subsystems. The main goal of MiMiC is to avoid interfering with the underlying parallelization of the external programs, including the operability on hybrid architectures (e.g., CPU/GPU), and keep their setup and execution as close as possible to the original. At the moment, MiMiC offers an efficient implementation of electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) that has demonstrated unprecedented parallel scaling in simulations of large biomolecules using CPMD and GROMACS as QM and MM engines, respectively. However, as it is designed for high flexibility with general multiscale models in mind, it can be straightforwardly extended beyond QM/MM. In this article, we illustrate the software design and the features of the framework, which make it a compelling choice for multiscale simulations in the upcoming era of exascale high-performance computing.

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters

Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This study introduces a comprehensive asymptotic parameter database encompassing atomic multipole moments, polarizabilities, and dispersion coefficients. Leveraging active learning, our database comprehensively represents protein fragments with up to 8 heavy atoms, capturing their conformational diversity with merely 78,000 data points. Additionally, the E(3) neural network (E3NN) is employed to predict the asymptotic parameters directly from the local geometry. The E3NN models demonstrate exceptional accuracy and transferability across all asymptotic parameters, achieving an R2 of 0.999 for both protein fragments and 20 amino acid dipeptide test sets. The long-range electrostatic and dispersion energies can be obtained using the E3NN-predicted parameters, with an error of 0.07 and 0.02 kcal/mol, respectively, when compared to symmetry-adapted perturbation theory (SAPT). Therefore, our force fields demonstrate the capability to accurately describe long-range interactions in proteins, paving the way for next-generation protein force fields.

Comparative Linkage of Novel Anti-Tumor Pd(II) Complex with Bio-Macromulecules: Fluorescence, UV-Vis, DFT, Molecular Docking and Molecular Dynamics Simulation Studies

A novel mononuclear palladium complex, [Pd(dach)(SSA)], where dach and SSA are diaminocyclohexane and sulfosalicylic acid ligands, respectively, has been synthesized and identified utilizing analytical and spectral methods. DFT calculations, namely geometry optimization, MEP, HOMO-LUMO and NBO analysis, have been conducted at B3LYP level by aug-ccpVTZ-PP and 6-311G(d, p) basis sets. NBO and HOMO-LUMO analysis exhibited that the palladium compound is stable. MEP showed the potential sites of molecule for the interaction. By employing MTT assay, the cytotoxicity activity of the aforesaid compound was examined on K562 cell line, which revealed a proper activity compared to cisplatin. To ascertain the lipophilicity of the newly made compound, the partition coefficient measurement was accomplished, which follows the order of cisplatin < Pd(II) complex. Next, investigation of binding properties of the studied compound with DNA of calf thymus and BSA were done by spectroscopic (CD, fluorescence emission and electronic adsorption) and non-spectroscopic (viscosity measurements, DNA gel electrophoresis, molecular docking and molecular dynamics simulation) methods. The outcomes of CD and UV-Vis spectroscopy demonstrated that the title compound refolded the protein via increasing the alpha helix percentage. The data obtained from UV-Vis studies indicated the non-intercalative mutual action between Pd(II) complex with DNA. It also revealed that the Kapp magnitude of CT-DNA (7.43 × 104 M- 1) is higher than the BSA (5.17 × 103 M- 1), and L1/2 (midpoint of transition) of CT-DNA (5 µM) is lower than the BSA (5.7 µM), indicating that the complex has a greater binding affinity to CT-DNA than BSA. Fluorescence quenching mechanism of the two biomolecules by the metal complex is static and the calculated thermodynamic parameters (ΔS° < 0 and ΔH° < 0) suggested the hydrogen bonding and/ or van der Waals forces with DNA and BSA. Further, molecular docking indicated that the studied compound fits into the groove of DNA and the site I of BSA. The stability of metal compound-DNA/-BSA in the presence of H2O solvent and over the time were validated via molecular dynamics simulation.

Porphyromonas gingivalis activates Heat-Shock-Protein 27 to drive a LC3C-specific probacterial form of select autophagy that is redox sensitive for intracellular bacterial survival in human gingival mucosa

Porphyromonas gingivalis , a major oral pathobiont, evades canonical host pathogen clearance in human primary gingival epithelial cells (GECs) by initiating a non-canonical variant of autophagy consisting of Microtubule-associated protein 1A/1B-light chain 3 (LC3)-rich autophagosomes, which then act as replicative niches. Simultaneously, P. gingivalis inhibits apoptosis and oxidative-stress, including extracellular-ATP (eATP)-mediated reactive-oxygen-species (ROS) production via phosphorylating Heat Shock Protein 27 (HSp27) with the bacterial nucleoside-diphosphate-kinase (Ndk). Here, we have mechanistically identified that P. gingivalis -mediated induction of HSp27 is crucial for the recruitment of the LC3 isoform, LC3C, to drive the formation of live P. gingivalis -containing Beclin1-ATG14-rich autophagosomes that are redox sensitive and non-degrading. HSp27 depletions of both infected GECs and gingiva-mimicking organotypic-culture systems resulted in the collapse of P. gingivalis -mediated autophagosomes, and abolished P. gingivalis -induced LC3C-specific autophagic-flux in a HSp27-dependent manner. Concurrently, HSp27 depletion accompanied by eATP treatment abrogated protracted Beclin 1-ATG14 partnering and decreased live intracellular P. gingivalis levels. These events were only partially restored via treatments with the antioxidant N-acetyl cysteine (NAC), which rescued the cellular redox environment independent of HSp27. Moreover, the temporal phosphorylation of HSp27 by the bacterial Ndk results in HSp27 tightly partnering with LC3C, hindering LC3C canonical cleavage, extending Beclin 1-ATG14 association, and halting canonical maturation. These findings pinpoint how HSp27 pleiotropically serves as a major platform-molecule, redox regulator, and stepwise modulator of LC3C during P. gingivalis -mediated non-canonical autophagy. Thus, our findings can determine specific molecular strategies for interfering with the host-adapted P. gingivalis ' successful mucosal colonization and oral dysbiosis.

Computational and experimental examinations of new antitumor palladium(II) complex: CT-DNA-/BSA-binding, in-silico prediction, DFT perspective, docking, molecular dynamics simulation and ONIOM

Since the design of metal complexes with better biological activities is important, herein a new palladium(II) complex bearing en and acac (en and acac stand for ethylenediamine and acetylacetonato, respectively) as its ligands, [Pd(en)(acac)]NO3 complex, was synthesized and fully characterized. Quantum chemical computations of the palladium(II) complex were done via DFT/B3LYP method. Cytotoxicity activity of the new compound on leukemia cell line (K562) was assessed via MTT method. The findings indicated that the metal complex has remarkable cytotoxic effect than cisplatin. OSIRIS DataWarrior software was employed to calculate in-silico physicochemical and toxicity parameters of the synthesized complex which rendered significant results. To comprehend the interaction type of new metal compound with macromolecules, the in depth investigation of interaction of mentioned complex with CT-DNA and BSA was accomplished by fluorescence, UV-Visible absorption spectroscopy, viscosity measurement, gel electrophoresis, FRET analysis and circular dichroism (CD) spectroscopy. On the other hand, computational molecular docking was carried out and the obtained data demonstrated that H-bond and van der Waals forces are the dominant forces for the binding of the compound to the mentioned biomolecules. Molecular dynamics simulation was also done and confirmed the stability of best docked pose of palladium(II) complex inside DNA or BSA over the time and in presence of water solvent. Also, Our own N-layered Integrated molecular Orbital and molecular Mechanics (ONIOM) methodology based on the hybridization of quantum mechanics and molecular mechanics (QM/MM) methodology was accomplished to inquire about binding of Pd(II) complex with DNA or BSA.HIGHLIGHTSNew biologically active Pd(II) complex was synthesized and characterized.The in silico studies of the designed complex and its ligands were accomplished by OSIRIS DataWarrior softwareInteraction with CT-DNA and BSA was assessed by various spectroscopic methods.Molecular docking simulation supported the interaction with both macromolecules.Based on ONIOM analysis, the structures of the complex and biomolecules are altered after binding. Communicated by Ramaswamy H. Sarma.

Implementation of Girsanov Reweighting in OpenMM and Deeptime

Classical molecular dynamics (MD) simulations provide invaluable insights into complex molecular systems but face limitations in capturing phenomena occurring on time scales beyond their reach. To bridge this gap, various enhanced sampling techniques have been developed, which are complemented by reweighting techniques to recover the unbiased dynamics. Girsanov reweighting is a reweighting technique that reweights simulation paths, generated by a stochastic MD integrator, without evoking an effective model of the dynamics. Instead, it calculates the relative path probability density at the time resolution of the MD integrator. Efficient implementation of Girsanov reweighting requires that the reweighting factors are calculated on-the-fly during the simulations and thus needs to be implemented within the MD integrator. Here, we present a comprehensive guide for implementing Girsanov reweighting into MD simulations. We demonstrate the implementation in the MD simulation package OpenMM by extending the library openmmtools. Additionally, we implemented a reweighted Markov state model estimator within the time series analysis package Deeptime.

Multistep Biooxidation of 5-(Hydroxymethyl)furfural to 2,5-Furandicarboxylic Acid with H2O2 by Unspecific Peroxygenases

5-(Hydroxymethyl)furfural (HMF) is a key platform chemical derived from renewable biomass sources, holding great potential as starting material for the synthesis of valuable compounds, thereby replacing petrochemical-derived counterparts. Among these valorised compounds, 2,5-furandicarboxylic acid (FDCA) has emerged as a versatile building block. Here we demonstrate the biocatalytic synthesis of FDCA from HMF via a one-pot three-step oxidative cascade performed via two operative steps under mild reaction conditions employing two unspecific peroxygenases (UPOs) using hydrogen peroxide as the only oxidant. The challenge of HMF oxidation by UPOs is the chemoselectivity of the first step, as one of the two possible oxidation products is only a poor substrate for further oxidation. The unspecific peroxygenase from Marasmius oreades (MorUPO) was found to oxidize 100 mM of HMF to 5-formyl-2-furoic acid (FFCA) with 95 % chemoselectivity. In the sequential one-pot cascade employing MorUPO (TON up to 13535) and the UPO from Agrocybe aegerita (AaeUPO, TON up to 7079), 100 mM of HMF were oxidized to FDCA reaching up to 99 % conversion and yielding 861 mg isolated pure crystalline FDCA, presenting the first example of a gram scale biocatalytic synthesis of FDCA involving UPOs.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Characterization of the Bottlenecks and Pathways for Inhibitor Dissociation from [NiFe] Hydrogenase

[NiFe] hydrogenases can act as efficient catalysts for hydrogen oxidation and biofuel production. However, some [NiFe] hydrogenases are inhibited by gas molecules present in the environment, such as O2 and CO. One strategy to engineer [NiFe] hydrogenases and achieve O2- and CO-tolerant enzymes is by introducing point mutations to block the access of inhibitors to the catalytic site. In this work, we characterized the unbinding pathways of CO in the complex with the wild-type and 10 different mutants of [NiFe] hydrogenase from Desulfovibrio fructosovorans using τ-random accelerated molecular dynamics (τRAMD) to enhance the sampling of unbinding events. The ranking provided by the relative residence times computed with τRAMD is in agreement with experiments. Extensive data analysis of the simulations revealed that from the two bottlenecks proposed in previous studies for the transit of gas molecules (residues 74 and 122 and residues 74 and 476), only one of them (residues 74 and 122) effectively modulates diffusion and residence times for CO. We also computed pathway probabilities for the unbinding of CO, O2, and H2 from the wild-type [NiFe] hydrogenase, and we observed that while the most probable pathways are the same, the secondary pathways are different. We propose that introducing mutations to block the most probable paths, in combination with mutations to open the main secondary path used by H2, can be a feasible strategy to achieve CO and O2 resistance in the [NiFe] hydrogenase from Desulfovibrio fructosovorans.

DNA as a perfect quantum computer based on the quantum physics principles

DNA is a complex multi-resolution molecule whose theoretical study is a challenge. Its intrinsic multiscale nature requires chemistry and quantum physics to understand the structure and quantum informatics to explain its operation as a perfect quantum computer. Here, we present theoretical results of DNA that allow a better description of its structure and the operation process in the transmission, coding, and decoding of genetic information. Aromaticity is explained by the oscillatory resonant quantum state of correlated electron and hole pairs due to the quantized molecular vibrational energy acting as an attractive force. The correlated pairs form a supercurrent in the nitrogenous bases in a single band π -molecular orbital ( π -MO). The MO wave function ( Φ ) is assumed to be the linear combination of the n constituent atomic orbitals. The central Hydrogen bond between Adenine (A) and Thymine (T) or Guanine (G) and Cytosine (C) functions like an ideal Josephson Junction. The approach of a Josephson Effect between two superconductors is correctly described, as well as the condensation of the nitrogenous bases to obtain the two entangled quantum states that form the qubit. Combining the quantum state of the composite system with the classical information, RNA polymerase teleports one of the four Bell states. DNA is a perfect quantum computer.

Genetic heterogeneity and respiratory chain enzyme analysis in pediatric Indian patients with mitochondrial disorder: Report of novel variants in POLG1 gene and their functional implication using molecular dynamic simulation

Mitochondrial disorders are a heterogeneous group of disorders caused by mutations in the mitochondrial DNA or in nuclear genes encoding the mitochondrial proteins and subunits. Polymerase Gamma (POLG) is a nuclear gene and mutation in the POLG gene are one of the major causes of inherited mitochondrial disorders. In this study, 15 pediatric patients, with a wide spectrum of clinical phenotypes were screened using blood samples (n = 15) and muscle samples (n = 4). Respiratory chain enzyme analysis in the muscle samples revealed multi-complex deficiencies with Complex I deficiency present in (1/4) patients, Complex II (2/4), Complex III (3/4) and Complex IV (2/4) patients. Multiple large deletions were observed in 4/15 patients using LR-PCR. Whole exome sequencing (WES) revealed a compound heterozygous mutation consisting of a POLG1 novel variant (NP_002684.1:p.Trp261X) and a missense variant (NP_002684.1:p. Leu304Arg) in one patient and another patient harboring a novel homozygous POLG1 variant (NP_002684.1:p. Phe750Val). These variants (NP_002684.1:p. Leu304Arg) and (NP_002684.1:p. Phe750Val) and their interactions with DNA were modelled using molecular docking and molecular dynamics (MD) simulation studies. The protein conformation was analyzed as root mean square deviation (RMSD), root mean square fluctuation (RMSF) which showed local fluctuations in the mutants compared to the wildtype. However, Solvent Accessible Surface Area (SASA) significantly increased for NP_002684.1:p.Leu304Arg and decreased in NP_002684.1:p.Phe750Val mutants. Further, Contact Order analysis indicated that the Aromatic-sulfur interactions were destabilizing in the mutants. Overall, these in-silico analysis has revealed a destabilizing mutations suggesting pathogenic variants in POLG1 gene.

Exploring the inhibitory action of betulinic acid on key digestive enzymes linked to diabetes via in vitro and computational models: approaches to anti-diabetic mechanisms

Phytochemicals are now increasingly exploited as remedial agents for the management of diabetes due to side effects attributable to commercial antidiabetic agents. This study investigated the structural and molecular mechanisms by which betulinic acid exhibits its antidiabetic effect via in vitro and computational techniques. In vitro antidiabetic potential was analysed via on α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin inhibitory assays. Its structural and molecular inhibitory mechanisms were investigated using Density Functional Theory (DFT) analysis, molecular docking and molecular dynamics (MD) simulation. Betulinic acid significantly (p < 0.05) inhibited α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin enzymes with IC50 of 70.02 μg/mL, 0.27 μg/mL, 1.70 μg/mL and 8.44 μg/mL, respectively. According to DFT studies, betulinic acid possesses similar reaction in gaseous phase and water due to close values observed for highest occupied molecular orbital (HOMO) and lowest occupied molecular orbital (LUMO) and the chemical descriptors. The dipole moment indicates that betulinic acid has high polarity. Molecular electrostatic potential surface revealed the electrophilic and nucleophilic attack-prone atoms of the molecule. Molecular dynamic studies revealed a stable complex between betulinic acid and α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin. The study elucidated the potent antidiabetic properties of betulinic acid by revealing its conformational inhibitory mode of action on enzymes involved in the onset of diabetes.

Coupling finite and boundary element methods to solve the Poisson-Boltzmann equation for electrostatics in molecular solvation

The Poisson-Boltzmann equation is widely used to model electrostatics in molecular systems. Available software packages solve it using finite difference, finite element, and boundary element methods, where the latter is attractive due to the accurate representation of the molecular surface and partial charges, and exact enforcement of the boundary conditions at infinity. However, the boundary element method is limited to linear equations and piecewise constant variations of the material properties. In this work, we present a scheme that couples finite and boundary elements for the linearised Poisson-Boltzmann equation, where the finite element method is applied in a confined solute region and the boundary element method in the external solvent region. As a proof-of-concept exercise, we use the simplest methods available: Johnson-Nédélec coupling with mass matrix and diagonal preconditioning, implemented using the Bempp-cl and FEniCSx libraries via their Python interfaces. We showcase our implementation by computing the polar component of the solvation free energy of a set of molecules using a constant and a Gaussian-varying permittivity. As validation, we compare against well-established finite difference solvers for an extensive binding energy data set, and with the finite difference code APBS (to 0.5%) for Gaussian permittivities. We also show scaling results from protein G B1 (955 atoms) up to immunoglobulin G (20,148 atoms). For small problems, the coupled method was efficient, outperforming a purely boundary integral approach. For Gaussian-varying permittivities, which are beyond the applicability of boundary elements alone, we were able to run medium to large-sized problems on a single workstation. The development of better preconditioning techniques and the use of distributed memory parallelism for larger systems remains an area for future work. We hope this work will serve as inspiration for future developments that consider space-varying field parameters, and mixed linear-nonlinear schemes for molecular electrostatics with implicit solvent models.

Mechanism of antagonist ligand binding to REV-ERBα

REV-ERBα, a therapeutically promising nuclear hormone receptor, plays a crucial role in regulating various physiological processes such as the circadian clock, inflammation, and metabolism. However, the availability of chemical probes to investigate the pharmacology of this receptor is limited, with SR8278 being the only identified synthetic antagonist. Moreover, no X-ray crystal structures are currently available that demonstrate the binding of REV-ERBα to antagonist ligands. This lack of structural information impedes the development of targeted therapeutics. To address this issue, we employed Gaussian accelerated molecular dynamics (GaMD) simulations to investigate the binding pathway of SR8278 to REV-ERBα. For comparison, we also used GaMD to observe the ligand binding process of STL1267, for which an X-ray structure is available. GaMD simulations successfully captured the binding of both ligands to the receptor's orthosteric site and predicted the ligand binding pathway and important amino acid residues involved in the antagonist SR8278 binding. This study highlights the effectiveness of GaMD in investigating protein-ligand interactions, particularly in the context of drug recognition for nuclear hormone receptors.

The OpenMMPol library for polarizable QM/MM calculations of properties and dynamics

We present a new library designed to provide a simple and straightforward way to implement QM/AMOEBA (Atomic Multipole Optimized Energetics for Biomolecular Applications) and other polarizable QM/MM (Molecular Mechanics) methods based on induced point dipoles. The library, herein referred to as OpenMMPol, is free and open-sourced and is engineered to address the increasing demand for accurate and efficient QM/MM simulations. OpenMMPol is specifically designed to allow polarizable QM/MM calculations of ground state energies and gradients and excitation properties. Key features of OpenMMPol include a modular architecture facilitating extensibility, parallel computing capabilities for enhanced performance on modern cluster architectures, a user-friendly interface for intuitive implementation, and a simple and flexible structure for providing input data. To show the capabilities offered by the library, we present an interface with PySCF to perform QM/AMOEBA molecular dynamics, geometry optimization, and excited-state calculation based on (time-dependent) density functional theory.

Type Label Framework for Bonded Force Fields in LAMMPS

New functionality is added to the LAMMPS molecular simulation package, which increases the versatility with which LAMMPS can interface with supporting software and manipulate information associated with bonded force fields. We introduce the "type label" framework that allows atom types and their higher-order interactions (bonds, angles, dihedrals, and impropers) to be represented in terms of the standard atom type strings of a bonded force field. Type labels increase the human readability of input files, enable bonded force fields to be supported by the OpenKIM repository, simplify the creation of reaction templates for the REACTER protocol, and increase compatibility with external visualization tools, such as VMD and OVITO. An introductory primer on the forms and use of bonded force fields is provided to motivate this new functionality and serve as an entry point for LAMMPS and OpenKIM users unfamiliar with bonded force fields. The type label framework has the potential to streamline modeling workflows that use LAMMPS by increasing the portability of software, files, and scripts for preprocessing, running, and postprocessing a molecular simulation.

DFT, molecular docking and molecular dynamics simulation studies on some recent natural products revealing their EGFR tyrosine kinase inhibition potential

Phytochemicals are important chemical compounds in pharmaceutical chemistry. These natural compounds have interesting biological activities, including anticancer, as well as many other functions. EGFR (epidermal growth factor receptor) tyrosine kinase inhibition is emerging as one of the accepted methods in the treatment of cancer. On the other hand, computer-aided drug design has become an increasingly important field of study due to its many important advantages such as efficient use of time and other resources. In this study, fourteen phytochemicals which have triterpenoid structure and have recently entered the literature were investigated computationally for their potential as EGFR tyrosine kinase inhibitors. In the study, DFT (density functional theory) calculations, molecular docking, molecular dynamics simulations, binding free energy calculations with the use of MM-PBSA (molecular mechanics Poisson-Boltzmann Surface Area) method, and ADMET predictions were performed. The obtained results were compared to the results obtained for reference drug Gefitinib. Results showed that the investigated natural compounds are promising structures for EGFR tyrosine kinase inhibition.Communicated by Ramaswamy H. Sarma.

Crocus genome reveals the evolutionary origin of crocin biosynthesis

Crocus sativus (saffron) is a globally autumn-flowering plant, and its stigmas are the most expensive spice and valuable herb medicine. Crocus specialized metabolites, crocins, are biosynthesized in distant species, Gardenia (eudicot) and Crocus (monocot), and the evolution of crocin biosynthesis remains poorly understood. With the chromosome-level Crocus genome assembly, we revealed that two rounds of lineage-specific whole genome triplication occurred, contributing important roles in the production of carotenoids and apocarotenoids. According to the kingdom-wide identification, phylogenetic analysis, and functional assays of carotenoid cleavage dioxygenases (CCDs), we deduced that the duplication, site positive selection, and neofunctionalization of Crocus-specific CCD2 from CCD1 members are responsible for the crocin biosynthesis. In addition, site mutation of CsCCD2 revealed the key amino acids, including I143, L146, R161, E181, T259, and S292 related to the catalytic activity of zeaxanthin cleavage. Our study provides important insights into the origin and evolution of plant specialized metabolites, which are derived by duplication events of biosynthetic genes.

The Role of Force Fields and Water Models in Protein Folding and Unfolding Dynamics

Protein folding is a fascinating, not fully understood phenomenon in biology. Molecular dynamics (MD) simulations are an invaluable tool to study conformational changes in atomistic detail, including folding and unfolding processes of proteins. However, the accuracy of the conformational ensembles derived from MD simulations inevitably relies on the quality of the underlying force field in combination with the respective water model. Here, we investigate protein folding, unfolding, and misfolding of fast-folding proteins by examining different force fields with their recommended water models, i.e., ff14SB with the TIP3P model and ff19SB with the OPC model. To this end, we generated long conventional MD simulations highlighting the perks and pitfalls of these setups. Using Markov state models, we defined kinetically independent conformational substates and emphasized their distinct characteristics, as well as their corresponding state probabilities. Surprisingly, we found substantial differences in thermodynamics and kinetics of protein folding, depending on the combination of the protein force field and water model, originating primarily from the different water models. These results emphasize the importance of carefully choosing the force field and the respective water model as they determine the accuracy of the observed dynamics of folding events. Thus, the findings support the hypothesis that the water model is at least equally important as the force field and hence needs to be considered in future studies investigating protein dynamics and folding in all areas of biophysics.

Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.

Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα

Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.

Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics

Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light-matter interaction does not simplify matters. Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations and molecular dynamics to accelerate such simulations. We then apply this approach to evaluate strong coupling, changes in reaction rate constant, and their influence on enthalpy and entropy for the deprotection reaction of 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally and using ab initio simulations. While we find qualitative agreement with critical experimental observations, especially with regard to the changes in kinetics, we also find differences in comparison with previous theoretical predictions. The features for which the ML-accelerated and ab initio simulations agree show the experimentally estimated kinetic behavior. Conflicting features indicate that a contribution of dynamic electronic polarization to the reaction process is more relevant than currently believed. Our work demonstrates the practical use of ML for polaritonic chemistry, discusses limitations of common approximations, and paves the way for a more holistic description of polaritonic chemistry.

The Interaction Mechanism of Picolinamide Fungicide Targeting on the Cytochrome bc1 Complex and Its Structural Modification

Picolinamide fungicides, structurally related to UK-2A and antimycin-A, bind into the Qi-site in the bc1 complex. However, the detailed binding mode of picolinamide fungicides remains unknown. In the present study, antimycin-A and UK-2A were selected to study the binding mode of picolinamide inhibitors with four protonation states in the Qi-site by integrating molecular dynamics simulation, molecular docking, and molecular mechanics Generalized Born surface area (MM/GBSA) calculations. Subsequently, a series of new picolinamide derivatives were designed and synthesized to further understand the effects of substituents on the tail phenyl ring. The computational results indicated that the substituted aromatic rings in antimycin-A and UK-2A were the pharmacophore fragments and made the primary contribution when bound to a protein. Compound 9g-hydrolysis formed H-bonds with Hie201 and Ash228 and showed an IC50 value of 6.05 ± 0.24 μM against the porcine bc1 complex. Compound 9c, with a simpler chemical structure, showed higher control effects than florylpicoxamid against cucumber downy mildew and expanded the fungicidal spectrum of picolinamide fungicides. The structural and mechanistic insights obtained from the present study will provide a valuable clue for the future designing of new promising Qi-site inhibitors.

System-Specific Parameter Optimization for Nonpolarizable and Polarizable Force Fields

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL. Furthermore, we present an open-source parametrization tool, called FFAFFURR, that enables the (system-specific) parametrization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a zinc-finger protein.

Identification and characterization of domain-specific inhibitors of DENV NS3 and NS5 proteins by in silico screening methods

The dengue virus (DENV) infects approximately 400 million people annually worldwide causing significant morbidity and mortality. Despite advances in understanding the virus life cycle and infectivity, no specific treatment for this disease exists due to the lack of therapeutic drugs. In addition, vaccines available currently are ineffective with severe side effects. Therefore, there is an urgent need for developing therapeutics suitable for effective management of DENV infection. In this study, we adopted a drug repurposing strategy to identify new therapeutic use of existing FDA approved drug molecules to target DENV2 non-structural proteins NS3 and NS5 using computational approaches. We used Drugbank database molecules for virtual screening and multiple docking analysis against a total of four domains, the NS3 protease and helicase domains and NS5 MTase and RdRp domains. Subsequently, MD simulations and MM-PBSA analysis were performed to validate the intrinsic atomic interactions and the binding affinities. Furthermore, the internal dynamics in all four protein domains, in presence of drug molecule binding were assessed using essential dynamics and free energy landscape analyses, which were further coupled with conformational dynamics-based clustering studies and cross-correlation analysis to map the regions that exhibit these structural variations. Our comprehensive analysis identified tolcapone, cefprozil, delavirdine and indinavir as potential inhibitors of NS5 MTase, NS5 RdRp, NS3 protease and NS3 helicase functions, respectively. These high-confidence candidate molecules will be useful for developing effective anti-DENV therapy to combat dengue infection.Communicated by Ramaswamy H. Sarma.

Upgrading of the general AMBER force field 2 for fluorinated alcohol biosolvents: A validation for water solutions and melittin solvation

The standard GAFF2 force field parameterization has been refined for the fluorinated alcohols 2,2,2-trifluoroethanol (TFE), 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP), and 1,1,1,3,3,3-hexafluoropropan-2-one (HFA), which are commonly used to study proteins and peptides in biomimetic media. The structural and dynamic properties of both proteins and peptides are significantly influenced by the biomimetic environment created by the presence of these cosolvents in aqueous solutions. Quantum mechanical calculations on stable conformers were used to parameterize the atomic charges. Different systems, such as pure liquids, aqueous solutions, and systems formed by melittin protein and cosolvent/water solutions, have been used to validate the new models. The calculated macroscopic and structural properties are in agreement with experimental findings, supporting the validity of the newly proposed models.

Novel Soybean Variety Lacking Raffinose Synthase 2 Activity

Variation in the raffinose family oligosaccharide (RFO) content in soybean is advantageous for livestock farming and health science. In this study, a soybean variety (GmJMC172) with a significantly low stachyose content in its seeds was identified in the NARO Genebank core collection. The results of the single-nucleotide polymorphism (SNP) analysis suggested that this phenomenon was related to a single-base deletion, inducing a frameshift mutation in raffinose synthase 2 (RS2), rather than the polymorphisms in the RS3, RS4, and stachyose synthase (STS) sequences. Differences in the enzymatic properties between the native RS2 and truncated RS2 were examined by using a three-dimensional model predicted using Alphafold2. In addition to revealing the missing active pocket in truncated RS2, the modeled structure explained the catalytic role of W331* and suggested a sufficient space to bind both sucrose and raffinose in the ligand-binding pocket. The soybean line, with seeds available from the NARO Genebank, could serve as breeding materials for manipulating the RFO content.