CHARMM: the biomolecular simulation program

Analysis of how antigen mutations disrupt antibody binding interactions toward enabling rapid and reliable antibody repurposing

Antibody repurposing is the process of changing a known antibody so that it binds to a mutated antigen. One of the findings to emerge from the Coronavirus Disease 2019 (COVID-19) pandemic was that it was possible to repurpose neutralizing antibodies for Severe Acute Respiratory Syndrome, a related disease, to work for COVID-19. Thus, antibody repurposing is a possible pathway to prepare for and respond to future pandemics, as well as personalizing cancer therapies. For antibodies to be successfully repurposed, it is necessary to know both how antigen mutations disrupt their binding and how they should be mutated to recover binding, with this work describing an analysis to address the first of these topics. Every possible antigen point mutation in the interface of 246 antibody-protein complexes were analyzed using the Rosetta molecular mechanics force field. The results highlight a number of features of how antigen mutations affect antibody binding, including the effects of mutating critical hotspot residues versus other positions, how many mutations are necessary to be likely to disrupt binding, the prevalence of indirect effects of mutations on binding, and the relative importance of changing attractive versus repulsive energies. These data are expected to be useful in guiding future antibody repurposing experiments.

Fine-tuning property domain weighting factors and the objective function in force-field parameter optimization

Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force-field parameters need to be adjusted. This force-field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties' errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the combined error for the reproduction of the properties targeted in this optimization is reduced. Furthermore, the transferability of the force field parameters (FFParams) to chemically similar systems is increased. One interesting outcome is a large variety in the resulting optimized FFParams and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.

InSty: A ProDy Module for Evaluating Protein Interactions and Stability

ProDy is a widely used application programming interface for analyzing the collective dynamics of proteins and their complexes, offering enhanced capabilities to address the growing needs of the computational biology community to bridge structure and function. Here, we introduce InSty, a new module integrated into ProDy to identify and quantify intra- and intermolecular interactions critical to protein stability and structural dynamics. InSty analyzes the non-covalent interactions using conformational ensemble data from both experiments and computational predictions, assesses their time evolution and persistence during molecular dynamics simulations as well as their conservation across homologs. It provides insights into the significance of these interactions in achieving function and/or supporting stability. InSty outputs lend themselves to statistical evaluation, visualization, and automated ensemble analysis for interpreting the significance of the interactions in the context of protein dynamics, sequence evolution, and allostery. Consolidation of InSty with various ProDy modules enables its efficient usage as a versatile tool that supports mutagenesis studies and identifies critical spots for functional interactions. The InSty module is available as part of the ProDy package at https://github.com/prody/ProDy.

E-FTMap: A Protein Structure Based Pharmacophore Identification Server for Guiding Fragment Expansion

In fragment-based drug design (FBDD), libraries of low molecular weight compounds are screened against a receptor. Due to their size, fragment hits typically bind with weak affinities by forming a handful of highly efficient interactions with the receptor. Such fragment hits must be expanded into more potent lead compounds in order to achieve higher binding affinities. Approaches for expanding fragments to leads include growing-the iterative expansion of the scaffold, and merging-the linking of two fragment hits. In both cases the design can be facilitated by information on the ligand binding preferences of the target protein. Here we describe a protocol for fragment expansion using E-FTMap, an automated web server that identifies important pharmacophore binding regions within a binding site of proteins using the receptor structure alone. E-FTMap distributes 119 small organic probes across a binding site, identifies energy minima in which similar probes bind, and clusters probes by their atom types to identify regions which preferably bind specific atom types. Unless a priori known, the binding site for this analysis can be identified by our FTMap server that uses only 16 probes to find binding hot spots that are generally preferable for ligand binding, whereas the subsequent use of E-FTMap provides atom-specific information. The utility of E-FTMap as a tool for guiding the expansion of fragments into higher affinity binders is demonstrated by its application to 17 proteins that have been targeted by FBDD. The E-FTMap webserver is publicly accessible at https://eftmap.bu.edu/.

Design and synthesis of novel 2-S-alkylated Quinazolinones as dual BRAFV600E and EGFR inhibitors in melanoma: Mechanistic insights from apoptosis and cell cycle modulation

Melanoma, an aggressive and highly metastatic form of skin cancer, remains challenging to treat due to its resistance to conventional therapies and frequent mutations in the BRAF signaling pathway. In this study, we report the design and synthesis of a novel series of thirteen quinazolinone derivatives, featuring a phenyl thiazole moiety linked via a triazole acetamide spacer. These compounds were developed as potential dual inhibitors of BRAFV600E and EGFR, which should offer a promising therapeutic strategy for melanoma treatment. The antiproliferative activity of these compounds was evaluated against the NCI-60 cell line panel, with six compounds advancing to a five-dose screening. Three compounds, 7k, 7l, and 7m, exhibited broad-spectrum anticancer activity, with mean growth inhibition (GI%) exceeding 100 %. Compound 7l demonstrated exceptional efficacy against melanoma subpanels (GI% = 152 %) and potent dual kinase inhibition, with IC50 values of 0.048 μM against B-RAFV600E and 0.037 μM against EGFR. In vitro studies of compound 7l revealed significant cytotoxicity against MALME-3 M (IC50 = 3.16 μM) and LOX-IMVI (IC50 = 2.50 μM) melanoma cell lines, with minimal toxicity towards normal Vero cells. Cell cycle analysis showed G1-phase arrest and disrupted DNA synthesis in melanoma cells, while apoptosis assays demonstrated a dramatic increase in early apoptotic cells from 7.28 % to 40.69 %. Compound 7l modulated key apoptotic markers, increasing the BAX/Bcl-2 ratio by 14.42-fold and elevating caspase 3 and 9 levels, indicating its potential to overcome drug resistance and enhance therapeutic efficacy in melanoma treatment.

In Silico Drug Repurposing Against PSMB8 as a Potential Target for Acute Myeloid Leukemia Treatment

PSMB8 emerges as a prominent gene associated with cancer survival, yet its potential therapeutic role in acute myeloid leukemia (AML) remains unexplored within the existing literature. The principal aim of this study is to systematically screen an expansive library of molecular entities, curated from various databases to identify the prospective inhibitory agents with an affinity for PSMB8. A comprehensive assortment of molecular compounds obtained from the ZINC15 database was subjected to molecular docking simulations with PSMB8 by using the AutoDock tool in PyRx (version 0.9.9) to elucidate binding affinities. Following the docking simulations, a select subset of molecules underwent further investigation through comprehensive ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis employing AdmetSar and SwissADME tools. Finally, RMSD, RMSF, Rg, and H bond analyses were conducted via GROMACS to determine the best conformationally dynamic molecule that represents the candidate agent for the study. Following rigorous evaluation, Adozelesin, Fiduxosin, and Rimegepant have been singled out based on considerations encompassing bioavailability scores, compliance with filter criteria, and acute oral toxicity levels. Additionally, ligand interaction analysis indicates that Adozelesin and Fiduxosin exhibit an augmented propensity for hydrogen bond formation, a factor recognized for its facilitative role in protein-ligand interactions. After final analyses, we report that Fiduxosin may offer a treatment possibility by reversing the low survival rates caused by PSMB8 high activation in AML. This study represents a strategic attempt to repurpose readily available pharmaceutical agents, potentially obviating the need for de novo drug development, and thereby offering promising avenues for therapeutic intervention in specific diseases.

Malyngamide C a potential inhibitor of protein synthesis Machinery targeting peptide deformylase enzyme

Due to the rising incidence of antibiotic-resistant and bacterial illnesses, new therapeutic drugs are essential to target vital bacterial enzymes. Peptide deformylase is an attractive antibacterial target because it plays a pivotal role in protein synthesis. The present study was guided to identify the potential inhibitors of peptide deformylase (PDF), viz., computational methods such as molecular docking, molecular dynamics (MD) simulations, thermodynamic stability, free energy calculations, and ADMET analysis. Here we observed the toxicity profile and drug-likeness of the in-house cyanopeptides database. The malyngamide C showed good oral bioavailability. Molecular docking experiments revealed that malyngamide C showed a better binding affinity of -8.81 kcal/mol than reference actinonin -7.08 kcal/mol. Next, MD simulations revealed that malyngamide C, tumonoic acid A, borophycin, and actinonin were found stable in the binding pocket of PDF observed for 300 ns. The binding posture was well-retained, with negligible RMSD, and found within permissible limits observed throughout the simulations. From the MM/PBSA calculations, the free binding energy of malyngamide C was found to be -145.281 kJ/mol, significantly exceeding other selected molecules, including actinonin. The malyngamide C could be a lead antibacterial candidate with a good safety profile. These computational findings strongly support its experimental validation and further clinical investigations as a novel antibacterial agent to combat drug-resistant bacterial infections.

Inhibition of FLT3-induced signalling in refractory acute myeloid leukaemia

Mutations in FLT3 make this receptor tyrosine kinase overactive. Such mutations found in ∼30 % of the patients who suffer from acute myeloid leukaemia (AML). FLT3 mediates signalling networks that lead to cell proliferation and survival. FLT3 inhibitors are used to treat AML but patients who are treated with them typically become resistant. Such resistance often emerges through secondary mutations which either restore the activity of FLT3 in the presence of drugs or activate a key player in a signalling network such as NRAS. We had developed AML-specific cell lines resistant to two advanced FLT3 inhibitors: gilteritinib and FF-10101. Resistance in these cell lines proceeds though different mechanisms. In this study, we followed on the efficacy of five FLT3 inhibitors (gilteritinib, FF-10101 and three promising inhibitors that are being developed), two pan-PI3K inhibitors (one of which also inhibits mTOR) and two c-KIT inhibitors in order to examine the significance of different signalling cascades in FLT3+-AML. In addition, we used molecular modelling and quantum chemistry calculations to explain why specific FLT3 mutations affect some inhibitors more than others. Two novel FLT3 inhibitors were found to be only weakly affected by resistance mutations against gilteritinib and FF-10101. The efficacy of most FLT3 inhibitors was only weakly (or not at all) affected by the NRAS/G12C activating mutation. Finally, no non-FLT3 inhibitor has shown sufficient efficacy in the cells, suggesting the central role of FLT3 in FLT3-mutated AML.

Conformational Landscape of the Di- and Tripeptide Permease A Transport Cycle

Dipeptide and tripeptide permease A (DtpA) transporter is a bacterial homologue of the human PepT that is responsible for the uptake of di- and tripeptides from the small intestine and transports them across the cell membrane utilizing an inward-directed proton electrochemical gradient. Despite its importance, the structural dynamics governing the conformational transitions of DtpA remain poorly understood. In this study, we employed Adaptive Bandit enhanced sampling molecular dynamics simulations to investigate the five major conformational states of DtpA adopted during the transport cycle. We identified key metastable states and transitions underlying the transport cycle using Markov State Models (MSMs). Our findings reveal that intra- and interhelical interactions drive conformational changes by inducing bending and rotation of helices lining the pore, resulting in its opening and closure. This study explains the substrate transport mechanism in DtpA, enhancing our understanding of bacterial proton-dependent oligopeptide transporters (POTs) and opening new drug design and development opportunities.

Studying Noncovalent Interactions in Molecular Systems with Machine Learning

Noncovalent interactions (NCIs) is an umbrella term for a multitude of typically weak interactions within and between molecules. Despite the low individual energy contributions, their collective effect significantly influences molecular behavior. Accordingly, understanding these interactions is crucial across fields like catalysis, drug design, materials science, and environmental chemistry. However, predicting NCIs is challenging, requiring at least molecular mechanics-level pairwise energy contributions or efficient quantum mechanical electron correlation treatment. In this review, we investigate the application of machine learning (ML) to study NCIs in molecular systems, an emerging research field. ML excels at modeling complex nonlinear relationships, and is capable of integrating vast data sets from experimental and theoretical sources. It offers a powerful approach for analyzing interactions across scales, from small molecules to large biomolecular assemblies. Specifically, we examine data sets characterizing NCIs, compare molecular featurization techniques, assess ML models predicting NCIs explicitly, and explore inverse design approaches. ML enhances predictive accuracy, reduces computational costs, and reveals overlooked interaction patterns. By identifying current challenges and future opportunities, we highlight how ML-driven insights could revolutionize this field. Overall, we believe that recent proof-of-concept studies foreshadow exciting developments for the study of NCIs in the years to come.

Recent advances in medicinal chemistry strategies for the development of METTL3 inhibitors

N6-methyladenosine (m6A), the most abundant RNA modification in eukaryotic cells, exerts a critical influence on RNA function and gene expression. It has attracted considerable attention within the rapidly evolving field of epitranscriptomics. METTL3 is a key enzyme for m6A modification and is essential for maintaining normal m6A levels. High expression of METTL3 is closely associated with various cancers, including gastric cancer, liver cancer, and leukemia. Inhibiting METTL3 has shown potential in slowing cancer progression, thereby driving the development of METTL3 inhibitors. In this work, we summarize recent advancements in the development of METTL3 inhibitor, with a focus on medicinal chemistry strategies employed during discovery and optimization phases. We explore the application of structure-activity relationship (SAR) studies and protein-targeted degradation techniques, while addressing key challenges associated with their characterization and clinical translation. This review underscores the therapeutic potential of METTL3 inhibitors in modulating epitranscriptomic pathways and aims to offer perspectives for future research in this rapidly evolving field.

Biophysical and structural insights into Azamethiphos-DNA interactions

Azamethiphos (AZA), an organophosphate pesticide, is well-known for its cholinesterase inhibition and associated toxic risks to non-target organisms. Its high-water solubility facilitates environmental contamination and persistence, increasing the risk of human exposure through bioaccumulation in agricultural products. This study investigates AZA's DNA-binding potential and underlying interaction mechanisms. Using in silico techniques, we analyzed AZA's interactions with DNA, revealing that hydrogen bonding plays a crucial role in stabilizing the AZA-DNA complex. The study found that AZA preferentially binds to AT-rich regions of Ct-DNA, suggesting it acts as a groove binder by fitting into the grooves of the DNA double helix Additionally, fluorescence spectroscopy studies of AZA with DNA were conducted at three temperatures (288 K, 298 K, and 308 K). These experiments demonstrated that AZA binds to Ct-DNA with a moderate binding affinity (3.868, 2.238 and 0.0061 x 104 LM-1 at 288, 298 and 308 K respectively). Thermodynamic analysis confirmed the binding process is spontaneous (ΔG < 0), enthalpy driven (ΔH < 0, ΔS < 0) and facilitated by the presence of hydrogen bonds and van der Waals. These findings provide molecular-level insights into AZA's interactions with Ct-DNA, emphasizing its potential effects on genetic material. Understanding these interactions is crucial for assessing AZA's biological risks.

Computer Integrated Dominant Epitopes Evoke Protective Immune Response Against Streptococcus pneumoniae

Streptococcus pneumoniae is a gram-positive bacterium responsible for various diseases like pneumonia, acute otitis media, sinusitis, meningitis and bacteraemia. These diseases cause a significant amount of morbidity and mortality. Although polysaccharide vaccines are available, the protection provided by these vaccines is serotype-dependent and not enough in sensitive populations like children and older people. Designing a subunit vaccine by using proteins that are responsible for the pathogenesis of diseases can provide better protection against bacterial infections. In this study, we present the design of a novel multi-epitope vaccine against Streptococcus pneumoniae using an immunoinformatic approach. More than 1170 epitopes were identified against B cells, cytotoxic T lymphocytes and helper T lymphocytes from more than 60 pneumococcal proteins. Epitopes were further screened, and potential epitopes were selected for vaccine development. Seven different vaccine combinations that harbour the 15 dominant B-cell, cytotoxic T cell and helper T cell epitopes were evaluated with linker and β-defensin adjuvant to finalise the best vaccine construct. Bioinformatics tools were used to analyse the construct's physicochemical properties, secondary and tertiary structures, allergenicity, antigenicity and immunogenicity. Docking studies with the TLR-4 receptor and molecular dynamics simulations indicated strong binding affinity and stability. In silico immune response simulations predicted robust IgG immune response generation and observed more than 200 000 IgG1 + IgG2 counts per mL. Similarly, cell-mediated immunity was also enhanced by the designed vaccine construct. The construct was codon-optimised and cloned in silico for expression in Escherichia coli. These findings suggest that the construct is a promising candidate for further experimental validation.

Influence of Radial Variations in Biochemical Concentrations in Collagen Type and Water on Mechanical Stability of Annulus Fibrosus' Collagen-Hyaluronan Interfaces at Nanoscale: A Molecular Dynamics Investigation

Multidirectional load transmission ability by annulus fibrosus (AF) requires substantial mechanical stability. Additionally, AF exhibits a unique biochemical concentration gradient with outer AF (OA) dominated by type I collagen (COL-I) and inner AF dominated by type II collagen (COL-II) with higher water and proteoglycan concentration. This indicates an intricate relationship between biochemistry and mechanical stability, which remains unclear. This study uses molecular dynamics (MD) simulations to investigate the impact of water, COL-I and COL-II, concentration gradients on mechanical stability of AF's collagen-hyaluronan (COL-HYL) nano-interfaces during tensile and compressive deformation. For this, COL-HYL atomistic models are created by increasing COL-II concentrations from 0% to 75% and water from 65% to 75%. Additional tensile and compressive deformation simulations are conducted for COL-I-HYL interface (COL-HYL interfaces with 0% COL-II) by increasing water concentration from 65% to 75% to segregate the effects of increasing water concentration alone. Results show that increasing water concentration alone to 75% results in marginal changes in local hydration indicating increase in bulk water. This enhances HYL and COL segment sliding-leading to reduction in mechanical stability in tension, indicated by drop in stress-strain characteristics. Additionally, increase in bulk water shifts load-bearing characteristics toward water-leading to reduction in modulus from 3.7 GPa to 1.9 GPa. Conversely, increasing COL-II and water concentration facilitates stable water bridge formation which impedes sliding in HYL and COL-enhancing mechanical stability. These water bridges further improve compressive load sustenance leading to lower reduction in compressive modulus from 3.7 GPa to 2.8 GPa.

A Computational Approach to Characterize the Protein S-Mer Tyrosine Kinase (PROS1-MERTK) Protein-Protein Interaction Dynamics

Protein S (PROS1) has recently been identified as a ligand for the TAM receptor MERTK, influencing immune response and cell survival. The PROS1-MERTK interaction plays a role in cancer progression, promoting immune evasion and metastasis in multiple cancers by fostering a tumor-supportive microenvironment. Despite its importance, limited structural insights into this interaction underscore the need for computational studies to explore their binding dynamics, potentially guiding targeted therapies. In this study, we investigated the PROS1-MERTK interaction using advanced computational analyses to support immunotherapy research. High-resolution structural models from ColabFold, an AlphaFold2 adaptation, provided a baseline structure, allowing us to examine the PROS1-MERTK interface with ChimeraX and map residue interactions through Van der Waals criteria. Molecular dynamics (MD) simulations were conducted in GROMACS over 100 ns to assess stability and conformational changes using RMSD, RMSF, and radius of gyration (Rg). The PROS1-MERTK interface was predicted to contain a heterogeneous mix of amino acid contacts, with lysine and leucine as frequent participants. MD simulations demonstrated prominent early structural shifts, stabilizing after approximately 50 ns with small conformational shifts occurring as the simulation completed. In addition, there are various regions in each protein that are predicted to have greater conformational fluctuations as compared to others, which may represent attractive areas to target to halt the progression of the interaction. These insights deepen our understanding of the PROS1-MERTK interaction role in immune modulation and tumor progression, unveiling potential targets for cancer immunotherapy.

Design and synthesis of novel cyclohexanecarboxamides with anticonvulsant effect by activating Nrf2-ARE pathway

This study investigated a series of novel cyclohexanecarboxamide derivatives 4a-e, 5a-e and 6a-e as anticonvulsant and neuroprotective agents. Compounds 4a-e, 5a-e, and 6a-e were synthesized starting from cyclohexanone and aniline derivatives and evaluated for their anticonvulsant effects using maximal electroshock (MES) and subcutaneous pentylenetetrazole (scPTZ) seizure models. The most potent compounds 4b, 5c, and 6d demonstrated 83.33 % protection in the scPTZ test, while compounds 5a and 6b showed 100 % protection in the MES test. Further quantitative evaluation showed that compound 6d was the most active derivative in scPTZ test (ED50 = 0.04 mmol/kg) and it was more potent than the two reference drugs phenobarbital and ethosuximide by 1.7 and 25.7-fold, respectively. Notably, all the compounds were free from neurotoxic side effects. Mechanistic studies indicated that the compounds 4b, 5c, and 6d exerted neuroprotective effects by modulating oxidative stress markers and activating the Nrf2/ARE pathway. Histopathological examination of brain of animals treated with compounds 4b, 5c, or 6d corroborated the neuroprotective properties. Additionally, molecular docking study and dynamic simulations were also carried out to study the mechanism of Nrf2 activation by the most active compound 6d. These results spotlight the potential of these derivatives as promising candidates for further development as anti-epileptic and neuroprotective agents.

Covalent binding of 5-tetradecyloxy-2-furoic acid (TOFA) and c(RGDfK) and its co-delivery with Lipusu, a novel synergistic strategy to inhibit the proliferation of nasopharyngeal cancer

As the world's only commercially available paclitaxel liposome, Lipusu (Lip) has been clinically used in chemotherapy for >20 years, but the design concept of Lip remains largely unchanged since its initial development. Based on the study of Acetyl-CoA-carboxylase 1 (ACC1) in nasopharyngeal carcinoma (NPC), we proposed the concept of next-generation liposomes (NGL) utilizing lipid demand balance. In this study, we evaluated the feasibility of ACC1 and integrin αVβ3 as NPC targets, and designed 10 conjugates of 5-tetradecyloxy-2-furoic acid (TOFA) and c(RGDfK) that can bind to Lip. Considering the results of chemical parameter prediction, molecular docking, molecular dynamics simulation (MD) and other aspects, we finally selected and synthesized the compound F, and successfully constructed F-Lip by simple incubation method. Compared with Lip, F-Lip showed stronger toxicity in both HONE-1 cells and corresponding tumor-bearing mice. In conclusion, by regulating the balance of lipid demand, the toxicity of Lip can be improved so as to achieve the goal of inhibiting the proliferation of NPC. This study provides a new model for the future design and development of Lip.

Structural and functional insights into the SARS-CoV-2 SUD domain and its interaction with RNA G-Quadruplexes

The SARS-CoV-2 pandemic has caused a global health crisis due to its high pathogenicity. The SARS-Unique Domain (SUD) in the non-structural protein Nsp3 of SARS-CoV-2 is hypothesized to play a critical role in viral replication and pathogenesis by interacting with host RNA G-quadruplex (G4) structures, but the molecular mechanisms remain unclear. In this study, we used a multidisciplinary approach, including fluorescence assays, CD, single-molecule FRET, SAXS, G4-unfolding experiments and MD simulations, to investigate the interaction between SUD and RNA G4 structures. We found that SUD exhibited a strong binding affinity for RNA three-layer G4 structures with 3'-tail preference but did not unfold G4; instead, it stabilized the G4 conformation, suggesting a role in modulating viral RNA stability and translation. SAXS revealed that G4 binds to a surface groove formed by the N- and C-termini of SUD, enhancing its conformational stability. MD simulations identified key interaction sites and confirmed the induced-fit binding mechanism. These findings provide critical insights into the role of SUD in modulating viral RNA stability and translation, and offers potential targets for antiviral strategies targeting SUD/G4 interactions.

Membrane disruption potential of endogenous opioid neuropeptide Dynorphin A and related clinical variants

Dynorphins are natural neuropeptides that act like opioids but can also cause harmful effects like neurological issues and cell death. Dynorphin A (DynA WT) and its variants (L5S, R6W, and R9C) may disrupt lipid bilayers, leading to pathophysiological effects. Using steered and conventional molecular dynamics simulations, we evaluated how DynA and its variants interact with and penetrate model lipid bilayers. We defined three lipid compositions: neutral, cholesterol-rich and negatively charged, representing different sections of a cell membrane to characterize specific lipid-protein interactions. The R6W peptide cannot find a stable state in any membrane, always returning to the water-bilayer interface. DynA L5S uniquely disturbs neutral lipid bilayers by forming proteolipid pores at the hydrophobic core. DynA WT and L5S are capable to form more stable proteolipid pores in neutral bilayers with cholesterol. L5S and R9C disrupt negatively charged bilayers with cholesterol, again, being able to form stable toroidal pores. The computational strategy presented here allows to study how single amino acid changes in DynA peptides affect their ability to disturb different bilayer compositions.

Assessment of dynamic removal mechanism of non-steroidal anti-inflammatory biomolecules in the aqueous environments by a novel covalent organic framework

Covalent Organic Frameworks (COFs) are a new class of highly porous crystalline substances which have demonstrated excellent potential as novel adsorbents for efficient depollution of pharmaceutical compounds from wastewater. Herein, the molecular mechanism involved in the removal process of non-steroidal anti-inflammatory drug residues, Ibuprofen (IBP) and Naproxen (NPX), from polluted water by an emerging novel COF functionalized with vinyl groups (COF-V), is evaluated through molecular dynamics (MD) simulations under various external electric fields (EFs). MD analyses show that COF-V is efficient in drug loading capacity of % 100 with total interaction energy value of -519.66 and -415.21 kJ/mol for NPX and IBP in single-component systems. In addition, both drug molecules can be simultaneously and efficiently removed in NPX/IBP/COF-V binary system. The van der Waals (vdW) potential is the primary force during the removal mechanism of drug residues. The efficacy of removing biomolecules from wastewater using COF-V substrate is reduced as the strength of EF is intensified in such a way an enhancement of solvent-accessible surface area (SASA) value of the adsorbent and the decreasing of Drug/COF-V contact area are found. The changes in the interaction energy and the RDF patterns are well in agreement with the adsorption mechanism observed in the presence of EFs. This work highlights using of COF as an effective adsorbent for removing pollutant from aqueous solution.

Quantifying the Cooperativity of Backbone Hydrogen Bonding

The hydrogen bonds (H-bonds) between backbone amide and carbonyl groups are fundamental to the stability, structure, and dynamics of proteins. A key feature of such hydrogen bonding interactions is that multiple H-bonds can enhance each other when aligned, as such in theα $$ \alpha $$ -helix orβ $$ \beta $$ -sheet secondary structures. To better understand this cooperative effect, we propose a new physical quantity to evaluate the cooperativity of intermolecular interactions. Using H-bond aligned N-methylacetamide molecules as the model system, we assess the cooperativity of protein backbone hydrogen bonds using quantum chemistry (QM) calculations at the MP2/aug-cc-pVTZ level, revealing cooperative energies ranging from 2 to 4.3 kcal/mol. A set of protein force fields was benchmarked against QM results. While the additive force field failed to reproduce cooperativity, polarizable force fields, including the Drude and AMOEBA protein force fields, have been found to reproduce the trend of QM results, albeit with smaller magnitude. This work demonstrates the theoretical utility of the proposed formula for quantifying cooperativity and its relevance in force field parameterization. Incorporating cooperative energy into polarizable models presents a pathway to achieving more accurate simulations of biomolecular systems.

Molecular simulations: From fundamental principles to applications in gaseous pollutant control

Removing gaseous pollutants from the environment is crucial for mitigating air pollution and safeguarding public health. Conventional laboratory methods for gaseous pollutant removal face significant challenges, including complex experimental setups, limited scalability, and difficulties in capturing molecular-level interactions under real-world conditions. Molecular simulations have emerged as a powerful tool to address these issues, with ongoing research focusing on improving computational efficiency and force field accuracy to model diverse pollutants and materials. These methods predict the absorption properties of gaseous pollutants, offering detailed insights at the molecular level that are challenging to achieve experimentally. This research begins by discussing the theory underlying molecular simulation methods, highlighting their relevance in understanding gas-solid interactions. Various absorbents' physical and chemical properties are analyzed, focusing on their effectiveness in trapping and neutralizing harmful gases. The study also examines the influence of molecular simulations in determining key transfer properties, such as permeability, solubility, and selectivity, which enhance the design and optimization of absorbent materials. The importance of this research lies in its potential to predict the removal efficiency of gaseous pollutants, providing valuable tools for developing effective pollution control strategies. This approach advances the understanding of gas absorption mechanisms and profoundly impacts the development of innovative solutions for environmental protection. By reviewing past achievements, present applications, and future directions, this article underscores the transformative role of molecular simulations in accelerating the development of novel materials for efficient gaseous pollutant control.

Dye intermediates N-phenyl-2-naphthylamine and o-tolidine are novel environmental androgens with reproductive toxicity in male rats

Endocrine disruptors are environmental chemicals that can interfere with the endocrine system. However, research on endocrine disruptors that interfere with androgen action is still limited. In this study, two new environmental androgens were identified. N-Phenyl-2-naphthylamine (PNA) and o-tolidine are dye intermediates widely used in industry to produce organic pigments. We found that both PNA and o-tolidine activated androgen receptor (AR) transcriptional activity in the luciferase reporter assay and bind to AR-ligand binding domain (LBD) directly in the surface plasmon resonance analysis. In vivo studies showed that PNA and o-tolidine induced the proliferation of seminal vesicle cells and increased seminal vesicle weight in immature male rats, indicating that these two compounds had androgenic activity in vivo. In addition, in mature male rats, PNA and o-tolidine reduced sperm concentration and motility and increased sperm deformities, and caused atrophy of the seminiferous tubules of the testis and decreased testosterone levels. These results suggest that PNA and o-tolidine are novel environmental androgens, which bind and activate AR and have toxicological effects on male reproductive function.

SHINE: Deterministic Many-to-Many Clustering of Molecular Pathways

State-of-the-art molecular dynamics (MD) simulation methods can generate diverse ensembles of pathways for complex biological processes. Analyzing these pathways using statistical mechanics tools demands identifying key states that contribute to both the dynamic and equilibrium properties of the system. This task becomes especially challenging when analyzing multiple MD simulations simultaneously, a common scenario in enhanced sampling techniques like the weighted ensemble strategy. Here, we present a new module of the MDANCE package designed to streamline the analysis of pathway ensembles. This module integrates n-ary similarity, cheminformatics-inspired tools, and hierarchical clustering to improve analysis efficiency. We present the theoretical foundation behind this approach, termed Sampling Hierarchical Intrinsic N-ary Ensembles (SHINE), and demonstrate its application to simulations of alanine dipeptide and adenylate kinase.

Design, synthesis, biological evaluation and in silico studies of 2-anilino- 4-(benzimidazol- 1-yl)pyrimidine scaffold as antitumor agents

In an attempt to rationalize the search for new potential Chemotherapeutic agents, a new series of 2-anilinobenzimidazol derivatives with CDK activity have been synthesized. The newly synthesized compounds have been assessed for their cytotoxic effects and CDK activity. These presented compounds showed strong inhibition of cell proliferation in various solid cancer cell lines, suggesting a promising approach for treating malignant tumors. Compounds 4 g, 4j, 4 m, and 4q displayed remarkably strong anticancer potencies against HepG2 cells, with IC50 of 7.59, 8.54, 3.56 and 5.88 µM, respectively, compared to the positive control, DOX (IC50 = 4.50 µM). while compound 4 m, and 4q had the highest anticancer activity against HeLa cells, with an IC50 of 6.39 and 9.71 µM, respectively, compared to the positive control DOX (IC50 = 5.57 µM). On the other hand, comparison of IC50 values against MCF-7 cells revealed that compounds 4 g, 4 m, and 4q showed significant anticancer potency with IC50 of 5.08, 2.18 and 8.19 µM, respectively compared to that of the positive control DOX (IC50 = 4.17 µM). Moreover, compound 4 m and 4q were the most potent CDK9 and CDK12 inhibitors. Furthermore, a molecular docking simulation were performed to explore the ability of compounds 4 m to adopt the common binding pattern of CDK9 and CDK12/T1 inhibitors. In silico ADMET results showed that all compounds have favourable drug-like properties since they met Lipinski's rule of five criteria. Overall, the synthesized anilinopyrimidine derivatives exhibit significant potential as chemotherapeutic agents.

Designing Ultraporous Mesostructured Silica Nanoparticles for the Remediation of Per- and Polyfluoroalkyl Substances

Concerns about per- and polyfluoroalkyl substances (PFAS) have been raised globally as they are bioaccumulative, highly persistent, and invoke a range of health risks. Although phytoremediation is a sustainable PFAS remediation strategy, its efficiency is highly dependent on the PFAS analyte chain length, with limited uptake and removal of longer-chain contaminants. This study aims to develop surface-modified ultraporous mesostructured silica nanoparticles (UMNs) to facilitate PFAS phytoremediation. UMNs were synthesized and functionalized to tune their hydrophobicity and surface charge to enhance UMN affinity for PFAS. Dynamic light scattering, ς-potential, and nitrogen physisorption show that the modified UMNs had similar physical characteristics. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis shows that positively charged UMNs have a higher affinity for PFAS than negatively charged UMNs (with 20% of perfluorooctanoic acid, or PFOA, remaining in solution vs 100% of PFOA remaining in solution, respectively). When incubated with multiple PFAS, UMNs show greater removal efficiency for longer-chain and more hydrophobic PFAS. Preliminary plant studies in soil show an increased PFOA bioconcentration when positively charged UMNs are present. Molecular dynamics simulations, which focused on interactions between the different functional groups on the silica surface and PFAS molecules, were completed and show the importance of the combination of hydrophobic and electrostatic interactions to drive PFAS uptake. Overall, this study highlights the potential of surface-modified UMNs to enhance the uptake of PFAS from the environmental matrix and promote phytoremediation.

Synergistic in silico exploration of some pyrazole-based potential anticancer agents: a DFT, molecular docking, and molecular dynamics study

CONTEXT

Understanding the interaction between therapeutic molecules with in vivo receptors is very essential in developing potential anticancer agents. In recent years, pyrazole derivatives have been evolving as a significant bioactive candidate due to their remarkable pharmacological properties in novel drug design and discovery. Herein, we present a comprehensive computational and theoretical analysis of some selected pyrazole derivatives with potential anticancer properties, employing quantum chemical calculations, molecular docking, and molecular dynamics simulation.

METHOD

In this study, quantum chemical calculations were employed using density functional theory (DFT) with B3LYP functional and 6-31G(d,p) basis set in Gaussian16 to investigate the electronic properties and intermolecular interactions of pyrazole derivatives. Natural bond orbital (NBO) analysis was performed to explore charge distribution and donor-acceptor interactions. Similarly, advanced topological analyses, viz., reduced density gradient (RDG), quantum theory of atoms in molecules (QTAIM), electron localisation function (ELF), localised orbital indicator (LOL), and electrostatic potential (ESP), to characterise intermolecular interactions and electron density features. Molecular docking studies were conducted to assess the binding affinity of the pyrazole derivatives with DNA (PDB ID: 2m2c), specifically focussing on interactions with base pairs. Molecular dynamics simulations were employed to examine the stability and characteristics of interactions over a prolonged timescale. This comprehensive approach integrates quantum chemical tools, molecular docking, and molecular dynamics simulations to elucidate the interaction mechanisms between pyrazole derivatives and DNA nucleobases, enhancing their potential novelty as anticancer agents.

The α/β3 complex of human voltage-gated sodium channel hNav1.7 to study mechanistic differences in presence and absence of auxiliary subunit β3

CONTEXT

In the context of structural interactomics, we generated a 3D model between α and β3 subunits for the hitherto unknown human voltage-gated sodium channel complex (hNa 1.7α/β3). We embedded our 3D model in a membrane lipid bilayer for molecular dynamics (MD) simulations of the sodium cation passage from the outer vestibule through the inner pore segment of our hNa 1.7 complex in presence and absence of auxiliary subunit β3 with remarkable changes close to electrophysiological study results. A complete passage could not be expected due to because the inactivated state of the underlying 3D template. A complete sodium ion passage would require an open state of the channel. The computed observations concerning side chain rearrangements for favorable cooperativity under evolutionary neighborhood conditions, favorable and unfavorable amino acid interactions, proline kink, loop, and helix displacements were all found in excellent keeping with the extant literature without any exception nor contradiction. Complex-stabilizing pairs of interacting amino acids with evolutionary neighborhood complementary were identified.

METHODS

The following tools were used: sequence search and alignment by FASTA and Clustal Omega; 3D model visualization and homology modeling by Vega ZZ, SPDBV, Chimera and Modeller, respectively; missing sections (loops) by Alphafold; geometry optimization prior to MD runs by GROMACS 2021.4 under the CHARMM 36 force field; local healing of bad contacts by SPDBV based on its Ramachandran plots; protein-protein docking by HDOCK 2.4; membrane insertion assisted by OPM; Berendsen V-rescaling for NVT; Parrinello-Rahman and Nose-Hoover for MPT; MD analyses by VMD and XMGRACE.

Insights into the solution structure of the actin-binding tail domain of metavinculin by small angle X-ray scattering and molecular dynamics simulations

Vinculin is a ubiquitously expressed focal adhesion protein that plays an important role in cell-matrix and cell-to-cell junctions. Metavinculin, a muscle-specific splice variant of vinculin, contains a 68-amino acid disordered insert region in its actin binding tail domain (MVt). Mutations in this insert are linked to cardiomyopathies. This study investigates the solution structures and structural ensembles of wild-type (WT) and two mutant MVts, ΔLeu954 and R975W, which have been associated with cardiomyopathies, using small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations. SAXS analyses revealed subtle differences in the estimated maximum dimensions and corroborated the elongated shape of the MVts. Quantitative comparisons of SAXS profiles indicated similarity between the WT and ΔLeu954, whereas R975W exhibited differences in the small-angle region. MD simulations demonstrated reduced conformational flexibility and greater packing of the insert in WT compared to mutants. Notably, a salt-bridge observed between R975 and D928 in a WT simulation provides a structural basis for the destabilization caused by the R975W mutation. These findings provide insights into the structure and dynamics of WT and mutant MVt, reflecting the promise of SAXS combined with MD simulations to elucidate the structural properties of proteins with structural disorder.

Explicitly accounting for background charges in a fast multipole method to simulate periodically replicated non neutral microscopic systems

We present two schemes coupling a Fast Multipole Method (FMM) (devoted to standard and polarizable force fields) and an approach to explicitly account for uniformly distributed background charges to simulate periodically replicated molecular systems with a net charge. These schemes rely on recent analytical relations allowing one to compute the electrostatic potential generated by a uniformly charged cube at any point in space. Whereas the first scheme considers the exact relations, the second one is based on grid interpolation of precomputed data for equal precision. Contrary to available approaches, our coupled schemes prevent the use of Ewald summation techniques as usually proposed in periodic FMM approaches. For a polarizable force field (based on induced dipole moments), we show the ability of our schemes to model molecular neutral and charged systems at the same level of accuracy as the Smooth Particle Mesh Ewald (SPME) approach, to predict usual properties. Moreover, our most efficient scheme, based on interpolating precomputed grid data, is already more efficient than SPME to simulate 3000 atom size periodic systems and one order of magnitude more efficient to compute electrostatic terms of periodic systems at the 100k atom size and above.

Enhancing binding affinity predictions through efficient sampling with a re-engineered BAR method: a test on GPCR targets

Computational approaches for predicting the binding affinity of ligand-receptor complex structures often fail to validate experimental results satisfactorily due to insufficient sampling. To address these challenges, recent emphasis has been placed on the re-sampling of new trajectories. In this study, we propose a simulation protocol that achieves efficient sampling by re-engineering the widely used Bennett acceptance ratio (BAR) method as a representative approach. We tested its efficacy across various membrane protein targets, including G-protein coupled receptors (GPCRs) with diverse structural landscapes and experimentally validated binding affinities, to verify its efficient applicability. Subsequently, using BAR-based binding free energy calculations, we confirmed correlations with experimental data, demonstrating the validity and performance of this computational approach.

Myelin sheaths can act as compact temporary oxygen storage units as modeled by an electrical RC circuit model

Oxygen is crucial for mitochondrial energy production in neurons and is efficiently stored and transported within the hydrophobic core of phospholipid bilayers. Using a diffusive model derived from molecular dynamics simulations, we demonstrate that oxygen storage in a bilayer follows first-order kinetics, which can be effectively represented by an RC (resistor-capacitor) circuit. For myelin, with multiple bilayers, oxygen transport is modeled through a ladder network of RC circuits, where oxygen permeation resistance and oxygen storage capacity scale linearly with bilayer count. Meanwhile, the characteristic time constant scales quadratically with myelin thickness, e.g. enhancing the characteristic time constant from 30 ns for one 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer to 506 μs for 200 POPC bilayers. This model shows that myelin sheaths serve as compact oxygen reservoirs, dampening sudden oxygen changes due to their slower release kinetics. During increased neuronal activity, the model suggests that myelination extends the ability to sustain elevated oxygen demand, implying a buffering role for myelin against oxygen fluctuations, while the need for vascular response remains critical in maintaining long-term oxygen homeostasis.

Ionization of DNA Nucleotides in Explicit Solution

QM/MM simulations were performed to investigate the ionizations of four DNA nucleotides in the explicit solution. The vertical ionization energies (VIEs) and adiabatic ionization energies (AIEs) were averaged over 40 snapshots to calculate mean values. The QM/MM VIEs (6.92-7.63 eV) were ~0.70 eV lower than those of the corresponding nucleosides. This suggests that the water environment cannot fully screen the effect of the phosphate group on ionizations. The result is inconsistent with computations using implicit solvent models. The distributions of holes in both adiabatic and vertical ionizations suggest that bulk-water polarization drives the hole transfer from first-shell water to nucleobases, resulting in increases in VIEs and AIEs. Moreover, we computed the released energies in the structural relaxations after ionizations. The results indicate that the minimal energies are released by the structural relaxations of both the bulk-water and the QM region. The redistributions of the electron density on first-shell water molecules and nucleobases produce the primary contributions to released energies.

Template-Based Docking Using Automated Maximum Common Substructure Identification with EnzyDock: Mechanistic and Inhibitor Docking

EnzyDock is a multistate, multiscale CHARMM-based docking program which enables mechanistic docking, i.e., modeling enzyme reactions by docking multiple reaction states, like substrates, intermediates, transition states, and products to the enzyme, in addition to standard protein-ligand docking. To achieve docking of multiple reaction states with similar poses (i.e., consensus docking), EnzyDock employs consensus pose restraints of the docked ligand states relative to a docking template. In the current work, we present an implementation of a Maximum Common Substructure (MCS)-guided docking strategy using EnzyDock, enabling the automatic detection of similarity among query ligands. Specifically, the MCS multistate approach is employed to efficiently dock ligands along enzyme reaction coordinates, including reactants, intermediates, and products, which allows efficient and robust mechanistic docking. To demonstrate the effectiveness of the MCS strategy in modeling enzymes, it is first applied to two highly complex enzyme reaction cascades catalyzed by the diterpene synthase CotB2 and the Diels-Alderase LepI. In addition, the MCS strategy is applied to dock enzyme inhibitors using cocrystallized inhibitors or substrates to guide the docking in the enzymes dihydrofolate reductase and the SARS-CoV-2 enzyme Mpro. The latter case exemplifies the use of MCS with EnzyDock's covalent docking capabilities and QM/MM scoring option. We show that different protocols of the implemented MCS algorithm are needed to obtain mechanistic consistency (i.e., similar poses) in mechanistic docking or to accurately dock chemically diverse ligands in inhibitor docking. Although the current implementation is specific for EnzyDock, the findings should be general and transferable to additional docking programs.

apoCHARMM: High-performance molecular dynamics simulations on GPUs for advanced simulation methods

We present apoCHARMM, a high-performance molecular dynamics (MD) engine optimized for graphics processing unit (GPU) architectures, designed to accelerate the simulation of complex molecular systems. The distinctive features of apoCHARMM include single-GPU support for multiple Hamiltonians, computation of a full virial tensor for each Hamiltonian, and full support for orthorhombic periodic systems in both P1 and P21 space groups. Multiple Hamiltonians on a single GPU permit rapid single-GPU multi-dimensional replica exchange methods, multi-state enveloping distribution sampling methods, and several efficient free energy methods where efficiency is gained by eliminating post-processing requirements. The combination of these capabilities enables constant-pH molecular dynamics in explicit solvent with enveloping distribution sampling, where Hamiltonian replica exchange can be performed on a single GPU with minimal host-GPU memory transfers. A full atomic virial tensor allows support for many different pressure, surface tension, and temperature ensembles. Support for orthorhombic P21 systems allows for the simulation of lipid bilayers, where the two leaflets have equalized chemical potentials. apoCHARMM uses CUDA and modern C++ to enable efficient computation of energy, force, restraint, constraint, and integration calculations directly on the GPU. This GPU-exclusive design focus minimizes host-GPU memory transfers, ensuring optimal performance during simulations, with such transfers occurring only during logging or trajectory saving. Benchmark tests demonstrate that apoCHARMM achieves competitive or superior performance when compared to other GPU-based MD engines, positioning it as a versatile and useful tool for the molecular dynamics community.

Structural Implications of H233L and H398P Mutations in Phospholipase Cζ: A Full-Atom Molecular Dynamics Study on Infertility-Associated Dysfunctions

Phospholipase Cζ (PLCζ), a sperm-specific enzyme, plays a critical role in mammalian fertilization. Mutations in PLCζ have been linked to male infertility, as they impair its ability to trigger calcium (Ca2+) oscillations necessary for egg activation and embryo development. During fertilization, PLCζ is introduced into the egg, where it hydrolyzes phosphatidylinositol 4,5-bisphosphate (PIP2) into inositol 1,4,5-trisphosphate and diacylglycerol, leading to Ca2+ release from the endoplasmic reticulum. Human infertility-associated mutations include H233L, H398P, and R553P, which disrupt PLCζ function. To elucidate the molecular consequences of the mutations, we employed full-atom molecular dynamics simulations to analyze structural perturbations and their impact on PIP2 and Ca2+ binding. Our results reveal that H233L and H398P mutations significantly reduce interactions with PIP2, disrupting hydrogen bonding and salt bridge formation, leading to misalignment of the substrate. Additionally, these mutations destabilize Ca2+ binding by altering its positioning within the active site. In contrast, the R553P mutation primarily affects intramolecular stability and enzyme dynamics without impairing substrate or ion binding. Free energy calculations indicate an increased affinity for PIP2 in H233L and H398P mutants, leading to an aberrant substrate positioning and compromised hydrolysis. These structural insights help explain the egg activation failure and infertility of patients carrying these mutations.

Structural insights into two thiamine diphosphate-dependent enzymes and their synthetic applications in carbon-carbon linkage reactions

The α-hydroxy-β-keto acid synthases are thiamine diphosphate-dependent enzymes catalysing carbon-carbon linkage reactions in the biosynthesis of primary metabolites and various secondary metabolites. However, the substrate selectivity and catalytic stereoselectivity of α-hydroxy-β-keto acid synthases are poorly understood, greatly hindering their synthetic application in generating diverse carbon frameworks. We here report the discovery of two new α-hydroxy-β-keto acid synthases CsmA and BbmA, which show different substrate selectivities in catalysing carbon-carbon coupling reactions between two β-keto acids. Four crystal structures of CsmA or BbmA complexed with thiamine diphosphate and their substrates were determined, clearly revealing their structural bases of substrate selectivity and catalytic stereoselectivity. Substrate scope expansion enables us to generate 120 α-hydroxy-β-keto acids together with 240 NaBH4-reduction products. Furthermore, we applied CsmA and BbmA into enzymatic total synthesis, generating 36 γ-butyrolactone-containing furanolides. These results provide new structural insights into the catalyses of α-hydroxy-β-keto acid synthases and highlight their great potential in carboligation catalysis and synthetic applications.

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation-free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pretrained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific pKa values, achieving high accuracy and generalizability. Notably, GSnet outperformed existing protein embeddings for SASA prediction and a locally charge-aware variant, aLCnet, approached the accuracy of simulation-based and empirical methods for pKa prediction. Our GNN framework demonstrated robustness across diverse data sets, including intrinsically disordered peptides, and scalability for high-throughput applications. These results highlight the potential of GNN-based embeddings and transfer learning to advance protein structure analysis, providing a foundation for integrating predictive models into proteome-wide studies and structural biology pipelines.

URB597 downregulates DJ-1 expression in the mouse striatum and induces neurodegeneration

DJ-1 is a multifunctional protein involved in diverse cellular processes, including defense against oxidative stress, regulation of gene transcription, and maintenance of mitochondrial function. Mutations in the DJ-1 gene are closely associated with early-onset Parkinson's disease, and loss of DJ-1 function increases the susceptibility of dopaminergic neurons to oxidative damage, potentially driving neurodegeneration. Therefore, DJ-1 represents an attractive therapeutic target for PD. In this study, we screened a library of blood-brain barrier-permeable small molecules to identify compounds that modulate DJ-1 expression in the mouse brain. Through molecular docking, we discovered that URB597, a selective fatty acid amide hydrolase inhibitor, binds to DJ-1 and forms a stable complex. URB597 treatment markedly reduced DJ-1 protein levels in SH-SY5Y cells, leading to decreased cell survival and impaired mitochondrial function under oxidative stress conditions. In addition, URB597-treated mice exhibited motor deficits and dopaminergic neuron loss, indicating that suppressing DJ-1 expression may adversely affect neuronal function. Gene expression and pathway enrichment analyses revealed that URB597 targets DJ-1 in the mouse striatum and regulates the expression of genes involved in protein acetylation. Collectively, these findings underscore the critical role of DJ-1 in protecting dopaminergic neurons from oxidative damage and uncover its potential implications in regulating protein acetylation.

Understanding the structure and function of HPI, a rubber tree serine protease inhibitor, and its interaction with subtilisin

Protease inhibitors are crucial in regulating enzymatic activity and have extensive applications in medicine, biotechnology, and agriculture. This study characterizes a recombinant protease inhibitor from Hevea brasiliensis (rHPI), highlighting its unique structural features and inhibitory potential. Using Matrix-Assisted Laser Desorption/Ionization (MALDI) analysis, the inhibitor exhibits one distinct peak around 7.54 kDa. Enzymatic assays using N-succinyl-Ala-Ala-Pro-Phe-p-nitroanilide as a substrate confirmed the inhibitor's activity against subtilisin Carlsberg, a widely utilized serine protease in industry and biotechnology. The crystal structure of rHPI, resolved at 1.73 Å, reveals a topology closely resembling eglin c, including a single alpha-helix, two parallel beta-strands, and a distinctive binding loop spanning residues 40-51. Disordered regions at the N- and C-termini contribute to its structural uniqueness. Despite lacking disulfide bonds and featuring an Arg residue instead of Trp at the P'8 position, rHPI maintains a high affinity for subtilisin. Isothermal titration calorimetry (ITC) showed that this interaction is entropically driven. Molecular docking and dynamics simulations of the rHPI-subtilisin complex revealed the formation of antiparallel β-sheets, hydrogen bonding involving the protein backbone, and a salt bridge between His64 of subtilisin and Asp47 of rHPI. These findings provide valuable insights into the molecular basis of rHPI's inhibitory activity and offer a framework for the rational design of novel subtilisin inhibitors with potential applications in agricultural and industrial settings.

Understanding the association of cell-surface proteins (ACE2 and GRP78) facilitating pathogen recognition: a computational approach

Angiotensin-converting enzyme 2 (ACE2) has been reported to be the primary host cell receptor for recognizing SARS-CoV and SARS-CoV-2 spike proteins. This host-cell element, despite having a crucial role in normal cells, may be hijacked by viruses to invade human cells. It has been reported that ACE2 trafficking to the cell membrane is mediated by other cellular factors, such as the endoplasmic reticulum resident chaperone, named glucose-regulated protein 78 (GRP78). GRP78 is the master of the unfolded protein response during cellular stress. This study uses sequence alignment, protein-protein docking, and molecular dynamics simulation (MDS) to predict the potential binding sites between the two proteins for the first time aiming to understand its role in viral recognition and infection. Results revealed three critical regions in ACE2 (C133-C141, C344-C361, and C530-C542), that could be the recognition site for GRP78 from which, the second region (C344-C361) is the suggested best region based on protein-protein docking, MDS, and MM-GBSA calculations. These cyclic regions show similarity (<38% identity) with the cyclic peptide Pep42, which is previously reported to target GRP78 over cancer cells. This approach paves the way toward suggesting potential inhibitors based on the prevention of the association between ACE2 and GRP78.

Introducing the Potential Binding Interface between the TRAIL-Mimicking Peptide and DR5 via Alanine Scan

Here we harnessed the unexplored binding interface between the 16-residue peptide (P) agonist and death receptor 5 (DR5). P is a solitary peptide ligand that mimics TRAIL (the natural ligand to death receptor) and is reported to control cancer growth in vivo selectively. We delved into the strategic merging of experimental and in silico structure-activity studies via the alanine scanning mutagenesis of P, wherein the disulfide bond was kept intact for structural integrity. Antiproliferative activity studies with these synthetic mutants on HCT116 cells enabled the mapping of the interaction engagement of each residue. Further, in silico docking and MD simulations led us to interpret and model the 3D interface of the binding site. Notably, Trp1, Leu4, Arg7, Ile8, Gln12, and Arg15 were projected experimentally as "hot-spot" residues crucial for primary interactions with DR5, which is predominantly supported via in silico investigations. This study is pivotal for developing new-generation peptide agonists that induce death receptor-mediated apoptosis.

Exploring the sensitivities of experimental techniques to various types of membrane asymmetry using atomistic simulations

Biological membranes have two leaflets that can differ in both lipid composition and total lipid abundance. These different types of asymmetries play a major role in determining the biophysical properties of the membrane; however, they have proven challenging to assay experimentally even in simpler model systems. Molecular dynamics simulations offer the means for detailed computational investigation of systematically varied interleaflet lipid distributions, but opportunities for critical validation with wet lab experiments are scarce. To help address this problem, here we use atomistic simulations of asymmetric bilayers to generate synthetic experimental data and thus investigate the sensitivity of various approaches to changes in relative lipid composition, number, and cholesterol distribution. Contrary to trends in symmetric bilayers, the simulations showed a decrease in lipid packing with increasing cholesterol in differentially stressed asymmetric bilayers, with more pronounced changes in the more loosely packed leaflet. Representative experimental data computed from the simulation trajectories indicated that the detection of asymmetry-induced changes in leaflet properties should be possible with environment-sensitive fluorescent probes and NMR observables, but may require optimization of sample preparation conditions. On the other hand, small-angle scattering data are already experimentally accessible and can reveal differential leaflet packing densities through a model-free analysis. We further show that computationally generated cryo-EM intensity profiles are highly sensitive to phospholipid imbalance between membrane leaflets. Together, these findings provide a roadmap for developing targeted applications of the in vitro techniques and obtaining experimental data critical for validating computationally derived principles related to membrane asymmetry.

Computational structure-guided approach to simulate delamanid and pretomanid binding to mycobacterial F420 redox cycling proteins: identification of key determinants of resistance

The recently approved delamanid (DLM) and pretomanid (PTM) improved the existing options to treat multidrug-resistant tuberculosis (MDR-TB). However, the high spontaneous mutation rates in mycobacterial F420 genes ddn, fgd1, fbiA, fbiB, fbiC, and fbiD create a bottleneck to successful anti-TB treatments. Of known mutations, identifying the therapeutically relevant ones is a prerequisite for understanding the drug resistance mechanism. Here, we applied a multistep computational pipeline to rank the mutations in F420 genes associated with DLM/PTM resistance. The DLM-/PTM-resistant protein mutants were built and simulated their innate sensitivity towards the drugs. The molecular dynamics (MD) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations quantified the effect of key mutations on drug union. The dynamic cross-correlated map (DCCM) and principal component analysis (PCA) showed a substantial link between the drug binding region and other sections in the mutants, hints to their potential role as an allosteric site. Also, the alterations induced conformationally unstable proteins with decreased DLM/PTM affinity. These investigations highlighted the DLM-tolerant G53D and Y65S and PTM-resilient Y133M (Ddn), L308P (FbiA), and C562W (FbiC) as candidate loss-of-function mutants of progressive research. The present results and interpretations could supply vital clues for protein engineering and drug development.

Inhibition of erectile dysfunction-related enzymes by ginger (Zingiber officinale)-derived compounds: molecular docking and dynamics studies

Erectile dysfunction (ED) is one of the common forms of sexual disorder that significantly impacts the psychosocial quality of life amongst male folks. Previous studies have evidenced the role of arginase-1 (Arg-1), angiotensin-I-converting enzyme (ACE), phosphodiesterase-5 (PDE-5) and acetylcholinesterase (AChE) in the progression of this pathology. In the current investigation, a library of compounds present in Zingiber officinale was screened to discover lead therapeutic agents for potential inhibitors of these metabolic enzymes. The compounds were subjected to molecular docking analysis with the various proteins' standard inhibitors. Subsequently, the thermodynamic stability of the protein-ligand complexes of two top-docked compounds with the highest binding affinities for each protein was studied further via molecular dynamics (MD) using the CHARMM-GUI website. Moreover, the Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) pharmacological properties and drug-likeness of the top-docked 5 compounds from the plant were investigated with the SuperPred and the SwissADME web servers. From the compounds' library, diacetoxy-6-gingerdiol, 10-gingerdione, alloaromadendrene, valencene, and 6-gingerdiol showed the strongest inhibitory capacities with the amino acids present at the catalytic pocket of the selected proteins. Nonetheless, valencene and alloaromadendrene displayed better stability with the various protein complexes. Given that all these compounds were predicted to be non-toxic and have acceptable drug-likeness profiles, this investigation revealed their potential as a source of lead phytochemicals from regularly consumed food substances to mitigate the pathophysiology of erectile dysfunction. However, additional lab-based experiments are required before these phytochemicals can be developed into clinically approved commercially available drugs.

Neutral Imidazole Lipid Analogues Exhibit Improved Properties for Artificial Model Biomembranes

In recent years, a variety of lipid-mimetic imidazolium salts have been developed and applied to investigate biological membranes and related processes. Despite their overall similar properties to natural lipids, there are potential drawbacks including cytotoxicity attributed to the cationic charge. Herein, we report the investigation of a novel class of electronically neutral imidazole-based lipids. In comparison to their positively charged congeners, they show improved biophysical properties and higher similarity to native lipids. By employing calorimetry, fluorescence spectroscopies, and fluorescence and atomic force microscopy, we examined changes in the thermotropic phase behavior, lipid order parameter, fluidity, and lateral membrane organization upon incorporation of the lipid mimetics. Depending on the characteristic of the lipid chains, charge of the headgroup, and substitution pattern, we observed changes in lipid order and fluidity, thus allowing modulation and fine-tuning of the physicochemical properties of the modified membrane. Notably, a newly synthesized imidazole-based cholesterol showed membrane properties very similar to natural cholesterol. Extensive computational studies indicate effective mimicking of cholesterol and reveal its capability to participate in raft formation. This new class of neutral imidazole lipid analogues is expected to lead to better molecular probes and tools.

Vibrational Spectroscopy Through Time Averaged Fourier Transform of Autocorrelated Molecular Dynamics Data: Introducing the Free SEMISOFT Web-Platform

Vibrational spectroscopy calculations based on classical molecular dynamics simulations are widely employed in a variety of popular fields, for instance, computational biochemistry and materials science. These calculations commonly rely on the Fourier transform of the velocity autocorrelation function. One major drawback of the method is that calculated spectra are difficult to interpret due to the large number of closely spaced signals. In this paper, we show how theory can help to overcome this issue by means of a time-average technique, and we introduce free software to perform such calculations for anyone who may take advantage of it. The studies presented here involve the classical vibrational spectra of aniline microsolvated by a water molecule and gas-phase deoxyguanosine. The software is made available in the form of a free web-platform, named SEMISOFT (http://semisoft.unimi.it/), whereby, upon upload of the classical trajectory, the user gets the corresponding time-averaged spectrum. Furthermore, since the evaluation of response functions through autocorrelated data is quite a general approach, the web-platform can be directly employed in many other research fields besides chemistry.

A key residue of the extracellular gate provides quality control contributing to ABCG substrate specificity

For G-type ATP-binding cassette (ABC) transporters, a hydrophobic "di-leucine motif" as part of a hydrophobic extracellular gate has been described to separate a large substrate-binding cavity from a smaller upper cavity and proposed to act as a valve controlling drug extrusion. Here, we show that an L704F mutation in the hydrophobic extracellular gate of Arabidopsis ABCG36/PDR8/PEN3 uncouples the export of the auxin precursor indole-3-butyric acid (IBA) from that of the defense compound camalexin (CLX). Molecular dynamics simulations reveal increased free energy for CLX translocation in ABCG36L704F and reduced CLX contacts within the binding pocket proximal to the extracellular gate region. Mutation L704Y enables export of structurally related non-ABCG36 substrates, IAA, and indole, indicating allosteric communication between the extracellular gate and distant transport pathway regions. An evolutionary analysis identifies L704 as a Brassicaceae family-specific key residue of the extracellular gate that controls the identity of chemically similar substrates. In summary, our work supports the conclusion that L704 is a key residue of the extracellular gate that provides a final quality control contributing to ABCG substrate specificity, allowing for balance of growth-defense trade-offs.

Evaluation of the antimicrobial efficiency of three novel chimeric peptides through biochemical and biophysical analyses

Three chimeric membrane-active antimicrobial peptides (AMPs) were designed from previously characterized parental molecules, namely pandinin-2, ascaphin-8, and maximin-3. The aim of constructing these chimeras was to obtain sequences with improved therapeutic indices or increased activity, while simultaneously investigating the functional roles of key segments of the parental peptides. Chimera-1 was the most active peptide against clinically relevant bacterial species, followed by chimera-2, and chimera-3, respectively, with no clear preference towards Gram-negative or Gram-positive strains. Escherichia coli and Pseudomonas aeruginosa were the most sensitive bacteria, while Klebsiella pneumoniae and Staphylococcus aureus were resistant to AMP activity. All peptides presented significantly lower activities towards human erythrocytes, with chimera-1 being the most selective. Additionally, only chimera-2 showed cytotoxicity towards Vero cells. Calcein leakage and dynamic light scattering assays using liposomal formulations indicated that the chimeras conserved the pore forming membrane perturbation mechanisms of the parental molecules. Peptide interaction also reduced membrane fluidity. Circular dichroism (CD) data showed disordered peptides in aqueous solution that transitioned into alpha helical structures lipid bilayer environments. In silico assessments correlated well with microbiological and in vitro experimental data. All peptides established greater contact with the bacterial biomimetic membrane compared to the erythrocyte system, as analyzed by distance from membrane surface, number of contacts, solvent accessible surface area, and number of hydrogen bonds. Additionally, the presence of the bilayer lipid patches favored peptide folding, consistent with CD experiments. Molecular dynamics simulations of peptide aggregation revealed that chimera-2 formed the largest oligomers, consistent with the predicted aggregation propensities and the predicted physico-chemical properties. Interaction with membrane surfaces resulted in smaller clusters while low or lack of interaction favored larger aggregates. Overall, the chimeric peptides displayed higher activity and selectivity compared to the parental ones. The contribution of the flanking regions of pandidin-2 and maximin-3 with respect to the core region of ascaphin-8 was not clear.

Astragalus polysaccharides inhibits tumor proliferation and enhances cisplatin sensitivity in bladder cancer by regulating the PI3K/AKT/FoxO1 axis

Cisplatin (DDP) resistance presents a major challenge in bladder cancer (BLCA) treatment. Recent evidence suggests that Astragalus polysaccharide (APS), extracted from Astragalus membranaceus, may sensitize tumors to DDP. However, the precise mechanisms by which APS modulates DDP sensitivity in BLCA are not fully elucidated. The study employed computational biology, bioinformatics, and both in vitro and in vivo experiments to explore the role of APS in BLCA. The results demonstrate that APS inhibits BLCA cell proliferation, induces apoptosis in vitro, and suppresses tumor growth in vivo. Additionally, APS induces G0/G1 cell cycle arrest in BLCA cells by downregulating CCND1 expression. Moreover, APS further enhances DDP-induced apoptosis by downregulating PI3K-p110β and p-AKT expression, while upregulating FoxO1 expression. Bioinformatics analysis indicates that APS may remodel the tumor microenvironment (TME) and influence cell-cell interactions, specifically through modulation of macrophage M2 polarization and CD8+ T cell exhaustion, thereby overcoming DDP resistance. In conclusion, APS potentiates DDP-induced apoptosis in BLCA cells via the PI3K/AKT/FoxO1 axis and may act as an immunomodulator to remodel the TME, offering a potential strategy to combat DDP resistance in BLCA.