Long-timescale atomistic simulations uncover loss-of-function mechanisms of uncharacterized Angiogenin mutants associated with ALS.

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease characterized by progressive degeneration of motor neurons, resulting in respiratory failure and mortality within 3-5 years. Mutations in the Angiogenin (ANG) cause loss of ribonucleolytic and nuclear translocation activities, contributing to ALS pathogenesis. This study focused on investigating two uncharacterized ANG mutations, T11S and R122H, newly identified in the Project Mine consortium. Using extensive computational analysis, including structural modeling and microsecond-timescale molecular dynamics (MD) simulations, we observed conformational changes in the catalytic residue His114 of ANG induced by T11S and R122H mutations. These alterations impaired ribonucleolytic activity, as inferred through molecular docking and binding free energy calculations. Gibbs free energy landscape and residue-residue interaction network analysis further supported our findings, revealing the energetic states and allosteric pathway from the mutated site to His114. Additionally, we assessed the binding of NCI-65828, an inhibitor of ribonucleolytic activity of ANG, and found reduced effectiveness in binding to T11S and R122H mutants when His114 assumed a non-native conformation. This highlights the crucial role of His114 and its association with ALS. Elucidating the relationship between physical structure and functional dynamics of frequently mutated ANG mutants is essential for understanding ALS pathogenesis and developing more effective therapeutic interventions.

Stereo-selectivity of enantiomeric inhibitors to ubiquitin-specific protease 7 (USP7) dissected by molecular docking, molecular dynamics simulations, and binding free energy calculations.

The ubiquitin-specific protease 7 (USP7), as a member of deubiquitination enzymes, represents an attractive therapeutic target for various cancers, including prostate cancer and liver cancer. The change of the inhibitor stereocenter from the S to R stereochemistry (S-ALM → R-ALM34) markedly improved USP7 inhibitory activity. However, the molecular mechanism for the stereo-selectivity of enantiomeric inhibitors to USP7 is still unclear. In this work, molecular docking, molecular dynamics (MD) simulations, molecular mechanics/Generalized-Born surface area (MM/GBSA) calculations, and free energy landscapes were performed to address this mystery. MD simulations revealed that S-ALM34 showed a high degree of conformational flexibility compared to the R-ALM34 counterpart, and S-ALM34 binding led to the enhanced intradomain motions of USP7, especially the BL1 and BL2 loops and the two helices α4 and α5. MM/GBSA calculations showed that the binding strength of R-ALM34 to USP7 was stronger than that of S-ALM34 by - 4.99 kcal/mol, a similar trend observed by experimental data. MM/GBSA free energy decomposition was further performed to differentiate the ligand-residue spectrum. These analyses not only identified the hotspot residues interacting with R-ALM34, but also revealed that the hydrophobic interactions from F409, K420, H456, and Y514 play the major determinants in the binding of R-ALM34 to USP7. This result is anticipated to shed light on energetic basis and conformational dynamics information to aid in the design of more potent and selective inhibitors targeting USP7.

AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease.

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.

LSD1-Based Reversible Inhibitors Virtual Screening and Binding Mechanism Computational Study.

As one of the crucial targets of epigenetics, histone lysine-specific demethylase 1 (LSD1) is significant in the occurrence and development of various tumors. Although several irreversible covalent LSD1 inhibitors have entered clinical trials, the large size and polarity of the FAD-binding pocket and undesired toxicity have focused interest on developing reversible LSD1 inhibitors. In this study, targeting the substrate-binding pocket of LSD1, structure-based and ligand-based virtual screenings were adopted to expand the potential novel structures with molecular docking and pharmacophore model strategies, respectively. Through drug-likeness evaluation, ADMET screening, molecular dynamics simulations, and binding free energy screening, we screened out one and four hit compounds from the databases of 2,029,554 compounds, respectively. Generally, these hit compounds can be divided into two categories, amide (Lig2 and Comp2) and 1,2,4-triazolo-4,3-α-quinazoline (Comp3, Comp4, Comp7). Among them, Comp4 exhibits the strongest binding affinity. Finally, the binding mechanisms of the hit compounds were further calculated in detail by the residue free energy decomposition. It was found that van der Waals interactions contribute most to the binding, and FAD is also helpful in stabilizing the binding and avoiding off-target effects. We believe this work not only provides a solid theoretical foundation for the design of LSD1 substrate reversible inhibitors, but also expands the diversity of parent nucleus, offering new insights for synthetic chemists.

Predicting antibiotic resistance in complex protein targets using alchemical free energy methods.

Drug resistant Mycobacterium tuberculosis, which mostly results from single nucleotide polymorphisms in antibiotic target genes, poses a major threat to tuberculosis treatment outcomes. Relative binding free energy (RBFE) calculations can rapidly predict the effects of mutations, but this approach has not been tested on large, complex proteins. We use RBFE calculations to predict the effects of M. tuberculosis RNA polymerase and DNA gyrase mutations on rifampicin and moxifloxacin susceptibility respectively. These mutations encompass a range of amino acid substitutions with known effects and include large steric perturbations and charged moieties. We find that moderate numbers (n = 3-15) of short RBFE calculations can predict resistance in cases where the mutation results in a large change in the binding free energy. We show that the method lacks discrimination in cases with either a small change in energy or that involve charged amino acids, and we investigate how these calculation errors may be decreased.

Definition of the Acceptor Substrate Binding Specificity in Plant Xyloglucan Endotransglycosylases Using Computational Chemistry.

Xyloglucan endotransglycosylases (XETs) play key roles in the remodelling and reconstruction of plant cell walls. These enzymes catalyse homo-transglycosylation reactions with xyloglucan-derived donor and acceptor substrates and hetero-transglycosylation reactions with a variety of structurally diverse polysaccharides. In this work, we describe the basis of acceptor substrate binding specificity in non-specific Tropaeolum majus (TmXET6.3) and specific Populus tremula x tremuloides (PttXET16A) XETs, using molecular docking and molecular dynamics (MD) simulations combined with binding free energy calculations. The data indicate that the enzyme-donor (xyloglucan heptaoligosaccharide or XG-OS7)/acceptor complexes with the linear acceptors, where a backbone consisted of glucose (Glc) moieties linked via (1,4)- or (1,3)-ß-glycosidic linkages, were bound stably in the active sites of TmXET6.3 and PttXET16A. Conversely, the acceptors with the (1,6)-ß-linked Glc moieties were bound stably in TmXET6.3 but not in PttXET16A. When in the (1,4)-ß-linked Glc containing acceptors, the saccharide moieties were replaced with mannose or xylose, they bound stably in TmXET6.3 but lacked stability in PttXET16A. MD simulations of the XET-donor/acceptor complexes with acceptors derived from (1,4;1,3)-ß-glucans highlighted the importance of (1,3)-ß-glycosidic linkages and side chain positions in the acceptor substrates. Our findings explain the differences in acceptor binding specificity between non-specific and specific XETs and associate theoretical to experimental data.

Phyto-Computational Intervention of Diabetes Mellitus at Multiple Stages Using Isoeugenol from Ocimum tenuiflorum: A Combination of Pharmacokinetics and Molecular Modelling Approaches.

In the present study, the anti-diabetic potential of Ocimum tenuiflorum was investigated using computational techniques for α-glucosidase, α-amylase, aldose reductase, and glycation at multiple stages. It aimed to elucidate the mechanism by which phytocompounds of O. tenuiflorum treat diabetes mellitus using concepts of druglikeness and pharmacokinetics, molecular docking simulations, molecular dynamics simulations, and binding free energy studies. Isoeugenol is a phenylpropene, propenyl-substituted guaiacol found in the essential oils of plants. During molecular docking modelling, isoeugenol was found to inhibit all the target enzymes, with a higher binding efficiency than standard drugs. Furthermore, molecular dynamic experiments revealed that isoeugenol was more stable in the binding pockets than the standard drugs used. Since our aim was to discover a single lead molecule with a higher binding efficiency and stability, isoeugenol was selected. In this context, our study stands in contrast to other computational studies that report on more than one compound, making it difficult to offer further analyses. To summarize, we recommend isoeugenol as a potential widely employed lead inhibitor of α-glucosidase, α-amylase, aldose reductase, and glycation based on the results of our in silico studies, therefore revealing a novel phytocompound for the effective treatment of hyperglycemia and diabetes mellitus.

Computer-Aided Screening of Phytoconstituents from Ocimum tenuiflorum against Diabetes Mellitus Targeting DPP4 Inhibition: A Combination of Molecular Docking, Molecular Dynamics, and Pharmacokinetics Approaches.

Diabetes mellitus is a major global health concern in the current scenario which is chiefly characterized by the rise in blood sugar levels or hyperglycemia. In the context, DPP4 enzyme plays a critical role in glucose homeostasis. DPP4 targets and inactivates incretin hormones such as glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP) as physiological substrates, which are essential to regulate the amount of insulin that is secreted after eating. Since the inactivation of incretins occurs, the hyperglycemic conditions continue to rise, and result in adverse physiological conditions linked with diabetes mellitus. Hence, inhibition of DPP4 has been the center of focus in the present antidiabetic studies. Although few DPP4 inhibitor drugs, such as alogliptin, saxagliptin, linagliptin, and sitagliptin, are available, their adverse effects on human metabolism are undeniable. Therefore, it becomes essential for the phytochemical intervention of the disease using computational methods prior to performing in vitro and in vivo studies. In this regard, we used an in-silico approach involving molecular docking, molecular dynamics simulations, and binding free energy calculations to investigate the inhibitory potential of Ocimum tenuiflorum phytocompounds against DPP4. In this regard, three phytocompounds (1S-α-pinene, ß-pinene, and dehydro-p-cymene) from O. tenuiflorum have been discovered as the potential inhibitors of the DPP4 protein. To summarize, from our in-silico experiment outcomes, we propose dehydro-p-cymene as the potential lead inhibitor of DPP4 protein, thereby discovering new a phytocompound for the effective management of hyperglycemia and diabetes mellitus. The reported compound can be taken for in vitro and in vivo analyses in near future.

SAMPL7: Host-guest binding prediction by molecular dynamics and quantum mechanics.

Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges provide routes to compare chemical quantities determined using computational chemistry approaches to experimental measurements that are shared after the competition. For this effort, several computational methods have been used to calculate the binding energies of Octa Acid (OA) and exo-Octa Acid (exoOA) host-guest systems for SAMPL7. The initial poses for molecular dynamics (MD) were generated by molecular docking. Binding free energy calculations were performed using molecular mechanics combined with Poisson-Boltzmann or generalized Born surface area solvation (MMPBSA/MMGBSA) approaches. The factors that affect the utility of the MMPBSA/MMGBSA approaches including solvation, partial charge, and solute entropy models were also analyzed. In addition to MD calculations, quantum mechanics (QM) calculations were performed using several different density functional theory (DFT) approaches. From SAMPL6 results, B3PW91-D3 was found to overestimate binding energies though it was effective for geometry optimizations, so it was considered for the DFT geometry optimizations in the current study, with single-point energy calculations carried out with B2PLYP-D3 with double-, triple-, and quadruple-ζ level basis sets. Accounting for dispersion effects, and solvation models was deemed essential for the predictions. MMGBSA and MMPBSA correlated better to experiment when used in conjunction with an empirical/linear correction.

Testing automatic methods to predict free binding energy of host-guest complexes in SAMPL7 challenge.

The design of new host-guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host-guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host-guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics.

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

Binding Free Energy (BFE) Calculations and Quantitative Structure-Activity Relationship (QSAR) Analysis of Schistosoma mansoni Histone Deacetylase 8 (smHDAC8) Inhibitors.

Histone-modifying proteins have been identified as promising targets to treat several diseases including cancer and parasitic ailments. In silico methods have been incorporated within a variety of drug discovery programs to facilitate the identification and development of novel lead compounds. In this study, we explore the binding modes of a series of benzhydroxamates derivatives developed as histone deacetylase inhibitors of Schistosoma mansoni histone deacetylase (smHDAC) using molecular docking and binding free energy (BFE) calculations. The developed docking protocol was able to correctly reproduce the experimentally established binding modes of resolved smHDAC8-inhibitor complexes. However, as has been reported in former studies, the obtained docking scores weakly correlate with the experimentally determined activity of the studied inhibitors. Thus, the obtained docking poses were refined and rescored using the Amber software. From the computed protein-inhibitor BFE, different quantitative structure-activity relationship (QSAR) models could be developed and validated using several cross-validation techniques. Some of the generated QSAR models with good correlation could explain up to ~73% variance in activity within the studied training set molecules. The best performing models were subsequently tested on an external test set of newly designed and synthesized analogs. In vitro testing showed a good correlation between the predicted and experimentally observed IC50 values. Thus, the generated models can be considered as interesting tools for the identification of novel smHDAC8 inhibitors.

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics.

Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson's disease associated symptoms can be treated using inhibitors of MAO-B as the dopamine degradation can be reduced. Currently, many inhibitors are available having micromolar to nanomolar binding affinities. However, still there is demand for compounds with superior binding affinity and binding specificity with favorable pharmacokinetic properties for treating Parkinson's disease and computational screening methods can be majorly recruited for this. However, the accuracy of currently available force-field methods for ranking the inhibitors or lead drug-like compounds should be improved and novel methods for screening compounds need to be developed. We studied the performance of various force-field-based methods and data driven approaches in ranking about 3753 compounds having activity against the MAO-B target. The binding affinities computed using autodock and autodock-vina are shown to be non-reliable. The force-field-based MM-GBSA also under-performs. However, certain machine learning approaches, in particular KNN, are found to be superior, and we propose KNN as the most reliable approach for ranking the complexes to reasonable accuracy. Furthermore, all the employed machine learning approaches are also computationally less demanding.

Molecular Interactions of Carbapenem Antibiotics with the Multidrug Efflux Transporter AcrB of Escherichia coli.

The drug/proton antiporter AcrB, engine of the major efflux pump AcrAB(Z)-TolC of Escherichia coli and other bacteria, is characterized by its impressive ability to transport chemically diverse compounds, conferring a multi-drug resistance (MDR) phenotype. Although hundreds of small molecules are known to be AcrB substrates, only a few co-crystal structures are available to date. Computational methods have been therefore intensively employed to provide structural and dynamical fingerprints related to transport and inhibition of AcrB. In this work, we performed a systematic computational investigation to study the interaction between representative carbapenem antibiotics and AcrB. We focused on the interaction of carbapenems with the so-called distal pocket, a region known for its importance in binding inhibitors and substrates of AcrB. Our findings reveal how the different physico-chemical nature of these antibiotics is reflected on their binding preference for AcrB. The molecular-level information provided here could help design new antibiotics less susceptible to the efflux mechanism.

Structure-Based Virtual Screening, Molecular Dynamics and Binding Free Energy Calculations of Hit Candidates as ALK-5 Inhibitors.

Activin-like kinase 5 (ALK-5) is involved in the physiopathology of several conditions, such as pancreatic carcinoma, cervical cancer and liver hepatoma. Cellular events that are landmarks of tumorigenesis, such as loss of cell polarity and acquisition of motile properties and mesenchymal phenotype, are associated to deregulated ALK-5 signaling. ALK-5 inhibitors, such as SB505154, GW6604, SD208, and LY2157299, have recently been reported to inhibit ALK-5 autophosphorylation and induce the transcription of matrix genes. Due to their ability to impair cell migration, invasion and metastasis, ALK-5 inhibitors have been explored as worthwhile hits as anticancer agents. This work reports the development of a structure-based virtual screening (SBVS) protocol aimed to prospect promising hits for further studies as novel ALK-5 inhibitors. From a lead-like subset of purchasable compounds, five molecules were identified as putative ALK-5 inhibitors. In addition, molecular dynamics and binding free energy calculations combined with pharmacokinetics and toxicity profiling demonstrated the suitability of these compounds to be further investigated as novel ALK-5 inhibitors.

Cross-interaction of tau PET tracers with monoamine oxidase B: evidence from in silico modelling and in vivo imaging.

PURPOSE: Several tracers have been designed for tracking the abnormal accumulation of tau pathology in vivo. Recently, concerns have been raised about the sources of off-target binding for these tracers; inconclusive data propose binding for some tracers to monoamine oxidase B (MAO-B). METHODS: Molecular docking and dynamics simulations were used to estimate the affinity and free energy for the binding of several tau tracers (FDDNP, THK523, THK5105, THK5317, THK5351, T807 [aka AV-1451, flortaucipir], T808, PBB3, RO-948, MK-6240, JNJ-311 and PI-2620) to MAO-B. These values were then compared with those for safinamide (MAO-B inhibitor). PET imaging was used with the tau tracer [18F]THK5317 and the MAO-B tracer [11C]DED in five patients with Alzheimer's disease to investigate the MAO-B binding component of this first generation tau tracer in vivo. RESULTS: The computational modelling studies identified a binding site for all the tau tracers on MAO-B; this was the same site as that for safinamide. The binding affinity and free energy of binding for the tau tracers to MAO-B was substantial and in a similar range to those for safinamide. The most recently developed tau tracers MK-6240, JNJ-311 and PI-2620 appeared, in silico, to have the lowest relative affinity for MAO-B. The in vivo investigations found that the regional distribution of binding for [18F]THK5317 was different from that for [11C]DED, although areas of suspected off-target [18F]THK5317 binding were detected. The binding relationship between [18F]THK5317 and [11C]DED depended on the availability of the MAO-B enzyme. CONCLUSIONS: The developed tau tracers show in silico and in vivo evidence of cross-interaction with MAO-B; the MAO-B component of the tracer binding was dependent on the regional concentration of the enzyme.

Exploring benzoxaborole derivatives as carbonic anhydrase inhibitors: a structural and computational analysis reveals their conformational variability as a tool to increase enzyme selectivity.

Recent studies identified the benzoxaborole moiety as a new zinc-binding group able to interact with carbonic anhydrase (CA) active site. Here, we report a structural analysis of benzoxaboroles containing urea/thiourea groups, showing that these molecules are very versatile since they can bind the enzyme assuming different binding conformations and coordination geometries of the catalytic zinc ion. In addition, theoretical calculations of binding free energy were performed highlighting the key role of specific residues for protein-inhibitor recognition. Overall, these data are very useful for the development of new inhibitors with higher selectivity and efficacy for various CAs.

Insights into the binding mode of sulphamates and sulphamides to hCA II: crystallographic studies and binding free energy calculations.

Sulphamate and sulphamide derivatives have been largely investigated as carbonic anhydrase inhibitors (CAIs) by means of different experimental techniques. However, the structural determinants responsible for their different binding mode to the enzyme active site were not clearly defined so far. In this paper, we report the X-ray crystal structure of hCA II in complex with a sulphamate inhibitor incorporating a nitroimidazole moiety. The comparison with the structure of hCA II in complex with its sulphamide analogue revealed that the two inhibitors adopt a completely different binding mode within the hCA II active site. Starting from these results, we performed a theoretical study on sulphamate and sulphamide derivatives, demonstrating that electrostatic interactions with residues within the enzyme active site play a key role in determining their binding conformation. These findings open new perspectives in the design of effective CAIs using the sulphamate and sulphamide zinc binding groups as lead compounds.

Computational Design and Optimization of Peptide Inhibitors for SIRT2.

Sirtuin 2 (SIRT2), an NAD+-dependent deacetylase, is crucial for regulating vital physiological processes, including aging, DNA repair, and cell cycle progression. Its abnormal activity is linked to diseases such as Parkinson's disease, cancer, and metabolic disorders, making it a potential target for therapeutic intervention. While small molecule inhibitors have been studied, peptide-based inhibitors offer a promising alternative due to their selectivity and bioavailability. This study explores the effects of converting the naturally occurring cyclic inhibitor peptide of SIRT2 (S2iL5) into a non-cyclic form by replacing a residue with FAK (LYS + CF3CO-). The new peptide sequence, Tyr-His-Thr-Tyr-His-Val-FAK (LYS)-Arg-Arg-Thr-Asn-Tyr-Tyr-Cys, was modeled to confirm its stable conformation. Docking studies and MM/GBSA calculations showed that the non-cyclic peptide had a better binding free energy (-50.66 kcal/mol) compared to the cyclic S2iL5 (-49.44 kcal/mol). Further mutations generated 160,000 unique peptides, screened using a machine learning-based QSAR model. Three promising peptides (Peptide 1: YGGNNVKRRTNYYC, Peptide 2: YMGEWVKRRTNYYC, and Peptide 3: YGGNGVKRRTNYYC) were selected and further modeled. Molecular dynamics (MD) analyses demonstrated that Peptide 1 and Peptide 2 had significant potential as SIRT2 inhibitors, showing moderate stability and some structural flexibility. Their best binding free energies were -59.07 kcal/mol and -46.01 kcal/mol, respectively. The study aimed to enhance peptide flexibility and binding affinity, suggesting that optimized peptide-based inhibitors can interact effectively with SIRT2. However, further experimental validation is necessary to confirm these computational predictions and evaluate the therapeutic potential of the identified peptides.

Identification of potential modulators for human GPD1 by docking-based virtual screening, molecular dynamics simulations, binding free energy calculations, and DeLA-drug analysis.

Cytosolic Glycerol-3-phosphate dehydrogenase 1 (GPD1, EC 1.1.1.8) plays a pivotal role in regulating the Embden-Meyerhof glucose glycolysis pathway (E-M pathway), as well as in conditions such as Huntington's disease, cancer, and its potential role as a specific marker for Dormant Glioma Stem Cells. In this study, we conducted virtual screening using the ZINC database ( http://zinc.docking.org/ ) and the GPD1 structure to identify potential GPD1 modulators. The investigation involved screening active candidate ligands using ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) parameters, combined with molecular docking, pose analysis, and interaction analysis based on Lipinski and Veber criteria. Subsequently, the top 10 ligands were subjected to 200 ns all-atom molecular dynamics (M.D.) simulations, and binding free energies were calculated. The findings revealed that specific residues, namely TRP14, PRO94, LYS120, ASN151, THR264, ASP260, and GLN298, played a crucial role in ensuring system stability. Furthermore, through a comprehensive analysis involving molecular docking, molecular M.D., and DeLA-Drug, we identified 10 promising small molecules. These molecules represent potential lead compounds for developing effective therapeutics targeting GPD1-associated diseases, thereby contributing to a deeper understanding of GPD1-associated mechanisms. This study's significance lies in identifying key residues associated with GPD1 and discovering valuable small molecules, providing a foundation for further research and development.