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Prolyl oligopeptidase (POP) is a potential therapeutic target for treatment of several neurological disorders and α-synucleinopathies including Parkinson's disease. Most of the known POP inhibitors failed in the clinical trials due to poor pharmacokinetic properties and blood-brain impermeability. Therefore, a training set of 30 structurally diverse compounds with a wide range of inhibitory activity against POP was used to generate a quantitative pharmacophore model, Hypo 3, to identify potential POP inhibitors with desirable drug-like properties. Validations through test set, cost analysis, and Fisher's randomization methods proved that Hypo 3 accurately predicted the known inhibitors among inactive compounds. Hypo 3 was employed as 3D query for virtual screening on an in-house drug-like chemical database containing compounds with good brain permeability and ADMET parameters. Database screening with Hypo 3 resulted in 99 compounds that were narrowed down to 21 compounds through molecular docking. Among them, five compounds were identified in our earlier studies, while two compounds showed in vitro POP inhibition. The current study proposed new 16 virtually screened compounds as potential inhibitors against POP that possess Gold docking score in the range of 64.61–75.74 and Chemscore of −32.25 to −38.35. Furthermore, the top scoring four hit compounds were subjected to molecular dynamics simulations to reveal their appropriate binding modes and assessing binding free energies. The hit compounds interacted with POP effectively via hydrogen bonds with important active site residues along with hydrophobic interactions. Moreover, the hit compounds had key inter-molecular interactions and better binding free energies as compared to the reference inhibitor. A potential new hydrogen bond interaction was discovered between Hit 2 with the Arg252 residue of POP. To conclude, we propose four hit compounds with new structural scaffolds against POP for the lead development of POP-based therapeutics for neurological disorders.

Breast cancer (BC) is the leading cause of death among women worldwide devoid of effective treatment. It is therefore important to develop agents that can reverse, reduce, or slow the growth of BC. The use of natural products as chemopreventive agents provides enormous advantages. The aim of the current investigation is to determine the efficacy of the phytochemicals against BC along with the approved drugs to screen the most desirable and effective phytocompound. In the current study, 36 phytochemicals have been evaluated against aromatase to identify the potential candidate drug along with the approved drugs employing the Cdocker module accessible on the Discovery Studio (DS) v4.5 and thereafter analysing the stability of the protein ligand complex using GROningen MAchine for Chemical Simulations v5.0.6 (GROMACS). Additionally, these compounds were assessed for the inhibitory features employing the structure-based pharmacophore (SBP). The Cdocker protocol available with the DS has computed higher dock scores for the phytochemicals complemented by lower binding energies. The top-ranked compounds that have anchored with key residues located at the binding pocket of the protein were subjected to molecular dynamics (MD) simulations employing GROMACS. The resultant findings reveal the stability of the protein backbone and further guide to comprehend on the involvement of key residues Phe134, Val370, and Met374 that mechanistically inhibit BC. Among 36 compounds, curcumin, capsaicin, rosmarinic acid, and 6-shogaol have emerged as promising phytochemicals conferred with the highest Cdocker interaction energy, key residue interactions, stable MD results than reference drugs, and imbibing the key inhibitory features. Taken together, the current study illuminates the use of natural compounds as potential drugs against BC. Additionally, these compounds could also serve as scaffolds in designing and development of new drugs.

DDX3 belongs to RNA helicase family that demonstrates oncogenic properties and has gained wider attention due to its role in cancer progression, proliferation and transformation. Mounting reports have evidenced the role of DDX3 in cancers making it a promising target to abrogate DDX3 triggered cancers. Dual pharmacophore models were generated and were subsequently validated. They were used as 3D queries to screen the InterBioScreen database, resulting in the selection of curcumin that was escalated to molecular dynamics simulation studies. In vitro anti-cancer analysis was conducted on three cell lines such as MCF-7, MDA-MB-231 and HeLa, which were evaluated along with exemestane. Curcumin was docked into the active site of the protein target (PDB code 2I4I) to estimate the binding affinity. The compound has interacted with two key residues and has displayed stable molecular dynamics simulation results. In vitro analysis has demonstrated that both the candidate compounds have reduced the expression of DDX3 in three cell lines. However, upon combinatorial treatment of curcumin (10 and 20 &mu, M) and exemestane (50 &mu, M) a synergism was exhibited, strikingly downregulating the DDX3 expression and has enhanced apoptosis in three cell lines. The obtained results illuminate the use of curcumin as an alternative DDX3 inhibitor and can serve as a chemical scaffold to design new small molecules.

Human epidermal growth factor receptors are implicated in several types of cancers characterized by aberrant signal transduction. This family comprises of EGFR (ErbB1), HER2 (ErbB2, HER2/neu), HER3 (ErbB3), and HER4 (ErbB4). Amongst them, HER2 is associated with breast cancer and is one of the most valuable targets in addressing the breast cancer incidences. For the current investigation, we have performed 3D-QSAR based pharmacophore search for the identification of potential inhibitors against the kinase domain of HER2 protein. Correspondingly, a pharmacophore model, Hypo1, with four features was generated and was validated employing Fischer's randomization, test set method and the decoy test method. The validated pharmacophore was allowed to screen the colossal natural compounds database (UNPD). Subsequently, the identified 33 compounds were docked into the proteins active site along with the reference after subjecting them to ADMET and Lipinski's Rule of Five (RoF) employing the CDOCKER implemented on the Discovery Studio. The compounds that have displayed higher dock scores than the reference compound were scrutinized for interactions with the key residues and were escalated to MD simulations. Additionally, molecular dynamics simulations performed by GROMACS have rendered stable root mean square deviation values, radius of gyration and potential energy values. Eventually, based upon the molecular dock score, interactions between the ligands and the active site residues and the stable MD results, the number of Hits was culled to two identifying Hit1 and Hit2 has potential leads against HER2 breast cancers.

HDAC8 inhibitors have become an attractive treatment for cancer. This study aimed to facilitate the identification of potential chemical scaffolds for the selective inhibition of histone deacetylase 8 (HDAC8) using in silico approaches. Non-linear QSAR classification and regression models of HDAC8 inhibitors were developed with support vector machine. Mean impact value-based sequential forward feature selection and grid search strategy were used for molecular descriptor selection and parameter optimization, respectively. The generated QSAR models were validated by leave-one-out cross validation and an external test set. The best QSAR classification model yielded 84 % of accuracy on the external test prediction and Matthews correlation coefficient is 0.69. The best QSAR regression model showed low root-mean-square error (0.63) and high squared correlation coefficient (0.53) for the test set. The validated QSAR models together with various drug-like properties, molecular docking and molecular dynamics simulation were sequentially used as a multi-step query in chemical database virtual screening. Finally, two hit compounds were discovered as new structural scaffolds which can be used for further in vitro and in vivo activity analyses. The strategy used in this study could be a promising computational strategy which can be utilized for other target drug design.

Aromatase inhibitors with an $\mathrm{IC}_{50}$ IC 50 value ranging from 1.4 to 49.7 µM are known to act as antiepileptic drugs besides being potential breast cancer inhibitors. The aim of the present study is to identify novel antiepileptic aromatase inhibitors with higher activity exploiting the ligand-based pharmacophore approach utilizing the experimentally known inhibitors. The resultant Hypo1 consists of four features and was further validated by using three different strategies. Hypo1 was allowed to screen different databases to identify lead molecules and were further subjected to Lipinski's Rule of Five and ADMET to establish their drug-like properties. Consequently, the obtained 68-screened molecules were subjected to molecular docking by GOLD v5.2.2. Furthermore, the compounds with the highest dock scores were assessed for molecular interactions. Later, the MD simulation was applied to evaluate the protein backbone stabilities and binding energies adapting GROMACS v5.0.6 and MM/PBSA which was followed by the density functional theory (DFT), to analyze their orbital energies, and further the energy gap between them. Eventually, the number of Hit molecules was culled to three projecting Hit1, Hit2, and Hit3 as the potential lead compounds based on their highest dock scores, hydrogen bond interaction, lowest energy gap, and the least binding energies and stable MD results.

Chagas disease is one of the primary causes of heart diseases accounting to 50,000 lives annually and is listed as the neglected tropical disease. Because the currently available therapies have greater toxic effects with higher resistance, there is a dire need to develop new drugs to combat the disease. In this pursuit, the 3D QSAR ligand-pharmacophore (pharm 1) and receptor-based pharmacophore (pharm 2) search was initiated to retrieve the candidate compounds from universal natural compounds database. The validated models were allowed to map the universal natural compounds database. The obtained lead candidates were subjected to molecular docking against cysteine protease (PDB code: 1ME3) employing -Cdocker available on the discovery studio. Subsequently, two Hits have satisfied the selection criteria and were escalated to molecular dynamics simulation and binding free energy calculations. These Hits have demonstrated higher dock scores, displayed interactions with the key residues portraying an ideal binding mode complemented by mapping to all the features of pharm 1 and pharm 2. Additionally, they have rendered stable root mean square deviation (RMSD) and potential energy profiles illuminating their potentiality as the prospective antichagastic agents. The study further demonstrates the mechanism of inhibition by tetrad residues compromising of Gly23 and Asn70 holding the ligand at each ends and the residues Gly65 and Gly160 clamping the Hits at the center. The notable feature is that the Hits lie in close proximity with the residues Glu66 and Leu67, accommodating within the S1, S2 and S3 subsites. Considering these findings, the study suggests that the Hits may be regarded as effective therapeutics against Chagas disease.

Proteins deacetylation by Histone deacetylase 6 (HDAC6) has been shown in various human chronic diseases like neurodegenerative diseases and cancer, and hence is an important therapeutic target. Since, the existing inhibitors have hydroxamate group, and are not HDAC6-selective, therefore, this study has designed to investigate non-hydroxamate HDAC6 inhibitors. Ligand-based pharmacophore was generated from 26 training set compounds of HDAC6 inhibitors. The statistical parameters of pharmacophore (Hypo1) included lowest total cost of 115.63, highest cost difference of 135.00, lowest RMSD of 0.70 and the highest correlation of 0.98. The pharmacophore was validated by Fischer’s Randomization and Test Set validation, and used as screening tool for chemical databases. The screened compounds were filtered by fit value ([Formula: see text]), estimated Inhibitory Concentration (IC[Formula: see text]) ([Formula: see text]), Lipinski’s Rule of Five and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Descriptors to identify drug-like compounds. Furthermore, the drug-like compounds were docked into the active site of HDAC6. The best docked compounds were selected having goldfitness score [Formula: see text] and [Formula: see text], and hydrogen bond interaction with catalytic active residues. Finally, three inhibitors having sulfamoyl group were selected by Molecular Dynamic (MD) simulation, which showed stable root mean square deviation (RMSD) (1.6–1.9[Formula: see text]Å), lowest potential energy ([Formula: see text][Formula: see text]kJ/mol), and hydrogen bonding with catalytic active residues of HDAC6.

Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches. The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations. The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer's randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10. Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors.

Protein tyrosine phosphatase 1B (PTP1B) plays important role in diabetes, obesity and cancer. The methanol extract of the gum resin of Garcinia hanburyi (G. hanburyi) showed potent PTP1B inhibition at 10µg/ml. The active compounds were identified as prenylated caged xanthones (1-9) which inhibited PTP1B in dose-dependent manner. Carboxybutenyl group within caged motif (A ring) was found to play a critical role in enzyme inhibition such as 1-6 (IC50s=0.47-4.69µM), whereas compounds having hydroxymethylbutenyl 7 (IC50=70.25µM) and methylbutenyl 8 (IC50>200µM) showed less activity. The most potent inhibitor, gambogic acid 1 (IC50=0.47µM) showed 30-fold more potency than ursolic acid (IC50=15.5µM), a positive control. In kinetic study, all isolated xanthones behaved as competitive inhibitors which were fully demonstrated with Km, Vmax and Kik/Kiv ratio. It was also proved that inhibitor 1 operated under the enzyme isomerization model having k5=0.0751µM-1S-1, k6=0.0249µM-1S-1 and Kiapp=0.499µM. To develop a pharmacophore model, we explored the binding sites of compound 1 and 7 in PTP1B. These modeling results were in agreement with our findings, which revealed that the inhibitory activities are tightly related to caged motif and prenyl group in A ring.

Breast cancer is one of the leading causes of death noticed in women across the world. Of late the most successful treatments rendered are the use of aromatase inhibitors (AIs). In the current study, a two-way approach for the identification of novel leads has been adapted. 81 chemical compounds were assessed to understand their potentiality against aromatase along with the four known drugs. Docking was performed employing the CDOCKER protocol available on the Discovery Studio (DS v4.5). Exemestane has displayed a higher dock score among the known drug candidates and is labeled as reference. Out of 81 ligands 14 have exhibited higher dock scores than the reference. In the second approach, these 14 compounds were utilized for the generation of the pharmacophore. The validated four-featured pharmacophore was then allowed to screen Chembridge database and the potential Hits were obtained after subjecting them to Lipinski’s rule of five and the ADMET properties. Subsequently, the acquired 3,050 Hits were escalated to molecular docking utilizing GOLD v5.0. Finally, the obtained Hits were consequently represented to be ideal lead candidates that were escalated to the MD simulations and binding free energy calculations. Additionally, the gene-disease association was performed to delineate the associated disease caused by CYP19A1.

Progeria is a rare genetic disorder characterized by premature aging that eventually leads to death and is noticed globally. Despite alarming conditions, this disease lacks effective medications; however, the farnesyltransferase inhibitors (FTIs) are a hope in the dark. Therefore, the objective of the present article is to identify new compounds from the databases employing pharmacophore based virtual screening. Utilizing nine training set compounds along with lonafarnib, a common feature pharmacophore was constructed consisting of four features. The validated Hypo1 was subsequently allowed to screen Maybridge, Chembridge, and Asinex databases to retrieve the novel lead candidates, which were then subjected to Lipinski’s rule of 5 and ADMET for drug-like assessment. The obtained 3,372 compounds were forwarded to docking simulations and were manually examined for the key interactions with the crucial residues. Two compounds that have demonstrated a higher dock score than the reference compounds and showed interactions with the crucial residues were subjected to MD simulations and binding free energy calculations to assess the stability of docked conformation and to investigate the binding interactions in detail. Furthermore, this study suggests that the Hits may be more effective against progeria and further the DFT studies were executed to understand their orbital energies.

Breast cancer is one of the major impediments affecting women globally. The ATP-dependant heat shock protein 90 (Hsp90) forms the central component of molecular chaperone machinery that predominantly governs the folding of newly synthesized peptides and their conformational maturation. It regulates the stability and function of numerous client proteins that are frequently upregulated and/or mutated in cancer cells, therefore, making Hsp90 inhibition a promising therapeutic strategy for the development of new efficacious drugs to treat breast cancer. In the present in silico investigation, a structure-based pharmacophore model was generated with hydrogen bond donor, hydrogen bond acceptor and hydrophobic features complementary to crucial residues Ala55, Lys58, Asp93, Ile96, Met98 and Thr184 directed at inhibiting the ATP-binding activity of Hsp90. Subsequently, the phytochemical dataset of 3210 natural compounds was screened to retrieve the prospective inhibitors after rigorous validation of the model pharmacophore. The retrieved 135 phytocompounds were further filtered by drug-likeness parameters including Lipinski's rule of five and ADMET properties, then investigated via molecular docking-based scoring. Molecular interactions were assessed using Genetic Optimisation for Ligand Docking program for 95 drug-like natural compounds against Hsp90 along with two clinical drugs as reference compounds - Geldanamycin and Radicicol. Docking studies revealed three phytochemicals are better than the investigated clinical drugs. The reference and hit compounds with dock scores of 48.27 (Geldanamycin), 40.90 (Radicicol), 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3) were further validated for their binding stability through molecular dynamics simulations. We propose that the non-macrocyclic scaffolds of three identified phytochemicals might aid in the development of novel therapeutic candidates against Hsp90-driven cancers.

Acetylcholinesterase (AChE) catalyzes the hydrolysis of neurotransmitter acetylcholine to acetate and choline in a synaptic cleft. Deficits in cholinergic neurotransmitters are linked closely with the progression of Alzheimer&rsquo, s disease (AD), which is a neurodegenerative disorder characterized by memory impairment, and a disordered cognitive function. Since the previously approved AChE inhibitors, donepezil (Aricept), galantamine (Reminyl), and rivastigmine (Exelon), have side effects and several studies are being carried out out to develop novel AD drugs, we have applied a three-dimensional quantitative structure&minus, activity relationship (3D QSAR) and structure-based pharmacophore modeling methodologies to identify potential candidate inhibitors against AChE. Herein, 3D QSAR and structure-based pharmacophore models were built from known inhibitors and crystal structures of human AChE in complex with donepezil, galantamine, huperzine A, and huprine W, respectively. The generated models were used as 3D queries to screen new scaffolds from various chemical databases. The hit compounds obtained from the virtual screening were subjected to an assessment of drug-like properties, followed by molecular docking. The final hit compounds were selected based on binding modes and molecular interactions in the active site of the enzyme. Furthermore, molecular dynamics simulations for AChE in complex with the final hits were performed to evaluate that they maintained stable interactions with the active site residues. The binding free energies of the final hits were also calculated using molecular mechanics/Poisson-Boltzmann surface area method. Taken together, we proposed that these hits can be promising candidates for anti-AD drugs.

Primary hypercholesterolemia is the root cause for major health issues like coronary heart disease and atherosclerosis. Regulating plasma cholesterol level, which is the product of biosynthesis as well as dietary intake, has become one of the major therapeutic strategies to effectively control these diseases. Human cholesterol esterase (hCEase) is an interesting target involved in the regulation of plasma cholesterol level and thus inhibition of this enzyme is highly effective in the treatment of hypercholesterolemia. This study was designed to understand the activation mechanism that enables the enzyme to accommodate long chain fatty acids and to identify the structural elements for the successful catalysis. Primarily the activation efficiencies of three different bile salts were studied and compared using molecular dynamics simulations. Based on the conformations of major surface loops, hydrogen bond interactions, and distance analyses, taurocholate was concluded as the preferred activator of the enzyme. Furthermore, the importance of two bile salt binding sites (proximal and remote) and the crucial role of 7α-OH group of the bile salts in the activation of hCEase was examined and evidenced. The results of our study explain the structural insights of the activation mechanism and show the key features of the bile salts responsible for the enzyme activation which are very useful in hypolipidemic drug designing strategies.

Mastoparans from the venom of social wasps have attracted considerable attention as effective antibiotic candidates. In this study, mastoparan V1 (MP-V1) from Vespula vulgaris was first disclosed to have a peptide amino acid sequence distinct from typical mastoparans and its biochemical properties and antimicrobial effects were compared with those of typical mastoparans MP-L, -X(V) and -B. Circular dichroism (CD) spectroscopy revealed that MP-V1 and -X(V) form more stable α-helical conformations in lipid membrane-like environments than MP-L and -B. In parallel, these two also showed more effective antimicrobial activities against the pathogens than did MP-L and -B. Although MP-V1 had a less stable α-helical conformation than MP-X(V), it showed stronger antimicrobial effects against Streptococcus mutans and Salmonella enterica than MP-X(V). In the meantime, analysis of hemolytic activity revealed a range of doses (~50 μM) that exhibited little potent cytotoxicity on human erythrocytes. Finally, the atypical MP-V1 peptide amino acid sequence provided important clues to understanding its antimicrobial mechanism from a structural perspective. Therefore, it has been concluded that MP-V1 is a de novo type of mastoparan with superior antimicrobial activities against both pathogenic bacteria and fungi, which may be useful in developing multipurpose antimicrobial drugs against infectious diseases.

The acetylcholinesterase (AChE) is important to terminate acetylcholine-mediated neurotransmission at cholinergic synapses. The pivotal role of AChE in apoptosome formation through the interactions with cytochrome c (Cyt c) was demonstrated in recent study. In order to investigate the proper binding conformation between the human AChE (hAChE) and human Cyt c (hCyt c), macro-molecular docking simulation was performed using DOT 2.0 program. The hCyt c was bound to peripheral anionic site (PAS) on hAChE and binding mode of the docked conformation was very similar to the reported crystal structure of the AChE and fasciculin-II (Fas-II) complex. Two 10 ns molecular dynamics (MD) simulations were carried out to refine the binding mode of docked structure and to observe the differences of the binding conformations between the absent (Apo) and presence (Holo) of heme group. The key hydrogen bonding residues between hAChE and hCyt c proteins were found in Apo and Holo systems, as well as each Tyr341 and Trp286 residue of hAChE was participated in cation-pi (π) interactions with Lys79 of hCyt c in Apo and Holo systems, respectively. From the present study, although the final structures of the Apo and Holo systems have similar binding pattern, several differences were investigated in flexibilities, interface interactions, and interface accessible surface areas. Based on these results, we were able to predict the reasonable binding conformation which is indispensable for apoptosome formation.

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is a rate-controlling enzyme in the mevalonate pathway which involved in biosynthesis of cholesterol and other isoprenoids. This enzyme catalyzes the conversion of HMG-CoA to mevalonate and is regarded as a drug target to treat hypercholesterolemia. In this study, ten qualitative pharmacophore models were generated based on chemical features in active inhibitors of HMGR. The generated models were validated using a test set. In a validation process, the best hypothesis was selected based on the statistical parameters and used for virtual screening of chemical databases to find novel lead candidates. The screened compounds were sorted by applying drug-like properties. The compounds that satisfied all drug-like properties were used for molecular docking study to identify their binding conformations at active site of HMGR. The final hit compounds were selected based on docking score and binding orientation. The HMGR structures in complex with the hit compounds were subjected to 10 ns molecular dynamics simulations to refine the binding orientation as well as to check the stability of the hits. After simulation, binding modes including hydrogen bonding patterns and molecular interactions with the active site residues were analyzed. In conclusion, four hit compounds with new structural scaffold were suggested as novel and potent HMGR inhibitors.

Background: Human LTA4H catalyzes the conversion of LTA4 to LTB4 and plays a key role in innate immune responses. Inhibition of this enzyme can be a valid method in the treatment of inflammatory response exhibited through LTB4. Results & discussion: The quantitative structure–activity relationship (QSAR) models were developed using genetic function approximation and validated. A training set of 26 diverse compounds and their molecular descriptors were used to develop highly correlating QSAR models. A six-descriptor model explaining the biological activity of the training and test sets with correlation values of 0.846 and 0.502, respectively, was selected as the best model and used in a database screening of drug-like Maybridge database followed by molecular docking. Conclusion: Based on the predicted potent inhibitory activities, expected binding mode and molecular interactions at the active site of hLTA4H final leads were selected as to be utilized in designing future hLTA4H inhibitors.

Sirtuin, NAD(+)-dependent histone deacetylase enzyme, emerged as a potential therapeutic target, and modulations by small molecules could be effective drugs for various diseases. Owing to the absence of complex structure of sirtuin 2 (SIRT2), sirtinol was docked in the NAD(+) binding site and subjected to 5-nseconds molecular dynamics (MD) simulation. LigandScout was used to develop hypotheses based on 3-representative SIRT2 complex structures from MD. Three structure-based hypotheses are generated and merged to form dynamics hypothesis. The dynamics hypothesis was validated using test and decoy sets. The results showed that dynamic hypothesis represents the complementary features of SIRT2 active site. Dynamic hypothesis was used to screen ChemDiv database, and hits were filtered through ADMET, rule of five, and two different molecular docking studies. Finally, 21 molecules were selected as potent leads based on consensus score from LigandFit, Gold fitness score, binding affinity from VINA as well as based on the important interactions with critical residues in SIRT2 active site. Hence, we suggest that the dynamic hypothesis will be reliable in the identification of SIRT2 new lead as well as to reduce time and cost in the drug discovery process.

Changes in meat quality traits are strongly associated with alterations in postmortem metabolism which depend on genetic variations, especially nonsynonymous single nucleotide variations (nsSNVs) having critical effects on protein structure and function. To selectively identify metabolism-related nsSNVs, next-generation transcriptome sequencing (RNA-Seq) was carried out using RNAs from porcine liver, which contains a diverse range of metabolic enzymes. The multiplex SNV genotyping analysis showed that various metabolism-related genes had different nsSNV alleles. Moreover, many nsSNVs were significantly associated with multiple meat quality traits. Particularly, ch7:g.22112616A>G SNV was identified to create a single amino acid change (Thr/Ala) at the 145th residue of H1.3-like protein, very close to the putative 147th threonine phosphorylation site, suggesting that the nsSNV may affect multiple meat quality traits by affecting the epigenetic regulation of postmortem metabolism-related gene expression. Besides, one nonsynonymous variation, probably generated by gene duplication, led to a stop signal in porcine testicular carbonyl reductase (PTCR), resulting in a C-terminal (E281-A288) deletion. Molecular docking and energy minimization calculations indicated that the binding affinity of wild-type PTCR to 5α-DHT, a C(21)-steroid, was superior to that of C-terminal-deleted PTCR or human carbonyl reductase, which was very consistent with experimental data, reported previously. Furthermore, P284 was identified as an important residue mediating the specific interaction between PTCR and 5α-DHT, and phylogenetic analysis showed that P284 is an evolutionarily conserved residue among animal carbonyl reductases, which suggests that the C-terminal tails of these reductases may have evolved under evolutionary pressure to increase the substrate specificity for C(21)-steroids and facilitate metabolic adaptation. Altogether, our RNA-Seq revealed that selective nsSNVs were associated with meat quality traits that could be useful for successful marker-assisted selection in pigs and also represents a useful resource to enhance understanding of protein folding, substrate specificity, and the evolution of enzymes such as carbonyl reductase.