Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models

Insights into seed coats of nine cultivars of Australian lupin: Unravelling LC-QTOF MS-based biochemical profiles, nutritional, functional, antioxidant, and antidiabetic properties together with rationalizing antidiabetic mechanism by in silico approaches

Lupins, and other legumes, have attained international interest due to their reported remarkable health benefits. Currently, the seed coats are discarded as waste or animal feed. The research presented here summarizes the potential for incorporating the seed coats into 'whole grain' foods. We aimed to identify metabolites found in the seed coats of nine commercial Australian cultivars of lupin (Lupinus angustifolius and L. albus species), and to evaluate and compare their functional, nutritional, antioxidant, and antidiabetic properties, along with in silico exploration of mechanisms of action for selected identified secondary metabolites. The seed coats were found to contain 79 to 90% dietary fibers and substantial quantity of essential macrometals. LC-QTOF MS-based, untargeted bioactive metabolite profiling explored a total of 673 chemical entities, and identified 63 bioactive secondary metabolites including: biophenols, unsaturated fatty acids, triterpenoids, alkaloids, and dietary prebiotics (insoluble fibers). The seed coats from these nine cultivars show substantial antioxidant activity. The cultivars of L. angustifolius inhibit α-amylase and α-glucosidase significantly in vitro. Moreover, in silico docking and dynamic simulation along with ADME/T analysis suggest that quercetin 3-methyl ether and 8-C-methylquercetin 3-methyl ether as molecules, novel in lupin seed coats, are responsible for the α-amylase and α-glucosidase inhibition. The findings indicated that lupin seed coats might be beneficial food components, rather than be discarded as 'mill waste'.

A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model

Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation. To our surprise, a new-scaffold AR antagonist C2 with comparable bioactivity with Enz was identified at the initial round of screening. C2 showed pronounced inhibition on the transcriptional function (IC50 = 0.63 μM) and nuclear translocation of AR and significant antiproliferative and antimetastatic activity on PCa cell line of LNCaP. In addition, C2 exhibited a stronger ability to block the cell cycle of LNCaP than Enz at lower dose and superior AR specificity. Our study highlights the success of MIEC-SVM in discovering AR antagonists, and compound C2 presents a promising new scaffold for the development of AR-targeted therapeutics.

RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning

Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.

Abrogation of ORF8-IRF3 binding interface with Carbon nanotube derivatives to rescue the host immune system against SARS-CoV-2 by using molecular screening and simulation approaches

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway. To block the binding of ORF8-IRF3 and inhibit viral pathogenesis a quick discovery of an inhibitor molecule is needed. Therefore, in the present study, the interface between the ORF8 and IRF3 was targeted on a high-affinity carbon nanotube by using computational tools. After analysis of 62 carbon nanotubes by multiple docking with the induced fit model, the top five compounds with high docking scores of - 7.94 kcal/mol, - 7.92 kcal/mol, - 7.28 kcal/mol, - 7.19 kcal/mol and - 7.09 kcal/mol (top hit1-5) were found to have inhibitory activity against the ORF8-IRF3 complex. Molecular dynamics analysis of the complexes revealed the high compactness of residues, stable binding, and strong hydrogen binding network among the ORF8-nanotubes complexes. Moreover, the total binding free energy for top hit1-5 was calculated to be - 43.21 ± 0.90 kcal/mol, - 41.17 ± 0.99 kcal/mol, - 48.85 ± 0.62 kcal/mol, - 43.49 ± 0.77 kcal/mol, and - 31.18 ± 0.78 kcal/mol respectively. These results strongly suggest that the identified top five nanotubes (hit1-5) possess significant potential for advancing and exploring innovative drug therapies. This underscores their suitability for subsequent in vivo and in vitro experiments, marking them as promising candidates worthy of further investigation.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Identification of potent BRD4-BD1 inhibitors using classical and steered molecular dynamics based free energy analysis

In the present work a combination of traditional and steered molecular dynamics based techniques were employed to identify potential inhibitors against the human BRD4 protein (BRD4- BD1); an established drug target for multiple illnesses including various malignancies. Quinoline derivatives that were synthesized in-house were tested for their potential as new BRD4-BD1 inhibitors. Initially molecular docking experiments were performed to determine the binding poses of BRD4-BD1 inhibitors. To learn more about the thermodynamics of inhibitor binding to the BRD4-BD1 active site, the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) free energy calculations were conducted afterwards. The findings of the MM-PBSA analysis were further reinforced by performing steered umbrella sampling simulations which revealed crucial details about the binding/unbinding process of the most potent quinoline derivatives at the BRD4-BD1 active site. We report a novel quinoline derivative which can be developed into a fully functional BRD4-BD1 inhibitor after experimental validation. The identified compound (4 g) shows better properties than the standard BRD4-BD1 inhibitors considered in the study. The study also highlights the crucial role of Gln78, Phe79, Trp81, Pro82, Phe83, Gln84, Gln85, Val87, Leu92, Leu94, Tyr97, Met105, Cys136, Asn140, Ile146 and Met149 in inhibitor binding. The study provides a possible lead candidate and key amino acids involved in inhibitor recognition and binding at the active site of BRD4-BD1 protein. The findings might be of significance to medicinal chemists involved in the development of potent BRD4-BD1 inhibitors.

Pharmacotherapeutic Potential of Natural Products to Target the SARS-CoV-2 PLpro Using Molecular Screening and Simulation Approaches

Because of the essential role of PLpro in the regulation of replication and dysregulation of the host immune sensing, it is considered a therapeutic target for novel drug development. To reduce the risk of immune evasion and vaccine effectiveness, small molecular therapeutics are the best complementary approach. Hence, we used a structure-based drug-designing approach to identify potential small molecular inhibitors for PLpro of SARS-CoV-2. Initial scoring and re-scoring of the best hits revealed that three compounds NPC320891 (2,2-Dihydroxyindene-1,3-Dione), NPC474594 (Isonarciclasine), and NPC474595 (7-Deoxyisonarciclasine) exhibit higher docking scores than the control GRL0617. Investigation of the binding modes revealed that alongside the essential contacts, i.e., Asp164, Glu167, Tyr264, and Gln269, these molecules also target Lys157 and Tyr268 residues in the active site. Moreover, molecular simulation demonstrated that the reported top hits also possess stable dynamics and structural packing. Furthermore, the residues' flexibility revealed that all the complexes demonstrated higher flexibility in the regions 120-140, 160-180, and 205-215. The 120-140 and 160-180 lie in the finger region of PLpro, which may open/close during the simulation to cover the active site and push the ligand inside. In addition, the total binding free energy was reported to be - 32.65 ± 0.17 kcal/mol for the GRL0617-PLpro, for the NPC320891-PLpro complex, the TBE was - 35.58 ± 0.14 kcal/mol, for the NPC474594-PLpro, the TBE was - 43.72 ± 0.22 kcal/mol, while for NPC474595-PLpro complex, the TBE was calculated to be - 41.61 ± 0.20 kcal/mol, respectively. Clustering of the protein's motion and FEL further revealed that in NPC474594 and NPC474595 complexes, the drug was seen to have moved inside the binding cavity along with the loop in the palm region harboring the catalytic triad, thus justifying the higher binding of these two molecules particularly. In conclusion, the overall results reflect favorable binding of the identified hits strongly than the control drug, thus demanding in vitro and in vivo validation for clinical purposes.

Antinociceptive effect of cyclic and linear diterpenoids as new atypical agonists of κ-opioid receptors obtained from four species of the Baccharis genus, and vehiculated in nanometric niosomes

New natural analgesic compounds that act in KORs are important alternatives for potential therapeutical use in medicine. In this work, we report and compare here the antinociceptive activity displayed by cyclic and linear diterpenes, obtained from the genus Baccharis. The antinociceptive activities determined were relatively strong, in comparison whit morphine. The antinociceptive mechanism of action was made through naloxone administration (a non-selective antagonist of opioid receptors). The more active compounds were vehiculized successfully in niosomes at nanometric scale. The observed antinociceptive activity for Bartemidiolide oxide (BARTO), obtain from Baccharis artemisioides, was greater than Flabeloic acid dimer (DACD), the first compound isolated from Baccharis flabellata that was reported possessing antinociceptive effects. We also conducted docking calculations and molecular dynamics simulations, which suggested that the newly identified diterpenes might share the molecular action mechanism reported for Salvinorin A (SalA). Molecular simulations have allowed us to appreciate some subtle differences between molecular interactions of these ligands stabilizing their respective complexes; such information might be useful for designing and searching for new inhibitors of KORs.

Discovery of Isojacareubin as a covalent inhibitor of SARS-CoV-2 main protease using structural and experimental approaches

The ongoing pandemic with the emergence of immune evasion potential and, particularly, the current omicron subvariants intensified the situation further. Although vaccines are available, the immune evasion capabilities of the recent variants demand further efficient therapeutic choices to control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Hence, considering the necessity of the small molecule inhibitor, we target the main protease (3CLpro), which is an appealing target for the development of antiviral drugs against SARS-CoV-2. High-throughput molecular in silico screening of South African natural compounds database reported Isojacareubin and Glabranin as the potential inhibitors for the main protease. The calculated docking scores were reported to be -8.47 and -8.03 kcal/mol, respectively. Moreover, the structural dynamic assessment reported that Isojacareubin in complex with 3CLpro exhibit a more stable dynamic behavior than Glabranin. Inhibition assay indicated that Isojacareubin could inhibit SARS-CoV-2 3CLpro in a time- and dose-dependent manner, with half maximal inhibitory concentration values of 16.00 ± 1.35 μM (60 min incubation). Next, the covalent binding sites of Isojacareubin on SARS-CoV-2 3CLpro was identified by biomass spectrometry, which reported that Isojacareubin can covalently bind to thiols or Cysteine through Michael addition. To evaluate the inactivation potency of Isojacareubin, the inactivation kinetics was further investigated. The inactivation kinetic curves were plotted according to various concentrations with gradient-ascending incubation times. The KI value of Isojacareubin was determined as 30.71 μM, whereas the Kinact value was calculated as 0.054 min-1 . These results suggest that Isojacareubin is a covalent inhibitor of SARS-CoV-2 3CLpro .

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.

Discovery of natural products to block SARS-CoV-2 S-protein interaction with Neuropilin-1 receptor: A molecular dynamics simulation approach

Neuropilin-1 (NRP1) is a widely expressed cell surface receptor protein characterized by its pleiotropic function. Recent reports highlighted NRP1 as an additional entry point of the SARS-CoV-2 virus, enhancing viral infectivity by interacting with the S-protein of SARS-CoV-2. The ubiquitous distribution and mechanism of action of NRP1 enable the SARS-CoV-2 virus to attack multiple organs in the body simultaneously. Therefore, blocking NRP1 is a potential therapeutic approach against SARS-CoV-2 infection. The current study screened the South African natural compounds database (SANCDB) for molecules that can disrupt the SARS-CoV-2 S protein-NRP1 interaction as a potential antiviral target for SARS-CoV-2 cellular entry. Following excessive screening and validation analysis 3-O-Methylquercetin and Esculetin were identified as potential compounds to disrupt the S-protein-NRP1 interaction. Furthermore, to understand the conformational stability and dynamic features between NRP1 interaction with the selected natural products, we performed 200 ns molecular dynamics (MD) simulations. In addition, molecular mechanics-generalized Born surface area (MM/GBSA) was utilized to calculate the free binding energies of the natural products interacting with NRP1. 3-O-methylquercetin showed an inhibitory effect with binding energies ΔG of -25.52 ± 0.04 kcal/mol to NRP1, indicating the possible disruption of the NRP1-S-protein interaction. Our analysis demonstrated that 3-O-methylquercetin presents a potential antiviral compound against SARS-CoV-2 infectivity. These results set the path for future functional in-vitro and in-vivo studies in SARS-CoV-2 research.

Abrogation of SARS-CoV-2 interaction with host (NRP1) neuropilin-1 receptor through high-affinity marine natural compounds to curtail the infectivity: A structural-dynamics data

The evolution of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants around the globe has made the coronavirus disease 2019 (COVID-19) pandemic more worrisome, pressuring the health care system and resulting in an increased mortality rate. Recent studies recognized neuropilin-1 (NRP1) as a key facilitator in the invasion of the new SARS-CoV-2 into the host cell. Therefore, it is considered an imperative drug target for the treatment of COVID-19. Hence, a thorough analysis was needed to understand the impact and to guide new therapeutics development. In this study, we used structural and biomolecular simulation techniques to identify novel marine natural products which could block this receptor and stop the virus entry. We discovered that the binding affinity of CMNPD10175, CMNPD10017, CMNPD10114, CMNPD10115, CMNPD10020. CMNPD10018, CMNPD10153, CMNPD10149 CMNPD10464 and CMNPD10019 were substantial during the virtual screening (VS). We further explored these compounds by analyzing their absorption, distribution, metabolism, and excretion and toxicity (ADMET) properties and structural-dynamics features. Free energy calculations further established that all the compounds exhibit stronger binding energy for NRP1. Consequently, we hypothesized that these compounds might be the best lead candidates for therapeutic interventions hindering virus binding to the host cell. This study provides a strong impetus to develop novel drugs against the SARS-CoV-2 by targeting NRP1.

Rescuing the Host Immune System by Targeting the Immune Evasion Complex ORF8-IRF3 in SARS-CoV-2 Infection with Natural Products Using Molecular Modeling Approaches

The perennial emergence of SARS-CoV-2 and its new variants causing upper respiratory complexities since December 2019 has aggravated the pandemic situation around the world. SARS-CoV-2 encodes several proteins among which ORF8 is a novel factor that is unique to SARS-CoV-2 only and is reported to help the virus in disease severity and immune evasion. ORF8-IRF3 complex induces endoplasmic reticulum stress, thus helps in the evasion of immune response. Consequently, targeting the ORF8-IRF3 complex is considered as a prime target for the discovery of novel drugs against SARS-CoV-2. In this regard, computational methods are of great interest to fast track the identification and development of novel drugs. Virtual screening of South African Natural Compounds Database (SANCDB), followed by docking and molecular dynamics (MD) simulation analysis, were performed to determine novel natural compounds. Computational molecular search and rescoring of the SANCDB database followed by induced-fit docking (IFD) protocol identified Quercetin 3-O-(6″-galloyl)-beta-D-galactopyranoside (SANC00850), Tribuloside (SANC01050), and Rutin (SANC00867) are the best scoring compounds. Structural-dynamic properties assessment revealed that these three compounds have stable dynamics, compactness, and a higher number of hydrogen bonds. For validation, we used MM/GBSA, in silico bioactivity estimation and dissociation constant (KD) approaches, which revealed that these compounds are the more potent inhibitors of the ORF8-IRF3 complex and would rescue the host immune system potentially. These compounds need further in vitro and in vivo validations to be used as therapeutics against SARS-CoV-2 to rescue the host immune system during COVID-19 infection.

Phenylethanoid glycosides as a possible COVID-19 protease inhibitor: a virtual screening approach

From the beginning of pandemic, more than 240 million people have been infected with a death rate higher than 2%. Indeed, the current exit strategy involving the spreading of vaccines must be combined with progress in effective treatment development. This scenario is sadly supported by the vaccine's immune activation time and the inequalities in the global immunization schedule. Bringing the crises under control means providing the world population with accessible and impactful new therapeutics. We screened a natural product library that contains a unique collection of 2370 natural products into the binding site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). According to the docking score and to the interaction at the active site, three phenylethanoid glycosides (forsythiaside A, isoacteoside, and verbascoside) were selected. In order to provide better insight into the atomistic interaction and test the impact of the three selected compounds at the binding site, we resorted to a half microsecond-long molecular dynamics simulation. As a result, we are showing that forsythiaside A is the most stable molecule and it is likely to possess the highest inhibitory effect against SARS-CoV-2 Mpro. Phenylethanoid glycosides also have been reported to have both protease and kinase activity. This kinase inhibitory activity is very beneficial in fighting viruses inside the body as kinases are required for viral entry, metabolism, and/or reproduction. The dual activity (kinase/protease) of phenylethanoid glycosides makes them very promising anit-COVID-19 agents.

Discovery of a small molecule inhibitor of cullin neddylation that triggers ER stress to induce autophagy

Protein neddylation is catalyzed by a three-enzyme cascade, namely an E1 NEDD8-activating enzyme (NAE), one of two E2 NEDD8 conjugation enzymes and one of several E3 NEDD8 ligases. The physiological substrates of neddylation are the family members of cullin, the scaffold component of cullin RING ligases (CRLs). Currently, a potent E1 inhibitor, MLN4924, also known as pevonedistat, is in several clinical trials for anti-cancer therapy. Here we report the discovery, through virtual screening and structural modifications, of a small molecule compound HA-1141 that directly binds to NAE in both in vitro and in vivo assays and effectively inhibits neddylation of cullins 1–5. Surprisingly, unlike MLN4924, HA-1141 also triggers non-canonical endoplasmic reticulum (ER) stress and PKR-mediated terminal integrated stress response (ISR) to activate ATF4 at an early stage, and to inhibit protein synthesis and mTORC1 activity at a later stage, eventually leading to autophagy induction. Biologically, HA-1141 suppresses growth and survival of cultured lung cancer cells and tumor growth in in vivo xenograft lung cancer models at a well-tolerated dose. Taken together, our study has identified a small molecule compound with the dual activities of blocking neddylation and triggering ER stress, leading to growth suppression of cancer cells.

Computational Evaluation of Abrogation of HBx-Bcl-xL Complex with High-Affinity Carbon Nanotubes (Fullerene) to Halt the Hepatitis B Virus Replication

Hepatitis B virus (HBV) is the world's most prevalent chronic viral infection. More than 350 million individuals are chronic carriers of the virus, with an estimated 2 billion infected persons. For instance, the role of HBx protein in attachment and infection is very obvious and consequently deemed as an important druggable target. Targeting the interface and discovering novel drugs greatly advanced the field of therapeutics development. Therefore, in the current study, HBx to Bcl-xL is abrogated on high-affinity carbon nanotubes using computational structural biology tools. Our analysis revealed that among the total 62 carbon fullerenes, only 13 compounds exhibited inhibitory activity against HBx, which was further confirmed through IFD-based rescoring. Structural dynamics investigation revealed stable binding, compactness, and hydrogen bonds reprogramming. Moreover, the binding free energy calculation results revealed that the top hits1-4 possess the total binding energy of -54.36 kcal/mol (hit1), -50.81 kcal/mol (hit2), -47.09 kcal/mol (hit3), and -45.59 kcal/mol for hit4. In addition, the predicted KD values and bioactivity scores further validated the inhibitory potential of these top hits. The identified compounds need further in vitro and in vivo validation to aid the treatment process of HBV.

Quinazoline-tethered hydrazone: A versatile scaffold toward dual anti-TB and EGFR inhibition activities in NSCLC

Globally, lung cancer and tuberculosis are considered to be very serious and complex diseases. Evidence suggests that chronic infection with tuberculosis (TB) can often lead to lung tumors; therefore, developing drugs that target both diseases is of great clinical significance. In our study, we designed and synthesized a suite of 14 new quinazolinones (5a-n) and performed biological investigations of these compounds in Mycobacterium tuberculosis (MTB) and cancer cell lines. In addition, we conducted a molecular modeling study to determine the mechanism of action of these compounds at the molecular level. Compounds that showed anticancer activity in the preliminary screening were further evaluated in three cancer cell lines (A549, Calu-3, and BT-474 cells) and characterized in an epidermal growth factor receptor (EGFR) binding assay. Cytotoxicity in noncancerous lung fibroblast cells was also evaluated to obtain safety data. Our theoretical and experimental studies indicated that our compounds showed a mechanism of action similar to that of erlotinib by inhibiting the EGFR tyrosine kinase. In turn, the antituberculosis activity of these compounds would be produced by the inhibition of enoyl-ACP-reductase. From our findings, we were able to identify two potential lead compounds (5i and 5l) with dual activity and elevated safety toward noncancerous lung fibroblast cells. In addition, our data identified three compounds with excellent anti-TB activities (compounds 5i, 5l, and 5n).

Differences in Gluco and Galacto Substrate-Binding Interactions in a Dual 6Pβ-Glucosidase/6Pβ-Galactosidase Glycoside Hydrolase 1 Enzyme from Bacillus licheniformis

Bacterial glycoside hydrolase 1 (GH1) enzymes with 6-phospho-β-galactosidase and 6-phospho-β-glucosidase activities have the important task of releasing phosphorylated and nonphosphorylated monosaccharides into the cytoplasm. Curiously, dual 6-phospho-β-galactosidase/6-phospho-β-glucosidase (dual-phospho) enzymes have broad specificity and are able to hydrolyze galacto- and gluco-derived substrates. This study investigates the structure and substrate specificity of a GH family 1 enzyme from Bacillus licheniformis, hereafter known as BlBglC. The enzyme structure has been solved, and sequence analysis, molecular dynamics simulations, and binding free energy calculations offered evidence of dual-phospho activity. Both test ligands p-nitrophenyl-β-d-galactoside-6-phosphate (PNP6Pgal) and p-nitrophenyl-β-d-glucoside-6-phosphate (PNP6Pglc) demonstrated strong binding to BlBglC although the pose and interactions of the PNP6Pglc triplicates were slightly more consistent. Interestingly, known specificity-inducing residues, Gln23 and Trp433, bind strongly to the ligand O3 hydroxyl group in the PNP6Pgal-BlBglC complex and to the ligand O4 hydroxyl group in the PNP6Pglc-BlBglC complex. Additionally, the BlBglC-His124 residue is a major contributor of hydrogen bonds to the PNP6Pgal O3 hydroxyl group but does not form any hydrogen bonds with PNP6Pglc. On the other hand, BlBglC residues Tyr173, Tyr301, Gln302, and Thr321 form hydrogen bonds with PNP6Pglc but not PNP6Pgal. These findings provide important details of the broad specificity of dual-phospho activity GH1 enzymes.

Combined drug repurposing and virtual screening strategies with molecular dynamics simulation identified potent inhibitors for SARS-CoV-2 main protease (3CLpro)

The current coronavirus (SARS-COV-2) pandemic and phenomenal spread to every nook and cranny of the world has raised major apprehensions about the modern public health care system. So far as a result of this epidemic, 4,434,653 confirmed cases and 302,169 deaths are reported. The growing infection rate and death toll demand the use of all possible approaches to design novel drugs and vaccines to curb this disease. In this study, we combined drugs repurposing and virtual drug screening strategies to target 3CLpro, which has an essential role in viral maturation and replication. A total of 31 FDA approved anti-HIV drugs, and Traditional Chinese medicines (TCM) database were screened to find potential inhibitors. As a result, Saquinavir, and five drugs (TCM5280805, TCM5280445, TCM5280343, TCM5280863, and TCM5458190) from the TCM database were found as promising hits. Furthermore, results from molecular dynamics simulation and total binding free energy revealed that Saquinavir and TCM5280805 target the catalytic dyad (His41 and Cys145) and possess stable dynamics behavior. Thus, we suggest that these compounds should be tested experimentally against the SARS-COV-2 as Saquinavir has been reported to inhibit HIV protease experimentally. Considering the intensity of coronavirus dissemination, the present research is in line with the idea of discovering the latest inhibitors against the coronavirus essential pathways to accelerate the drug development cycle.Communicated by Ramaswamy H. Sarma.

Deciphering the selective binding mechanisms of anaplastic lymphoma kinase-derived neuroblastoma tumor neoepitopes to human leukocyte antigen

Neuroblastoma (NB), as a metastatic form of solid tumor, has a high fatality rate found in early childhood. The two anaplastic lymphoma kinase (ALK) neoepitopes nonamer and decamer used in cancer immunotherapy against NB cancer can selectively bind to the human leukocyte antigen (HLA-B*15:01) groove with high affinities, whereas the native self-peptide is unable to interact with the HLA-B*15:01. Here, we performed molecular dynamics (MD) simulations and subsequent molecular mechanics-generalized born surface area (MM-GBSA) binding free energy calculations to explore the selective binding mechanisms of nonamer and decamer to the HLA-B*15:01 against the self-peptide. MD simulations revealed the significant conformational dynamics of the self-peptide in the HLA-B*15:01 groove against the nonamer and decamer. Binding free energy calculations showed that the binding affinities of HLA-B*15:01-neoepitope complexes were followed in the order decamer > nonamer > self-peptide. Detailed analysis of HLA-B*15:01-neoepitope structural complexes showed that compared to the nonamer, the self-peptide tended to move outward to the solvent, whereas the decamer moved deep to the HLA-B*15:01 groove. These different dynamic observations of the ALK neoepitopes can explain the distinct binding affinities of self-peptide, nonamer, and decamer to the HLA-B*15:01. The results may be useful for the design of more selective ALK neoepitopes.

Exploring the Distinct Binding and Activation Mechanisms for Different CagA Oncoproteins and SHP2 by Molecular Dynamics Simulations

CagA is a major virulence factor of Helicobacter pylori. H. pylori CagA is geographically subclassified into East Asian CagA and Western CagA, which are characterized by the presence of a EPIYA-D or EPIYA-C segment. The East Asian CagA is more closely associated with gastric cancer than the Western CagA. In this study, molecular dynamic (MD) simulations were performed to investigate the binding details of SHP2 and EPIYA segments, and to explore the allosteric regulation mechanism of SHP2. Our results show that the EPIYA-D has a stronger binding affinity to the N-SH2 domain of SHP2 than EPIYA-C. In addition, a single EPIYA-D binding to N-SH2 domain of SHP2 can cause a deflection of the key helix B, and the deflected helix B could squeeze the N-SH2 and PTP domains to break the autoinhibition pocket of SHP2. However, a single EPIYA-C binding to the N-SH2 domain of SHP2 cannot break the autoinhibition of SHP2 because the secondary structure of the key helix B is destroyed. However, the tandem EPIYA-C not only increases its binding affinity to SHP2, but also does not significantly break the secondary structure of the key helix B. Our study can help us better understand the mechanism of gastric cancer caused by Helicobacter pylori infection.

Modifications on the Tetrahydroquinoline Scaffold Targeting a Phenylalanine Cluster on GPER as Antiproliferative Compounds against Renal, Liver and Pancreatic Cancer Cells

The implementation of chemo- and bioinformatics tools is a crucial step in the design of structure-based drugs, enabling the identification of more specific and effective molecules against cancer without side effects. In this study, three new compounds were designed and synthesized with suitable absorption, distribution, metabolism, excretion and toxicity (ADME-tox) properties and high affinity for the G protein-coupled estrogen receptor (GPER) binding site by in silico methods, which correlated with the growth inhibitory activity tested in a cluster of cancer cell lines. Docking and molecular dynamics (MD) simulations accompanied by a molecular mechanics/generalized Born surface area (MMGBSA) approach yielded the binding modes and energetic features of the proposed compounds on GPER. These in silico studies showed that the compounds reached the GPER binding site, establishing interactions with a phenylalanine cluster (F206, F208 and F278) required for GPER molecular recognition of its agonist and antagonist ligands. Finally, a 3-(4,5-dimethylthiazol-2-yl)2,5-diphenyltetrazolium bromide (MTT) assay showed growth inhibitory activity of compounds 4, 5 and 7 in three different cancer cell lines-MIA Paca-2, RCC4-VA and Hep G2-at micromolar concentrations. These new molecules with specific chemical modifications of the GPER pharmacophore open up the possibility of generating new compounds capable of reaching the GPER binding site with potential growth inhibitory activities against nonconventional GPER cell models.

Exploring the Allosteric Mechanism of Src Homology-2 Domain-Containing Protein Tyrosine Phosphatase 2 (SHP2) by Molecular Dynamics Simulations

The Src homology-2 (SH2) domain-containing protein tyrosine phosphatase 2 (SHP2, encoded by PTPN11) is a critical allosteric phosphatase for many signaling pathways. Programmed cell death 1 (PD-1) could be phosphorylated at its immunoreceptor tyrosine-based inhibitory motif (ITIM) and immunoreceptor tyrosine-based switch motif (ITSM) and can bind to SHP2 to initiate T cell inactivation. Although the interaction of SHP2-PD-1 plays an important role in the immune process, the complex structure and the allosteric regulation mechanism remain unknown. In this study, molecular dynamics (MD) simulations were performed to study the binding details of SHP2 and PD-1, and explore the allosteric regulation mechanism of SHP2. The results show that ITIM has a preference to bind to the N-SH2 domain and ITSM has almost the same binding affinity to the N-SH2 and C-SH2 domain. Only when ITIM binds to the N-SH2 domain and ITSM binds to the C-SH2 domain can the full activation of SHP2 be obtained. The binding of ITIM and ITSM could change the motion mode of SHP2 and switch it to the activated state.

Molecular simulation studies to reveal the binding mechanisms of shikonin derivatives inhibiting VEGFR-2 kinase

Traditional vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors can manage angiogenesis; however, severe toxicity and resistance limit their long-term applications in clinical therapy. Shikonin (SHK) and its derivatives could be promising to inhibit the VEGFR-2 mediated angiogenesis, as they are reported to bind in the catalytic kinase domain with low affinity. However, the detailed molecular insights and binding dynamics of these natural inhibitors are unknown, which is crucial for potential SHK based lead design. Therefore, the present study employed molecular modeling and simulations techniques to get insight into the binding behaviors of SHK and its two derivates, β-hydroxyisovalerylshikonin (β-HIVS) and acetylshikonin (ACS). Here the intermolecular interactions between protein and ligands were studied by induced fit docking approach, which were further evaluated by treating QM/MM (quantum mechanics/molecular mechanics) and molecular dynamics (MD) simulation. The result showed that the naphthazarin ring of the SHK derivates is vital for strong binding to the catalytic domain; however, the binding stability can be modulated by the side chain modification. Because of having electrostatic potential, this ring makes essential interactions with the DFG (Asp1046 and Phe1047) motif and also allows interacting with the allosteric binding site. Taken together, the studies will advance our knowledge and scope for the development of new selective VEGFR-2 inhibitors based on SHK and its analogs.

X-ray Structure, Bioinformatics Analysis, and Substrate Specificity of a 6-Phospho-β-glucosidase Glycoside Hydrolase 1 Enzyme from Bacillus licheniformis

In bacteria, mono- and disaccharides are phosphorylated during the uptake processes through the vastly spread transport system phosphoenolpyruvate-dependent phosphotransferase. As an initial step in the phosphorylated disaccharide metabolism pathway, 6-phospho-β-glucosidases and 6-phospho-β-galactosidases play a crucial role by releasing phosphorylated and nonphosphorylated monosaccharides. However, structural determinants for the specificity of these enzymes still need to be clarified. Here, an X-ray structure of a glycoside hydrolase family 1 enzyme from Bacillus licheniformis, hereafter known as BlBglH, was determined at 2.2 Å resolution, and its substrate specificity was investigated. The sequence of BlBglH was compared to the sequences of 58 other GH1 enzymes using sequence alignments, sequence identity calculations, phylogenetic analysis, and motif discovery. Through these various analyses, BlBglH was found to have sequence features characteristic of the 6-phospho-β-glucosidase activity enzymes. Motif and structural observations highlighted the importance of loop L8 in 6-phospho-β-glucosidase activity enzymes. To further affirm enzyme specificity, molecular docking and molecular dynamics simulations were performed using the crystallographic structure of BlBglH. Docking was carried out with a 6-phospho-β-glucosidase enzyme activity positive and negative control ligand, followed by 400 ns of MD simulations. The positive and negative control ligands were PNP6Pglc and PNP6Pgal, respectively. PNP6Pglc maintained favorable interactions within the active site until the end of the MD simulation, while PNP6Pgal exhibited instability. The favorable binding of substrate stabilized the loops that surround the active site. Binding free energy calculations showed that the PNP6Pglc complex had a substantially lower binding energy compared to the PNP6Pgal complex. Altogether, the findings of this study suggest that BlBglH possesses 6-phospho-β-glucosidase enzymatic activity and revealed sequence and structural differences between bacterial GH1 enzymes of various activities.

Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors

Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors.

Application of per-residue energy decomposition to identify the set of amino acids critical for in silico prediction of COX-2 inhibitory activity

The enormous magnitude of scientific research carried out in the field of NSAIDs and cyclooxygenases (COXs) is known. They are crucial in pain management. COX-2 inhibitors have evolved over the years; from traditional NSAIDs to isoform-specific. The present study is aimed to identify a cluster of amino acids in the catalytic site whose energy contribution can better explain COX-2 inhibitory activity accurately than the binding energy of the whole protein. Initially, MD simulations (25 ns) and MM-PBSA calculations were performed for 8 diarylheterocyclic inhibitors. Per-residue energy decomposition studies were carried out to elucidate the energy contribution of each amino acid, and their correlation with COX-2 inhibitory activity was enumerated. A cluster of catalytic amino acids whose free energy sum has a high correlation with biological data was identified. The cluster of Gln178, Ser339, Tyr341, Arg499, Phe504, Val509 and Ala513 showed the correlation of -0.60. Further, the study was extended to a total of 26 COX-2 inhibitors belonging to different classes to validate the applicability of the cluster of amino acids identified. Results clearly suggest that the cluster of amino acids identified provide accurate screening method, and can be applied to predict COX-2 inhibitory activity of small molecules.

In silico characterisation of olive phenolic compounds as potential cyclooxygenase modulators. Part 1

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used to reduce pain. These target cyclooxygenase (COX) enzymes which produce inflammatory mediators. Adverse effects associated with the use of traditional NSAIDs have led to a rise in the development of alternative therapies. Derived from Olea Europaea, olive oil is a main component of the Mediterranean diet, containing phenolic compounds that contribute to its antioxidant and anti-inflammatory properties. It has previously been found that oleocanthal, a phenolic compound derived from the olive, had similar effects to ibuprofen, a commonly used NSAID. There is an abundance of olive phenolic compounds that have yet to be investigated for their anti-inflammatory properties. In this study, it was sought to identify potential olive-derived compounds with the ability to inhibit COX enzymes, and study the mechanisms using in silico approaches. Molecular docking was employed to determine the COX inhibitory potential of an olive phenolic compound library. From docking, it was determined that 1-oleyltyrosol (1OL) and ligstroside derivative 2 (LG2) demonstrated the greatest binding affinity to both COX-1 and COX-2. Interactions with these compounds were further examined using molecular dynamics simulations. The residue contributions to binding free energy were computed using Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) methods, revealing that residues Leu93, Val116, Leu352, and Ala527 in COX-1 and COX-2 were key determinants of potential inhibition. Along with part 2 of this study, this work aims to identify and characterise novel phenolic compounds which may possess COX inhibitory properties.

Structural insights of catalytic mechanism in mutant pyrazinamidase of Mycobacterium tuberculosis

Pyrazinamidase (PZase) is a member of Fe-dependent amidohydrolases that activates pyrazinamide (PZA) into active pyrazinoic acid (POA). PZA, a nicotinamide analogue, is an essential first-line drug used in Mycobacterium tuberculosis (Mtb) treatment. The active form of PZA, POA, is toxic and potently inhibits the growth of latent Mtb, which makes it possible to shorten the conventional 9-month tuberculosis treatment to 6 months. In this study, an extensive molecular dynamics simulation was carried out to the study the resistance mechanism offered by the three mutations Q10P and D12A and G97D. Our results showed that two regions Gln10-His43, Phe50-Gly75 are profoundly affected by these mutations. Among the three mutations, Q10P and D12A mutations strongly disturb the communication among the catalytic triad (Asp8, Lys98 and Cys138). The oxyanion hole is formed between the backbone nitrogen atoms of A134 and C138 which stabilizes the hydroxyl anion of nicotinamide. The D12A mutation greatly disturbs the oxyanion hole formation followed by the Q10P and G97D. Our results also showed that these mutations destabilize the interaction between Fe²⁺ ion and Asp49, His51, His57 and His71. The binding pocket analysis showed that these mutations increase the cavity volume, which results in loose binding of PZA. MMGBSA analyzes have shown that these mutations reduce the binding affinity to the PZA drug. Our results may provide useful information for the design of new and effective PZase inhibitors based on structural information of WT and mutant PZases.Communicated by Ramaswamy H. Sarma.

HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA

Protein–protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction of the three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, a free and open accessed web server, was developed to predict and analyze the structures of PPIs. In the HawkDock server, the ATTRACT docking algorithm, the HawkRank scoring function developed in our group and the MM/GBSA free energy decomposition analysis were seamlessly integrated into a multi-functional platform. The structures of PPIs were predicted by combining the ATTRACT docking and the HawkRank re-scoring, and the key residues for PPIs were highlighted by the MM/GBSA free energy decomposition. The molecular visualization was supported by 3Dmol.js. For the structural modeling of PPIs, HawkDock could achieve a better performance than ZDOCK 3.0.2 in the benchmark testing. For the prediction of key residues, the important residues that play an essential role in PPIs could be identified in the top 10 residues for ∼81.4% predicted models and ∼95.4% crystal structures in the benchmark dataset. To sum up, the HawkDock server is a powerful tool to predict the binding structures and identify the key residues of PPIs. The HawkDock server is accessible free of charge at http://cadd.zju.edu.cn/hawkdock/.

Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses

Emerging evidences indicate bromodomain-containing proteins 2 and 4 (BRD2 and BRD4) play critical roles in cancers, inflammations, cardiovascular diseases and other pathologies. Multiple short molecular dynamics (MSMD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were applied to investigate the binding selectivity of three inhibitors 87D, 88M and 89G towards BRD2 over BRD4. The root-mean-square fluctuation (RMSF) analysis indicates that the structural flexibility of BRD4 is stronger than that of BRD2. Moreover the calculated distances between the C_α atoms in the centres of the ZA_loop and BC_loop of BRD4 are also bigger than that of BRD2. The rank of binding free energies calculated using MM-GBSA method agrees well with that determined by experimental data. The results show that 87D can bind more favourably to BRD2 than BRD4, while 88M has better selectivity on BRD4 over BRD2. Residue-based free-energy decomposition method was utilized to estimate the inhibitor-residue interaction spectrum and the results not only identify the hot interaction spots of inhibitors with BRD2 and BRD4, but also demonstrate that several common residues, including (W370, W374), (P371, P375), (V376, V380) and (L381, L385) belonging to (BRD2, BRD4), generate significant binding difference of inhibitors to BRD2 and BRD4.

BREED based de novo hybridization approach: generating novel T790M/C797S-EGFR tyrosine kinase inhibitors to overcome the problem of mutation and resistance in non small cell lung cancer (NSCLC)

Third generation EGFR inhibitor osimertinib was approved as the first-line treatment for EGFR T790M mutation-positive Non-Small Cell Lung Cancer (NSCLC) patients in 2017. However, EGFR tertiary Cys797 to Ser797 (C797S) point mutation emanate rapidly after treatment of osimertinib, which is undruggable mutation to the all existing drugs. In this work, we have reported the novel T790M/C797S-EGFR Tyrosine Kinase inhibitors using BREED based de novo hybridization approach. BREED generates novel inhibitors from structures of known ligands bound to a common target. Among the generated hybridised breed compounds, the top best scorer breed molecules were breed 436, breed 530, breed 450, breed 562 and breed 313. Molecular Dynamics simulation of breed 436 for 10 ns further suggested that docked compound was stable into the pocket of the T790M/C797S-EGFR Tyrosine Kinase. In silico pharmacokinetic predictions of the breed hybridised compounds were within the defined range described for human use.Communicated by Ramaswamy H. Sarma.

Unveiling the Structural Insights into the Selective Inhibition of Protein Kinase D1

BACKGROUND

Although protein kinase D1 (PKD1) has been proved to be an efficient target for anticancer drug development, lack of structural details and substrate binding mechanisms are the main obstacles for the development of selective inhibitors with therapeutic benefits.

OBJECTIVE

The present study described the in silico dynamics behaviors of PKD1 in binding with selective and non-selective inhibitors and revealed the critical binding site residues for the selective kinase inhibition.

METHODS

Here, the three dimensional model of PKD1 was initially constructed by homology modeling along with binding site characterization to explore the non-conserved residues. Subsequently, two known inhibitors were docked to the catalytic site and the detailed ligand binding mechanisms and post binding dyanmics were investigated by molecular dynamics simulation and binding free energy calculations.

RESULTS

According to the binding site analysis, PKD1 serves several non-conserved residues in the G-loop, hinge and catalytic subunits. Among them, the residues including Leu662, His663, and Asp665 from hinge region made polar interactions with selective PKD1 inhibitor in docking simulation, which were further validated by the molecular dynamics simulation. Both inhibitors strongly influenced the structural dynamics of PKD1 and their computed binding free energies were in accordance with experimental bioactivity data.

CONCLUSION

The identified non-conserved residues likely to play critical role on molecular reorganization and inhibitor selectivity. Taken together, this study explained the molecular basis of PKD1 specific inhibition, which may help to design new selective inhibitors for better therapies to overcome cancer and PKD1 dysregulated disorders.

Computational investigation on the effect of Oleuropein aglycone on the α-synuclein aggregation

Parkinson's disease (PD) is considered to be the second most common progressive neurodegenerative brain disorder after Alzheimer's disease, which is caused by misfolding and aggregation of Alpha-synuclein (α-synuclein). It is characterized by distinct aggregated fibrillary form of α-synuclein known as the Lewy bodies and Lewy neurites. The most promising approach to combat PD is to prevent the misfolding and subsequent aggregation of α-synuclein. Recently, Oleuropein aglycone (OleA) has been reported to stabilize the monomeric structure of α-synuclein, subsequently favoring the growth of nontoxic aggregates. Therefore, understanding the conformational dynamics of α-synuclein monomer in the presence of OleA is significant. Here, we have investigated the effect of OleA on the conformational dynamics and the aggregation propensity of α-synuclein using molecular dynamics simulation. From molecular dynamics trajectory analysis, we noticed that when OleA is bound to α-synuclein, the intramolecular distance between non-amyloid-β component domain and C-terminal domain of α-synuclein was increased, whereas long-range hydrophobic interactions between the two region were reduced. Oleuropein aglycone was found to interact with the N-terminal domain of α-synuclein, making this region unavailable for interaction with membranes and lipids for the formation of cellular toxic aggregates. From the binding-free energy analysis, we found binding affinity between α-synuclein and OleA to be indeed high (ΔG_bind = -12.56 kcal mol^-1 from MM-PBSA and ΔG_bind = -27.41 kcal mol^-1from MM-GBSA). Our findings in this study thus substantiate the effect of OleA on the structure and stabilization of α-synuclein monomer that subsequently favors the growth of stable and nontoxic aggregates.Communicated by Ramaswamy H. Sarma.

How Different Substitution Positions of F, Cl Atoms in Benzene Ring of 5-Methylpyrimidine Pyridine Derivatives Affect the Inhibition Ability of EGFRL858R/T790M/C797S Inhibitors: A Molecular Dynamics Simulation Study

Lung cancer is the most frequent cause of cancer-related deaths worldwide, and mutations in the kinase domain of the epidermal growth factor receptor (EGFR) are a common cause of non-small-cell lung cancers, which is a major subtype of lung cancers. Recently, a series of 5-methylpyrimidine-pyridinone derivatives have been designed and synthesized as novel selective inhibitors of EGFR and EGFR mutants. However, the binding-based inhibition mechanism has not yet been determined. In this study, we carried out molecular dynamic simulations and free-energy calculations for EGFR derivatives to fill this gap. Based on the investigation, the three factors that influence the inhibitory effect of inhibitors are as follows: (1) The substitution site of the Cl atom is the main factor influencing the activity through steric effect; (2) The secondary factors are repulsion between the F atom (present in the inhibitor) and Glu762, and the blocking effect of Lys745 on the phenyl ring of the inhibitor. (3) The two factors function synergistically to influence the inhibitory capacity of the inhibitor. The theoretical results of this study can provide further insights that will aid the design of oncogenic EGFR inhibitors with high selectivity.

Molecular mechanism of inhibitor bindings to bromodomain-containing protein 9 explored based on molecular dynamics simulations and calculations of binding free energies

Recently, bromodomain-containing protein 9 (BRD9) has been a prospective therapeutic target for anticancer drug design. Molecular dynamics (MD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were adopted to explore binding modes of three inhibitors (5SW, 5U2, and 5U6) to BRD9 and identify the hot spot of the inhibitor-BRD9 binding. The results indicate that the inhibitor 5SW has the strongest binding ability to BRD9 among the current three inhibitors. Furthermore, the rank of the binding free energies predicted by MM-GBSA approach agrees with that determined by the experimental values. In addition, inhibitor-residue interactions were computed by using residue-based free-energy decomposition method and the results suggest that residue His42 produces the CH-H interactions, residues Asn100, Ile53 and Val49 produce the CH-[Formula: see text] interactions with three inhibitors and Tyr106, Phe45 and Phe44 generate the π-π interactions with inhibitors. Notably, the residue Asn140 forms hydrogen bonding interactions with three inhibitors. This research is expected to provide useful molecular basis and dynamics information at atomic levels for the design of potent inhibitors inhibiting the activity of BRD9.

Dynamics Insights Into the Gain of Flexibility by Helix-12 in ESR1 as a Mechanism of Resistance to Drugs in Breast Cancer Cell Lines

Incidents of breast cancer (BC) are on the rise on a daily basis and have proven to be the most prevelant cause of death for women in both developed and developing countries. Among total BC cases diagnosed after menopause, 70% of cases are Estrogen Receptor (ER) positive (ER-positive or ER+). Mutations in the LBD (ligand-binding domain) of the ER have recently been reported to be the major cause of resistance to potent antagonists. In this study, the experimentally reported mutations K303R, E380Q, V392I, S463P, V524E, P535H, P536H, Y537C, Y537N, Y537S, and D538G were analyzed, and the most significant mutations were shortlisted based on multiple analyses. Initial analyses, such as mCSM stability, occluded depth analysis, mCSM-binding affinity, and FoldX energy changes shortlisted only six mutations as being highly resistant. Finally, simulations of force field-based molecular dynamics (MD on wild type (WT) ERα) on six mERα variants (E380Q, S463P, Y537S, Y537C, Y537N, and D538G) were carried out to justify mechanism of the resistance. It was observed that these mutations increased the flexibility of the H12. A bonding analysis suggested that previously reported important residue His524 lost bonding upon mutation. Other parameters, such as PCA (principal component analysis), DCCM (dynamics cross-correlation), and FEL (free energy landscape), verified that the shortlisted mutations affect the H12 helix, which opens up the co-activator binding conformation. These results provide deep insight into the mechanism of relative resistance posed to fulvestrant due to mutations in breast cancer. This study will facilitate further understanding of the important aspects of designing specific and more effective drugs.

Deciphering Molecular Mechanism of the Neuropharmacological Action of Fucosterol through Integrated System Pharmacology and In Silico Analysis

Fucosterol is an algae-derived unique phytosterol having several medicinal properties, including antioxidant, anti-inflammatory, anticholinesterase, neuroprotective, and so on. Accumulated evidence suggests a therapeutic promise of fucosterol in neurodegeneration; however, the in-depth pharmacological mechanism of its neuroprotection is poorly understood. Here, we employed system pharmacology and in silico analysis to elucidate the underlying mechanism of neuropharmacological action of fucosterol against neurodegenerative disorders (NDD). Network pharmacology revealed that fucosterol targets signaling molecules, receptors, enzymes, transporters, transcription factors, cytoskeletal, and various other proteins of cellular pathways, including tumor necrosis factor (TNF), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), neurotrophin, and toll-like receptor (TLR) signaling, which are intimately associated with neuronal survival, immune response, and inflammation. Moreover, the molecular simulation study further verified that fucosterol exhibited a significant binding affinity to some of the vital targets, including liver X-receptor-beta (LXR-β), glucocorticoid receptor (GR), tropomyosin receptor kinase B (TrkB), toll-like receptor 2/4 (TLR2/4), and β-secretase (BACE1), which are the crucial regulators of molecular and cellular processes associated with NDD. Together, the present system pharmacology and in silico findings demonstrate that fucosterol might play a significant role in modulating NDD-pathobiology, supporting its therapeutic application for the prevention and treatment of NDD.

Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3–4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6–2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.

Machine-learning techniques for the prediction of protein-protein interactions

Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.

Molecular Docking and Molecular Dynamics Studies on Selective Synthesis of α-Amyrin and β-Amyrin by Oxidosqualene Cyclases from Ilex Asprella

Amyrins are the immediate precursors of many pharmaceutically important pentacyclic triterpenoids. Although various amyrin synthases have been identified, little is known about the relationship between protein structures and the constituent and content of the products. IaAS1 and IaAS2 identified from Ilex asprella in our previous work belong to multifunctional oxidosqualene cyclases and can produce α-amyrin and β-amyrin at different ratios. More than 80% of total production of IaAS1 is α-amyrin; while IaAS2 mainly produces β-amyrin with a yield of 95%. Here, we present a molecular modeling approach to explore the underlying mechanism for selective synthesis. The structures of IaAS1 and IaAS2 were constructed by homology modeling, and were evaluated by Ramachandran Plot and Verify 3D program. The enzyme-product conformations generated by molecular docking indicated that ASP484 residue plays an important role in the catalytic process; and TRP611 residue of IaAS2 had interaction with β-amyrin through π–σ interaction. MM/GBSA binding free energy calculations and free energy decomposition after 50 ns molecular dynamics simulations were performed. The binding affinity between the main product and corresponding enzyme was higher than that of the by-product. Conserved amino acid residues such as TRP257; TYR259; PHE47; TRP534; TRP612; and TYR728 for IaAS1 (TRP257; TYR259; PHE473; TRP533; TRP611; and TYR727 for IaAS2) had strong interactions with both products. GLN450 and LYS372 had negative contribution to binding affinity between α-amyrin or β-amyrin and IaAS1. LYS372 and ARG261 had strong repulsive effects for the binding of α-amyrin with IaAS2. The importance of Lys372 and TRP612 of IaAS1, and Lys372 and TRP611 of IaAS2, for synthesizing amyrins were confirmed by site-directed mutagenesis. The different patterns of residue–product interactions is the cause for the difference in the yields of two products.

Discovery of SBF1 as an allosteric inhibitor targeting the PIF-pocket of 3-phosphoinositide-dependent protein kinase-1

3-phosphoinositide-dependent protein kinase-1 (PDK1) plays a crucial role in the signal transduction of massive growth-related protein kinases. In this work, a computational study has been performed to investigate the binding pose of the hydrolyzed product of SBF1 (SBF1^-) with PDK1. The binding pose was predicted by Vina and was further refined in a molecular dynamics simulation. For comparison, four published low molecular weight compounds (PS48, PS171, PS182, and PS210) binding with PDK1 were also studied. SBF1^- was anchored in the PIF-pocket of PDK1 with salt bridge interaction using its carboxylate moiety, which is a common feature among the known ligands. Hydrogen bonds to THR148 and vdW interactions with GLN150 also have contributions to the association affinity. The allosteric regulation on PDK1 via the binding of SBF1^- was further addressed. The binding affinity of SBF1^- in complex with PDK1 is comparable to those of PS171 and PS182, with an estimated IC50 in a range from 2.0 to 10.0 μ molar. Comparison between the free energy profiles with the presence or absence of SBF1^- in the binding pocket indicates that the binding of SBF1^- enhances the hinge motion and suppresses the fluctuation of the end-to-end distance in α B of PDK1. These results demonstrate that SBF1^- is a promising allosteric regulator of PDK1 targeting the PIF-binding pocket and can serve as a new scaffold template for the design of new drugs targeting PDK1.

Insight into the selective binding mechanism of DNMT1 and DNMT3A inhibitors: a molecular simulation study

DNA methyltransferases (DNMTs), responsible for the regulation of DNA methylation, have been regarded as promising drug targets for cancer therapy. However, high structural conservation of the catalytic domains of DNMTs poses a big challenge to design selective inhibitors for a specific DNMT isoform. In this study, molecular dynamics (MD) simulations, end-point free energy calculations and umbrella sampling (US) simulations were performed to reveal the molecular basis of the binding selectivity of three representative DNMT inhibitors towards DNMT1 and DNMT3A, including SFG (DNMT1 and DNMT3A dual inhibitors), DC-05 (DNMT1 selective inhibitor) and GSKex1 (DNMT3A selective inhibitor). The binding selectivity of the studied inhibitors reported in previous experiments is reproduced by the MD simulation and binding free energy prediction. The simulation results also suggest that the driving force to determine the binding selectivity of the studied inhibitors stems from the difference in the protein-inhibitor van der Waals interactions. Meanwhile, the per-residue free energy decomposition reveals that the contributions from several non-conserved residues in the binding pocket of DNMT1/DNMT3A, especially Val1580/Trp893, Asn1578/Arg891 and Met1169/Val665, are the key factors responsible for the binding selectivity of DNMT inhibitors. In addition, the binding preference of the studied inhibitors was further validated by the potentials of mean force predicted by the US simulations. This study will provide valuable information for the rational design of novel selective inhibitors targeting DNMT1 and DNMT3A.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Background:

Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.

Methods:

We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.

Results:

Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.

Conclusion:

There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

The structural basis of the autoinhibition mechanism of glycogen synthase kinase 3β (GSK3β): molecular modeling and molecular dynamics simulation studies

The autoinhibition phenomenon has been frequently observed in enzymes and represents an important regulatory strategy to fine-tune enzyme activity. Evolution has exploited this mechanism to reduce enzymatic activity. Glycogen synthase kinase 3β (GSK3β) undergoes autoinhibition via the phosphorylation of Ser9 at the N-terminus of the kinase, which, acting as a pseudosubstrate, occupies the catalytic domain of GSK3β and subsequently blocks primed substrates from having access to the catalytic domain. The detailed structural basis of the autoinhibition mechanism of GSK3β by the pseudosubstrate, however, has not yet been fully resolved. Here, a three-dimensional model of the binary GSK3β-pseudosubstrate complex was built via the molecular modeling method. Based on the constructed model, extensive molecular dynamics (MD) simulations and subsequent molecular mechanics generalized Born/surface area (MM_GBSA) calculations were performed on the wild-type GSK3β-pseudosubstrate complex and three mutated systems (R4A, R6A, and S9A). Analyses of MD simulations and binding free energies revealed that the phosphorylation of Ser9 is the prerequisite for the autoinhibition of GSK3β, and both mutations of Arg4 and Arg6 to alanine markedly reduced the binding affinities of the mutated pseudosubstrate to the GSK3β catalytic domain, thereby disrupting the autoinhibition of the kinase. This study highlights the importance of Ser9, Arg6, and Arg4 in modulating the autoinhibition mechanism of GSK3β, contributing to a deeper understanding of GSK3β biology.Communicated by Ramaswamy H. Sarma.

Importance of protein flexibility in ranking ERK2 Type I1/2 inhibitor affinities: a computational study

Extracellular-regulated kinase (ERK2) has been regarded as an essential target for various cancers, especially melanoma. Recently, pyrrolidine piperidine derivatives were reported as Type I1/2 inhibitors of ERK2, which occupy both the ATP binding pocket and the allosteric pocket. Due to the dynamic behavior of ERK2 upon the binding of Type I1/2 inhibitors, it is difficult to predict the binding structures and relative binding potencies of these inhibitors with ERK2 accurately. In this work, the binding mechanism of pyrrolidine piperidines was discussed by using different simulation techniques, including molecular docking, ensemble docking based on multiple receptor conformation, molecular dynamics simulations and free energy calculations. Our computational results show that the traditional docking method cannot predict the relative binding ability of the studied inhibitors with high accuracy, but incorporating ERK2 protein flexibility into docking is an effective method to improve the prediction accuracy. It is worth noting that the binding free energies predicted by MM/GBSA or MM/PBSA based on the MD simulations for the docked poses have the highest correlation with the experimental data, which highlights the importance of protein flexibility for accurately predicting the binding ability of Type I1/2 inhibitors of ERK2. In addition, the comprehensive analysis of several representative inhibitors indicates that hydrogen bonds and hydrophobic interactions are of significance for improving the binding affinities of the inhibitors. We hope this work will provide valuable information for further design of novel and efficient Type I1/2 ERK2 inhibitors.