Discovery of novel inhibitors for Pseudomonas aeruginosa lipase enzyme from in silico and in vitro studies.

Pseudomonas aeruginosa is an opportunistic pathogen prone to developing drug-resistance and is a major cause of infection for burn patients and patients suffering from cystic fibrosis or are hospitalized in intensive care units. One of the virulence factors of this bacterium is the lipase enzyme that degrades the extracellular matrix of the host tissue and promotes invasion. Bromhexine is a mucolytic drug and has recently been reported to function as a competitive inhibitor of lipase with an IC50 value of 49 µM. In the present study, an attempt was made to identify stronger inhibitors from the ChEMBL database of bioactive compounds, as compared to the reference compound Bromhexine. Following docking and MD simulations, four hit compounds (N1-N4) were selected that showed promising binding modes and low RMSD values indicative of stable protein-ligand complexes. From subsequent binding pose metadynamics (BPMD) simulations, two of these (N2 and N4) stood out as more potent than Bromhexine, displaying stable interactions with residues in the catalytic site of the enzyme. Biological investigations were performed for all four compounds. Among them, the same two hit compounds were found to be the most effective binders with IC50 values of 22.1 and 27.5 µM, respectively; i.e. roughly twice as efficient as the reference Bromhexine. Taken together, our results show that these hits can be promising new candidates to use as leads for the development of drugs targeting the P. aeruginosa lipase enzyme.Communicated by Ramaswamy H. Sarma.

Harnessing the druggability at orthosteric and allosteric sites of PD-1 for small molecule discovery by an integrated in silico pipeline.

The PD-1/PD-L1 interaction is a promising target for small molecule inhibitors in cancer immunotherapy, but targeting this interface has been challenging. While efforts have been made to identify compounds that target the orthosteric sites, no reports have explored the potential of small molecules to target the allosteric region of PD-1. Therefore, our study aims to establish a pipeline to identify small molecules that can effectively bind to either the orthosteric or allosteric pockets of PD-1. We categorized the PD-1 interface into two hot-spot zones (P-and N-zones) based on extensive analysis of its structural, dynamical, and energetic properties. These zones correspond to the orthosteric and allosteric PPI sites, respectively, targeted by monoclonal antibodies. We used a guided virtual screening workflow to identify hits from ∼7 million compounds library, which were then clustered based on structural similarity and assessed by interaction fingerprinting. The selective and diverse chemical representatives were subjected to MD simulations and binding energetics calculations to filter out false positives and identify actual binders. Binding poses metadynamics calculations confirmed the stability of the final hits in the pocket. This study emphasizes the need for an integrated pipeline that uses molecular dynamics simulations and binding energetics to identify potential binders for the dynamic PD-1/PD-L1 interface, due to the lack of small molecule co-crystals. Only a few potential binders were discovered from a large pool of molecules targeting both the allosteric and orthosteric zones. Our results suggest that the allosteric site has more potential than the orthosteric site for inhibitor design. The identified "computational hits" hold potential as starting points for in vitro evaluations followed by hit-to-lead optimization. Overall, this study represents an effort to establish a computational pipeline for exploring and enriching both the allosteric and orthosteric sites of PPI interfaces, "a tough but indispensable nut to crack".

Integrated molecular modeling and dynamics approaches revealed the mechanism of selective inhibition of HDAC6/8.

The high structural homology of histone deacetylases 6 and 8 (HDAC6/8) poses a challenge in achieving isoform selectivity and has resulted in adverse side effects due to pan-inhibition in clinical applications. Additionally, the rational design of dual-target inhibitors, centered on HDAC6/8, demands a profound understanding of their selectivity mechanisms. Addressing the urgent need for enhanced specificity in the development of inhibitors targeting specific isoforms, we elucidate the mechanism underpinning the selective inhibition of HDAC6/8 inhibitors through in-silico strategies. The hydrogen bonding interaction with Asp101 and Tyr306 is a key factor that enables compound 12b to selectively inhibit HDAC8. Its favorable spatial orientation places the Cap group of 12b between Tyr306 and Tyr100, resulting in an overall L-shaped conformation. These two factors significantly contribute to the selective inhibitory activity of 12b against HDAC8. The zinc binding group (ZBG) of compound NN-390 forms a hydrogen bond with His610, a key residue of HDAC6, facilitating stable chelation with zinc ions. In addition, the Cap group of NN-390 interacts with Phe620 and Phe680 via van der Waals forces, leading to an overall Y-shaped conformation. The aforementioned factors are the main reasons for the selective inhibition of HDAC6 by NN-390. Furthermore, whether the Cap group is in the para or meta-position will influence the selective inhibition of either HDAC6 or HDAC8. We believe these clues can offer valuable insights for the rational design of selective inhibitors targeting HDAC6/8 and pave the way for rational design of dual-target HDAC6/8-based inhibitors.Communicated by Ramaswamy H. Sarma.

Identification of Potential JNK3 Inhibitors: A Combined Approach Using Molecular Docking and Deep Learning-Based Virtual Screening.

JNK3, a member of the MAPK family, plays a pivotal role in mediating cellular responses to stress signals, with its activation implicated in a myriad of inflammatory conditions. While JNK3 holds promise as a therapeutic target for neurodegenerative disorders such as Huntington's, Parkinson's, and Alzheimer's diseases, there remains a gap in the market for effective JNK3 inhibitors. Despite some pan-JNK inhibitors reaching clinical trials, no JNK-targeted therapies have achieved market approval. To bridge this gap, our study introduces a sophisticated virtual screening approach. We begin with an energy-based screening, subsequently integrating a variety of rescoring techniques. These encompass glide docking scores, MM/GBSA, and artificial scoring mechanisms such as DeepDock and advanced Graph Neural Networks. This virtual screening workflow is designed to evaluate and identify potential small-molecule inhibitors with high binding affinity. We have implemented a virtual screening workflow to identify potential candidate molecules. This process has resulted in the selection of ten molecules. Subsequently, these ten molecules have undergone biological activity evaluation to assess their potential efficacy. Impressively, molecule compound 6 surfaced as the most promising, exhibiting a potent kinase inhibitory activity marked by an IC50 of 130.1 nM and a notable reduction in TNF-α release within macrophages. This suggests that compound 6 could potentially serve as an effective inhibitor for the treatment of neuroinflammation and neurodegenerative diseases. The prospect of further medicinal modifications to optimize compound 6 presents a promising avenue for future research and development in this field. Utilizing binding pose metadynamics coupled with molecular dynamics simulations, we delved into the explicit binding mode of compound 6 to JNK3. Such insights pave the way for refined drug development strategies. Collectively, our results underscore the efficacy of the hybrid virtual screening workflow in the identification of robust JNK3 inhibitors, holding promise for innovative treatments against neuroinflammation and neurodegenerative disorders.

A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for monkeypox virus.

Monkeypox virus (MPXV) was confirmed in May 2022 and designated a global health emergency by WHO in July 2022. MPX virions are big, enclosed, brick-shaped, and contain a linear, double-stranded DNA genome as well as enzymes. MPXV particles bind to the host cell membrane via a variety of viral-host protein interactions. As a result, the wrapped structure is a potential therapeutic target. DeepRepurpose, an artificial intelligence-based compound-viral proteins interaction framework, was used via a transfer learning setting to prioritize a set of FDA approved and investigational drugs which can potentially inhibit MPXV viral proteins. To filter and narrow down the lead compounds from curated collections of pharmaceutical compounds, we used a rigorous computational framework that included homology modeling, molecular docking, dynamic simulations, binding free energy calculations, and binding pose metadynamics. We identified Elvitegravir as a potential inhibitor of MPXV virus using our comprehensive pipeline.

Identification of potential ATP-competitive cyclin-dependent kinase 1 inhibitors: De novo drug generation, molecular docking, and molecular dynamics simulation.

Cyclin-dependent kinases 1 (CDK1) has been identified as a potential target for the search for new antitumor drugs. However, no clinically effective CDK1 inhibitors are now available for cancer treatment. Therefore, this study aimed to offer potential CDK1 inhibitors using de novo drug generation, molecular docking, and molecular dynamics (MD) simulation studies. We first utilized the BREED algorithm (a de novo drug generation approach) to produce a novel library of small molecules targeting CDK1. To initially obtain novel potential CDK1 inhibitors with favorable physicochemical properties and excellent druggability, we performed a virtual rule-based rational drug screening on our generated library and found ten initial hits. Then, the molecular interactions and dynamic stability of these ten initial hits and CDK1 complexes during their all-atom MD simulations (total 18 µs) and binding pose metadynamics simulations were investigated, resulting in five final hits. Furthermore, another MD simulation (total 2.1 µs) with different force fields demonstrated the binding ability of the five hits to CDK1. It was found that these five hits, CBMA001 (ΔG = -29.88 kcal/mol), CBMA002 (ΔG = -34.89 kcal/mol), CBMA004 (ΔG = -32.47 kcal/mol), CBMA007 (ΔG = -31.16 kcal/mol), and CBMA008 (ΔG = -34.78 kcal/mol) possessed much greater binding affinity to CDK1 than positive compound Flavopiridol (FLP, ΔG = -25.38 kcal/mol). Finally, CBMA002 and CBMA004 were identified as excellent selective CDK1 inhibitors in silico. Together, this study provides a workflow for rational drug design and two promising selective CDK1 inhibitors that deserve further investigation.

Identifying Dopamine D3 Receptor Ligands through Virtual Screening and Exploring the Binding Modes of Hit Compounds.

The dopamine D3 receptor (D3R) is an important central nervous system target for treating various neurological diseases. D3R antagonists modulate the improvement of psychostimulant addiction and relapse, while D3R agonists can enhance the response to dopaminergic stimulation and have potential applications in treating Parkinson's disease, which highlights the importance of identifying novel D3R ligands. Therefore, we performed auto dock Vina-based virtual screening and D3R-binding-affinity assays to identify human D3R ligands with diverse structures. All molecules in the ChemDiv library (>1,500,000) were narrowed down to a final set of 37 molecules for the binding assays. Twenty-seven compounds exhibited over 50% inhibition of D3R at a concentration of 10 µM, and 23 compounds exhibited over 70% D3R inhibition at a concentration of 10 µM. Thirteen compounds exhibited over 80% inhibition of D3R at a concentration of 10 µM and the IC50 values were measured. The IC50 values of the five compounds with the highest D3R-inhibition rates ranged from 0.97 µM to 1.49 µM. These hit compounds exhibited good structural diversity, which prompted us to investigate their D3R-binding modes. After trial and error, we combined unbiased molecular dynamics simulation (MD) and molecular mechanics generalized Born surface area (MM/GBSA) binding free-energy calculations with the reported protein−ligand-binding pose prediction method using induced-fit docking (IFD) and binding pose metadynamics (BPMD) simulations into a self-consistent and computationally efficient method for predicting and verifying the binding poses of the hit ligands to D3R. Using this IFD-BPMD-MD-MM/GBSA method, we obtained more accurate and reliable D3R−ligand-binding poses than were obtained using the reported IFD-BPMD method. This IFD-BPMD-MD-MM/GBSA method provides a novel paradigm and reference for predicting and validating other protein−ligand binding poses.

A combination strategy of structure-based virtual screening, MM-GBSA, cross docking, molecular dynamics and metadynamics simulations used to investigate natural compounds as potent and specific inhibitors of tumor linked human carbonic anhydrase IX.

Cancer remains a serious health concern representing one of the leading causes of deaths worldwide. The enzyme human carbonic anhydrase IX (hCA IX) is found to be over-expressed in many cancer types and its selective inhibition over its cytosolic off-target isoform, human carbonic anhydrase II (hCA II), represents a potential area of research in the development of novel anticancer compounds. This work is concerned with the use of various in silico tools for the identification of natural product based molecules that can selectively inhibit hCA IX over hCA II. MM-GBSA assisted structure-based virtual screening against hCA IX was performed for nearly 225,000 natural products imported from the ZINC15 database. The obtained hits were checked for their potency by considering acetazolamide, the bound inhibitor of hCA IX, as the reference molecule, and 121 molecules were identified as potent hCA IX inhibitors. After ensuring their potency, cross-docking, followed by MM-GBSA calculations of the hits with hCA II, was performed, and their selectivity was assessed by considering the hCA IX selective compound SLC-0111 as the reference molecule, and 50 natural products were identified as potent as well as selective hCA IX inhibitors. Molecules with the quinoline scaffold showed the highest selectivity, and their selectivity was attributed to the strong electrostatic interactions of the zinc binding group (ZBG) with the active site Zn(II) ion. Furthermore, the stability of the binding modes of the top hCA IX selective hits was ensured by performing molecular dynamics (MD) simulations, which clearly proved that one of the short-listed molecules is truly selective, as it does not interact with the active site Zn(II) ion of hCA II, but interacts strongly with this ion in hCA IX. Bonding pose metadynamics studies revealed that the ligand moves to a more stable binding site from the one predicted by the docking studies and shows stronger interaction with the protein and Zn(II) at this binding site. The ligand is not likely to have issues with bioavailability. As a result, this ligand can be taken for bioassay testing and subsequently used as a feasible therapeutic treatment for a variety of cancer types. Communicated by Ramaswamy H. Sarma.

Exploring new catechin derivatives as SARS-CoV-2 Mpro inhibitors from tea by molecular networking, surface plasma resonance, enzyme inhibition, induced fit docking, and metadynamics simulations.

SARS-CoV-2 Mpro (Mpro) is the critical cysteine protease in coronavirus viral replication. Tea polyphenols are effective Mpro inhibitors. Therefore, we aim to isolate and synthesize more novel tea polyphenols from Zhenghedabai (ZHDB) white tea methanol-water (MW) extracts that might inhibit COVID-19. Through molecular networking, 33 compounds were identified and divided into 5 clusters. Further, natural products molecular network (MN) analysis showed that MN1 has new phenylpropanoid-substituted ester-catechin (PSEC), and MN5 has the important basic compound type hydroxycinnamoylcatechins (HCCs). Thus, a new PSEC (1, PSEC636) was isolated, which can be further detected in 14 green tea samples. A series of HCCs were synthesized (2-6), including three new acetylated HCCs (3-5). Then we used surface plasmon resonance (SPR) to analyze the equilibrium dissociation constants (KD) for the interaction of 12 catechins and Mpro. The KD values of PSEC636 (1), EGC-C (2), and EC-CDA (3) were 2.25, 2.81, and 2.44 µM, respectively. Moreover, compounds 1, 2, and 3 showed the potential Mpro inhibition with IC50 5.95 ± 0.17, 9.09 ± 0.22, and 23.10 ± 0.69 µM, respectively. Further, we used induced fit docking (IFD), binding pose metadynamics (BPMD), and molecular dynamics (MD) to explore the stable binding pose of Mpro-1, showing that 1 could tightly bond with the amino acid residues THR26, HIS41, CYS44, TYR54, GLU166, and ASP187. The computer modeling studies reveal that the ester, acetyl, and pyrogallol groups could improve inhibitory activity. Our research suggests that these catechins are effective Mpro inhibitors, and might be developed as therapeutics against COVID-19.