Advances in Computational Methods to Discover New NS2B-NS3 Inhibitors Useful Against Dengue and Zika Viruses

Structure-guided identification and characterization of potent inhibitors targeting PhoP and MtrA to combat mycobacteria

Mycobacteria are causative agents of tuberculosis (TB), which is a global health concern. Drug-resistant TB strains are rapidly emerging, thereby necessitating the urgent development of new drugs. Two-component signal transduction systems (TCSs) are signaling pathways involved in the regulation of various bacterial behaviors and responses to environmental stimuli. Applying specific inhibitors of TCSs can disrupt bacterial signaling, growth, and virulence, and can help combat drug-resistant TB. We conducted a comprehensive pharmacophore-based inhibitor screening and biochemical and biophysical examinations to identify, characterize, and validate potential inhibitors targeting the response regulators PhoP and MtrA of mycobacteria. The constructed pharmacophore model Phar-PR-n4 identified effective inhibitors of formation of the PhoP-DNA complex: ST132 (IC50 = 29 ± 1.6 µM) and ST166 (IC50 = 18 ± 1.3 µM). ST166 (KD = 18.4 ± 4.3 μM) and ST132 (KD = 14.5 ± 0.1 μM) strongly targeted PhoP in a slow-on, slow-off manner. The inhibitory potency and binding affinity of ST166 and ST132 for MtrAC were comparable to those of PhoP. Structural analyses and molecular dynamics simulations revealed that ST166 and ST132 mainly interact with the α8-helix and C-terminal β-hairpin of PhoP, with functionally essential residue hotspots for structure-based inhibitor optimization. Moreover, ST166 has in vitro antibacterial activity against Macrobacterium marinum. Thus, ST166, with its characteristic 1,2,5,6-tetrathiocane and terminal sulphonic groups, has excellent potential as a candidate for the development of novel antimicrobial agents to combat pathogenic mycobacteria.

Current trends to design antimalarial drugs targeting N-myristoyltransferase

Malaria is a disease caused by Plasmodium spp., of which Plasmodium falciparum and Plasmodium vivax are the most prevalent. Unfortunately, traditional and some current treatment regimens face growing protozoan resistance. Thus, searching for and exploring new drugs and targets is necessary. One of these is N-myristoyltransferase (NMT). This enzyme is responsible for the myristoylation of several protein substrates in eukaryotic cells, including Plasmodium spp., thus enabling the assembly of protein complexes and stabilization of protein-membrane interactions. Given the importance of this target in developing new antiparasitic drugs, this review aims to explore the recent advances in the design of antimalarial drugs to target Plasmodium NMT.

Mechanisms underlying the therapeutic effects of Gang Huo Qing wen granules in the treatment of influenza based on network pharmacology, molecular docking and molecular dynamics

Influenza (Flu) is a severe health, medical, and economic problem, but no medication that has excellent outcomes and lowers the occurrence of these problems is now available. GanghuoQingwenGranules (GHQWG) is a common Chinese herbal formula for the treatment of influenza (flu). However, its methods of action remain unknown. We used network pharmacology, molecular docking, and molecular dynamics simulation techniques to investigate the pharmacological mechanism of GHQWG in flu. TCMSP and various types of literature were used to obtain active molecules and targets of GHQWG. Flu-related targets were found in the Online Mendelian Inheritance in Man (OMIM) database, the DisFeNET database, the Therapeutic Target Database (TTD), and the DrugBank database. To screen the key targets, a protein-protein interaction (PPI) network was constructed. DAVID was used to analyze GO and KEGG pathway enrichment. Target tissue and organ distribution was assessed. Molecular docking was used to evaluate interactions between possible targets and active molecules. For the ideal core protein-compound complexes obtained using molecular docking, a molecular dynamics simulation was performed. In total, 90 active molecules and 312 GHQWG targets were discovered. The PPI network's topology highlighted six key targets. GHQWG's effects are mediated via genes involved in inflammation, apoptosis, and oxidative stress, as well as the TNF and IL-17 signaling pathways, according to GO and KEGG pathway enrichment analysis. Molecular docking and molecular dynamics simulations demonstrated that the active compounds and tested targets had strong binding capabilities. This analysis accurately predicts the effective components, possible targets, and pathways involved in GHQWG flu treatment. We proposed a novel study strategy for future studies on the molecular processes of GHQWG in flu treatment. Furthermore, the possible active components provide a dependable source for flu drug screening.

Discovery of a potent inhibitor, D-132, targeting AsfvPolX, via protein-DNA complex-guided pharmacophore screening and in vitro molecular characterizations

The heightened transmissibility and capacity of African swine fever virus (ASFV) induce fatal diseases in domestic pigs and wild boars, posing significant economic repercussions and global threats. Despite extensive research efforts, the development of potent vaccines or treatments for ASFV remains a persistent challenge. Recently, inhibiting the AsfvPolX, a key DNA repair enzyme, emerges as a feasible strategy to disrupt viral replication and control ASFV infections. In this study, a comprehensive approach involving pharmacophore-based inhibitor screening, coupled with biochemical and biophysical analyses, were implemented to identify, characterize, and validate potential inhibitors targeting AsfvPolX. The constructed pharmacophore model, Phar-PolX-S, demonstrated efficacy in identifying a potent inhibitor, D-132 (IC50 = 2.8 ± 0.2 µM), disrupting the formation of the AsfvPolX-DNA complex. Notably, D-132 exhibited strong binding to AsfvPolX (KD = 6.9 ± 2.2 µM) through a slow-on-fast-off binding mechanism. Employing molecular modeling, it was elucidated that D-132 predominantly binds in-between the palm and finger domains of AsfvPolX, with crucial residues (R42, N48, Q98, E100, F102, and F116) identified as hotspots for structure-based inhibitor optimization. Distinctively characterized by a 1,2,5,6-tetrathiocane with modifications at the 3 and 8 positions involving ethanesulfonates, D-132 holds considerable promise as a lead compound for the development of innovative agents to combat ASFV infections.

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

Targeting SmCB1: Perspectives and Insights to Design Antischistosomal Drugs

Neglected tropical diseases (NTDs) are prevalent in tropical and subtropical countries, and schistosomiasis is among the most relevant diseases worldwide. In addition, one of the two biggest problems in developing drugs against this disease is related to drug resistance, which promotes the demand to develop new drug candidates for this purpose. Thus, one of the drug targets most explored, Schistosoma mansoni Cathepsin B1 (SmCB1 or Sm31), provides new opportunities in drug development due to its essential functions for the parasite's survival. In this way, here, the latest developments in drug design studies targeting SmCB1 were approached, focusing on the most promising analogs of nitrile, vinyl sulphones, and peptidomimetics. Thus, it was shown that despite being a disease known since ancient times, it remains prevalent throughout the world, with high mortality rates. The therapeutic arsenal of antischistosomal drugs (ASD) consists only of praziquantel, which is widely used for this purpose and has several advantages, such as efficacy and safety. However, it has limitations, such as the impossibility of acting on the immature worm and exploring new targets to overcome these limitations. SmCB1 shows its potential as a cysteine protease with a catalytic triad consisting of Cys100, His270, and Asn290. Thus, design studies of new inhibitors focus on their catalytic mechanism for designing new analogs. In fact, nitrile and sulfonamide analogs show the most significant potential in drug development, showing that these chemical groups can be better exploited in drug discovery against schistosomiasis. We hope this manuscript guides the authors in searching for promising new antischistosomal drugs.

Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction

Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks for side-effect-drug-association prediction (TCSD). The method encodes and integrates attributes from multiple types of neighbor nodes, connection semantics, and multi-view pairwise information. In each drug-side-effect heterogeneous graph, a target node has two types of neighbor nodes, the drug nodes and the side-effect ones. We proposed a new heterogeneous graph transformer-based context representation learning module. The module is able to encode specific topology and the contextual relations among multiple kinds of nodes. There are similarity and association connections between the target node and its various types of neighbor nodes, and these connections imply semantic diversity. Therefore, we designed a new strategy to measure the importance of a neighboring node to the target node and incorporate different semantics of the connections between the target node and its multi-type neighbors. Furthermore, we designed attentions at the neighbor node type level and at the graph level, respectively, to obtain enhanced informative neighbor node features and multi-graph features. Finally, a pairwise multi-view feature learning module based on capsule networks was built to learn the pairwise attributes from the heterogeneous graphs. Our prediction model was evaluated using a public dataset, and the cross-validation results showed it achieved superior performance to several state-of-the-art methods. Ablation experiments undertaken demonstrated the effectiveness of heterogeneous graph transformer-based context encoding, the position enhanced pairwise attribute learning, and the neighborhood node category-level attention. Case studies on five drugs further showed TCSD's ability in retrieving potential drug-related side-effect candidates, and TCSD inferred the candidate side-effects for 708 drugs.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.