Software for molecular docking: a review

Phomopsterone B Alleviates Liver Fibrosis through mTOR-Mediated Autophagy and Apoptosis Pathway

Liver fibrosis is the initial pathological process of many chronic liver diseases. Targeting hepatic stellate cell (HSC) activation is an available strategy for the therapy of liver fibrosis. We aimed to explore the anti-liver fibrosis activity and potential mechanism of phomopsterone B (PB) in human HSCs. The results showed that PB effectively attenuated the proliferation of TGF-β1-stimulated LX-2 cells in a concentration-dependent manner at doses of 1, 2, and 4 μM. Quantitative real-time PCR and Western blot assays displayed that PB significantly reduced the expression levels of α-SMA and collagen I/III. AO/EB and Hoechst33342 staining and flow cytometry assays exhibited that PB promoted the cells' apoptosis. Meanwhile, PB diminished the number of autophagic vesicles and vacuolated structures, and the LC3B fluorescent spots indicated that PB could effectively inhibit the accretion of autophagosomes in LX-2 cells. Moreover, rapamycin and MHY1485 were utilized to further investigate the effect of mTOR in autophagy and apoptosis. The results demonstrated that PB regulated autophagy and apoptosis via the mTOR-dependent pathway in LX-2 cells. In summary, this is the first evidence that PB effectively alleviates liver fibrosis in TGF-β1-stimulated LX-2 cells, and PB may be a promising candidate for the prevention of liver fibrosis.

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking

To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.

Structure-based, deep-learning models for protein-ligand binding affinity prediction

The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been generated. To compensate for the lack of valid comparisons among those models, we realized and evaluated representatives from a uniform basis, with the advantages and shortcomings discussed. This review will potentially benefit structure-based drug discovery and related areas.

Repurposing dye ligands as antivirals via a docking approach on viral membrane and globular proteins - SARS-CoV-2 and HPV-16

A series of dye ligands are docked to three different proteins, E and 3a of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and E6 of human papilloma virus type 16 (HPV-16) using three different software. A four-level selection algorithm is used based on nonparametric statistics of numerical key values such as the "rank" derived from (i) averaged estimated binding energies (EBEs) and (ii) absolute EBE value of each of the software, (iii) frequency of ranking and (iv) rank of the area-under-curve values (AUCs) from decoy docking. A series of repurposing drugs and known antivirals used in experimental studies are docked for comparison. One dye ligand is ranked best for all proteins using the selection algorithm levels i - iii. Another three dye ligands are ranked top for the proteins individually when using all four levels.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Recent Deep Learning Applications to Structure-Based Drug Design

Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.

The Search for Antidotes Against Ricin

The castor plant (Ricinus communis) is primarily known for its seeds, which contain a unique fatty acid called ricinoleic acid with several industrial and commercial applications. Castor seeds also contain ricin, a toxin considered a chemical and biological warfare agent. Despite years of investigation, there is still no effective antidote or vaccine available. However, some progress has been made, and the development of an effective treatment may be on the horizon. To provide an updated overview of this issue, we have conducted a comprehensive review of the literature on the current state of research in the fight against ricin. This review is based on the reported research and aims to address the challenges faced by researchers, as well as highlight the most successful cases achieved thus far. Our goal is to encourage the scientific community to continue their efforts in this critical search.

Identification of novel inhibitors against Med15a KIX domain of Candida glabrata

Candida glabrata, the second most common cause of invasive fungal infections, exhibits multi-drug resistance to commonly used antifungal drugs. To counter this resistance, there is a critical need for novel antifungals. This study identifies small molecule inhibitors that target a three-helix bundle KIX domain in the Med15a Mediator subunit of Candida glabrata (CgMed15a KIX). This domain plays a crucial role by interacting with the Pleiotropic Drug Resistance transcription factor Pdr1, a key regulator of the multidrug resistance pathway in Candida glabrata. We performed high throughput computational screening of large chemical datasets against the binding sites of the CgMed15a KIX domain to identify novel inhibitors. We selected six potential candidates with high affinity and confirmed their binding with the CgMed15a KIX domain. A phytochemical compound, Chebulinic acid binds to the CgMed15a KIX domain with a KD value of 0.339 μM and shows significant inhibitory effects on the growth of Candida glabrata. Molecular dynamics simulation studies further revealed the structural stability of the CgMed15a KIX-Chebulinic acid complex. Thus, in conclusion, this study highlights Chebulinic acid as a novel potential antifungal compound against Candida glabrata.

The Discovery of Novel Agents against Staphylococcus aureus by Targeting Sortase A: A Combination of Virtual Screening and Experimental Validation

Staphylococcus aureus (S. aureus), commonly known as "superbugs", is a highly pathogenic bacterium that poses a serious threat to human health. There is an urgent need to replace traditional antibiotics with novel drugs to combat S. aureus. Sortase A (SrtA) is a crucial transpeptidase involved in the adhesion process of S. aureus. The reduction in virulence and prevention of S. aureus infections have made it a significant target for antimicrobial drugs. In this study, we combined virtual screening with experimental validation to identify potential drug candidates from a drug library. Three hits, referred to as Naldemedine, Telmisartan, and Azilsartan, were identified based on docking binding energy and the ratio of occupied functional sites of SrtA. The stability analysis manifests that Naldemedine and Telmisartan have a higher binding affinity to the hydrophobic pockets. Specifically, Telmisartan forms stable hydrogen bonds with SrtA, resulting in the highest binding energy. Our experiments prove that the efficiency of adhesion and invasion by S. aureus can be decreased without significantly affecting bacterial growth. Our work identifies Telmisartan as the most promising candidate for inhibiting SrtA, which can help combat S. aureus infection.

Annonaceous acetogenins as promising DNA methylation inhibitors to prevent and treat leukemogenesis - an in silico approach

Leukemia is a haematological malignancy affecting blood and bone marrow, ranking 10th among the other common cancers. DNA methylation is an epigenetic dysregulation that plays a critical role in leukemogenesis. DNA methyltransferases (DNMTs) such as DNMT1, DNMT3A and DNMT3B are the key enzymes catalysing DNA methylation. Inhibition of DNMT1 with secondary metabolites from medicinal plants helps reverse DNA methylation. The present study focuses on inhibiting DNMT1 protein (PDB ID: 3PTA) with annonaceous acetogenins through in-silico studies. The docking and molecular dynamic (MD) simulation study was carried out using Schrödinger Maestro and Desmond, respectively. These compounds' drug likeliness, ADMET properties and bioactivity scores were analysed. About 76 different acetogenins were chosen for this study, among which 17 showed the highest binding energy in the range of -8.312 to -10.266 kcal/mol. The compounds with the highest negative binding energy were found to be annohexocin (-10.266 kcal/mol), isoannonacinone (-10.209 kcal/mol) and annonacin (-9.839 kcal/mol). MD simulation results reveal that annonacin remains stable throughout the simulation time of 100 ns and also binds to the catalytic domain of DNMT1 protein. From the above results, it can be concluded that annonacin has the potential to inhibit the DNA methylation process and prevent leukemogenesis.Communicated by Ramaswamy H. Sarma.

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.

Inhibition of human glutathione transferase by catechin and gossypol: comparative structural analysis by kinetic properties, molecular docking and their efficacy on the viability of human MCF-7 cells

Glutathione transferase Pi (GSTP1) expression is increased in many cancer types and is associated with multidrug resistance and apoptosis inhibition. Inhibitors of GSTP1-1 have the potential to overcome drug resistance and improve chemotherapy efficacy as adjuvant agents. This study investigated the effects of catechin and gossypol on human glutathione transferase Pi (GSTP1-1) activity and their cytotoxic effects on breast cancer cells (MCF-7) individually and in combination with tamoxifen (TAM). Gossypol effectively inhibited the enzyme with an IC50 value of 40 μM, compared to 200 μM for catechin. Gossypol showed stronger inhibition of GSTP1-1 activity (Ki = 63.3 ± 17.5 μM) compared to catechin (Ki = 220 ± 44 μM). Molecular docking analysis revealed their binding conformations to GSTP1-1, with gossypol binding at the subunit interface in an un-competitive manner and catechin showing mixed non-competitive inhibition. Gossypol had severe cytotoxic effects on both MCF-7 cells and normal BJ1 cells, while catechin had a weak cytotoxic effect on MCF-7 cells only. Combination therapy with TAM resulted in cytotoxicity of 27.3% and 35.2% when combined with catechin and gossypol, respectively. Gossypol showed higher toxicity to MCF-7 cells, but its strong effects on normal cells raised concerns about selectivity and potential side effects.

Research advances of molecular docking and molecular dynamic simulation in recognizing interaction between muscle proteins and exogenous additives

During storage and processing, muscle proteins, e.g. myosin and myoglobin, will inevitably undergo degeneration, which is thus accompanied by quality deterioration of muscle foods. Some exogenous additives have been widely used to interact with muscle proteins to stabilize the quality of muscle foods. Molecular docking and molecular dynamics simulation (MDS) are regarded as promising tools for recognizing dynamic molecular information at atomic level. Molecular docking and MDS can explore chemical bonds, specific binding sites, spatial structure changes, and binding energy between additives and muscle proteins. Development and workflow of molecular docking and MDS are systematically summarized in this review. Roles of molecular simulations are, for the first time, comprehensively discussed in recognizing the interaction details between muscle proteins and exogenous additives aimed for stabilizing color, texture, flavor, and other properties of muscle foods. Finally, research directions of molecular docking and MDS for improving the qualities of muscle foods are discussed.

Countenance and implication of Β-sitosterol, Β-amyrin and epiafzelechin in nickel exposed Rat: in-silico and in-vivo approach

The detrimental impact of reactive oxygen species on D.N.A. repair processes is one of the contributing factors to colon cancer. The idea that oxidative stress may be a significant etiological element for carcinogenesis is currently receiving more and more support. The goal of the current study is to evaluate the anti-inflammatory and anticancer activity of three powerful phytocompounds-sitosterol, amyrin, and epiafzelechin-alone and in various therapeutic combinations against colon cancer to identify the critical mechanisms that mitigate nickel's carcinogenic effect. To evaluate the ligand-protein interaction of four selected components against Vascular endothelial growth factor (VEGF), Matrix metalloproteinase-9 (MMP9) inhibitor and Interleukin-10 (IL-10) molecular docking approach was applied using PyRx bioinformatics tool. For in vivo analysis, hundred albino rats were included, divided into ten groups, each containing ten rats of weight 160-200 g. All the groups were injected with 1 ml/kg nickel intraperitoneally per week for three months, excluding the negative control group. Three of the ten groups were treated with β-sitosterol (100 mg/kg b wt), β-amyrin (100 mg/kg b wt), and epiafzelechin (200 mg/kg b wt), respectively, for one month. The later four groups were fed with combinatorial treatments of the three phyto compounds for one month. The last group was administered with commercial drug Nalgin (500 mg/kg b wt). The biochemical parameters Creatinine, Protein carbonyl, 8-hydroxydeoxyguanosine (8-OHdG), VEGF, MMP-9 Inhibitor, and IL-10 were estimated using ELISA kits and Glutathione (G.S.H.), Superoxide dismutase (S.O.D.), Catalase (C.A.T.) and Nitric Oxide (NO) were analyzed manually. The correlation was analyzed through Pearson's correlation matrix. All the parameters were significantly raised in the positive control group, indicating significant inflammation. At the same time, the levels of the foresaid biomarkers were decreased in the serum in all the other groups treated with the three phytocompounds in different dose patterns. However, the best recovery was observed in the group where the three active compounds were administered concomitantly. The correlation matrix indicated a significant positive correlation of IL-10 vs VEGF (r = 0.749**, p = 0.009), MMP-9 inhibitor vs SOD (r = 0.748**, p = 0.0 21). The study concluded that the three phytocompounds β-sitosterol, β-amyrin, and epiafzelechin are important anticancer agents which can target the cancerous biomarkers and might be used as a better therapeutic approach against colon cancer soon.

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination

Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds promise for drug development. However, experimental confirmation of the peculiarities of PPIs is challenging due to their dynamic and transient nature. As a complement to experimental technologies, multiple computational molecular docking (MD) methods have been developed to predict the structures of protein-protein complexes and their dynamics, still requiring further improvements in several issues. Here, we report an improved MD method, namely three-software docking (3SD), by employing three popular protein-peptide docking software (CABS-dock, HPEPDOCK, and HADDOCK) in combination to ensure constant quality for most targets. We validated our 3SD performance in known protein-peptide interactions (PpIs). We also enhanced MD performance in proteins having intrinsically disordered regions (IDRs) by applying the modified 3SD strategy, the three-software docking after removing random coiled IDR (3SD-RR), to the comparable crystal PpI structures. At the end, we applied 3SD-RR to the AlphaFold2-predicted receptors, yielding an efficient prediction of PpI pose with high relevance to the experimental data regardless of the presence of IDRs or the availability of receptor structures. Our study provides an improved solution to the challenges in studying PPIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. SIGNIFICANCE STATEMENT: Protein-protein interactions (PPIs) are integral to life, and abnormal PPIs are associated with diseases such as cancer. Studying protein-peptide interactions (PpIs) is challenging due to their dynamic and transient nature. Here we developed improved docking methods (3SD and 3SD-RR) to predict the PpI poses, ensuring constant quality in most targets and also addressing issues like intrinsically disordered regions (IDRs) and artificial intelligence-predicted structures. Our study provides an improved solution to the challenges in studying PpIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery.

Ensemble-based virtual screening of African natural products to target human thymidylate synthase

Human thymidylate synthase (hTS) is a validated drug target for chemotherapy. A virtual screening experiment was used to prioritize a list of compounds from African Natural Products Databases docked against the orthosteric binding pocket of hTS. Consensus scores of binding affinities from ensemble-based virtual screening, hydrated docking and MM-PBSA calculations ranked compounds NEA4433 and NEA4434 as the best candidates owing to binding affinity scores in the picomolar order, their excellent ADMET profiles and the good stability of the protein-ligand complexes formed. The current study demonstrates the role of water in small molecule binding to hTS in mediating protein-ligand interactions. Similarly, the robust ensemble docking (relaxed scheme complex) ranked NEA4433 and NEA4434 as the best candidates. Furthermore, the best candidates prioritized were shown to strongly interact with the same residues that interacted with hTS substrate and cofactor.

Target-ligand binding affinity from single point enthalpy calculation and elemental composition

Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.

Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning

Flexible modeling of the protein-ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies limit their accuracy and efficiency because they retain massive sampling pressure and lack consideration for flexible biomolecular changes. In this study, we propose FlexPose, a geometric graph network capable of direct flexible modeling of complex structures in Euclidean space without the following conventional sampling and scoring strategies. Our model adopts two key designs: scalar-vector dual feature representation and SE(3)-equivariant network, to manage dynamic structural changes, as well as two strategies: conformation-aware pretraining and weakly supervised learning, to boost model generalizability in unseen chemical space. Benefiting from these paradigms, our model dramatically outperforms all tested popular docking tools and recently advanced deep learning methods, especially in tasks involving protein conformation changes. We further investigate the impact of protein and ligand similarity on the model performance with two conformation-aware strategies. Moreover, FlexPose provides an affinity estimation and model confidence for postanalysis.

GraphGPT: A Graph Enhanced Generative Pretrained Transformer for Conditioned Molecular Generation

Condition-based molecular generation can generate a large number of molecules with particular properties, expanding the virtual drug screening library, and accelerating the process of drug discovery. In this study, we combined a molecular graph structure and sequential representations using a generative pretrained transformer (GPT) architecture for generating molecules conditionally. The incorporation of graph structure information facilitated a better comprehension of molecular topological features, and the augmentation of a sequential contextual understanding of GPT architecture facilitated molecular generation. The experiments indicate that our model efficiently produces molecules with the desired properties, with valid and unique metrics that are close to 100%. Faced with the typical task of generating molecules based on a scaffold in drug discovery, our model is able to preserve scaffold information and generate molecules with low similarity and specified properties.

Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulation for the Discovery of Novel SARS-CoV-2 NSP3 Mac1 Domain Inhibitors

Since the emergence of SARS-CoV-2, many genetic variations within its genome have been identified, but only a few mutations have been found in nonstructural proteins (NSPs). Among this class of viral proteins, NSP3 is a multidomain protein with 16 different domains, and its largest domain is known as the macrodomain or Mac1 domain. In this study, we present a virtual screening campaign in which we computationally evaluated the NCI anticancer library against the NSP3 Mac1 domain, using Molegro Virtual Docker. The top hits with the best MolDock and Re-Rank scores were selected. The physicochemical analysis and drug-like potential of the top hits were analyzed using the SwissADME data server. The binding stability and affinity of the top NSC compounds against the NSP3 Mac1 domain were analyzed using molecular dynamics (MD) simulation, using Desmond software, and their interaction energies were analyzed using the MM/GBSA method. In particular, by applying subsequent computational filters, we identified 10 compounds as possible NSP3 Mac1 domain inhibitors. Among them, after the assessment of binding energies (ΔGbind) on the whole MD trajectories, we identified the four most interesting compounds that acted as strong binders of the NSP3 Mac1 domain (NSC-358078, NSC-287067, NSC-123472, and NSC-142843), and, remarkably, it could be further characterized for developing innovative antivirals against SARS-CoV-2.

Antifungal Constituents of Piper crocatum and Their Activities as Ergosterol Biosynthesis Inhibitors Discovered via In Silico Study Using ADMET and Drug-Likeness Analysis

Along with the increasing resistance of Candida spp. to some antibiotics, it is necessary to find new antifungal drugs, one of which is from the medicinal plant Red Betel (Piper crocatum). The purpose of this research is to isolate antifungal constituents from P. crocatum and evaluate their activities as ergosterol biosynthesis inhibitors via an in silico study of ADMET and drug-likeness analysis. Two new active compounds 1 and 2 and a known compound 3 were isolated, and their structures were determined using spectroscopic methods, while their bioactivities were evaluated via in vitro and in silico studies, respectively. Antifungal compound 3 was the most active compared to 1 and 2 with zone inhibition values of 14.5, 11.9, and 13.0 mm, respectively, at a concentration of 10% w/v, together with MIC/MFC at 0.31/1.2% w/v. Further in silico study demonstrated that compound 3 had a stronger ΔG than the positive control and compounds 1 and 2 with -11.14, -12.78, -12.00, and -6.89 Kcal/mol against ERG1, ERG2, ERG11, and ERG24, respectively, and also that 3 had the best Ki with 6.8 × 10-3, 4 × 10-4, 1.6 × 10-3, and 8.88 μM. On the other hand, an ADMET analysis of 1-3 met five parameters, while 1 had one violation of Ro5. Based on the research data, the promising antifungal constituents of P. crocatum allow P. crocatum to be proposed as a new antifungal candidate to treat and cure infections due to C. albicans.

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.

A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases

In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

The in-vitro development of novel enzyme-based chemo-mechanical caries removal agents

OBJECTIVES

Bromelain is a potent proteolytic enzyme that has a unique functionality makes it valuable for various therapeutic purposes. This study aimed to develop three novel formulations based on bromelain to be used as chemomechanical caries removal agents.

METHODS

The novel agents were prepared using different concentrations of bromelain (10-40 wt. %), with and without 0.1-0.3 wt. % chloramine T or 0.5-1.5 wt. % chlorhexidine (CHX). Based on the enzymatic activity test, three formulations were selected; 30 % bromelain (F1), 30 % bromelain-0.1 % chloramine (F2) and 30 % bromelain-1.5 % CHX (F3). The assessments included molecular docking, Fourier-transform infrared spectroscopy (FTIR), viscosity and pH measurements. The efficiency of caries removal was assessed by DIAGNOdent pen, measuring the excavation time and number of applications, followed by a morphological evaluation of the remaining dentine using scanning electron microscopy (SEM). The results were compared to Brix 3000 as a control.

RESULTS

The chloramine and chlorhexidine were chemically compatible with bromelain without compromising the enzyme activity. All experimental formulations showed higher viscosity and pH in comparison to Brix 3000. The DIAGNOdent readings were <20 in all groups, and the lowest readings were observed in F2. The excavation time and number of applications were lowest in F2 and F1. Both F2 and F3 produced smooth dentine surfaces with less tissue debris, but more patent dentine tubules were observed in F1 and F2.

CONCLUSIONS

The bromelain-contained formulations showed a potential to be used as chemomechanical caries removal agents in vitro. Further laboratory and clinical studies are needed to validate this claim.

CLINICAL SIGNIFICANCE

The bromelain from pineapple stem has broad specificity for cleavage the peptide bonds in denatured protein to facilitate their removal. The study proved the efficiency of this enzyme to remove the dental caries chemomechanically when used alone or conjugated with chloramine and/or chlorhexidine to enhance the disinfecting and cleansing properties.

On the Role of Temperature in the Depolymerization of PET by FAST-PETase: An Atomistic Point of View on Possible Active Site Pre-Organization and Substrate-Destabilization Effects

Enzyme FAST-PETase, recently obtained by a machine learning approach, can depolymerize poly(ethylene terephthalate) (PET), a synthetic resin employed in plastics and in clothing fibers. Therefore it represents a promising solution for the recycling of PET-based materials. In this study, a model of PET was adopted to describe the substrate, and all-atoms classical molecular dynamics (MD) simulations on apo- and substrate-bound FAST-PETase were carried out at 30 and 50 °C to provide atomistic details on the binding step of the catalytic cycle. Comparative analysis shed light on the interactions occurring between the FAST-PETase and 4PET at 50 °C, the optimal working conditions of the enzyme. Pre-organization of the enzyme active and binding sites has been highlighted, while MD simulations of FAST-PETase:4PET pointed out the occurrence of solvent-inaccessible conformations of the substrate promoted by the enzyme. Indeed, neither of these conformations was observed during MD simulations of the substrate alone in solution performed at 30, 50 and 150 °C. The analysis led us to propose that, at 50 °C, the FAST-PETase is pre-organized to bind the PET and that the interactions occurring in the binding site can promote a more reactive conformation of PET substrate, thus enhancing the catalytic activity of the enzyme.

Unveiling the Anti-Cancer Potential of Onoceranoid Triterpenes from Lansium domesticum Corr. cv. kokosan: An In Silico Study against Estrogen Receptor Alpha

Breast cancer is a significant global concern, with tamoxifen, the standard treatment, raising long-term safety issues due to side effects. In this study, we evaluated the potential of five onoceranoid triterpenes from Lansium domesticum Corr. cv. kokosan against estrogen receptor alpha (ERα) using in silico techniques. Utilizing molecular docking, Lipinski's rule of five, in silico ADMET, and molecular dynamics simulations, we assessed the potency of five onoceranoid triterpenes against ERα. Molecular docking indicated competitive binding energies for these triterpenes relative to the active form of tamoxifen (4OHT) and estradiol, an ERα native ligand. Three triterpenes met drug-likeness criteria with favorable ADMET profiles. Notably, 2 demonstrated superior binding affinity in molecular dynamics simulations, outperforming estradiol, closely followed by 3 and 4. Hierarchical clustering on principal components (HCPC) and the spatial distribution of contact surface area (CSA) analyses suggest that these triterpenes, especially 2, may act as antagonist ligands akin to 4OHT. These findings highlight the potential of onoceranoid triterpenes in treating ERα-related breast cancer.

Evaluation of the Binding Properties of A New Phenylurea Appended Carbazole Compound to Pepsin/Trypsin by Computational and Multi-Spectral Analysis

A novel carbazole compound, named 1-(9-ethyl-9H-carbazol-3-yl)-3-phenylurea (Cpu) was synthesized and its binding properties with protease enzymes (pepsin and trypsin) has been examined by steady-state fluorescence measurements, UV/vis absorption, infrared (FT-IR) and circular dicroism (CD) spectroscopies and also computational methods. The fluorescence experimental results indicated that the quenching mechanism of enzyme by Cpu is static process. The thermodynamic parameters (both negative ΔH/ΔS) and molecular docking results suggested that the binding of Cpu to pepsin/trypsin were driven by hydrogen bonds and van der Waals forces. Based on Förster's theory, the binding distance (r) between pepsin/trypsin and Cpu was calculated to be 3.072/2.784 nm, which implies that non-radiative energy transfer occurs from enzyme to Cpu. Furthermore, absorption, CD, and FT-IR spectral analysis provided an evidence that the presence of Cpu induced notable changes in the secondary structures and microenvironmental of both pepsin and trypsin, supporting its significant influence on these enzymes.

Unraveling the potential mechanisms of the anti-osteoporotic effects of the Achyranthes bidentata-Dipsacus asper herb pair: a network pharmacology and experimental study

Background: Osteoporosis is a prevalent bone metabolism disease characterized by a reduction in bone density, leading to several complications that significantly affect patients' quality of life. The Achyranthes bidentata-Dipsacus asper (AB-DA) herb pair is commonly used in Traditional Chinese Medicine (TCM) to treat osteoporosis. This study aimed to investigate the therapeutic compounds and potential mechanisms of AB-DA using network pharmacology, molecular docking, molecular dynamics simulation, and experimental verification. Methods: Identified compounds of AB-DA were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicine Information Database (TCM-ID), TCM@Taiwan Database, BATMAN-TCM, and relevant literature. The main bioactive ingredients were screened based on the criteria of "OB (oral bioavailability) ≥ 30, DL (drug-likeness) ≥ 0.18." Potential targets were predicted using the PharmMapper and SwissTargetPrediction websites, while disease (osteoporosis)-related targets were obtained from the GeneCards, DisGeNET, and OMIM databases. The PPI network and KEGG/GO enrichment analysis were utilized for core targets and pathway screening in the STRING and Metascape databases, respectively. A drug-compound-target-pathway-disease network was constructed using Cytoscape software to display core regulatory mechanisms. Molecular docking and dynamics simulation techniques explored the binding reliability and stability between core compounds and targets. In vitro and in vivo validation experiments were utilized to explore the anti-osteoporosis efficiency and mechanism of sitogluside. Results: A total of 31 compounds with 83 potential targets for AB-DA against osteoporosis were obtained. The PPI analysis revealed several hub targets, including AKT1, CASP3, EGFR, IGF1, MAPK1, MAPK8, and MAPK14. GO/KEGG analysis indicated that the MAPK cascade (ERK/JNK/p38) is the main pathway involved in treating osteoporosis. The D-C-T-P-T network demonstrated therapeutic compounds that mainly consisted of iridoids, steroids, and flavonoids, such as sitogluside, loganic acid, and β-ecdysterone. Molecular docking and dynamics simulation analyses confirmed strong binding affinity and stability between core compounds and targets. Additionally, the validation experiments showed preliminary evidence of antiosteoporosis effects. Conclusion: This study identified iridoids, steroids, and flavonoids as the main therapeutic compounds of AB-DA in treating osteoporosis. The underlying mechanisms may involve targeting core MAPK cascade (ERK/JNK/p38) targets, such as MAPK1, MAPK8, and MAPK14. In vivo experiments preliminarily validated the anti-osteoporosis effect of sitogluside. Further in-depth experimental studies are required to validate the therapeutic value of AB-DA for treating osteoporosis in clinical practice.

Synthesis and in silico study of chenodeoxycholic acid and its analogues as an alternative inhibitor of spike glycoprotein of SARS-CoV-2

COVID-19, caused by SARS-CoV-2, is a viral infection that has generated one of the most significant health problems in the world. Spike glycoprotein is a crucial enzyme in viral replication and transcription mediation. There are reports in the literature on using bile acid in the fight against this virus through in vitro tests. This work presents the synthesis of nine chenodeoxycholic acid derivatives (1-9), which were prepared by oxidation, acetylation, formylation, and esterification reactions, and the analogs 6-9 have not yet been reported in the literature and the possibility of conducting an in silico study of bile acid derivatives as a therapeutic alternative to combat the virus using glycoprotein as a macromolecular target. As a result, five compounds (1, 6-9) possessed favorable competitive interactions with the lowest energies compared to the native ligand (BLA), and the highlighted compound 9 got the best scores. At the same time, analog 1 presented the best ADME filter result. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. Physical and spectroscopic data have fully characterized all the compounds.Communicated by Ramaswamy H. Sarma.

Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions

One of the most important aspects of successful cancer therapy is the identification of a target protein for inhibition interaction. Conventionally, this consists of screening a panel of genes to assess which is mutated and then developing a small molecule to inhibit the interaction of two proteins or to simply inhibit a specific protein from all interactions. In previous work, we have proposed computational methods that analyze protein-protein networks using both topological approaches and thermodynamic quantification provided by Gibbs free energy. In order to make these approaches both easier to implement and free of arbitrary topological filtration criteria, in the present paper, we propose a modification of the topological-thermodynamic analysis, which focuses on the selection of the most thermodynamically stable proteins and their subnetwork interaction partners with the highest expression levels. We illustrate the implementation of the new approach with two specific cases, glioblastoma (glioma brain tumors) and chronic lymphatic leukoma (CLL), based on the publicly available patient-derived datasets. We also discuss how this can be used in clinical practice in connection with the availability of approved and investigational drugs.

Ormosianines A-P, Structurally Diverse Quinolizidine Alkaloids with AChE Inhibitory Effects from Ormosia yunnanensis

Sixteen new quinolizidine alkaloids (QAs), named ormosianines A-P (1-16), and 18 known congeners (17-34) were isolated from the stems and leaves of Ormosia yunnanensis. The structures were elucidated based on spectroscopic analyses and electron circular dichroism (ECD) calculations. Structurally, ormosianines A (1) and B (2) are the first examples of cytisine and Ormosia-type alkaloids with the cleavage of the piperidine ring. Results of the acetylcholinesterase (AChE) inhibitory assay revealed that the pentacycline Ormosia-type QAs, including 1, 16, 24, and 27-29, are good AChE inhibitors. Ormosianine A (1) exhibited more potent AChE inhibitory activity with an IC50 value of 1.55 μM. Molecular docking revealed that 1 might bind to the protein 1DX4, forming two hydrogen bonds with residues SER-238 and HIS-480.

Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery

In recent years machine learning has transformed many aspects of the drug discovery process including small molecule design for which the prediction of the bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches, but is fundamentally limited by the accuracy with which protein:ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase:inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario. The assembled benchmark data set focuses on the well-studied protein kinase family and comprises a subset of 589 protein structures co-crystallized with 423 ATP-competitive ligands. We find that the docking methods biased by the co-crystallized ligand-utilizing shape overlap with or without maximum common substructure matching-are more successful in recovering binding poses than standard physics-based docking alone. Also, docking into multiple structures significantly increases the chance to generate a low RMSD docking pose. Docking utilizing an approach that combines all three methods (Posit) into structures with the most similar co-crystallized ligands according to shape and electrostatics proofed to be the most efficient way to reproduce binding poses achieving a success rate of 66.9 % across all included systems. The studied docking and pose selection strategies-which utilize the OpenEye Toolkit-were implemented into pipelines of the KinoML framework allowing automated and reliable protein:ligand complex generation for future downstream machine learning tasks. Although focused on protein kinases, we believe the general findings can also be transferred to other protein families.

Advanced virtual screening enables the discovery of a host-targeting and broad-spectrum antiviral agent

We employed an advanced virtual screening (AVS) approach to identify potential inhibitors of human dihydroorotate dehydrogenase (DHODH), a validated target for development of broad-spectrum antivirals. We screened a library of 495118 compounds and identified 495 compounds that exhibited better binding scores than the reference ligands involved in the screening. From the top 100 compounds, we selected 28 based on their consensus docking scores and structural novelty. Then, we conducted in vitro experiments to investigate the antiviral activity of selected compounds on HSV-1 infection, which is susceptible to DHODH inhibitors. Among the tested compounds, seven displayed statistically significant antiviral effects, with Comp 19 being the most potent inhibitor. We found that Comp 19 exerted its antiviral effect in a dose-dependent manner (IC50 = 1.1 μM) and exhibited the most significant antiviral effect when added before viral infection. In the biochemical assay, Comp 19 inhibited human DHODH in a dose-dependent manner with the IC50 value of 7.3 μM. Long-timescale molecular dynamics simulations (1000 ns) revealed that Comp 19 formed a very stable complex with human DHODH. Comp 19 also displayed broad-spectrum antiviral activity and suppressed cytokine production in THP-1 cells. Overall, our study provides evidence that AVS could be successfully implemented to discover novel DHODH inhibitors with broad-spectrum antiviral activity.

An in-silico investigation and network pharmacology based approach to explore the anti-breast-cancer potential of Tecteria coadunata (Wall.) C. Chr

Uncontrolled cell proliferation is a common definition of cancer. After lung carcinoma, breast neoplasm is the second-most prevalent kind of cancer. The majority of breast cancer cells and healthy breast cells both have receptors for circulating oestrogen and progesterone. In order to promote the development and division of cancer cells, oestrogen and progesterone bind to the receptors and may collaborate with growth factors (such as oncogenes and mutant tumour suppressor genes). As per the literature, Tecteria coadunata (Wall.) C. Chr. has anticancer, antioxidant and anti-inflammatory potential. After the hydroalcoholic extraction of this rhizome, total of 200 phytochemicals were retrieved from HR-LCMS analysis. In this current study, Network pharmacology was carried out to explore the rationale of Tecteria coadunata (Wall.) C. Chr. by using different database using Cytoscape software. The network depicted the interaction of Bioactives with their targets and their association with several disease, especially breast cancer. Tecteria coadunata (Wall.) C. Chr. has offered new relationship with variety of genes and its applications in different types of breast cancers. Further Gene Ontology was carried out and it showed key targets were TP53, BRCA2, PGR and CHEK 2. Further Signalling pathways were also enriched. Flex-X software was used for molecular docking studies, and it verified that Dopaxanthin, Dantrolene and Orotidin shows the highest binding affinities with key targets. Additionally, Pharmacokinetic analysis revealed that all top three lead compounds which follows the Lipinski Rule (Rule of three) without interrupting the conditions of bioavailability with minimal toxicity.Communicated by Ramaswamy H. Sarma.

Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets

Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset-Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (n = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein-ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques.Communicated by Ramaswamy H. Sarma.

Mechanism of icariin for the treatment of osteoarthritis based on network pharmacology and molecular docking method

BACKGROUND

Icarin's mechanism of action in osteoarthritis (OA) was explored using network pharmacology and the GEO database, and then further validated using molecular docking.

METHODS

GEO database using network pharmacology identified differential genes in OA based on Icariin's possible targets predicted by pharmmapper database. Combining the differentially expressed genes in OA with the OA-related targets, the overlapping targets were removed. In order to determine what Icariin's core targets are for treating OA, PPI network analysis was performed using OA-related targets and possible Icariin targets. Furthermore, molecular docking was used to verify the chemical's binding to the targets. Final steps included Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Cytoscape was used to construct a network of compound-target-pathway-disease.

RESULTS

Protein-protein interactions between overlapping targets revealed 151 intersection targets based on a network analysis. The top ten targets with the highest enrichment scores were SRC, MAPK1, HSP90AA1, AKT1, PTPN11, ESR1, EGFR, RhoA, JAK2, and MAPK14. KEGG enrichment analysis showed that the pathways at which Icariin intervention occurs include the OA including FOXO signaling pathway, and estrogen signaling pathway. The GO analysis result showed that various biologic processes such as proteolysis, angiogenesis, innate immune response, and positive regulation of inflammatory response were involved in treatment. Molecular docking analysis confirmed that Icariin could bind well to the targets through intermolecular forces.

CONCLUSION

With its multi-targeting and multi-pathway characteristics, Icariin is a promising candidate drug for treating OA.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

Design, combinatorial synthesis and cytotoxic activity of 2-substituted furo[2,3-d]pyrimidinone and pyrrolo[2,3-d]pyrimidinone library

A facile protocol was developed for the combinatorial synthesis of furo[2,3-d]pyrimidinone and pyrrolo[2,3-d]pyrimidinone library via a one-pot condensation, from 2-amino furans/pyrroles. Herein reported process required a similar reaction condition, providing mild access to two diverse series of natural product-like heterocycles. Both furo[2,3-d]pyrimidinones and pyrrolo[2,3-d]pyrimidinones were evaluated in vitro against a panel of human cancer cell lines including against human cancer HeLa (cervical), MCF-7 (breast) and HT-29 (colon) cell lines. Derivative 12n ((2-(4-chlorophenyl)-1-methyl-6,7,8,9-tetrahydropyrido[1,2-a]pyrrolo[2,3-d]pyrimidin-4(1H)-one)) showed high activity (IC50 = 6.55 ± 0.31 µM) against the HeLa cell line. These products could be subjected to a various modification and therefore represent important skeletons for the anticancer drug discovery.

Antifungal therapy: Novel drug delivery strategies driven by new targets

In patients with compromised immunity, invasive fungal infections represent a significant cause of mortality. Given the limited availability and drawbacks of existing first-line antifungal drugs, there is a growing interest in exploring novel targets that could facilitate the development of new antifungal agents or enhance the effectiveness of conventional ones. While previous studies have extensively summarized new antifungal targets inherent in fungi for drug development purposes, the exploration of potential targets for novel antifungal drug delivery strategies has received less attention. In this review, we provide an overview of recent advancements in new antifungal drug delivery strategies that leverage novel targets, including those located in the physio-pathological barrier at the site of infection, the infection microenvironment, fungal-host interactions, and the fungal pathogen itself. The objective is to enhance therapeutic efficacy and mitigate toxic effects in fungal infections, particularly in challenging cases such as refractory, recurrent, and drug-resistant invasive fungal infections. We also discuss the current challenges and future prospects associated with target-driven antifungal drug delivery strategies, offering important insights into the clinical implementation of these innovative approaches.

Molecular Glue Discovery: Current and Future Approaches

The intracellular interactions of biomolecules can be maneuvered to redirect signaling, reprogram the cell cycle, or decrease infectivity using only a few dozen atoms. Such "molecular glues," which can drive both novel and known interactions between protein partners, represent an enticing therapeutic strategy. Here, we review the methods and approaches that have led to the identification of small-molecule molecular glues. We first classify current FDA-approved molecular glues to facilitate the selection of discovery methods. We then survey two broad discovery method strategies, where we highlight the importance of factors such as experimental conditions, software packages, and genetic tools for success. We hope that this curation of methodologies for directed discovery will inspire diverse research efforts targeting a multitude of human diseases.

Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (r_ext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC₅₀ values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

The Anti-Inflammatory Mechanism of Flaxseed Linusorbs on Lipopolysaccharide-Induced RAW 264.7 Macrophages by Modulating TLR4/NF-κB/MAPK Pathway

Flaxseed linusorbs (FLs), cyclic peptides derived from flaxseed oils, have shown multiple activities such as anticancer, antibacterial, and anti-inflammatory effects. However, the anti-inflammatory monomers of FLs and their mechanisms are still unclear. In this study, we have elucidated that FLs suppress the modulation of NF-κB/MAPK signaling pathways by targeting the inhibition of activating TLR4 in LPS-induced RAW 264.7 cells. Therefore, the transcription and expression of inflammatory cytokines (i.e., TNF-α, IL-1β, and IL-6) and inflammatory mediator proteins (i.e., iNos and Cox-2) were significantly suppressed by FLs. In addition, an in silico study discovered that eight monomers of FLs showed high-affinity bindings with TLR4. In silico data combined with HPLC results indicated that FLA and FLE, accounting for 44%, were likely the major anti-inflammatory monomers in FLs. In summary, FLA and FLE were proposed as the main anti-inflammatory active cyclopeptides via hindering TLR4/NF-κB/MAPK signaling pathways, suggesting the potential use of food-derived FLs as natural anti-inflammatory supplements in a daily diet.

Structure-Based Design of Novel MAO-B Inhibitors: A Review

With the significant growth of patients suffering from neurodegenerative diseases (NDs), novel classes of compounds targeting monoamine oxidase type B (MAO-B) are promptly emerging as distinguished structures for the treatment of the latter. As a promising function of computer-aided drug design (CADD), structure-based virtual screening (SBVS) is being heavily applied in processes of drug discovery and development. The utilization of molecular docking, as a helping tool for SBVS, is providing essential data about the poses and the occurring interactions between ligands and target molecules. The current work presents a brief discussion of the role of MAOs in the treatment of NDs, insight into the advantages and drawbacks of docking simulations and docking software, and a look into the active sites of MAO-A and MAO-B and their main characteristics. Thereafter, we report new chemical classes of MAO-B inhibitors and the essential fragments required for stable interactions focusing mainly on papers published in the last five years. The reviewed cases are separated into several chemically distinct groups. Moreover, a modest table for rapid revision of the revised works including the structures of the reported inhibitors together with the utilized docking software and the PDB codes of the crystal targets applied in each study is provided. Our work could be beneficial for further investigations in the search for novel, effective, and selective MAO-B inhibitors.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

End-to-end protein-ligand complex structure generation with diffusion-based generative models

BACKGROUND

Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods.

RESULTS

We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein-ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available.

CONCLUSION

The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein-ligand complexes, and we expect further improvements and wide applications.

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.