Protein-ligand Interactions Research Articles

Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions.

Quantum-chemical fragmentation methods offer an attractive approach for the accurate calculation of protein-ligand interaction energies. While the molecular fractionation with conjugate caps (MFCC) scheme offers a rather straightforward approach for this purpose, its accuracy is often not sufficient. Here, we upgrade the MFCC scheme for the calculation of protein-ligand interactions by including many-body contributions. The resulting fragmentation scheme is an extension of our previously developed MFCC-MBE(2) scheme [J. Comput. Chem. 2023, 44, 1634-1644]. For a diverse test set of protein-ligand complexes, we demonstrate that by upgrading the MFCC scheme with many-body contributions, the error in protein-ligand interaction energies can be reduced significantly, and one generally achieves errors below 20 kJ/mol. Our scheme allows for systematically reducing these errors by including higher-order many-body contributions. As it combines the use of single amino acid fragments with high accuracy, our scheme provides an ideal starting point for the parametrization of accurate machine learning potentials for proteins and protein-ligand interactions.

Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands. Research studies have shown that our fusion model, PLA-STGCNnet, outperforms individual algorithms in accurately predicting binding affinity. The advantage of a fusion model is the ability to fully combine the advantages of multiple different models and improve overall performance by combining their features and outputs. Our fusion model shows satisfactory performance on different data sets, which proves its generalization ability and stability. The fusion-based model showed good performance in protein-ligand affinity prediction, and we successfully applied the model to drug screening. Our research underscores the promise of fusion spatial-temporal graph neural networks in addressing complex challenges in protein-ligand affinity prediction. The Python scripts for implementing various model components are accessible at https://github.com/ligaili01/PLA-STGCN.

Nonspecific Yet Selective Interactions Contribute to Small Molecule Condensate Binding.

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by nonspecific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity toward specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

Benzylidenehydrazine Derivatives: Synthesis, Antidiabetic Evaluation, Antioxidation, Mode Of Inhibition, DFT And Molecular Docking Studies.

Diabetes mellitus (DM) is a metabolic condition that is a profound health concern across the globe due to its contribution to the increased mortality rate. It affects millions of people around the world and is associated with severe complications among people diagnosed with it. Herein, we report the synthesis of benzylidenehydrazine derivatives as well as their evaluation as α-glucosidase and α-amylase inhibitors including their antioxidant testing. Generally, all the synthesized derivatives were more potent inhibitors of α-amylase than of α-glucosidase. Specifically, 2,4 fluoro substituted analogue 9 (IC50 =116.19 µM) emerged as the strongest α-amylase inhibitor with ~5-fold superior activity in comparison to the standard drug, acarbose (IC50 = 600 µM). Compounds 18 (IC50= 240.59) and 19 (IC50 = 198.32 µM) displayed the strongest NO scavenging activity compared to Trolox (IC50 = 272.36 µM). In addition, the enzyme kinetic studies indicated that compound 9 acts as a non-competitive inhibitor of α-amylase. Finally, density functional theory and molecular docking studies for compound 9 were conducted to explore its structural and electronic properties as well as to determine protein-ligand interactions, respectively to decipher its observed activity.

Spectroscopic, quantum computational, molecular dynamic simulations, and molecular docking investigation of the biologically important compound-2,6-diaminopyridine

To establish the role in future drug development, 2,6-diamino pyridine (2,6-DAP) was investigated experimentally using computational tools. NMR (1H,13C), FTIR, and UV–Vis spectra were analyzed for the titled compound. The molecular structure was optimized via DFT with the “B3LYP method with 6-311++G(d,p)” The optimized parameters of the structure were assessed with the observed bond characteristics. The vibrational frequencies were studied, PED was carried out, and the calculated vibrational frequency obtained was compared with the experimentally obtained FTIR. NMR (1H,13C) was calculated and compared with the experimentally obtained spectra. UV–vis spectra are calculated in the gaseous phase and different mediums of solvents (methanol, DMSO) by employing the PCM solvent model and TD-DFT method, and the estimated value was compared with experimentally measured data. NBO analysis was done to study the donor-acceptor interaction. The extent of electron charge transfer inside the molecular structure was examined using HOMO–LUMO energy values. The study of excitation analysis led to the creation of E.D.D and H.D.D maps in methanol and DMSO. The AIM and ELF analysis was employed to compute the iso-surface projections, ellipticity, and binding energies. The MEP and Fukui functions evaluate the molecule’s reactive zones. Hirshfeld research was done to analyze interactions among the molecules in the structure of the crystal, and 3D Hirshfeld tops and 2D fingerprint layouts were developed. The molecular docking procedure was used, followed by a 100 ns MD simulation and computation of binding free energy found to delineate the binding affinity of the molecule toward human carbonic anhydrase II (HCA II). Molecular docking studies with several receptor proteins were also conducted to determine the most effective protein-ligand interactions. Biological assessments like drug-likeness also act upon the compound to verify the molecule’s drug-like nature.

DSDPFlex: Flexible-Receptor Docking with GPU Acceleration.

Molecular docking is an essential tool in structure-based drug discovery, widely utilized to model ligand-protein interactions and enrich potential hits. Among the different docking strategies, semiflexible docking (rigid-receptor and flexible-ligand model) is the most popular, benefiting from its balance of docking accuracy and speed. However, this approach ignores the conformational changes of proteins and hence demands suitable protein conformations as input. When the binding interaction adheres to an induced-fit model, flexible methods such as molecular dynamics simulation can be utilized, but they are computationally demanding. To balance between speed and accuracy, the flexible docking approach is an effective choice, as exemplified by AutoDock Vina and AutoDockFR, which treat selected protein side chains as flexible parts. However, the efficiency of flexible docking methods is yet to be improved for virtual screening usage. In this article, we introduce DSDPFlex, an improved flexible-receptor docking method accelerated by GPU parallelization. Beyond acceleration, optimizations with respect to sampling, scoring, and search space are implemented in DSDPFlex to further improve its capability in flexible tasks. In cross-docking evaluation, DSDPFlex demonstrates superior accuracy compared to AutoDock Vina and is 100 times faster than Vina in flexible-receptor tasks. We also show the advantage of flexible-receptor methods on suboptimal pockets and validate the advantage of DSDPFlex in screening on apo and AlphaFold2-predicted structures. With improvements in both efficiency and accuracy, DSDPFlex is expected to hold potential in future docking-based studies.

In Situ and Label-Free Quantification of Membrane Protein–Ligand Interactions Using Optical Imaging Techniques: A Review

Membrane proteins are crucial for various cellular processes and are key targets in pharmacological research. Their interactions with ligands are essential for elucidating cellular mechanisms and advancing drug development. To study these interactions without altering their functional properties in native environments, several advanced optical imaging methods have been developed for in situ and label-free quantification. This review focuses on recent optical imaging techniques such as surface plasmon resonance imaging (SPRi), surface plasmon resonance microscopy (SPRM), edge tracking approaches, and surface light scattering microscopy (SLSM). We explore the operational principles, recent advancements, and the scope of application of these methods. Additionally, we address the current challenges and explore the future potential of these innovative optical imaging strategies in deepening our understanding of biomolecular interactions and facilitating the discovery of new therapeutic agents.

Improving the Performance of Screening MM/PBSA in Protein-Ligand Interactions via Machine Learning

Abstract Accurately estimating protein-ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) method in combination with various machine learning techniques to compute the binding free energies of protein-ligand interactions. Our results demonstrate that machine learning outperforms direct screening MM/PBSA calculations in predicting protein-ligand binding free energies. Notably, the random forest (RF) method exhibited the best predictive performance, with a Pearson correlation coefficient (r p ) of 0.702 and a mean absolute error (MAE) of 1.379 kcal/mol. Furthermore, we analyzed feature importance rankings in the gradient boosting (GB), adaptive boosting (AdaBoost), and RF methods, and found that feature selection significantly impacted predictive performance. In particular, molecular weight (MW) and van der Waals (VDW) energies played a decisive role in the prediction. Overall, this study highlights the potential of combining machine learning methods with screening MM/PBSA for accurately predicting binding free energies in biosystems.

StreaMD: the toolkit for high-throughput molecular dynamics simulations.

Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.

Targeting mitochondrial dynamics: an in-silico approach for repurposing antifungal drugs in OSCC treatment

Drug repurposing for cancer treatment is a valuable strategy to identify existing drugs with known safety profiles that could combat the neoplasm, by reducing costs. Oral squamous cell carcinoma, an ulcer-proliferative lesion on the mucosal epithelium, is the most common oral malignancy. About 10% of cancer patients within the Indian subcontinent suffer from OSCC, primarily due to chewing of betel plant derivatives. Concomitant administration of the chemotherapeutic agent (Cisplatin/Paclitaxel) is the treatment of choice. Analysis of the oral mycobiome of OSCC patients has projected the role of Candida albicans in potentiating OSCC. Hence, repurposing antifungal drugs emerges as a promising approach, as these drugs could target both the cancer cells and the infection. Cancer cells often have heightened energy requirements, and targeting mitochondrial proteins to disrupt mitochondrial division and induce dysfunction contributing to cell death, offers a method for treating OSCC. We identified 18 mitochondrial targets playing a crucial role in the maintenance of mitochondrial homeostasis. They were docked against 125 antifungal ligand molecules sourced from PUBCHEM. Ligand profiling was performed using Lipinski’s rule of 5, SwissADME and ProTox. Also, molecular dynamics and MM-PBSA were performed to validate our results. Among all protein ligand interactions, we observed that targeting DRP1 with itraconazole yielded superior binding and stability. Overall, lower toxicity and thumping ADME properties solidified the choice of ligand. We hope this experimental approach will enable us to provide a basis for selecting a lead molecule for a possible novel nano-formulation and validate our finding through in-vitro cell line-based testing.

Accurate prediction of protein–ligand interactions by combining physical energy functions and graph-neural networks

We introduce an advanced model for predicting protein–ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein–ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein–ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein–ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model’s efficiency and generalizability. The model’s efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery.Scientific contributionOur work introduces a novel training strategy for a protein–ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.

Understanding and Predicting Ligand Efficacy in the μ-Opioid Receptor through Quantitative Dynamical Analysis of Complex Structures.

The μ-opioid receptor (MOR) is a G-protein coupled receptor involved in nociception and the primary target of opioid drugs. Understanding the relationships among the ligand structure, receptor dynamics, and efficacy in activating MOR is crucial for drug discovery and development. Here, we use coarse-grained normal-mode analysis to predict ligand-induced changes in receptor dynamics with the Quantitative Dynamics Activity Relationship (QDAR) DynaSig-ML methodology, training a LASSO regression model on the entropic signatures (ESs) computed from ligand-receptor complexes. We train and validate the methodology using a data set of 179 MOR ligands with experimentally measured efficacies split into strictly chemically different cross-validation sets. By analyzing the coefficients of the ES LASSO model, we identified key residues involved in MOR activation, several of which have mutational data supporting their role in MOR activation. Additionally, we explored a contact-only LASSO model based on ligand-protein interactions. While the model showed predictive power, it failed at predicting efficacy for ligands with low structural similarity to the training set, emphasizing the importance of receptor dynamics for predicting ligand-induced receptor activation. Moreover, the low computational cost of our approach, at 3 CPU s per ligand-receptor complex, opens the door to its application in large-scale virtual screening contexts. Our work contributes to a better understanding of dynamics-function relationships in the μ-opioid receptor and provides a framework for predicting ligand efficacy based on ligand-induced changes in receptor dynamics.

Correction: Giri et al. Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge. Biomolecules 2023, 13, 132

There was an error in the original publication [...]

Revealing Missing Protein–Ligand Interactions Using AlphaFold Predictions

Protein–ligand interactions represent an essential step to understand the bases of molecular recognition, an intense field of research in many scientific areas. Structural biology has played a central role in unveiling protein–ligand interactions, but current techniques are still not able to reliably describe the interactions of ligands with highly flexible regions. In this work, we explored the capacity of AlphaFold2 (AF2) to estimate the presence of interactions between ligands and residues belonging to disordered regions. As these interactions are missing in the crystallographic-derived structures, we called them “ghost interactions”. Using a set of protein structures experimentally obtained after AF2 was trained, we found that the obtained models are good predictors of regions associated with order–disorder transitions. Additionally, we found that AF2 predicts residues making ghost interactions with ligands, which are mostly buried and show differential evolutionary conservation with the rest of the residues located in the flexible region. Our findings could fuel current areas of research that consider, given their biological relevance and their involvement in diseases, intrinsically disordered proteins as potentially valuable targets for drug development.

Purification, characterization and molecular docking studies of analogous alpha amylase inhibitor compounds

BackgroundA large number of protein inhibitors are found in higher plants, cereals and legumes. These inhibitors are helpful in the prevention as well as medical treatment of metabolic syndromes such as type 2 diabetes. Basically, the diabetes mellitus is a chronic disorder that occurs either due to inadequate insulin secretions or when body fails to utilize the produced insulin. The alpha amylase inhibitors are termed as starch blockers. They catalyze the hydrolysis of α-(1,4)-D-glycosidic linkages of starch and other glucose polymer. They play a significant role in inhibition of the activity of salivary and pancreatic amylase in vitro and in vivo. MethodThe present study was based on the purification, characterization and molecular docking studies of the alpha amylase inhibitor isolated from the kidney bean sample. The seed sample was collected from G.B. Pant Nagar University. The crude extract was prepared, the in-vitro studies and heat stability were determined. Following it, purification was carried out using ammonium sulphate precipitation and size exclusion chromatography was done. Later, characterization and molecular docking studies of the purified sample was done after obtaining GC–MS results. ResultThe in-vitro analysis was done and noted that the inhibitory activity was 96.5 ± 0.84 % post size exclusion chromatography. The molecular weight was about 54 kDa. The molecular docking studies revealed that there were interactions between the ligand molecules (the constituents that were selected from the GC–MS chromatogram peaks based on the height and area) and the human pancreatic alpha amylase (1HNY). The hydrogen bonding as affinity binding capacity was also been observed in each of the ligand–protein interaction. ConclusionFrom the tests, it has been elucidated that the alpha amylase inhibitor isolated from the sample has a great potential to serve as an anti-diabetic drug. However, in order to check the potential effects and to explore the optimization and development of the anti-diabetic drug the in-vivo studies can also be done further.

Identification of new potential inhibitors of pteridine reductase-1 (PTR1) via biophysical and biochemical mechanism-based approaches: Step towards the treatment of Leishmaniasis

Leishmaniasis is a parasitic disease, which spreads from the bite of an infected Phlebotomine fly to human hosts. The disease is characterized by a number of clinical manifestations, such as ulcerative lesions at the site of sandfly bite (cutaneous form), inflammation of mucosal membranes (mucosal leishmaniasis) or the deadly visceral form. This study was aimed to target pteridine reductase-1 (PTR1), a member of short chain dehydrogenases, which accounts for the reduction of conjugated and unconjugated pterins in Leishmania parasite. The ptr1-pET28a+-tev construct was expressed using BL21 (DE3) cells, followed by two tandem purification steps including affinity and gel permeation chromatography. In the next phase, functional studies of PTR1 were performed via screening of an in-house library of 500 compounds. The biochemical-mechanism based assay of PTR1 identified 11 hits that were also found to be non-cytotoxic against human fibroblast cell line (BJ) (except compound 6), and thus further studied via computational technique and saturation transfer difference-nuclear magnetic resonance (STD-NMR) spectroscopy. These high throughput techniques identified six compounds 2, 4, 5, 7, 9, and 11 as active, which were then assessed via in-vitro assay. Among them, compounds 2, 4, and 7 showed substantial leishmanicidal activity, comparable to the standard drug, miltefosine (IC50 value: 31.8 ± 0.2 μM). These results narrowed down the search to 3 compounds as potential leads, with prominent protein-ligand interaction profiles. Hence, the respective compounds can be further assessed for their therapeutic potential against leishmaniasis.

Discovery of potential FDA-approved SARS-CoV-2 Papain-like protease inhibitors: A multi-phase in silico approach

Papain-like protease (PLpro) is a crucial enzyme for SARS-CoV-2 replication and immune evasion. Inhibiting PLpro could be a promising strategy to fight against COVID-19. This study aimed to identify potent inhibitors of PLpro among FDA-approved drugs using an in silico approach. The study also aimed to examine and confirm the binding of the selected compounds to the active pocket of PLpro using a multi-phased in silico approach, involving the screening of 3009 FDA-approved drugs to pinpoint the most similar compounds to, TTT, the co-crystallized ligand TTT of PLpro. The selected compounds were subjected to further analysis, including molecular docking, molecular dynamics simulations, MM-GPSA (molecular mechanics generalized Born surface area), and PLIP (Protein-Ligand Interaction Profiler) studies, to examine and confirm their binding to the active pocket of PLpro. Seven candidates (Vismodegib, Celecoxib, Ketoprofen, Indomethacin, Naphazoline, Valdecoxib, and Eslicarbazepine) showed promising in silico activities against the PLpro. The computational analysis confirmed the binding of Celecoxib to the active pocket of PLpro, suggesting its potential in the fight against COVID-19. This study identified seven FDA-approved drugs as potential inhibitors of PLpro, providing a feasible approach for drug repurposing against COVID-19. The results obtained from the in silico approach hold promise, but further in vitro and in vivo studies are warranted to validate the potential of these compounds.

Network-Pharmacology-Based Study on Active Phytochemicals and Molecular Mechanism of Cnidii Fructus in Treating Colorectal Cancer.

Cnidii Fructus (CF) is known for its antibacterial, anti-inflammatory, and antitumor properties, as well as its activities against kidney deficiency and impotence. In this study, we aimed to explore the anti-CRC cancer effect and molecular mechanism of CF via network pharmacology and in vitro antitumor experiments. Network pharmacology was used to investigate the anti-CRC mechanism of CF. First, a series of databases was used to screen the active phytochemical targets and anti-CRC core targets. Then, the GO and KEGG pathways were analyzed to predict possible mechanisms. Molecular docking analysis explore core targets-phytochemicals interactions. In vitro antitumor experiments were carried on verifying anti-CRC mechanism of CF. In this study, 20 active ingredient targets and 50 intersecting targets were analyzed by Cytoscape software 3.9.1 to obtain the core genes and phytochemicals. Then, the GO and KEGG pathways of 50 intersecting targets were analyzed to predict possible mechanisms. The results from GO and KEGG indicated that CF has significant antitumor efficacy, which involves many signaling pathways, such as PI3K/AKT and p53. The five core targets and five core phytochemicals were screened for molecular docking to show protein-ligand interactions. According to the results of molecular docking, the compound O-acetylcolumbianetin was selected for the anti- CRC functional verification in vitro. MTT assay showed that O-acetylcolumbianetin significantly inhibited the proliferation of colorectal HCT116 cells in a time- and quantity-dependent manner. O-acetylcolumbianetin can promote the expression of CASP3 protein, induce HCT116 cells apoptosis, thus exert anti-CRC effect. This study preliminarily verified the anti-CRC effect and molecular mechanism of CF and provided a reference for Traditional Chinese Medicine anti-tumor subsequent research.

Identification of novel SARS-CoV-2 3CLpro inhibitors by molecular docking, in vitro assays, molecular dynamics simulations and DFT analyses.

SARS-CoV-2 pandemic has presented a significant threat to global health and the economy, necessitating urgent efforts to develop effective antiviral drugs. The main protease (3CLpro) of SARS-CoV-2 is a critical target for antiviral therapy due to its essential role in viral replication. In order to find new structural types of 3CLpro inhibitors to facilitate the solution to the problem of new virus resistance. Six potential pharmacologically bioactive compounds were identified by utilizing structure-based virtual screening and in vitro assays from the Topscience database containing 10 million compounds. Among these, compounds 34 and 36 exhibited potent inhibitory activity with IC50 values of 6.12 ± 0.42μM and 4.47 ± 0.39μM, respectively. To elucidate their binding mechanisms with 3CLpro, all-atom molecular dynamics (MD) simulations were conducted. Principal component analysis (PCA), free energy landscapes (FEL) and dynamic cross-correlation maps (DCCM) revealed that the binding of compounds 34 and 36 to 3CLpro significantly enhanced the structural stability of 3CLpro, reducing conformational flexibility and internal motions. The results of protein-ligand interaction showed that compounds 34 and 36 formed strong and stable interactions to key residues at active site of 3CLpro with different binding modes from S-217622. And HOMO-LUMO gap and molecular electrostatic potential distribution revealed the quantum chemical properties of compounds 34 and 36. These findings suggested that compounds 34 and 36 can be as novel SARS-CoV-2 3CLpro inhibitors and promising lead-like drug candidates for developing COVID-19 treatments.

Repurposed pharmacotherapy: targeting cathepsin L with repurposed drugs in virtual screening.

Proteolytic enzymes are closely associated with cancer and are important in different phases, including tumor growth, angiogenesis, and metastasis. Despite efforts to target matrix metalloproteases (MMPs), clinical trials have often resulted in various side effects such as musculoskeletal pain, joint stiffness, and tendinitis, making them less optimal for chronic cancer treatment. Thus, there is a need for the identification of other protease targets that would provide different approaches towards the management of cancer. Of these targets, Cathepsin L (CatL) is a lysosomal cysteine protease that has been identified as a therapeutic target that is implicated in cancer development and metastasis. In this study, we performed an integrated approach of virtual screening and molecular dynamics (MD) simulations to identify the potential inhibitors of CatL from a library of drugs that have been used for different treatments. Towards this goal, we performed virtual screening of the DrugBank database and found two repurposed drugs, Irinotecan and Nilotinib, against CatL based on their docking profiles, favorable docking scores, and specific interaction with the CatL binding pocket. MD simulations of the Irinotecan and Nilotinib bound structures with CatL were carried out, and the analysis showed that both these compounds could function as CatL inhibitors as the protein-ligand interactions were stable for 300 ns. This study highlights the robustness of these drugs bound to CatL and indicates that they could be repurposed for the treatment of cancer. These findings endorse the use of computer-based approaches for the identification of new inhibitors, and the present study will be a useful resource for future experimental research towards the targeting of CatL in cancer therapeutics.