LigBind: Identifying Binding Residues for Over 1000 Ligands with Relation-Aware Graph Neural Networks.

Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind's efficacy on large-scale ligand-specific benchmark datasets, and it generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The web server and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.

Targeting the I7L Protease: A Rational Design for Anti-Monkeypox Drugs?

The latest monkeypox virus outbreak in 2022 showcased the potential threat of this viral zoonosis to public health. The lack of specific treatments against this infection and the success of viral protease inhibitors-based treatments against HIV, Hepatitis C, and SARS-CoV-2, brought the monkeypox virus I7L protease under the spotlight as a potential target for the development of specific and compelling drugs against this emerging disease. In the present work, the structure of the monkeypox virus I7L protease was modeled and thoroughly characterized through a dedicated computational study. Furthermore, structural information gathered in the first part of the study was exploited to virtually screen the DrugBank database, consisting of drugs approved by the Food and Drug Administration (FDA) and clinical-stage drug candidates, in search for readily repurposable compounds with similar binding features as TTP-6171, the only non-covalent I7L protease inhibitor reported in the literature. The virtual screening resulted in the identification of 14 potential inhibitors of the monkeypox I7L protease. Finally, based on data collected within the present work, some considerations on developing allosteric modulators of the I7L protease are reported.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics.

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge.

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.

Screening and identification of potential MERS-CoV papain-like protease (PLpro) inhibitors; Steady-state kinetic and Molecular dynamic studies.

MERS-CoV belongs to the coronavirus group. Recent years have seen a rash of coronavirus epidemics. In June 2012, MERS-CoV was discovered in the Kingdom of Saudi Arabia, with 2,591 MERSA cases confirmed by lab tests by the end of August 2022 and 894 deaths at a case-fatality ratio (CFR) of 34.5% documented worldwide. Saudi Arabia reported the majority of these cases, with 2,184 cases and 813 deaths (CFR: 37.2%), necessitating a thorough understanding of the molecular machinery of MERS-CoV. To develop antiviral medicines, illustrative investigation of the protein in coronavirus subunits are required to increase our understanding of the subject. In this study, recombinant expression and purification of MERS-CoV (PLpro), a primary goal for the development of 22 new inhibitors, were completed using a high throughput screening methodology that employed fragment-based libraries in conjunction with structure-based virtual screening. Compounds 2, 7, and 20, showed significant biological activity. Moreover, a docking analysis revealed that the three compounds had favorable binding mood and binding free energy. Molecular dynamic simulation demonstrated the stability of compound 2 (2-((Benzimidazol-2-yl) thio)-1-arylethan-1-ones) the strongest inhibitory activity against the PLpro enzyme. In addition, disubstitutions at the meta and para locations are the only substitutions that may boost the inhibitory action against PLpro. Compound 2 was chosen as a MERS-CoV PLpro inhibitor after passing absorption, distribution, metabolism, and excretion studies; however, further investigations are required.

Thermal Titration Molecular Dynamics (TTMD): Not Your Usual Post-Docking Refinement.

Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease.

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model.

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts have been made and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein-ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and exhibited the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors.

In silico investigation of oxindole alkaloids from Uncaria perrottetii and Uncaria lanosa f. philippinensis for COVID-19 therapy: Binding site prediction and molecular docking

On January 31, 2020, WHO declared the global outbreak of novel Coronavirus as a public health emergency of international concern. The biological origin of COVID-19 is caused by three primary pathophysiological conditions: immunosuppression, viral infection, and inflammation. In the Philippines, nine alkaloids with potential antiviral and anti-inflammatory effects have been isolated from two plants, Uncaria perrottetii and Uncaria lanosa f. philippinensis. The binding site of A2AR was proven to be pocket 0, which is similar to the literature. Two drug candidates showed the best result for molecular docking: mitraphylline and rauniticine-allo-oxindole A for A2AR and 3CLpro receptors. Mitraphylline candidate showed the lowest free energy scores and RMSD scores. This study is extended to other in silico tests to prove the impact of the alkaloid against COVID-19. © 2022 ACM.

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions.

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

Development of Effective Therapeutic Molecule from Natural Sources against Coronavirus Protease.

The SARS-CoV-2 main protease (Mpro) is one of the molecular targets for drug design. Effective vaccines have been identified as a long-term solution but the rate at which they are being administered is slow in several countries, and mutations of SARS-CoV-2 could render them less effective. Moreover, remdesivir seems to work only with some types of COVID-19 patients. Hence, the continuous investigation of new treatments for this disease is pivotal. This study investigated the inhibitory role of natural products against SARS-CoV-2 Mpro as repurposable agents in the treatment of coronavirus disease 2019 (COVID-19). Through in silico approach, selected flavonoids were docked into the active site of Mpro. The free energies of the ligands complexed with Mpro were computationally estimated using the molecular mechanics-generalized Born surface area (MM/GBSA) method. In addition, the inhibition process of SARS-CoV-2 Mpro with these ligands was simulated at 100 ns in order to uncover the dynamic behavior and complex stability. The docking results showed that the selected flavonoids exhibited good poses in the binding domain of Mpro. The amino acid residues involved in the binding of the selected ligands correlated well with the residues involved with the mechanism-based inhibitor (N3) and the docking score of Quercetin-3-O-Neohesperidoside (-16.8 Kcal/mol) ranked efficiently with this inhibitor (-16.5 Kcal/mol). In addition, single-structure MM/GBSA rescoring method showed that Quercetin-3-O-Neohesperidoside (-87.60 Kcal/mol) is more energetically favored than N3 (-80.88 Kcal/mol) and other ligands (Myricetin 3-Rutinoside (-87.50 Kcal/mol), Quercetin 3-Rhamnoside (-80.17 Kcal/mol), Rutin (-58.98 Kcal/mol), and Myricitrin (-49.22 Kcal/mol). The molecular dynamics simulation (MDs) pinpointed the stability of these complexes over the course of 100 ns with reduced RMSD and RMSF. Based on the docking results and energy calculation, together with the RMSD of 1.98 ± 0.19 Å and RMSF of 1.00 ± 0.51 Å, Quercetin-3-O-Neohesperidoside is a better inhibitor of Mpro compared to N3 and other selected ligands and can be repurposed as a drug candidate for the treatment of COVID-19. In addition, this study demonstrated that in silico docking, free energy calculations, and MDs, respectively, are applicable to estimating the interaction, energetics, and dynamic behavior of molecular targets by natural products and can be used to direct the development of novel target function modulators.

Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2.

BACKGROUND: The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. OBJECTIVE: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4. METHODS: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. RESULTS: The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. CONCLUSION: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

Protein-ligand binding interactions of imidazolium salts with SARS CoV-2.

The disease called severe acute respiratory syndrome (SARS) is a lifestyle intimidating viral contamination affected by a positive, single stranded novel RNA virus (COVID-2019) from the enveloped coronaviruse family. The COVID-2019 virus has affected many people, scattering promptly, and researchers are attempting to find out medicines for its effectual cure in all over the globe. Chloroquine (ChQ) and its derivatives, an older drug used for the cure of malaria, is exposed to encompass a perceptible feasibility and commendable well-being in opposition to SARS CoV-2 associated pneumonia clinical trials conducted in China. Later on, a few investigations have been directed to find and present SARS CoV-2 antiviral medications. The aim of this present work deals with the potential binding interactions of some imidazolium salts with Nsp9 (Nonstructural protein 9) RNA binding protein of SARS CoV-2.

Are Hsp90 inhibitors good candidates against Covid-19?

Drug reposition, or repurposing, has become a promising strategy in therapeutics due to its advantages in several aspects of drug therapy. General drug development is expensive and can take more than 10 years to go through the designing, development, and necessary approval steps. However, established drugs have already overcome these steps and thus a potential candidate may be already available decreasing the risks and costs involved. Viruses invade cells, usually provoking biochemical changes, leading to tissue damage, alteration of normal physiological condition in organisms and can even result in death. Inside the cell, the virus finds the machinery necessary for its multiplication, as for instance the protein quality control system, which involves chaperones and Hsps (heat shock proteins) that, in addition to physiological functions, help in the stabilization of viral proteins. Recently, many inhibitors of Hsp90 have been developed as therapeutic strategies against diseases such as the Hsp90 inhibitors used in anticancer therapy. Several shreds of evidence indicate that these inhibitors can also be used as therapeutic strategies against viruses. Therefore, since a drug treatment for COVID-19 is urgently needed, this review aims to discuss the potential use of Hsp90 inhibitors in the treatment of this globally threatening disease.