ABSTRACT
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.
ABSTRACT
Gyrolab® is an open immunoassay platform that automates the complete immunoassay protocol in a microfluidic disc. The column profiles generated with Gyrolab immunoassays are used to gain more information about biomolecular interactions that can be useful in assay development or quantify analytes in samples. Gyrolab immunoassays can be used to cover a broad concentration range and diversity of matrices in applications ranging from biomarker monitoring, pharmacodynamics and pharmacokinetics studies, to bioprocess development in many areas, including therapeutic antibodies, vaccines, and cell and gene therapy.This chapter is an overview of Gyrolab technology, including system components and the assay development workflow, including the process of selecting affinity reagents, Gyrolab Bioaffy CDs, and assay conditions to optimize immunoassays. Two case studies are included. The first involves an assay for the humanized antibody pembrolizumab used in cancer immunotherapy that can generate data for pharmacokinetics studies. The second case study involves quantification of the biomarker and biotherapeutic interleukin-2 (IL-2) in human serum and buffer. IL-2 has been implicated in the cytokine storm associated with COVID-19, and cytokine release syndrome (CRS), which can occur during chimeric antigen receptor T cell (CART) therapy used in treating cancer. These molecules also have therapeutic relevance in combination.
Subject(s)
COVID-19 , Interleukin-2 , Humans , Workflow , Immunoassay/methods , Automation , Miniaturization , BiomarkersABSTRACT
Approximately 280 people from pharmaceutical industries, contractors, academic institutions and regulatory authorities attended the 13th Japan Bioanalysis Forum Symposium. The symposium was held via web to prevent the spread of COVID-19 from the 28 February to 2 March 2022. The theme of the symposium was 'All for One Goal', and the event has provided an opportunity for open discussion among researchers with different backgrounds but who share a common goal: "to deliver more effective and safe pharmaceuticals to patients as quickly as possible". The speakers focused on hot topics in bioanalysis, including chromatography, biomarker analysis, cell and gene therapy, COVID-19 and antidrug antibody. This symposium provided a great opportunity for the participants to have meaningful discussions, even though 'on the web' was a limited space.
Subject(s)
COVID-19 , Humans , Japan , Antibodies , Drug IndustryABSTRACT
Traditional medicines contain natural products (NPs) as main ingredient which always give new direction and paths to develop new advanced medicines. In the COVID-19 pandemic, NPs can be used or can help to find new compound against it. The SARS coronavirus-2 main protease (SARS CoV-2 Mpro) enzyme, arbitrate viral replication and transcription, is target here. The study show that, from the electronic features and binding affinity of all the NPs with the enzyme, the compounds with higher hydrophobicity and lower flexibility can be more favorable inhibitor. More than fifty NPs were screened for the target and one terpenoid (T3) from marine sponge Cacospongia mycofijiensis shows excellent SARS CoV-2 Mpro inhibitory activity in comparison with known peptide based inhibitors. The molecular dynamics simulation studies of the terpenoids with the protein indicates that the complex is stable and hydrogen bonds are involved during the complexation. Considering binding affinity, bioavailability, pharmacokinetics and toxicity of the compounds, it is proposed that the NP T3 can act as a potential drug candidate against COVID-19 virus.
ABSTRACT
The 68-kDa homodimeric 3C-like protease of SARS-CoV-2, Mpro (3CLpro /Nsp5), is a key antiviral drug target. NMR spectroscopy of this large system proved challenging and resonance assignments have remained incomplete. Here we present the near-complete (>97 %) backbone assignments of a C145A variant of Mpro (Mpro C145A ) both with, and without, the N-terminal auto-cleavage substrate sequence, in its native homodimeric state. We also present SILLY (Selective Inversion of thioL and Ligand for NOESY), a simple yet effective pseudo-3D NMR experiment that utilizes NOEs to identify interactions between Cys-thiol or aliphatic protons, and their spatially proximate backbone amides in a perdeuterated protein background. High protection against hydrogen exchange is observed for 10 of the 11 thiol groups in Mpro C145A , even those that are partially accessible to solvent. A combination of SILLY methods and high-resolution triple-resonance NMR experiments reveals site-specific interactions between Mpro , its substrate peptides, and other ligands, which present opportunities for competitive binding studies in future drug design efforts.
Subject(s)
COVID-19 , Protons , Amides , Antiviral Agents/chemistry , Coronavirus 3C Proteases , Cysteine Endopeptidases/metabolism , Humans , Ligands , Magnetic Resonance Spectroscopy , Peptides/metabolism , Protease Inhibitors , SARS-CoV-2 , Solvents , Sulfhydryl CompoundsABSTRACT
Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Ligands , Protein Binding , Proteins/chemistryABSTRACT
Graph neural networks (GNNs) are the most promising deep learning models that can revolutionize non-Euclidean data analysis. However, their full potential is severely curtailed by poorly represented molecular graphs and features. Here, we propose a multiphysical graph neural network (MP-GNN) model based on the developed multiphysical molecular graph representation and featurization. All kinds of molecular interactions, between different atom types and at different scales, are systematically represented by a series of scale-specific and element-specific graphs with distance-related node features. From these graphs, graph convolution network (GCN) models are constructed with specially designed weight-sharing architectures. Base learners are constructed from GCN models from different elements at different scales, and further consolidated together using both one-scale and multi-scale ensemble learning schemes. Our MP-GNN has two distinct properties. First, our MP-GNN incorporates multiscale interactions using more than one molecular graph. Atomic interactions from various different scales are not modeled by one specific graph (as in traditional GNNs), instead they are represented by a series of graphs at different scales. Second, it is free from the complicated feature generation process as in conventional GNN methods. In our MP-GNN, various atom interactions are embedded into element-specific graph representations with only distance-related node features. A unique GNN architecture is designed to incorporate all the information into a consolidated model. Our MP-GNN has been extensively validated on the widely used benchmark test datasets from PDBbind, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016. Our model can outperform all existing models as far as we know. Further, our MP-GNN is used in coronavirus disease 2019 drug design. Based on a dataset with 185 complexes of inhibitors for severe acute respiratory syndrome coronavirus (SARS-CoV/SARS-CoV-2), we evaluate their binding affinities using our MP-GNN. It has been found that our MP-GNN is of high accuracy. This demonstrates the great potential of our MP-GNN for the screening of potential drugs for SARS-CoV-2. Availability: The Multiphysical graph neural network (MP-GNN) model can be found in https://github.com/Alibaba-DAMO-DrugAI/MGNN. Additional data or code will be available upon reasonable request.
Subject(s)
COVID-19 Drug Treatment , Data Analysis , Drug Design , Humans , Neural Networks, Computer , SARS-CoV-2ABSTRACT
Previous studies suggest that berberine, an isoquinoline alkaloid, has antiviral potential and is a possible therapeutic candidate against SARS-CoV-2. The molecular underpinnings of its action are still unknown. Potential targets include quadruplexes (G4Q) in the viral genome as they play a key role in modulating the biological activity of viruses. While several DNA-G4Q structures and their binding properties have been elucidated, RNA-G4Qs such as RG-1 of the N-gene of SARS-CoV-2 are less explored. Using biophysical techniques, the berberine binding thermodynamics and the associated conformational and hydration changes of RG-1 could be characterized and compared with human telomeric DNA-G4Q 22AG. Berberine can interact with both quadruplexes. Substantial changes were observed in the interaction of berberine with 22AG and RG-1, which adopt different topologies that can also change upon ligand binding. The strength of interaction and the thermodynamic signatures were found to dependent not only on the initial conformation of the quadruplex, but also on the type of salt present in solution. Since berberine has shown promise as a G-quadruplex stabilizer that can modulate viral gene expression, this study may also contribute to the development of optimized ligands that can discriminate between binding to DNA and RNA G-quadruplexes.
Subject(s)
Berberine , COVID-19 Drug Treatment , Berberine/pharmacology , DNA/chemistry , Humans , RNA/metabolism , SARS-CoV-2ABSTRACT
Vascular endothelial growth factors (VEGFs) are the key regulators of blood and lymphatic vessels' formation and function. Each of the proteins from the homologous family VEGFA, VEGFB, VEGFC and VEGFD employs a core cysteine-knot structural domain for the specific interaction with one or more of the cognate tyrosine kinase receptors. Additional diversity is exhibited by the involvement of neuropilins-transmembrane co-receptors, whose b1 domain contains the binding site for the C-terminal sequence of VEGFs. Although all relevant isoforms of VEGFs that interact with neuropilins contain the required C-terminal Arg residue, there is selectivity of neuropilins and VEGF receptors for the VEGF proteins, which is reflected in the physiological roles that they mediate. To decipher the contribution made by the C-terminal sequences of the individual VEGF proteins to that functional differentiation, we determined structures of molecular complexes of neuropilins and VEGF-derived peptides and examined binding interactions for all neuropilin-VEGF pairs experimentally and computationally. While X-ray crystal structures and ligand-binding experiments highlighted similarities between the ligands, the molecular dynamics simulations uncovered conformational preferences of VEGF-derived peptides beyond the C-terminal arginine that contribute to the ligand selectivity of neuropilins. The implications for the design of the selective antagonists of neuropilins' functions are discussed.
Subject(s)
Neuropilins , Vascular Endothelial Growth Factor A , Ligands , Neuropilins/chemistry , Neuropilins/genetics , Neuropilins/metabolism , Peptides , Vascular Endothelial Growth Factor A/genetics , Vascular Endothelial Growth Factor A/metabolism , Vascular Endothelial Growth FactorsABSTRACT
In this study, we investigated the binding affinities between the main protease of SARS-CoV-2 virus (Mpro) and its various ligands to identify the hot spot residues of the protease. To benchmark the influence of various force fields on hot spot residue identification and binding free energy calculation, we performed MD simulations followed by MM-PBSA analysis with three different force fields: CHARMM36, AMBER99SB, and GROMOS54a7. We performed MD simulations with 100 ns for 11 protein-ligand complexes. From the series of MD simulations and MM-PBSA calculations, it is identified that the MM-PBSA estimations using different force fields are weakly correlated to each other. From a comparison between the force fields, AMBER99SB and GROMOS54a7 results are fairly correlated while CHARMM36 results show weak or almost no correlations with the others. Our results suggest that MM-PBSA analysis results strongly depend on force fields and should be interpreted carefully. Additionally, we identified the hot spot residues of Mpro, which play critical roles in ligand binding through energy decomposition analysis. It is identified that the residues of the S4 subsite of the binding site, N142, M165, and R188, contribute strongly to ligand binding. In addition, the terminal residues, D295, R298, and Q299 are identified to have attractive interactions with ligands via electrostatic and solvation energy. We believe that our findings will help facilitate developing the novel inhibitors of SARS-CoV-2.
ABSTRACT
Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.