In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation

APPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT

Human health, one of the major topics in Life Science, is facing intensified challenges, including cancer, pandemic outbreaks, and antimicrobial resistance. Thus, new medicines with unique advantages, including peptide-based vaccines and permeable small molecule antimicrobials, are in urgent need. However, the drug development process is long, complex, and risky with no guarantee of success. Also, the improvements in techniques applied in genomics, proteomics, computational biology, and clinical trials significantly increase the data complexity and volume, which imposes higher requirements on the drug development pipeline. In recent years, machine learning (ML) methods were employed to support drug development in various aspects and were shown to be highly effective. Here, we explored the application of advanced ML approaches to empower the development of peptide-based vaccines and permeable antimicrobials. First, the peptide-based vaccines targeting pancreatic cancer and COVID-19 were predicted and screened via multiple approaches. Next, novel structure-based methods to improve the performance of peptide: MHC binding affinity prediction were developed, including an HLA modeling pipeline that provides structures for docking-based peptide binder validation, and hierarchical clustering of HLA I into supertypes and subtypes that have similar peptide binding specificity. Finally, the physicochemical properties governing the permeability of small molecules into multidrug-resistant Pseudomonas aeruginosa cells were selected using a random forest model. In conclusion, the use of machine learning methods could accelerate the drug development process at a lower cost and promote data-based decision-making if used properly