A PIPELINE FOR PROTEIN-LIGAND INTERACTION ANALYSIS

Abstract

The drug discovery and development is a very complex, time and cost intensive process with multiple steps. One of the key steps in this process is the identification of the binding sites between protein and ligands. There are various data resources that can serve in the step of high throughput in silico screening, a process for drug candidate search such as to find a molecule for specific target that has the required chemical and biological properties, and can be used in the further downstream process of the drug development. These large databases in combination with big data processing methods, such as data mining, data fusion and data integration, are attracting much attention from various scientific communities in studying the problem of the automation of drug design. Big data processing methods and techniques are efficient and capable of implementing screening for molecular properties and drug design for millions of chemical compounds. In particular, the automated analysis and prediction methods of protein binding sites and potential ligand conformations can accelerate and effectively advance the drug development process. There are many gaps that would need an improvement in the current automated drug design in particular for the processing and analysis of protein-ligand interaction that would increase the accuracy of the final prediction. Some of the areas for development would be the removal of inaccuracies and errors in the initial dataset creation and processing for the used training sets of compounds. These problems are critical to solve because mistakes can lead to serious health and economic consequences such as harm to patients and severe financial losses for the stakeholders. In the proposed approach, a multi-step pipeline for protein-ligand interaction analysis based on the large dataset of compounds from the Protein Data Bank was considered. The result of this pipeline would ultimately provide a practical way to manipulate large-scale chemical data using familiar software for specialists in molecular chemistry without consuming large amounts of computing power.