In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Abstract

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Conformational changes in the AdeB transmembrane efflux pump by amphiphilic peptide Mastoparan-B, down-regulates expression of the ade B gene and restores antibiotics susceptibility

“…Moreover, most of the current drug targets are proteins, and the 3D structures of these proteins need to be known in target identification. 48 Therefore, Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the drug binding sites and evaluating the druggability of the predicted protein. 47 The results from this study provide novel insight into the inhibitory activity of Mastoparan-B on the RND efflux pump.…”

Dynamic regulation and key roles of ribonucleic acid methylation

“…Given that RNA sequence numbers show explosive growth in the post-genomic era, wet-lab approaches are obviously not suitable for systematic and in-depth analysis of the relevant mechanisms and functions of RNA methylation modification. Therefore, many researchers have developed predictive and computational tools for identifying epigenetic modifications ( Chen et al, 2020c ; Guo et al, 2021 ), which have evolved rapidly in recent years ( Rehman et al, 2021b , 2022b ; Liao et al, 2022 ). These tools are mainly based on machine learning (ML) or deep learning (DL) algorithms ( Abbas et al, 2022 ).…”

Integrated Molecular Modeling and Machine Learning for Drug Design

“…One of the main tasks in analyzing the protein surfaces is identifying binding sites. There are a number of algorithms that do this, and these can be broadly classified as geometry- ,,− or energy-based methods. ,− Geometry-based methods map out the concavities on the surface using grids or alpha-spheres, while energy-based methods calculate the interaction energy or surface accessibility between the protein and diverse small-molecule probes. The next step usually requires ML methods to characterize the binding site.…”

“…In the case of binding site prediction, the introduction of the protein dynamics to the model can lead to the detection of cryptic pockets that are not seen in the static crystal structures and to better predictors of binding site ligandability . Cryptic pockets can be teased out using standard MD simulations or specialized MD simulations where the solvent molecules are altered to act as probes to tease out these pockets . Researchers are also using ML models that have learned the aspects of protein flexibility that lead to cryptic pockets from MD simulations to predict cryptic sites. , …”