Protein-ligand Interaction Profiling Research Articles

The Relevance of Protein-Ligand Interaction Profiles in Computer-Aided Novel Compound Design and Applications

Advancements in methods and tools used in the solving of crystal structures account for the continuously increasing number of compounds available in databases. Various methods and tools are employed in current bioinformatics to screen, retrieve and analyze lead compounds from databases for drug design and industrial use, bringing marked advantages in terms of reducing the time and cost of studies. However, a pivotal point in lead compounds retrieval is the proper understanding and representation of their binding interactions. In this review we compare current methods used in virtual screening and post screening analysis of lead compounds which employ interaction profiles as a key feature. We also emphasize the use of such methods in mining novel compounds for manufacturing nutritional supplements, cosmetics and other industrial products.

TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features

BackgroundThe increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.ResultsUsing the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.ConclusionBy representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment.