3D-BioInfo webinar: Prediction of ligands, pockets and tunnels for enzymes.

You are invited to join this webinar run by the 3D-BioInfo Community. 

Abstract

Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme–ligand complexes Ondrej Vavra, Ph.D., Enantis Slides

Abstract: Tunnels in enzymes that have buried active sites facilitate substrate entry and product release. Targeting the bottlenecks of enzyme tunnels is a powerful strategy in protein engineering, but identifying them across thousands of enzymes requires computational methods. In a recent study, we developed a pipeline combining automated structural analysis with an in-house machine-learning predictor to annotate protein pockets, as well as energy analysis of ligand binding and unbinding by CaverDock. The pipeline was used to analyse the transport of cognate ligands in over 29,000 tunnels from 13,000 enzyme structures. Our analysis of ligand passage revealed that in 75 % of cases, the top-priority tunnel identified by CAVER had the most favourable energies. Additionally, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks in only 50 % of cases. The study provided valuable information for interpreting results from tunnel calculation and energy profiling, useful for protein engineering.

Incorporating Molecular Dynamics and Biophysical Studies into a Structure-Based Approach to Identify Non-Amino Acidic Ligands of DDAH-1. Antonio Macchiarulo, Department of Pharmaceutical Sciences, University of Perugia, Italy Slides

Dimethylarginine dimethylaminohydrolase 1 (DDAH-1) catalyzes the hydrolysis of asymmetric Nω-methylated L-arginine metabolites (Nω ,Nω-dimethyl-L-arginine, ADMA; Nω-monomethyl-L-arginine, NMMA). Since ADMA and NMMA are endogenous inhibitors of nitric oxide (NO) synthases, the catalytic inhibition of DDAH-1 is sought to achieve an indirect tissue-specific inhibition of NOS in diseases wherein an excessive level of NO contributes to the pathogenetic mechanism. As result of intense endeavors to designing and developing inhibitors of DDAH-1, many substrate analogues and few non-aminoacidic ligands have been reported in literature, yet showing low potency and selectivity. This is likely due to the shape of the catalytic site which is roughly flat and highly solvent-exposed, scoring a difficult druggable binding pocket. In this work, we have used molecular dynamics to sample the conformational space of DDAH- 1, starting from its apo and holo forms. Molecular docking has then been applied using a selection of distinct enzyme conformations to screen an in-house library of 824 small molecules. Virtual hits have been validated for their binding activity to recombinant human DDAH-1 using microscale thermophoresis (MST). As a result, we have successfully identified three non-amino acidic ligands of DDAH-1 (VIS212, VIS268, VIS726) that show higher binding efficiency than ADMA. We also show that one of them, purpurogallin (VIS726), is a potent ligand of DDAH-1, featuring a mixed behavior of enzymatic inhibition in a biochemical assay. While this finding widens the panel of known molecular targets of purpurogallin, it further unravels the molecular mechanisms of purpurogallin’s cellular NO inhibition activity as well as its anti- inflammatory and neuroprotective effects. Incorporating molecular dynamics and biophysical studies may thus provide a useful strategy to address challenging druggable binding pocket for the identification of novel lead compounds.

This is part of a continuing series building on the success of previous years, recordings of which are available here