3 research outputs found
A computer-based approach for developing linamarase inhibitory agents
This research article published by Walter de Gruyter GmbH, 2020Cassava is a strategic crop, especially for developing countries. However, the presence of cyanogenic compounds in cassava products limits the proper nutrients utilization. Due to the poor availability of structure
discovery and elucidation in the Protein Data Bank is limiting the full understanding of the enzyme, how to
inhibit it and applications in different fields. There is a need to solve the three-dimensional structure (3-D) of
linamarase from cassava. The structural elucidation will allow the development of a competitive inhibitor and
various industrial applications of the enzyme. The goal of this review is to summarize and present the available
3-D modeling structure of linamarase enzyme using different computational strategies. This approach could
help in determining the structure of linamarase and later guide the structure elucidation in silico and experimentall
Cheminformatics Approaches to Structure Based Virtual Screening: Methodology Development and Applications
Structure-based virtual screening (VS) using 3D structures of protein targets has
become a popular in silico drug discovery approach. The success of VS relies on the quality
of underlying scoring functions. Despite of the success of structure-based VS in several
reported cases, target-dependent VS performance and poor binding affinity predictions are
well-known drawbacks in structure-based scoring functions. The goal of my dissertation is to
use cheminformatics approaches to address above problems of the existing structure-based
scoring methods.
In Aim 1, cheminformatics practices are applied to those problems which
conventional structure-based scoring functions find difficult (anti-bacterial leads efflux study)
or fail to address (AmpC β-lactamase study). Predictive binary classification QSAR models
can be constructed to classify complex efflux properties (low vs. high) and to differentiate
AmpC β-lactamase binders from binding decoys (i.e., the false positives generated by scoring
functions). The above models are applied to virtual screening and many computational hits
are experimentally confirmed.
In Aim 2, novel statistical binding and pose scoring functions (or pose filter in Aim 3)
are developed, to accurately predict protein-ligand binding affinity and to discriminate
native-like poses of ligands from pose decoys respectively. In my approach, the proteinligand
interface is represented at the atomic level resolution and transformed via a special
computational geometry approach called Delaunay tessellation to a collection of atom
quadruplet motifs. And individual atom members of the motifs are characterized by
conceptual Density Functional Theory (DFT)-based atomic properties. The binding scoring
function shows acceptable prediction accuracy towards Community Structure-Activity
Resources (CSAR) data sets with diverse protein families.
In Aim 3, a two-step scoring protocol for target-specific virtual screening is
developed and validated using the challenging Directory of Useful Decoys (DUD) data sets.
In the first step our target-specific pose (-scoring) filter developed in Aim 2 is used to filter
out/penalize putative pose decoys for every compound. Then in the second step the
remaining putative native-like poses are scored with MedusaScore, which is a conventional
force-field-based scoring function. This novel screening protocol can consistently improve
MedusaScore VS performance, suggesting it possible applications to practical
pharmaceutically relevant targets