Biomedical subjects

Ravinder Abrol

Publications and source records attributed to Ravinder Abrol.

4 recordsLinked to original sources

PubMed2025

IgStrand: A universal residue numbering scheme for the immunoglobulin-fold (Ig-fold) to study Ig-proteomes and Ig-interactomes.

The Immunoglobulin fold (Ig-fold) is found in proteins from all domains of life and represents the most populous fold in the human genome, with current estimates ranging from 2 to 3% of protein coding regions. That proportion is much higher in the surfaceome where Ig and Ig-like domains orchestrate cell-cell recognition, adhesion and signaling. The ability of Ig-domains to reliably fold and self-assemble through highly specific interfaces represents a remarkable property of these domains, making them key elements of molecular interaction systems: the immune system, the nervous system, the vascular system and the muscular system. We define a universal residue numbering scheme, common to all domains sharing the Ig-fold in order to study the wide spectrum of Ig-domain variants constituting the Ig-proteome and Ig-Ig interactomes at the heart of these systems. The "IgStrand numbering scheme" enables the identification of Ig structural proteomes and interactomes in and between any species, and comparative structural, functional, and evolutionary analyses. We review how Ig-domains are classified today as topological and structural variants and highlight the "Ig-fold irreducible structural signature" shared by all of them. The IgStrand numbering scheme lays the foundation for the systematic annotation of structural proteomes by detecting and accurately labeling Ig-, Ig-like and Ig-extended domains in proteins, which are poorly annotated in current databases and opens the door to accurate machine learning. Importantly, it sheds light on the robust Ig protein folding algorithm used by nature to form beta sandwich supersecondary structures. The numbering scheme powers an algorithm implemented in the interactive structural analysis software iCn3D to systematically recognize Ig-domains, annotate them and perform detailed analyses comparing any domain sharing the Ig-fold in sequence, topology and structure, regardless of their diverse topologies or origin. The scheme provides a robust fold detection and labeling mechanism that reveals unsuspected structural homologies among protein structures beyond currently identified Ig- and Ig-like domain variants. Indeed, multiple folds classified independently contain a common structural signature, in particular jelly-rolls. Examples of folds that harbor an "Ig-extended" architecture are given. Applications in protein engineering around the Ig-architecture are straightforward based on the universal numbering.

Humans↗

PubMed2006

Predictions of CCR1 chemokine receptor structure and BX 471 antagonist binding followed by experimental validation.

A major challenge in the application of structure-based drug design methods to proteins belonging to the superfamily of G protein-coupled receptors (GPCRs) is the paucity of structural information (1). The 19 chemokine receptors, belonging to the Class A family of GPCRs, are important drug targets not only for autoimmune diseases like multiple sclerosis but also for the blockade of human immunodeficiency virus type 1 entry (2). Using the MembStruk computational method (3), we predicted the three-dimensional structure of the human CCR1 receptor. In addition, we predicted the binding site of the small molecule CCR1 antagonist BX 471, which is currently in Phase II clinical trials (4). Based on the predicted antagonist binding site we designed 17 point mutants of CCR1 to validate the predictions. Subsequent competitive ligand binding and chemotaxis experiments with these mutants gave an excellent correlation to these predictions. In particular, we find that Tyr-113 and Tyr-114 on transmembrane domain 3 and Ile-259 on transmembrane 6 contribute significantly to the binding of BX 471. Finally, we used the predicted and validated structure of CCR1 in a virtual screening validation of the Maybridge data base, seeded with selective CCR1 antagonists. The screen identified 63% of CCR1 antagonists in the top 5% of the hits. Our results indicate that rational drug design for GPCR targets is a feasible approach.

Animals↗

PubMed2005

Biological chiral recognition: the substrate's perspective.

A novel stereocenter-recognition (SR) model has been proposed recently for describing the stereoselectivity of biological and other macromolecules towards substrates that have multiple stereocenters, based on the topology of substrate stereocenters (Sundaresan and Abrol, Prot Sci 11:1330-1339, 2002). The SR model provides the minimum number of substrate locations interacting with receptor sites that need to be considered for understanding stereoselectivity characteristics. Interactions between substrate locations and receptor sites may be binding, nonbinding or repulsive in nature and may occur in a many-to-one or one-to-many fashion, but for a receptor to be stereoselective, its interactions with substrate stereoisomers have to involve a minimum number of locations, in the correct geometry. The SR model is topologically rigorous, explains several previous experimental observations, and is predictive in nature. It predicts that stereoselectivity towards a substrate with N stereocenters in a linear structure involves a minimum of N + 2 substrate locations, distributed over all stereocenters in the substrate, such that effectively at least three locations per stereocenter interact with one or more receptor sites. This article uses the SR model to provide an insight into the chiral recognition process from a substrate's perspective that is intuitive and simple, furnishing a rigorous stereochemical basis for explaining stereoselectivity characteristics of many biological systems.

Aquaporins↗

PubMed2002

Towards a general model for protein-substrate stereoselectivity.

Protein-substrate interactions in enzymatic, neurological, and immunological systems are typically characterized by a high degree of stereoselectivity towards complex substrates. We propose a novel stereocenter-recognition (SR) model for stereoselectivity of proteins (or receptors in general) towards substrates that have multiple stereocenters, based on the topology of substrate stereocenters. The model provides the minimum number of substrate locations that need to enter into binding, nonbinding, or repulsive interactions with receptor sites, for stereoselectivity to occur. According to this model, a substrate location may interact with multiple receptor sites, or multiple substrate locations may interact with a single receptor site, but a stereoselective receptor has to offer, in the correct geometry, at least as many interactions as the required minimum number of substrate locations. The SR model predicts that stereoselectivity towards an acyclic substrate with N stereocenters distributed along a single chain requires interactions involving a minimum of N + 2 substrate locations, distributed over all stereocenters in the substrate, such that effectively three locations exist per stereocenter. Thus, enantioselective recognition of molecules with one chiral center requires a protein to interact with a minimum of three substrate locations, while stereoselectivity towards substrates with two or three stereocenters requires interactions with a minimum of four or five substrate locations, respectively, and so on. We demonstrate the general applicability of this model to protein-substrate interactions by interpreting several previous experimental observations.

Animals↗