Biology subjects

Shanmugasundaram, V.

Publications and source records attributed to Shanmugasundaram, V..

5 recordsLinked to original sources

bioRxiv2025

Protein Frustration Reveals Active Sites in Co-Evolved GPCR:G Protein Complexes and in Engineered Targeted Degrader Complexes

The folded structure of a protein is understood to be an optimal energy state. However, previous studies have shown that certain amino acid residue positions that play a critical role in protein function are often in a suboptimal energy state or "frustrated". Here, we leverage over 1200 three-dimensional structures of G protein-coupled receptors (GPCRs) to demonstrate that residues at the interface between GPCR and its ligand or G protein contain a higher density of frustrated residues compared to other structural regions in the receptor. Likewise, the G subunit of the trimeric G proteins shows multiple clusters of highly frustrated residues on its surface that overlap with their effector protein (G{beta}{gamma}, RGS, Adenyl cyclase, Ric8) binding interfaces. Compared to the co-evolved GPCR:G protein complexes, engineered protein complexes, such as those facilitated by molecular degraders, show a much greater density of highly frustrated residues in the degrader interface. Our study highlights the use of protein frustration as an invaluable tool to evaluate both native protein-protein interfaces and design strategies to facilitate engineered protein complexes.

biophysics↗

bioRxiv2025

Insights From Protein Frustration Analysis Of BRD4-Cereblon Degrader Ternary Complexes Show Separation of Strong from Weak Degraders

PROteolysis TArgeting Chimeras (PROTACs), also known as Ligand-Directed Degraders (LDDs), are an innovative class of small molecules that leverage the ubiquitin-proteasome system to induce the degradation of target proteins. Structure based design methods are not readily applicable for designing LDDs due to the dynamic nature of the ternary complexes. This study investigates the dynamic properties of five LDD-mediated BRD4-Cereblon complexes, focusing on the challenges of evaluating linker efficiency due to the difficulty in identifying suitable computational metrics that correlate well with the cooperativity or degradation propensity of LDDs. We uncovered that protein frustration, a concept originally developed to understand protein folding, calculated for the residues in the protein-protein interface of the LDD-mediated ternary complexes recapitulate the strength of degradation of the LDDs. Our findings indicated that hydrophobic residues in the interface are among the highly frustrated residues pairs, and they are crucial in distinguishing strong degraders from weak ones. By analyzing frustration patterns, we identified key residues and interactions critical to the effectiveness of the ternary complex. These insights provide practical guidelines for designing and prioritizing more efficient degraders, paving the way for the development of next-generation LDDs with improved therapeutic potential.

biophysics↗

bioRxiv2021

Small molecule allosteric inhibitors of RORγt block Th17-dependent inflammation and associated gene expression in vivo.

Retinoic acid receptor-related orphan nuclear receptor (ROR) {gamma}t is a member of the RORC nuclear hormone receptor family of transcription factors. ROR{gamma}t functions as a critical regulator of thymopoiesis and immune responses. ROR{gamma}t is expressed in multiple immune cell populations including Th17 cells, where its primary function is regulation of immune responses to bacteria and fungi through IL-17A production. However, excessive IL-17A production has been linked to numerous autoimmune diseases. Moreover, Th17 cells have been shown to elicit both pro- and anti-tumor effects. Thus, modulation of the ROR{gamma}t/IL-17A axis may represent an attractive therapeutic target for the treatment of autoimmune disorders and some cancers. Herein we report the design, synthesis and characterization of three selective allosteric ROR{gamma}t inhibitors in preclinical models of inflammation and tumor growth. We demonstrate that these compounds can inhibit Th17 differentiation and maintenance in vitro and Th17-dependent inflammation and associated gene expression in vivo, in a dose-dependent manner. Finally, ROR{gamma}t inhibitors were shown to inhibit tumor formation in pancreatic ductal adenocarcinoma (PDAC) organoids.

immunology↗

bioRxiv2019

Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

Although the three-dimensional structures of G-protein-coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence, structure and dynamics based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available dataset. A blind prediction for thermostable mutations of the Complement factor C5a Receptor retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.\n\nStatement Of SignifiganceG-protein-coupled receptors (GPCRs), the largest superfamily of membrane proteins play a vital role in cellular physiology and are targets to blockbuster drugs. Hence it is imperative to solve the three dimensional structures of GPCRs in various conformational states with different types of ligands bound. To reduce the experimental burden in identifying thermostable GPCR mutants, we report a computational framework using machine learning algorithms trained on thermostability data for 1231 mutants and features calculated from analysis of GPCR sequences, structure and dynamics to predict thermostable mutations ahead of experiments. This work represents a significant advancement in the development, validation and testing of a computational framework that can be extended to other class A GPCRs and helical membrane proteins.

biophysics↗

bioRxiv2016

Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter

Two-pore domain potassium (K2P) channel ion conductance is regulated by diverse stimuli that directly or indirectly gate the channel selectivity filter (SF). Recent crystal structures for the TREK-2 member of the K2P family reveal distinct \"up\" and \"down\" states assumed during activation via mechanical stretch. We performed 195 ps of all-atom, unbiased molecular dynamics simulations of the TREK-2 channel to probe how membrane stretch regulates the SF gate. Markov modeling reveals a novel \"pinched\" SF configuration that stretch activation rapidly destabilizes. Free-energy barrier heights calculated for critical steps in the conduction pathway indicate that this pinched state impairs ion conduction. Our simulations predict that this low-conductance state is accessed exclusively in the compressed, \"down\" conformation in which the intracellular helix arrangement allosterically pinches the SF. By explicitly relating structure to function, we contribute a critical piece of understanding to the evolving K2P puzzle.

biophysics↗