Biology subjects

Muk, S.

Publications and source records attributed to Muk, S..

2 recordsLinked to original sources

bioRxiv2024

Frustration in the Protein-Protein interface Plays a Central Role in the Cooperativity of PROTAC Ternary Complexes

Targeted protein degradation of a protein of interest (POI) by Proteolysis Targeting Chimeras (PROTACs) is an attractive approach for dealing with formerly undruggable protein targets. PROTACs are heterobifunctional molecules that connect a POI-binding and an E3-ligase (E3) binding motif with a linker. The simultaneous binding and formation of a ternary POI::PROTAC::E3 complex (TC) induces proximity between the POI and the E3, allowing for POI-ubiquitination and subsequent induction of proteasomal degradation. Despite the availability of many three-dimensional structures of TCs, unveiling the structure-function relationships for the design of PROTACs remains a challenge. This is because the TCs can be dynamic with a complex conformational landscape that individual crystal structures may not capture. In this work, we used SMARCA2 as the POI and VHL as the E3-ligase and solved the X-ray crystal structures of the respective ternary complexes with four different PROTACs. Molecular dynamics (MD) simulations were used to show that the SMARCA2-VHL interface is flexible with multiple energy minima. The protein-protein (POI-E3) interactions are largely formed by residues located in structurally disordered loops in both VHL and SMARCA2. The residue pairs in the SMARCA2-VHL interface are frustrated, i.e., adopt a suboptimal energetic state. The number of frustrated residue pairs averaged over the MD ensemble shows a positive correlation with the experimentally determined cooperativity of the PROTACs. This indicates that protein-protein interface frustration can play an important role in PROTAC function. The TC ensembles of VHL, SMARCA2 and 11 different PROTACs were modeled by comparative modeling followed by MD. The frustration was subsequently calculated from the MD trajectories and correlated with the cooperativity. We found that identification of the dynamic protein-protein contacts and frustrated residue pairs in the interface can provide a rational framework for the structure-based design of PROTACs.

biophysics↗

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↗