We have vaccines, treatments, and diagnostics to combat the spread of COVID-19, yet the coronavirus is always one step ahead. A parade of variants has emerged over the years, each harbouring mutations that help it to evade pandemic countermeasures. Now, an innovation that combines computer science and protein biology advances could help us regain control.
Igor Berezovsky, Senior Principal Investigator at A*STAR’s Bioinformatics Institute (BII) leads a team interested in the relationship between a protein’s structure and its function. They have been studying a mechanistic phenomenon called allostery, where changes on one protein site—such as binding of a ligand—trigger changes at a distant site via dynamics of the whole structure. Allostery is an incredibly complex and sophisticated tool that can help in the development of more durable and effective treatments for a range of conditions.
For over five years, Berezovsky’s team has been building computational frameworks to crack the allostery code. The researchers developed a theoretical model called SBSMMA (structure-based statistical mechanical model of allostery) that offers key advantages over previous phenomenological approaches.
“SBSMMA can calculate the energetics of allosteric signalling at the single amino acid level, giving users the ability to predict the intra-protein communication provided by protein dynamics with unprecedented precision,” Berezovsky explained.
In their latest study, the team took SBSMMA to the next level, developing comprehensive protocols for analysing allosteric signalling and targeting new sites and effectors. They demonstrated the computational framework’s utility in the design of allosteric drugs for tackling mutating targets in two disease models, COVID-19 and cancer.
The researchers mapped the mutational blueprints of these conditions by analysing the allosteric effects of both individual amino acid mutations and ligand binding. They compiled these data in the Allosteric Signalling Maps (ASMs) and Allosteric Probing Maps (APMs), computational tools that help scientists predict how allostery and mutations can impact protein signalling.
A hypothetical energy landscape containing orthosterically/allosterically regulated (pink/purple) protein structures can be explored with SBSMMA. Activated (A) or inhibited (I) protein states are separated from the native state by energy barriers which can be passed with the help of ligands and/or mutations.
© A*STAR Research
ASMs and APMs can also be used to design new allosteric site-effector pairs, according to Berezovsky, and can be particularly useful when the allosteric sites of a given protein are still unknown. “The procedure can start from using available information or it can start from what we call agnostic analysis,” Berezovsky said.
Protein scientists have full access to these tools online through the AlloSigMA web server and the AlloMAPS, which Berezovsky’s team is continually refining. “We just updated AlloMAPS with exhaustive data on allosteric signalling in the SARS-CoV-2 Spike protein,” Berezovsky said, which allows researchers to pinpoint specific mutations that act as viral drivers and develop therapeutic strategies to make the virus less harmful.
The A*STAR-affiliated researchers contributing to this research are from the Bioinformatics Institute (BII).
