Researchers at Yale say they used Google to help unravel the complex structure and regulation of enzymes. In a new study, “Eigenvector centrality for characterization of protein allosteric pathways,” published online in PNAS, chemistry professor Victor Batista, Ph.D., and his colleagues used the Google algorithm PageRank to identify key amino acids in the regulation of a bacterial enzyme essential for most microorganisms.
The team believes its work paves the way for additional experiments that may lead to the development of new antibiotics, pesticides, and herbicides.
“Determining the principal energy-transfer pathways responsible for allosteric communication in biomolecules remains challenging, partially due to the intrinsic complexity of the systems and the lack of effective characterization methods. In this work, we introduce the eigenvector centrality metric based on mutual information to elucidate allosteric mechanisms that regulate enzymatic activity. Moreover, we propose a strategy to characterize the range of correlations that underlie the allosteric processes. We use the V-type allosteric enzyme imidazole glycerol phosphate synthase (IGPS) to test the proposed methodology. The eigenvector centrality method identifies key amino acid residues of IGPS with high susceptibility to effector binding,” wrote the investigators.
“The findings are validated by solution NMR measurements yielding important biological insights, including direct experimental evidence for interdomain motion, the central role played by helix hα1α1, and the short-range nature of correlations responsible for the allosteric mechanism. Beyond insights on IGPS allosteric pathways and the nature of residues that could be targeted by therapeutic drugs or site-directed mutagenesis, the reported findings demonstrate the eigenvector centrality analysis as a general cost-effective methodology to gain fundamental understanding of allosteric mechanisms at the molecular level.”
Despite decades of study, it is still poorly understood how information is transferred from the enzyme’s allosteric site to the active site. Much of the difficulty has to do with the large number of atoms involved and the great structural flexibility of enzymes.
The Yale team noted that a similar question had been addressed years earlier in the realm of computer science. Researchers at Google had studied the flow of information on the Internet, using PageRank to indicate the importance of each web page in terms of the number and quality of links to other Internet sites.
“This problem is completely analogous to the exchange of information between distant sites that characterizes allosterism,” said Uriel Morzan, Ph.D., a postdoctoral associate in Dr. Batista’s lab and co-first author of the study. “By finding out how the information flow through each atom changes with the binding of an allosteric activator to the enzyme, it is possible to find the information channels that are being activated.”
The Yale researchers identified important amino acids for the allosteric process in IGPS, a bacterial enzyme found in most microorganisms.
“It’s exciting that data science methods are starting to percolate into the field of theoretical chemistry, providing new tools for understanding fundamental aspects of catalytic molecular systems when combined with state-of-the-art molecular dynamics simulations and NMR spectroscopy,” said Dr. Batista, who is also a member of the Energy Sciences Institute at Yale’s West Campus.
According to co-author J. Patrick Loria, Ph.D., a Yale professor of chemistry and of molecular biophysics and biochemistry, “It is the synergistic combination of experimental NMR and computational tools that enables this deeper insight into biological function and demonstrates the importance of collaboration between theorists and experimentalists.”