4.6 Article

IRAA: A statistical tool for investigating a protein-protein interaction interface from multiple structures

Journal

PROTEIN SCIENCE
Volume 32, Issue 1, Pages -

Publisher

WILEY
DOI: 10.1002/pro.4523

Keywords

ACE2 receptor; Bayesian statistics; Buried surface area; Computational structural biology; Monte Carlo method; Protein-protein interactions; Spike protein; SARS-CoV-2

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Understanding protein-protein interactions is essential for understanding the function of molecular systems. We developed a Monte Carlo method-based algorithm, IRAA, to calculate the buried surface area (BSA) of a given complex. By applying IRAA to the ACE2 and SARS-CoV-2 Spike protein complex, we found a broader distribution of BSA compared to using only bound structures, suggesting important implications for biomolecular recognition.
Understanding protein-protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.

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