Development of the TSR-based method for probing the interactions between Spike and a monoclonal antibody

Tarikul I Milon ¹ Titli Sarkar ¹ Yixin Chen ² Jordan M Grider ¹ Feng Chen ³ Jun-Yuan Ji ⁴ Seetharama Jois ³ Konstantin G Kousoulas ³ Vijay Raghavan ¹ Wu Xu ^5*

• ¹ University of Louisiana at Lafayette, Lafayette, Louisiana, United States
• ² University of Mississippi, Oxford, Mississippi, United States
• ³ Louisiana State University, Baton Rouge, Louisiana, United States
• ⁴ Tulane University, New Orleans, Louisiana, United States
• ⁵ Chemistry, University of Louisiana at Lafayette, Lafayette, Louisiana, United States

Introduction. Monoclonal antibody (mAb) drug treatments have proven effective in reducing COVID-19-related hospitalizations or fatalities, particularly among high-risk patients.Numerous experimental studies have explored the structures of spike proteins and their complexes with ACE2 or mAbs. These 3D structures provide crucial insight into the interactions between spike proteins and ACE2 or mAb, forming a basis for the development of diagnostic tools and therapeutics. However, the field of computational biology has faced substantial challenges due to the lack of methods for precise protein structural comparisons and accurate prediction of molecular interactions. In our previous studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which represents a protein's 3D structure using a vector of integers (keys). These earlier studies, however, were limited to individual proteins.Purpose. This study introduces new extensions of the TSR-based algorithm, enhancing its ability to study interactions between two molecules. We apply these extensions to gain a mechanistic understanding of spike -mAb interactions.Method. We expanded the basic TSR method in three novel ways: (1) TSR keys encompassing all atoms, (2) cross keys for interactions between two molecules, and (3) intraresidual keys for amino acids. This TSR-based representation of 3D structures offers a unique advantage by simplifying the search for similar substructures within structural datasets.Results. The study's key findings include: (i) The method effectively quantified and interpreted conformational changes and steric effects using the newly introduced TSR keys. (ii) Six clusters for CDRH3 and three clusters for CDRL3 were identified using all-atom keys. (iii)We constructed the TSR-STRSUM (TSR-STRucture SUbstitution Matrix), a matrix that represents pairwise similarities between amino acid structures, providing valuable applications in protein sequence and structure comparison. (iv) Intra-residual keys revealed two distinct Tyr clusters characterized by specific triangle geometries.This study presents an advanced computational approach that not only quantifies and interprets conformational changes in protein backbones, entire structures, or individual amino acids, but also facilitates the search for substructures induced by molecular binding across protein datasets. In some instances, a direct correlation between structures and functions was successfully established.