Development of the TSR-based computational method to investigate spike and monoclonal antibody interactions
IntroductionMonoclonal 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...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2025.1395374/full |
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| author | Tarikul I. Milon Titli Sarkar Titli Sarkar Yixin Chen Jordan M. Grider Feng Chen Jun-Yuan Ji Seetharama D. Jois Konstantin G. Kousoulas Vijay Raghavan Wu Xu |
| author_facet | Tarikul I. Milon Titli Sarkar Titli Sarkar Yixin Chen Jordan M. Grider Feng Chen Jun-Yuan Ji Seetharama D. Jois Konstantin G. Kousoulas Vijay Raghavan Wu Xu |
| author_sort | Tarikul I. Milon |
| collection | DOAJ |
| description | IntroductionMonoclonal 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 insights 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.PurposeThis 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.MethodWe 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) intra-residual 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.ResultsThe 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.ConclusionThis 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. |
| format | Article |
| id | doaj-art-a89ce4c0c0ca4d7392fa8d84cdf91348 |
| institution | DOAJ |
| issn | 2296-2646 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Chemistry |
| spelling | doaj-art-a89ce4c0c0ca4d7392fa8d84cdf913482025-08-20T02:50:34ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462025-03-011310.3389/fchem.2025.13953741395374Development of the TSR-based computational method to investigate spike and monoclonal antibody interactionsTarikul I. Milon0Titli Sarkar1Titli Sarkar2Yixin Chen3Jordan M. Grider4Feng Chen5Jun-Yuan Ji6Seetharama D. Jois7Konstantin G. Kousoulas8Vijay Raghavan9Wu Xu10Department of Chemistry, University of Louisiana at Lafayette, Lafayette, LA, United StatesDepartment of Chemistry, University of Louisiana at Lafayette, Lafayette, LA, United StatesThe Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA, United StatesDepartment of Computer and Information Science, The University of Mississippi, University, MS, United StatesDepartment of Chemistry, University of Louisiana at Lafayette, Lafayette, LA, United StatesHigh Performance Computing, 329 Frey Computing Services Center, Louisiana State University, Baton Rouge, LA, United StatesDepartment of Biochemistry and Molecular Biology, Tulane University School of Medicine, Louisiana Cancer Research Center, New Orleans, LA, United StatesDepartment of Pathobiological Sciences, LSU School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, United StatesDepartment of Pathobiological Sciences, LSU School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, United StatesThe Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA, United StatesDepartment of Chemistry, University of Louisiana at Lafayette, Lafayette, LA, United StatesIntroductionMonoclonal 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 insights 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.PurposeThis 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.MethodWe 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) intra-residual 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.ResultsThe 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/fchem.2025.1395374/fullTSR-based methodmolecular interactionmonoclonal antibodyspikeamino acid structuremachine learning |
| spellingShingle | Tarikul I. Milon Titli Sarkar Titli Sarkar Yixin Chen Jordan M. Grider Feng Chen Jun-Yuan Ji Seetharama D. Jois Konstantin G. Kousoulas Vijay Raghavan Wu Xu Development of the TSR-based computational method to investigate spike and monoclonal antibody interactions Frontiers in Chemistry TSR-based method molecular interaction monoclonal antibody spike amino acid structure machine learning |
| title | Development of the TSR-based computational method to investigate spike and monoclonal antibody interactions |
| title_full | Development of the TSR-based computational method to investigate spike and monoclonal antibody interactions |
| title_fullStr | Development of the TSR-based computational method to investigate spike and monoclonal antibody interactions |
| title_full_unstemmed | Development of the TSR-based computational method to investigate spike and monoclonal antibody interactions |
| title_short | Development of the TSR-based computational method to investigate spike and monoclonal antibody interactions |
| title_sort | development of the tsr based computational method to investigate spike and monoclonal antibody interactions |
| topic | TSR-based method molecular interaction monoclonal antibody spike amino acid structure machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2025.1395374/full |
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