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Milon TI, Wang Y, Fontenot RL, Khajouie P, Villinger F, Raghavan V, Xu W. Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions. Comput Biol Chem 2024; 112:108117. [PMID: 38852360 DOI: 10.1016/j.compbiolchem.2024.108117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/13/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.
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Affiliation(s)
- Tarikul I Milon
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Yuhong Wang
- National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ryan L Fontenot
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Poorya Khajouie
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA; The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Francois Villinger
- Department of Biology, University of Louisiana at Lafayette, New Iberia, LA 70560, USA
| | - Vijay Raghavan
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA
| | - Wu Xu
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
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Sarkar T, Chen Y, Wang Y, Chen Y, Chen F, Reaux CR, Moore LE, Raghavan V, Xu W. Introducing mirror-image discrimination capability to the TSR-based method for capturing stereo geometry and understanding hierarchical structure relationships of protein receptor family. Comput Biol Chem 2023; 103:107824. [PMID: 36753783 PMCID: PMC9992349 DOI: 10.1016/j.compbiolchem.2023.107824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 01/17/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023]
Abstract
We have developed a Triangular Spatial Relationship (TSR)-based computational method for protein structure comparison and motif discovery that is both sequence and structure alignment-free. A protein 3D structure is modeled by all possible triangles that are constructed with every three Cα atoms of amino acids as vertices. Every triangle is represented using an integer (a key). The keys are calculated by a rule-based formula which is a function of a representative length, a representative angle, and the vertex labels associated with amino acids. A 3D structure is thereby represented by a vector of integers (TSR keys). Global or local structure comparisons are achieved by computing all keys or a set of keys, respectively. Many enzymatic reactions and notable marketed drugs are highly stereospecific. Thus, in this paper, we propose a modified key calculation formula by including a mechanism for discriminating mirror-image keys to capture stereo geometry. We assign a positive or a negative sign to the integers representing mirror-image keys. Applying the new key calculation function provides the ability to further discriminate mirror-image keys that were previously considered identical. As the result, applying the mirror-image discrimination capability (i) significantly increases the number of distinct keys; (ii) decreases the number of common keys; (iii) decreases structural similarity; (iv) increases the opportunity to identify specific keys for each type of the receptors. The specific keys identified in this study for the cases of without (not applying) and with (applying) mirror-image discrimination can be considered as the structure signatures that exclusively belong to a certain type of receptors. Applying mirror-image discrimination introduces stereospecificity to keys for allowing more precise modeling of ligand - target interactions. The development of mirror-image TSR keys of Cα atom, in conjunction with the integration of Cα TSR keys with all-atom TSR keys for amino acids and drugs, will lead to a new and promising computational method for aiding drug design and discovery.
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Affiliation(s)
- Titli Sarkar
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA; The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Yuwu Chen
- San Diego Supercomputer Center, University of California San Diego, Gilman Drive, La Jolla, CA 92093, USA
| | - Yu Wang
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Yixin Chen
- Department of Computer and Information Science, The University of Mississippi, MS 38677, USA
| | - Feng Chen
- High Performance Computing, Frey Computing Services Center, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Camille R Reaux
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Laura E Moore
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA
| | - Vijay Raghavan
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
| | - Wu Xu
- Department of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
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Kondra S, Chen F, Chen Y, Chen Y, Collette CJ, Xu W. A study of a hierarchical structure of proteins and ligand binding sites of receptors using the triangular spatial relationship-based structure comparison method and development of a size-filtering feature designed for comparing different sizes of protein structures. Proteins 2021; 90:239-257. [PMID: 34392570 DOI: 10.1002/prot.26215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 07/28/2021] [Accepted: 08/09/2021] [Indexed: 12/15/2022]
Abstract
The presence of receptors and the specific binding of the ligands determine nearly all cellular responses. Binding of a ligand to its receptor causes conformational changes of the receptor that triggers the subsequent signaling cascade. Therefore, systematically studying structures of receptors will provide insight into their functions. We have developed the triangular spatial relationship (TSR)-based method where all possible triangles are constructed with Cα atoms of a protein as vertices. Every triangle is represented by an integer denoted as a "key" computed through the TSR algorithm. A structure is thereby represented by a vector of integers. In this study, we have first defined substructures using different types of keys. Second, using different types of keys represents a new way to interpret structure hierarchical relations and differences between structures and sequences. Third, we demonstrate the effects of sequence similarity as well as sample size on the structure-based classifications. Fourth, we show identification of structure motifs, and the motifs containing multiple triangles connected by either an edge or a vertex are mapped to the ligand binding sites of the receptors. The structure motifs are valuable resources for the researchers in the field of signal transduction. Next, we propose amino-acid scoring matrices that capture "evolutionary closeness" information based on BLOSUM62 matrix, and present the development of a new visualization method where keys are organized according to evolutionary closeness and shown in a 2D image. This new visualization opens a window for developing tools with the aim of identification of specific and common substructures by scanning pixels and neighboring pixels. Finally, we report a new algorithm called as size filtering that is designed to improve structure comparison of large proteins with small proteins. Collectively, we provide an in-depth interpretation of structure relations through the detailed analyses of different types of keys and their associated key occurrence frequencies, geometries, and labels. In summary, we consider this study as a new computational platform where keys are served as a bridge to connect sequence and structure as well as structure and function for a deep understanding of sequence, structure, and function relationships of the protein family.
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Affiliation(s)
- Sarika Kondra
- The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, Louisiana, USA
| | - Feng Chen
- High Performance Computing, 329 Frey Computing Services Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Yixin Chen
- Department of Computer and Information Science, The University of Mississippi, University, Mississippi, USA
| | - Yuwu Chen
- High Performance Computing, 329 Frey Computing Services Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Caleb J Collette
- Department of Chemistry, University of Louisiana at Lafayette, Lafayette, Louisiana, USA
| | - Wu Xu
- Department of Chemistry, University of Louisiana at Lafayette, Lafayette, Louisiana, USA
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