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Yuan L, Guo J. PharmaRedefine: A database server for repurposing drugs against pathogenic bacteria. Methods 2024; 227:78-85. [PMID: 38754711 DOI: 10.1016/j.ymeth.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/03/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024] Open
Abstract
Pathogenic bacteria represent a formidable threat to human health, necessitating substantial resources for prevention and treatment. With the escalating concern regarding antibiotic resistance, there is a pressing need for innovative approaches to combat these pathogens. Repurposing existing drugs offers a promising solution. Our present work hypothesizes that proteins harboring ligand-binding pockets with similar chemical environments may be able to bind the same drug. To facilitate this drug-repurposing strategy against pathogenic bacteria, we introduce an online server, PharmaRedefine. Leveraging a combination of sequence and structure alignment and protein pocket similarity analysis, this platform enables the prediction of potential targets in representative bacteria for specific FDA-approved drugs. This novel approach holds tremendous potential for drug repositioning that effectively combat infections caused by pathogenic bacteria. PharmaRedefine is freely available at http://guolab.mpu.edu.mo/pharmredefine.
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Affiliation(s)
- Longxiao Yuan
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999097, China
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999097, China.
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Sankar S, Vasudevan S, Chandra N. CRD: A de novo design algorithm for the prediction of cognate protein receptors for small molecule ligands. Structure 2024; 32:362-375.e4. [PMID: 38194962 DOI: 10.1016/j.str.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/20/2023] [Accepted: 12/13/2023] [Indexed: 01/11/2024]
Abstract
While predicting a ligand that binds to a protein is feasible with current methods, the opposite, i.e., the prediction of a receptor for a ligand remains challenging. We present an approach for predicting receptors of a given ligand that uses de novo design and structural bioinformatics. We have developed the algorithm CRD, comprising multiple modules combining fragment-based sub-site finding, a machine learning function to estimate the size of the site, a genetic algorithm that encodes knowledge on protein structures and a physics-based fitness scoring scheme. CRD includes a pseudo-receptor design component followed by a mapping component to identify proteins that might contain these sites. CRD recovers the sites and receptors of several natural ligands. It designs similar sites for similar ligands, yet to some extent can distinguish between closely related ligands. CRD correctly predicts receptor classes for several drugs and might become a valuable tool for drug discovery.
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Affiliation(s)
- Santhosh Sankar
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Sneha Vasudevan
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India; Department of Bioengineering, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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Zhao Y, Liang Y, Luo G, Li Y, Han X, Wen M. Sequence-Structure Analysis Unlocking the Potential Functional Application of the Local 3D Motifs of Plant-Derived Diterpene Synthases. Biomolecules 2024; 14:120. [PMID: 38254720 PMCID: PMC10813164 DOI: 10.3390/biom14010120] [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: 12/12/2023] [Revised: 12/31/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
Plant-derived diterpene synthases (PdiTPSs) play a critical role in the formation of structurally and functionally diverse diterpenoids. However, the specificity or functional-related features of PdiTPSs are not well understood. For a more profound insight, we collected, constructed, and curated 199 functionally characterized PdiTPSs and their corresponding 3D structures. The complex correlations among their sequences, domains, structures, and corresponding products were comprehensively analyzed. Ultimately, our focus narrowed to the geometric arrangement of local structures. We found that local structural alignment can rapidly localize product-specific residues that have been validated by mutagenesis experiments. Based on the 3D motifs derived from the residues around the substrate, we successfully searched diterpene synthases (diTPSs) from the predicted terpene synthases and newly characterized PdiTPSs, suggesting that the identified 3D motifs can serve as distinctive signatures in diTPSs (I and II class). Local structural analysis revealed the PdiTPSs with more conserved amino acid residues show features unique to class I and class II, whereas those with fewer conserved amino acid residues typically exhibit product diversity and specificity. These results provide an attractive method for discovering novel or functionally equivalent enzymes and probing the product specificity in cases where enzyme characterization is limited.
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Affiliation(s)
- Yalan Zhao
- National Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China; (Y.Z.); (Y.L.); (G.L.); (X.H.)
- Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, Yunnan Institute of Microbiology, School of Life Sciences, Yunnan University, Kunming 650091, China
| | - Yupeng Liang
- National Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China; (Y.Z.); (Y.L.); (G.L.); (X.H.)
- Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, Yunnan Institute of Microbiology, School of Life Sciences, Yunnan University, Kunming 650091, China
| | - Gan Luo
- National Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China; (Y.Z.); (Y.L.); (G.L.); (X.H.)
- Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, Yunnan Institute of Microbiology, School of Life Sciences, Yunnan University, Kunming 650091, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali 671003, China
| | - Xiulin Han
- National Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China; (Y.Z.); (Y.L.); (G.L.); (X.H.)
- Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, Yunnan Institute of Microbiology, School of Life Sciences, Yunnan University, Kunming 650091, China
| | - Mengliang Wen
- National Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming 650091, China; (Y.Z.); (Y.L.); (G.L.); (X.H.)
- Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, Yunnan Institute of Microbiology, School of Life Sciences, Yunnan University, Kunming 650091, China
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Sankar S, Preeti P, Ravikumar K, Kumar A, Prasad Y, Pal S, Rao DN, Savithri HS, Chandra N. Structural similarities between SAM and ATP recognition motifs and detection of ATP binding in a SAM binding DNA methyltransferase. Curr Res Struct Biol 2023; 6:100108. [PMID: 38106461 PMCID: PMC10724544 DOI: 10.1016/j.crstbi.2023.100108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 10/05/2023] [Accepted: 10/24/2023] [Indexed: 12/19/2023] Open
Abstract
S-adenosylmethionine (SAM) is a ubiquitous co-factor that serves as a donor for methylation reactions and additionally serves as a donor of other functional groups such as amino and ribosyl moieties in a variety of other biochemical reactions. Such versatility in function is enabled by the ability of SAM to be recognized by a wide variety of protein molecules that vary in their sequences and structural folds. To understand what gives rise to specific SAM binding in diverse proteins, we set out to study if there are any structural patterns at their binding sites. A comprehensive analysis of structures of the binding sites of SAM by all-pair comparison and clustering, indicated the presence of 4 different site-types, only one among them being well studied. For each site-type we decipher the common minimum principle involved in SAM recognition by diverse proteins and derive structural motifs that are characteristic of SAM binding. The presence of the structural motifs with precise three-dimensional arrangement of amino acids in SAM sites that appear to have evolved independently, indicates that these are winning arrangements of residues to bring about SAM recognition. Further, we find high similarity between one of the SAM site types and a well known ATP binding site type. We demonstrate using in vitro experiments that a known SAM binding protein, HpyAII.M1, a type 2 methyltransferase can bind and hydrolyse ATP. We find common structural motifs that explain this, further supported through site-directed mutagenesis. Observation of similar motifs for binding two of the most ubiquitous ligands in multiple protein families with diverse sequences and structural folds presents compelling evidence at the molecular level in favour of convergent evolution.
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Affiliation(s)
- Santhosh Sankar
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Preeti Preeti
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Kavya Ravikumar
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Amrendra Kumar
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Yedu Prasad
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Sukriti Pal
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Desirazu N. Rao
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Handanahal S. Savithri
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560012, Karnataka, India
- Department of BioEngineering, Indian Institute of Science, Bangalore, 560012, Karnataka, India
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Drug Discov Today 2023; 28:103551. [PMID: 36907321 PMCID: PMC10238671 DOI: 10.1016/j.drudis.2023.103551] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023]
Abstract
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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