1
|
Wang L, Sun F, Li Q, Ma H, Zhong J, Zhang H, Cheng S, Wu H, Zhao Y, Wang N, Xie Z, Zhao M, Zhu P, Zheng H. CytoSIP: an annotated structural atlas for interactions involving cytokines or cytokine receptors. Commun Biol 2024; 7:630. [PMID: 38789577 PMCID: PMC11126726 DOI: 10.1038/s42003-024-06289-0] [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: 05/23/2023] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
Therapeutic agents targeting cytokine-cytokine receptor (CK-CKR) interactions lead to the disruption in cellular signaling and are effective in treating many diseases including tumors. However, a lack of universal and quick access to annotated structural surface regions on CK/CKR has limited the progress of a structure-driven approach in developing targeted macromolecular drugs and precision medicine therapeutics. Herein we develop CytoSIP (Single nucleotide polymorphisms (SNPs), Interface, and Phenotype), a rich internet application based on a database of atomic interactions around hotspots in experimentally determined CK/CKR structural complexes. CytoSIP contains: (1) SNPs on CK/CKR; (2) interactions involving CK/CKR domains, including CK/CKR interfaces, oligomeric interfaces, epitopes, or other drug targeting surfaces; and (3) diseases and phenotypes associated with CK/CKR or SNPs. The database framework introduces a unique tri-level SIP data model to bridge genetic variants (atomic level) to disease phenotypes (organism level) using protein structure (complexes) as an underlying framework (molecule level). Customized screening tools are implemented to retrieve relevant CK/CKR subset, which reduces the time and resources needed to interrogate large datasets involving CK/CKR surface hotspots and associated pathologies. CytoSIP portal is publicly accessible at https://CytoSIP.biocloud.top , facilitating the panoramic investigation of the context-dependent crosstalk between CK/CKR and the development of targeted therapeutic agents.
Collapse
Affiliation(s)
- Lu Wang
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China
| | - Fang Sun
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
- Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410006, China
| | - Qianying Li
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Haojie Ma
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Juanhong Zhong
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Huihui Zhang
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Siyi Cheng
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China
| | - Hao Wu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China
| | - Yanmin Zhao
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China
| | - Nasui Wang
- Division of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College, No. 57 Changping Road, Shantou, 515041, China
| | - Zhongqiu Xie
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
| | - Mingyi Zhao
- Department of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410006, China.
| | - Ping Zhu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510100, China.
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, Guangdong, 510100, China.
| | - Heping Zheng
- Bioinformatics Center, Hunan University College of Biology, Changsha, Hunan, 410082, China.
| |
Collapse
|
2
|
Lenkiewicz J, Bijak V, Poonuganti S, Szczygiel M, Gucwa M, Murzyn K, Minor W. Structural biology and public health response to biomedical threats. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2023; 10:034701. [PMID: 37350851 PMCID: PMC10284607 DOI: 10.1063/4.0000186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 04/27/2023] [Indexed: 06/24/2023]
Abstract
Over the course of the pandemic caused by SARS-CoV-2, structural biologists have worked hand in hand with groups developing vaccines and treatments. However, relying solely on in vitro and clinical studies may be insufficient to guide vaccination and treatment developments, and other healthcare policies during virus mutations or peaks in infections and fatalities. Therefore, it is crucial to track statistical data related to the number of infections, deaths, and vaccinations in specific regions and present it in an easy-to-understand way.
Collapse
Affiliation(s)
- Joanna Lenkiewicz
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908, USA
| | - Vanessa Bijak
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908, USA
| | - Shrisha Poonuganti
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908, USA
| | | | | | - Krzysztof Murzyn
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University, Krakow, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville 22908, USA
| |
Collapse
|
3
|
Bijak V, Gucwa M, Lenkiewicz J, Murzyn K, Cooper DR, Minor W. Continuous Validation Across Macromolecular Structure Determination Process. NIHON KESSHO GAKKAI SHI 2023; 65:10-16. [PMID: 37416056 PMCID: PMC10321142 DOI: 10.5940/jcrsj.65.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
The overall quality of the experimentally determined structures contained in the PDB is exceptionally high, mainly due to the continuous improvement of model building and structural validation programs. Improving reproducibility on a large scale requires expanding the concept of validation in structural biology and all other disciplines to include a broader framework that encompasses the entire project. A successful approach to science requires diligent attention to detail and a focus on the future. An earnest commitment to data availability and reuse is essential for scientific progress, be that by human minds or artificial intelligence.
Collapse
Affiliation(s)
- Vanessa Bijak
- Department of Molecular Physiology and Biological Physics, University of Virginia
| | - Michal Gucwa
- Department of Molecular Physiology and Biological Physics, University of Virginia
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University
| | - Joanna Lenkiewicz
- Department of Molecular Physiology and Biological Physics, University of Virginia
| | - Krzysztof Murzyn
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University
| | - David R Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia
| |
Collapse
|
4
|
Andreini C, Rosato A. Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications. Int J Mol Sci 2022; 23:7684. [PMID: 35887033 PMCID: PMC9323969 DOI: 10.3390/ijms23147684] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 02/04/2023] Open
Abstract
All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.
Collapse
Affiliation(s)
- Claudia Andreini
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Magnetic Resonance Center (CERM), Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Antonio Rosato
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Magnetic Resonance Center (CERM), Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| |
Collapse
|