1
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Moldovean-Cioroianu NS. Reviewing the Structure-Function Paradigm in Polyglutamine Disorders: A Synergistic Perspective on Theoretical and Experimental Approaches. Int J Mol Sci 2024; 25:6789. [PMID: 38928495 PMCID: PMC11204371 DOI: 10.3390/ijms25126789] [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: 05/16/2024] [Revised: 06/13/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Polyglutamine (polyQ) disorders are a group of neurodegenerative diseases characterized by the excessive expansion of CAG (cytosine, adenine, guanine) repeats within host proteins. The quest to unravel the complex diseases mechanism has led researchers to adopt both theoretical and experimental methods, each offering unique insights into the underlying pathogenesis. This review emphasizes the significance of combining multiple approaches in the study of polyQ disorders, focusing on the structure-function correlations and the relevance of polyQ-related protein dynamics in neurodegeneration. By integrating computational/theoretical predictions with experimental observations, one can establish robust structure-function correlations, aiding in the identification of key molecular targets for therapeutic interventions. PolyQ proteins' dynamics, influenced by their length and interactions with other molecular partners, play a pivotal role in the polyQ-related pathogenic cascade. Moreover, conformational dynamics of polyQ proteins can trigger aggregation, leading to toxic assembles that hinder proper cellular homeostasis. Understanding these intricacies offers new avenues for therapeutic strategies by fine-tuning polyQ kinetics, in order to prevent and control disease progression. Last but not least, this review highlights the importance of integrating multidisciplinary efforts to advancing research in this field, bringing us closer to the ultimate goal of finding effective treatments against polyQ disorders.
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Affiliation(s)
- Nastasia Sanda Moldovean-Cioroianu
- Institute of Materials Science, Bioinspired Materials and Biosensor Technologies, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany;
- Faculty of Physics, Babeș-Bolyai University, Kogălniceanu 1, RO-400084 Cluj-Napoca, Romania
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2
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Ray Chaudhuri N, Ghosh Dastidar S. Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1's Allosteric Network. Biochemistry 2024; 63:1474-1492. [PMID: 38743619 DOI: 10.1021/acs.biochem.3c00643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states of TAK1, differing just by the presence or absence of its allosteric activator TAB1, from an ensemble mixture of conformations (obtained from 2.4 μs molecular dynamics (MD) simulations). The novelty, however, lies in understanding the deeper algorithmic potentials to systematically derive a diverse set of differential residue connectivity features that reconstruct the essential mechanistic architecture for TAK1-TAB1 allostery in such a close-to-active biochemical scenario. While the recursive, random forest-based workflow displays the potential of conducting discretized, hierarchical derivation of allosteric features, a multilayer perceptron-based approach gains considerable efficacy in revealing fluid connected patterns of features when hybridized with mutual information scoring. Interestingly, both pipelines benchmark similar directions of functional conformational changes for TAK1's activation. The findings significantly advance the depth of mechanistic understanding by highlighting crucial activation signatures along a directed C-lobe → activation loop → ATP pocket channel of information flow, including (1) the αF-αE biterminal alignments and (2) the "catalytic" drift of the activation loop toward kinase active site. Besides, some novel allosteric hotspots (K253, Y206, N189, etc.) are further recognized as TAB1 sensors, transducers, and responders, including a benchmark E70 mutation site, precisely mapping the important structural segments for sequential allosteric execution. Hence, our work demonstrates how to navigate through greater structural depths and dimensions of dynamic allosteric machineries just by leveraging standard ML methods in suitable streamlined workflows adaptive to the specific system and objectives.
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Affiliation(s)
- Nibedita Ray Chaudhuri
- Biological Sciences, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata 700091, India
| | - Shubhra Ghosh Dastidar
- Biological Sciences, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata 700091, India
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3
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Zhang G, Zhang C, Cai M, Luo C, Zhu F, Liang Z. FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics. Int J Biol Macromol 2024; 266:131180. [PMID: 38552697 DOI: 10.1016/j.ijbiomac.2024.131180] [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: 12/21/2023] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/01/2024]
Abstract
Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.
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Affiliation(s)
- Guangyu Zhang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Cai Zhang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Mingyue Cai
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Cheng Luo
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Fei Zhu
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China.
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4
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Del Conte A, Camagni GF, Clementel D, Minervini G, Monzon AM, Ferrari C, Piovesan D, Tosatto SCE. RING 4.0: faster residue interaction networks with novel interaction types across over 35,000 different chemical structures. Nucleic Acids Res 2024:gkae337. [PMID: 38686797 DOI: 10.1093/nar/gkae337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 04/09/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024] Open
Abstract
Residue interaction networks (RINs) are a valuable approach for representing contacts in protein structures. RINs have been widely used in various research areas, including the analysis of mutation effects, domain-domain communication, catalytic activity, and molecular dynamics simulations. The RING server is a powerful tool to calculate non-covalent molecular interactions based on geometrical parameters, providing high-quality and reliable results. Here, we introduce RING 4.0, which includes significant enhancements for identifying both covalent and non-covalent bonds in protein structures. It now encompasses seven different interaction types, with the addition of π-hydrogen, halogen bonds and metal ion coordination sites. The definitions of all available bond types have also been refined and RING can now process the complete PDB chemical component dictionary (over 35000 different molecules) which provides atom names and covalent connectivity information for all known ligands. Optimization of the software has improved execution time by an order of magnitude. The RING web server has been redesigned to provide a more engaging and interactive user experience, incorporating new visualization tools. Users can now visualize all types of interactions simultaneously in the structure viewer and network component. The web server, including extensive help and tutorials, is available from URL: https://ring.biocomputingup.it/.
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Affiliation(s)
- Alessio Del Conte
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Giorgia F Camagni
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Damiano Clementel
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | | | - Carlo Ferrari
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova, Italy
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5
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Nerín-Fonz F, Cournia Z. Machine learning approaches in predicting allosteric sites. Curr Opin Struct Biol 2024; 85:102774. [PMID: 38354652 DOI: 10.1016/j.sbi.2024.102774] [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: 10/08/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024]
Abstract
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked the development of numerous computational approaches, such as the identification of allosteric binding sites, to facilitate allosteric drug discovery. Building on the success of machine learning (ML) models for solving complex problems in biology and chemistry, several ML models for predicting allosteric sites have been developed. In this review, we provide an overview of these models and discuss future perspectives powered by the field of artificial intelligence such as protein language models.
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Affiliation(s)
- Francho Nerín-Fonz
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
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6
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Spanò A, Fanton L, Pizzolato D, Moi J, Vinci F, Pesce A, Dongmo Foumthuim CJ, Giacometti A, Simeoni M. Rinmaker: a fast, versatile and reliable tool to determine residue interaction networks in proteins. BMC Bioinformatics 2023; 24:336. [PMID: 37697267 PMCID: PMC10496328 DOI: 10.1186/s12859-023-05466-y] [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: 06/02/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and non-covalent bonds as edges. Determination and visualization of a protein as a RIN provides insights on the topological properties (and hence their related biological functions) of large proteins without dealing with the full complexity of the three-dimensional description, and hence it represents an invaluable tool of modern bioinformatics. RESULTS We present RINmaker, a fast, flexible, and powerful tool for determining and visualizing RINs that include all standard non-covalent interactions. RINmaker is offered as a cross-platform and open source software that can be used either as a command-line tool or through a web application or a web API service. We benchmark its efficiency against the main alternatives and provide explicit tests to show its performance and its correctness. CONCLUSIONS RINmaker is designed to be fully customizable, from a simple and handy support for experimental research to a sophisticated computational tool that can be embedded into a large computational pipeline. Hence, it paves the way to bridge the gap between data-driven/machine learning approaches and numerical simulations of simple, physically motivated, models.
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Affiliation(s)
- Alvise Spanò
- Department of Environmental Science, Computer Science and Statistics, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
| | - Lorenzo Fanton
- Department of Environmental Science, Computer Science and Statistics, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
| | - Davide Pizzolato
- Department of Environmental Science, Computer Science and Statistics, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
| | - Jacopo Moi
- Department of Molecular Science and Nanosystems, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
| | - Francesco Vinci
- Department of Environmental Science, Computer Science and Statistics, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
| | - Alberto Pesce
- Department of Environmental Science, Computer Science and Statistics, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
| | - Cedrix J Dongmo Foumthuim
- Department of Molecular Science and Nanosystems, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
| | - Achille Giacometti
- Department of Molecular Science and Nanosystems, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy
- European Centre for Living Technology (ECLT), Dorsoduro 3246, 30123, Venice, Italy
| | - Marta Simeoni
- Department of Environmental Science, Computer Science and Statistics, University Ca' Foscari of Venice, Via Torino 155, 30172, Venice, Italy.
- European Centre for Living Technology (ECLT), Dorsoduro 3246, 30123, Venice, Italy.
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7
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Abstract
A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.
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Affiliation(s)
- Ke-Wei Chen
- Lab of Computional Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Tian-Yu Sun
- Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yun-Dong Wu
- Lab of Computional Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Shenzhen Bay Laboratory, Shenzhen 518132, China
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8
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Liu X, Zhang H, Zhou Z, Prabhakaran P, Vongsangnak W, Hu G, Xiao F. Functional insight into Cordyceps militaris sugar transporters by structure modeling, network analysis and allosteric regulation. Phys Chem Chem Phys 2023; 25:14311-14323. [PMID: 37183444 DOI: 10.1039/d2cp05611a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Insights into the structures, functions and dynamics of Cordyceps militaris (C. militaris) sugar transporters are necessary for understanding their versatile metabolic capability for fungal growth. The sequence-function relationship study of 85 C. militaris sugar transporters showed that there is a gap between phylogenetic-based subfamily classification and their functions. Beyond protein sequences, structural modeling and principal component analysis of the structural ensemble revealed the different folds of the Car and Org subfamilies. Performing channel detection and network analysis found that the Alp and Hex subfamilies can be specifically distinguished from others by the betweenness of channel residues. Signature dynamics analysis further suggested that the Hex subfamily demonstrates different dynamics, with high flexibility at the H1 region in TM11. Furthermore, the H1 region as an allosteric site was examined by network parameter calculations that guided allosteric pathways between this region and the channel cavity. Together with gene expression data of C. militaris, e.g., Hex06741 in the Hex subfamily, it was promisingly expressed when sugar utilization was altered. This work demonstrates an in silico framework for investigating C. militaris sugar transporters as an example case study of the allosteric activity of the Hex subfamily and can facilitate sugar transporter engineering design that can further optimize the preferable sugar utilization and fermentation process of C. militaris.
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Affiliation(s)
- Xin Liu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
- Institute of Blood and Marrow Transplantation, Medical College of Soochow University, Jiangsu Institute of Hematology, The first Affiliated Hospital of Soochow University, Collaborative Innovation Center of Hematology, National Clinical Research Center for Hematologic Diseases, Soochow University, Suzhou 215123, China
| | - Hanyang Zhang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
| | - Ziyun Zhou
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, 215123, China
| | - Pranesha Prabhakaran
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
| | - Wanwipa Vongsangnak
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok 10900, Thailand
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand.
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, 215123, China
| | - Fei Xiao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China.
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9
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Zhu F, Deng L, Dai Y, Zhang G, Meng F, Luo C, Hu G, Liang Z. PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk. Brief Bioinform 2023; 24:7035113. [PMID: 36781207 DOI: 10.1093/bib/bbad052] [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: 10/12/2022] [Revised: 01/11/2023] [Accepted: 01/26/2023] [Indexed: 02/15/2023] Open
Abstract
Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.
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Affiliation(s)
- Fei Zhu
- School of Computer Science and Technology, Soochow University, 215006, Suzhou, China
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Lei Deng
- School of Computer Science and Technology, Soochow University, 215006, Suzhou, China
| | - Yuhao Dai
- School of Computer Science and Technology, Soochow University, 215006, Suzhou, China
| | - Guangyu Zhang
- School of Computer Science and Technology, Soochow University, 215006, Suzhou, China
| | - Fanwang Meng
- Department of Chemistry and Chemical Biology, McMaster University, L8S 4L8, Ontario, Canada
| | - Cheng Luo
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 201203, Shanghai, China
- Key Laboratory of Systems Biomedicine (Ministry of Education), Center for Systems Biomedicine, Shanghai Jiao Tong University, 200240, Shanghai, China
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10
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Zeng L, Lu Y, Yan W, Yang Y. A Protein Co-Conservation Network Model Characterizes Mutation Effects on SARS-CoV-2 Spike Protein. Int J Mol Sci 2023; 24:ijms24043255. [PMID: 36834664 PMCID: PMC9960056 DOI: 10.3390/ijms24043255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/03/2023] [Accepted: 02/04/2023] [Indexed: 02/10/2023] Open
Abstract
The emergence of numerous variants of SARS-CoV-2 has presented challenges to the global efforts to control the COVID-19 pandemic. The major mutation is in the SARS-CoV-2 viral envelope spike protein that is responsible for virus attachment to the host, and is the main target for host antibodies. It is critically important to study the biological effects of the mutations to understand the mechanisms of how mutations alter viral functions. Here, we propose a protein co-conservation weighted network (PCCN) model only based on the protein sequence to characterize the mutation sites by topological features and to investigate the mutation effects on the spike protein from a network view. Frist, we found that the mutation sites on the spike protein had significantly larger centrality than the non-mutation sites. Second, the stability changes and binding free energy changes in the mutation sites were positively significantly correlated with their neighbors' degree and the shortest path length separately. The results indicate that our PCCN model provides new insights into mutations on spike proteins and reflects the mutation effects on protein function alternations.
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Affiliation(s)
- Lianjie Zeng
- School of Computer Science & Technology, Soochow University, Suzhou 215000, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Yitan Lu
- Department of Bioinformatics, School of Biology and Basic Medical Sciences, Medical College of Soochow University, Suzhou 215123, China
| | - Wenying Yan
- Department of Bioinformatics, School of Biology and Basic Medical Sciences, Medical College of Soochow University, Suzhou 215123, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou 215123, China
- Correspondence: (W.Y.); (Y.Y.)
| | - Yang Yang
- School of Computer Science & Technology, Soochow University, Suzhou 215000, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
- Correspondence: (W.Y.); (Y.Y.)
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11
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Weigle AT, Feng J, Shukla D. Thirty years of molecular dynamics simulations on posttranslational modifications of proteins. Phys Chem Chem Phys 2022; 24:26371-26397. [PMID: 36285789 PMCID: PMC9704509 DOI: 10.1039/d2cp02883b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.
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Affiliation(s)
- Austin T Weigle
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Jiangyan Feng
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
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12
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Pacini L, Lesieur C. GCAT: A network model of mutational influences between amino acid positions in PSD95pdz3. Front Mol Biosci 2022; 9:1035248. [PMID: 36387271 PMCID: PMC9659846 DOI: 10.3389/fmolb.2022.1035248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/13/2022] [Indexed: 12/05/2022] Open
Abstract
Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e.g., amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences. In the GCAT, a position is a node with edges that leave the node (out-edges) for the influences of the mutation of the position on other positions and edges that enter the position (in-edges) for the influences of the mutation of other positions on the position. 1BE9 positions split into four influence categories called G, C, A and T going from positions influencing on average less other positions and influenced on average by less other positions (category C) to positions influencing on average more others positions and influenced on average by more other positions (category T). The four categories depict position neighborhoods in the protein structure with different tolerance to mutations.
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Affiliation(s)
- Lorenza Pacini
- University Lyon, CNRS, INSA Lyon, Ecole Centrale de Lyon, UMR5005, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
| | - Claire Lesieur
- University Lyon, CNRS, INSA Lyon, Ecole Centrale de Lyon, UMR5005, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
- *Correspondence: Claire Lesieur,
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13
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Petrizzelli F, Biagini T, Bianco SD, Liorni N, Napoli A, Castellana S, Mazza T. Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks. FRONTIERS IN BIOINFORMATICS 2022; 2:1045368. [PMID: 36438625 PMCID: PMC9689706 DOI: 10.3389/fbinf.2022.1045368] [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: 09/15/2022] [Accepted: 10/05/2022] [Indexed: 01/25/2023] Open
Abstract
Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.
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Affiliation(s)
- Francesco Petrizzelli
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tommaso Biagini
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Salvatore Daniele Bianco
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy,Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Niccolò Liorni
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy,Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Alessandro Napoli
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Stefano Castellana
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tommaso Mazza
- Bioinformatics Laboratory, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy,*Correspondence: Tommaso Mazza,
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14
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Nussinov R, Zhang M, Maloney R, Liu Y, Tsai CJ, Jang H. Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs. J Mol Biol 2022; 434:167569. [PMID: 35378118 PMCID: PMC9398924 DOI: 10.1016/j.jmb.2022.167569] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/11/2022] [Accepted: 03/25/2022] [Indexed: 01/12/2023]
Abstract
Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, 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 in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Ryan Maloney
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Yonglan Liu
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
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15
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Large-scale prediction of key dynamic interacting proteins in multiple cancers. Int J Biol Macromol 2022; 220:1124-1132. [PMID: 36027989 DOI: 10.1016/j.ijbiomac.2022.08.125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 11/21/2022]
Abstract
Tracking cancer dynamic protein-protein interactions (PPIs) and deciphering their pathogenesis remain a challenge. We presented a dynamic PPIs' hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted >1400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. Two prominent functional characteristics, "Cell cycle-related" and "Immune-related", were presented for KCG, suggesting that these might be their general characteristics. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.
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16
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In Search of a Dynamical Vocabulary: A Pipeline to Construct a Basis of Shared Traits in Large-Scale Motions of Proteins. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.
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17
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Zhu F, Yang S, Meng F, Zheng Y, Ku X, Luo C, Hu G, Liang Z. Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models. J Chem Inf Model 2022; 62:3331-3345. [PMID: 35816597 DOI: 10.1021/acs.jcim.2c00484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, machine learning models largely enriched the prediction of PTMs. However, most machine learning models only rely on protein sequence and little structural information. The lack of the systematic dynamics analysis underlying PTMs largely limits the PTM functional predictions. In this research, we present two dynamics-centric deep learning models, namely, cDL-PAU and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based features to elucidate the molecular basis and underlying functional landscape of PTMs. cDL-PAU achieved satisfactory area under the curve (AUC) scores of 0.804-0.888 for predicting phosphorylation, acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos) sites, displaying reliable improvements. Through a feature selection, the dynamics-based coupling and commute ability show large contributions in discovering PAU sites and FuncPhos sites, suggesting the allosteric propensity for important PTMs. The application of cDL-FuncPhos in three oncoproteins not only corroborates its strong performance in FuncPhos prioritization but also gains insight into the physical basis for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos are available at https://github.com/ComputeSuda/PTM_ML.
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Affiliation(s)
- Fei Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China.,School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Sijie Yang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Fanwang Meng
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton L8S 4L8, Ontario, Canada
| | - Yuxiang Zheng
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Xin Ku
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Cheng Luo
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China.,Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.,State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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18
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Clementel D, Del Conte A, Monzon AM, Camagni GF, Minervini G, Piovesan D, Tosatto SCE. RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles. Nucleic Acids Res 2022; 50:W651-W656. [PMID: 35554554 PMCID: PMC9252747 DOI: 10.1093/nar/gkac365] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/15/2022] [Accepted: 04/30/2022] [Indexed: 12/18/2022] Open
Abstract
Residue interaction networks (RINs) are used to represent residue contacts in protein structures. Thanks to the advances in network theory, RINs have been proved effective as an alternative to coordinate data in the analysis of complex systems. The RING server calculates high quality and reliable non-covalent molecular interactions based on geometrical parameters. Here, we present the new RING 3.0 version extending the previous functionality in several ways. The underlying software library has been re-engineered to improve speed by an order of magnitude. RING now also supports the mmCIF format and provides typed interactions for the entire PDB chemical component dictionary, including nucleic acids. Moreover, RING now employs probabilistic graphs, where multiple conformations (e.g. NMR or molecular dynamics ensembles) are mapped as weighted edges, opening up new ways to analyze structural data. The web interface has been expanded to include a simultaneous view of the RIN alongside a structure viewer, with both synchronized and clickable. Contact evolution across models (or time) is displayed as a heatmap and can help in the discovery of correlating interaction patterns. The web server, together with an extensive help and tutorial, is available from URL: https://ring.biocomputingup.it/.
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Affiliation(s)
- Damiano Clementel
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy
| | - Alessio Del Conte
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy
| | | | - Giorgia F Camagni
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy
| | - Giovanni Minervini
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy
| | - Silvio C E Tosatto
- Department of Biomedical Sciences, University of Padova, Padova 35131, Italy
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19
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Liu C, Li Z, Liu Z, Yang S, Wang Q, Chai Z. Understanding the P-Loop Conformation in the Determination of Inhibitor Selectivity Toward the Hepatocellular Carcinoma-Associated Dark Kinase STK17B. Front Mol Biosci 2022; 9:901603. [PMID: 35620482 PMCID: PMC9127184 DOI: 10.3389/fmolb.2022.901603] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/22/2022] [Indexed: 12/26/2022] Open
Abstract
As a member of the death-associated protein kinase family of serine/threonine kinases, the STK17B has been associated with diverse diseases such as hepatocellular carcinoma. However, the conformational dynamics of the phosphate-binding loop (P-loop) in the determination of inhibitor selectivity profile to the STK17B are less understood. Here, a multi-microsecond length molecular dynamics (MD) simulation of STK17B in the three different states (ligand-free, ADP-bound, and ligand-bound states) was carried out to uncover the conformational plasticity of the P-loop. Together with the analyses of principal component analysis, cross-correlation and generalized correlation motions, secondary structural analysis, and community network analysis, the conformational dynamics of the P-loop in the different states were revealed, in which the P-loop flipped into the ADP-binding site upon the inhibitor binding and interacted with the inhibitor and the C-lobe, strengthened the communication between the N- and C-lobes. These resulting interactions contributed to inhibitor selectivity profile to the STK17B. Our results may advance our understanding of kinase inhibitor selectivity and offer possible implications for the design of highly selective inhibitors for other protein kinases.
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Affiliation(s)
- Chang Liu
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University (Navy Medical University), Shanghai, China
| | - Zhizhen Li
- Department of Biliary Surgery I, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University (Navy Medical University), Shanghai, China
| | - Zonghan Liu
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University (Navy Medical University), Shanghai, China
| | - Shiye Yang
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University (Navy Medical University), Shanghai, China
| | - Qing Wang
- Oncology Department, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Qing Wang, ; Zongtao Chai,
| | - Zongtao Chai
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University (Navy Medical University), Shanghai, China
- Department of Hepatic Surgery, Shanghai Geriatric Center, Shanghai, China
- *Correspondence: Qing Wang, ; Zongtao Chai,
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20
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Barozi V, Musyoka TM, Sheik Amamuddy O, Tastan Bishop Ö. Deciphering Isoniazid Drug Resistance Mechanisms on Dimeric Mycobacterium tuberculosis KatG via Post-molecular Dynamics Analyses Including Combined Dynamic Residue Network Metrics. ACS OMEGA 2022; 7:13313-13332. [PMID: 35474779 PMCID: PMC9025985 DOI: 10.1021/acsomega.2c01036] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 03/22/2022] [Indexed: 05/12/2023]
Abstract
Resistance mutations in Mycobacterium tuberculosis (Mtb) catalase peroxidase protein (KatG), an essential enzyme in isoniazid (INH) activation, reduce the sensitivity of Mtb to first-line drugs, hence presenting challenges in tuberculosis (TB) management. Thus, understanding the mutational imposed resistance mechanisms remains of utmost importance in the quest to reduce the TB burden. Herein, effects of 11 high confidence mutations in the KatG structure and residue network communication patterns were determined using extensive computational approaches. Combined traditional post-molecular dynamics analysis and comparative essential dynamics revealed that the mutant proteins have significant loop flexibility around the heme binding pocket and enhanced asymmetric protomer behavior with respect to wild-type (WT) protein. Heme contact analysis between WT and mutant proteins identified a reduction to no contact between heme and residue His270, a covalent bond vital for the heme-enabled KatG catalytic activity. Betweenness centrality calculations showed large hub ensembles with new hubs especially around the binding cavity and expanded to the dimerization domain via interface in the mutant systems, providing possible compensatory allosteric communication paths for the active site as a result of the mutations which may destabilize the heme binding pocket and the loops in its vicinity. Additionally, an interesting observation came from Eigencentrality hubs, most of which are located in the C-terminal domain, indicating relevance of the domain in the protease functionality. Overall, our results provide insight toward the mechanisms involved in KatG-INH resistance in addition to identifying key regions in the enzyme functionality, which can be used for future drug design.
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Affiliation(s)
- Victor Barozi
- Research Unit in Bioinformatics
(RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140 South Africa
| | - Thommas Mutemi Musyoka
- Research Unit in Bioinformatics
(RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140 South Africa
| | - Olivier Sheik Amamuddy
- Research Unit in Bioinformatics
(RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140 South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics
(RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140 South Africa
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21
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Verkhivker GM, Agajanian S, Kassab R, Krishnan K. Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants. J Chem Inf Model 2022; 62:1956-1978. [PMID: 35377633 DOI: 10.1021/acs.jcim.2c00124] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.
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Affiliation(s)
- Gennady M Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States.,Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
| | - Steve Agajanian
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Ryan Kassab
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Keerthi Krishnan
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
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22
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Tastan Bishop Ö, Mutemi Musyoka T, Barozi V. Allostery and missense mutations as intermittently linked promising aspects of modern computational drug discovery. J Mol Biol 2022; 434:167610. [DOI: 10.1016/j.jmb.2022.167610] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/15/2022]
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23
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Xiao F, Zhou Z, Song X, Gan M, Long J, Verkhivker G, Hu G. Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation. PLoS Comput Biol 2022; 18:e1010009. [PMID: 35320273 PMCID: PMC8979438 DOI: 10.1371/journal.pcbi.1010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/04/2022] [Accepted: 03/10/2022] [Indexed: 11/18/2022] Open
Abstract
Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.
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Affiliation(s)
- Fei Xiao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Ziyun Zhou
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Xingyu Song
- Department of Chemistry, Multiscale Research Institute of Complex Systems and Institute of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mi Gan
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Jie Long
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Gennady Verkhivker
- Department of Computational and Data Sciences, Chapman University, One University Drive, Orange, California, United States of America
- Department of Biomedical and Pharmaceutical Sciences, Chapman University Pharmacy School 9401 Jeronimo Rd, Irvine, California, United States of America
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
- * E-mail:
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24
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Zha J, Li M, Kong R, Lu S, Zhang J. Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques. J Mol Biol 2022; 434:167481. [DOI: 10.1016/j.jmb.2022.167481] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/25/2022] [Accepted: 01/31/2022] [Indexed: 12/17/2022]
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25
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Zhang H, He J, Hu G, Zhu F, Jiang H, Gao J, Zhou H, Lin H, Wang Y, Chen K, Meng F, Hao M, Zhao K, Luo C, Liang Z. Dynamics of Post-Translational Modification Inspires Drug Design in the Kinase Family. J Med Chem 2021; 64:15111-15125. [PMID: 34668699 DOI: 10.1021/acs.jmedchem.1c01076] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Post-translational modification (PTM) on protein plays important roles in the regulation of cellular function and disease pathogenesis. The systematic analysis of PTM dynamics presents great opportunities to enlarge the target space by PTM allosteric regulation. Here, we presented a framework by integrating the sequence, structural topology, and particular dynamics features to characterize the functional context and druggabilities of PTMs in the well-known kinase family. The machine learning models with these biophysical features could successfully predict PTMs. On the other hand, PTMs were identified to be significantly enriched in the reported allosteric pockets and the allosteric potential of PTM pockets were thus proposed through these biophysical features. In the end, the covalent inhibitor DC-Srci-6668 targeting the PTM pocket in c-Src kinase was identified, which inhibited the phosphorylation and locked c-Src in the inactive state. Our findings represent a crucial step toward PTM-inspired drug design in the kinase family.
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Affiliation(s)
- Huimin Zhang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China.,Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,School of Life Science and Technology, Shanghai Tech University, 100 Haike Road, Shanghai 201210, China.,University of Chinese Academy of Sciences (UCAS), 19 Yuquan Road, Beijing 100049, China
| | - Jixiao He
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Fei Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Hao Jiang
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences (UCAS), 19 Yuquan Road, Beijing 100049, China
| | - Jing Gao
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences (UCAS), 19 Yuquan Road, Beijing 100049, China
| | - Hu Zhou
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,University of Chinese Academy of Sciences (UCAS), 19 Yuquan Road, Beijing 100049, China
| | - Hua Lin
- Biomedical Research Center of South China, College of Life Sciences, Fujian Normal University, 1 Keji Road, Fuzhou 350117, China
| | - Yingjuan Wang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Kaixian Chen
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,School of Life Science and Technology, Shanghai Tech University, 100 Haike Road, Shanghai 201210, China.,University of Chinese Academy of Sciences (UCAS), 19 Yuquan Road, Beijing 100049, China
| | - Fanwang Meng
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Minghong Hao
- Ensem Therapeutics, Inc., 200 Boston Avenue, Medford, Massachusetts 02155, United States
| | - Kehao Zhao
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai 264005, China
| | - Cheng Luo
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.,School of Life Science and Technology, Shanghai Tech University, 100 Haike Road, Shanghai 201210, China.,University of Chinese Academy of Sciences (UCAS), 19 Yuquan Road, Beijing 100049, China.,School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
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26
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Pacini L, Dorantes-Gilardi R, Vuillon L, Lesieur C. Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures. Front Mol Biosci 2021; 8:744646. [PMID: 34708077 PMCID: PMC8543124 DOI: 10.3389/fmolb.2021.744646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/19/2021] [Indexed: 11/25/2022] Open
Abstract
Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined.
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Affiliation(s)
- Lorenza Pacini
- Ecole Centrale de Lyon, Ampère, UMR5005, Univ. Lyon, CNRS, INSA Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
| | - Rodrigo Dorantes-Gilardi
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
- USMB, CNRS, LAMA UMR5127, Le Bourget du Lac, France
| | | | - Claire Lesieur
- Ecole Centrale de Lyon, Ampère, UMR5005, Univ. Lyon, CNRS, INSA Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France
- Institut Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon, France
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27
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Okeke CJ, Musyoka TM, Sheik Amamuddy O, Barozi V, Tastan Bishop Ö. Allosteric pockets and dynamic residue network hubs of falcipain 2 in mutations including those linked to artemisinin resistance. Comput Struct Biotechnol J 2021; 19:5647-5666. [PMID: 34745456 PMCID: PMC8545671 DOI: 10.1016/j.csbj.2021.10.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 09/30/2021] [Accepted: 10/03/2021] [Indexed: 10/29/2022] Open
Abstract
Continually emerging resistant strains of malarial parasites to current drugs present challenges. Understanding the underlying resistance mechanisms, especially those linked to allostery is, thus, highly crucial for drug design. This forms the main concern of the paper through a case study of falcipain 2 (FP-2) and its mutations, some of which are linked to artemisinin (ART) drug resistance. Here, we applied a variety of in silico approaches and tools that we developed recently, together with existing computational tools. This included novel essential dynamics and dynamic residue network (DRN) analysis algorithms. We identified six pockets demonstrating dynamic differences in the presence of some mutations. We observed striking allosteric effects in two mutant proteins. In the presence of M245I, a cryptic pocket was detected via a unique mechanism in which Pocket 2 fused with Pocket 6. In the presence of the A353T mutation, which is located at Pocket 2, the pocket became the most rigid among all protein systems analyzed. Pocket 6 was also highly stable in all cases, except in the presence of M245I mutation. The effect of ART linked mutations was more subtle, and the changes were at residue level. Importantly, we identified an allosteric communication path formed by four unique averaged BC hubs going from the mutated residue to the catalytic site and passing through the interface of three identified pockets. Collectively, we established and demonstrated that we have robust tools and a pipeline that can be applicable to the analysis of mutations.
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Affiliation(s)
| | | | - Olivier Sheik Amamuddy
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
| | - Victor Barozi
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
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28
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Sheik Amamuddy O, Glenister M, Tshabalala T, Tastan Bishop Ö. MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics. Comput Struct Biotechnol J 2021; 19:5059-5071. [PMID: 34589183 PMCID: PMC8455658 DOI: 10.1016/j.csbj.2021.08.043] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/28/2021] [Accepted: 08/28/2021] [Indexed: 11/18/2022] Open
Abstract
The web server, MDM-TASK-web, combines the MD-TASK and MODE-TASK software suites, which are aimed at the coarse-grained analysis of static and all-atom MD-simulated proteins, using a variety of non-conventional approaches, such as dynamic residue network analysis, perturbation-response scanning, dynamic cross-correlation, essential dynamics and normal mode analysis. Altogether, these tools allow for the exploration of protein dynamics at various levels of detail, spanning single residue perturbations and weighted contact network representations, to global residue centrality measurements and the investigation of global protein motion. Typically, following molecular dynamic simulations designed to investigate intrinsic and extrinsic protein perturbations (for instance induced by allosteric and orthosteric ligands, protein binding, temperature, pH and mutations), this selection of tools can be used to further describe protein dynamics. This may lead to the discovery of key residues involved in biological processes, such as drug resistance. The server simplifies the set-up required for running these tools and visualizing their results. Several scripts from the tool suites were updated and new ones were also added and integrated with 2D/3D visualization via the web interface. An embedded work-flow, integrated documentation and visualization tools shorten the number of steps to follow, starting from calculations to result visualization. The Django-powered web server (available at https://mdmtaskweb.rubi.ru.ac.za/) is compatible with all major web browsers. All scripts implemented in the web platform are freely available at https://github.com/RUBi-ZA/MD-TASK/tree/mdm-task-web and https://github.com/RUBi-ZA/MODE-TASK/tree/mdm-task-web.
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Affiliation(s)
- Olivier Sheik Amamuddy
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
| | - Michael Glenister
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
| | - Thulani Tshabalala
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
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29
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Zhu Y, Ye F, Zhou Z, Liu W, Liang Z, Hu G. Insights into Conformational Dynamics and Allostery in DNMT1-H3Ub/USP7 Interactions. Molecules 2021; 26:molecules26175153. [PMID: 34500587 PMCID: PMC8434485 DOI: 10.3390/molecules26175153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/23/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023] Open
Abstract
DNA methyltransferases (DNMTs) including DNMT1 are a conserved family of cytosine methylases that play crucial roles in epigenetic regulation. The versatile functions of DNMT1 rely on allosteric networks between its different interacting partners, emerging as novel therapeutic targets. In this work, based on the modeling structures of DNMT1-ubiquitylated H3 (H3Ub)/ubiquitin specific peptidase 7 (USP7) complexes, we have used a combination of elastic network models, molecular dynamics simulations, structural residue perturbation, network modeling, and pocket pathway analysis to examine their molecular mechanisms of allosteric regulation. The comparative intrinsic and conformational dynamics analysis of three DNMT1 systems has highlighted the pivotal role of the RFTS domain as the dynamics hub in both intra- and inter-molecular interactions. The site perturbation and network modeling approaches have revealed the different and more complex allosteric interaction landscape in both DNMT1 complexes, involving the events caused by mutational hotspots and post-translation modification sites through protein-protein interactions (PPIs). Furthermore, communication pathway analysis and pocket detection have provided new mechanistic insights into molecular mechanisms underlying quaternary structures of DNMT1 complexes, suggesting potential targeting pockets for PPI-based allosteric drug design.
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Affiliation(s)
- Yu Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; (Y.Z.); (Z.Z.); (W.L.)
| | - Fei Ye
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China;
| | - Ziyun Zhou
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; (Y.Z.); (Z.Z.); (W.L.)
| | - Wanlin Liu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; (Y.Z.); (Z.Z.); (W.L.)
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; (Y.Z.); (Z.Z.); (W.L.)
- Correspondence: (Z.L.); (G.H.)
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; (Y.Z.); (Z.Z.); (W.L.)
- Correspondence: (Z.L.); (G.H.)
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30
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Sladek V, Yamamoto Y, Harada R, Shoji M, Shigeta Y, Sladek V. pyProGA-A PyMOL plugin for protein residue network analysis. PLoS One 2021; 16:e0255167. [PMID: 34329304 PMCID: PMC8323899 DOI: 10.1371/journal.pone.0255167] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 07/11/2021] [Indexed: 11/18/2022] Open
Abstract
The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.
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Affiliation(s)
- Vladimir Sladek
- Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Yuta Yamamoto
- Department of Chemistry, Rikkyo University, Nishi-Ikebukuro, Tokyo, Japan
| | - Ryuhei Harada
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Mitsuo Shoji
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yasuteru Shigeta
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Vladimir Sladek
- Institute of Construction and Architecture, Slovak Academy of Sciences, Bratislava, Slovakia
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31
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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32
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Sequeiros-Borja CE, Surpeta B, Brezovsky J. Recent advances in user-friendly computational tools to engineer protein function. Brief Bioinform 2021; 22:bbaa150. [PMID: 32743637 PMCID: PMC8138880 DOI: 10.1093/bib/bbaa150] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/03/2020] [Accepted: 06/16/2020] [Indexed: 12/14/2022] Open
Abstract
Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.
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Affiliation(s)
- Carlos Eduardo Sequeiros-Borja
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University and the International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Bartłomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University and the International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University and the International Institute of Molecular and Cell Biology in Warsaw
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33
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Verkhivker GM, Agajanian S, Oztas DY, Gupta G. Comparative Perturbation-Based Modeling of the SARS-CoV-2 Spike Protein Binding with Host Receptor and Neutralizing Antibodies: Structurally Adaptable Allosteric Communication Hotspots Define Spike Sites Targeted by Global Circulating Mutations. Biochemistry 2021; 60:1459-1484. [PMID: 33900725 PMCID: PMC8098775 DOI: 10.1021/acs.biochem.1c00139] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/12/2021] [Indexed: 12/11/2022]
Abstract
In this study, we used an integrative computational approach to examine molecular mechanisms and determine functional signatures underlying the role of functional residues in the SARS-CoV-2 spike protein that are targeted by novel mutational variants and antibody-escaping mutations. Atomistic simulations and functional dynamics analysis are combined with alanine scanning and mutational sensitivity profiling of the SARS-CoV-2 spike protein complexes with the ACE2 host receptor and the REGN-COV2 antibody cocktail(REG10987+REG10933). Using alanine scanning and mutational sensitivity analysis, we have shown that K417, E484, and N501 residues correspond to key interacting centers with a significant degree of structural and energetic plasticity that allow mutants in these positions to afford the improved binding affinity with ACE2. Through perturbation-based network modeling and community analysis of the SARS-CoV-2 spike protein complexes with ACE2, we demonstrate that E406, N439, K417, and N501 residues serve as effector centers of allosteric interactions and anchor major intermolecular communities that mediate long-range communication in the complexes. The results provide support to a model according to which mutational variants and antibody-escaping mutations constrained by the requirements for host receptor binding and preservation of stability may preferentially select structurally plastic and energetically adaptable allosteric centers to differentially modulate collective motions and allosteric interactions in the complexes with the ACE2 enzyme and REGN-COV2 antibody combination. This study suggests that the SARS-CoV-2 spike protein may function as a versatile and functionally adaptable allosteric machine that exploits the plasticity of allosteric regulatory centers to fine-tune response to antibody binding without compromising the activity of the spike protein.
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Affiliation(s)
- Gennady M. Verkhivker
- Keck Center for Science and Engineering, Schmid
College of Science and Technology, Chapman University, One
University Drive, Orange, California 92866, United States
- Depatment of Biomedical and Pharmaceutical Sciences,
Chapman University School of Pharmacy, Irvine, California
92618, United States
| | - Steve Agajanian
- Keck Center for Science and Engineering, Schmid
College of Science and Technology, Chapman University, One
University Drive, Orange, California 92866, United States
| | - Deniz Yazar Oztas
- Keck Center for Science and Engineering, Schmid
College of Science and Technology, Chapman University, One
University Drive, Orange, California 92866, United States
| | - Grace Gupta
- Keck Center for Science and Engineering, Schmid
College of Science and Technology, Chapman University, One
University Drive, Orange, California 92866, United States
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34
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Hu G, Doruker P, Li H, Demet Akten E. Editorial: Understanding Protein Dynamics, Binding and Allostery for Drug Design. Front Mol Biosci 2021; 8:681364. [PMID: 33968997 PMCID: PMC8097160 DOI: 10.3389/fmolb.2021.681364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 03/25/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Pemra Doruker
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Hongchun Li
- Research Center for Computer-Aided Drug Discovery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ebru Demet Akten
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey
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35
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Yan FF, Gao F. Comparison of the binding characteristics of SARS-CoV and SARS-CoV-2 RBDs to ACE2 at different temperatures by MD simulations. Brief Bioinform 2021; 22:1122-1136. [PMID: 33611368 PMCID: PMC7929385 DOI: 10.1093/bib/bbab044] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/14/2021] [Accepted: 01/28/2021] [Indexed: 12/12/2022] Open
Abstract
Temperature plays a significant role in the survival and transmission of SARS-CoV (severe acute respiratory syndrome coronavirus) and SARS-CoV-2. To reveal the binding differences of SARS-CoV and SARS-CoV-2 receptor-binding domains (RBDs) to angiotensin-converting enzyme 2 (ACE2) at different temperatures at atomic level, 20 molecular dynamics simulations were carried out for SARS-CoV and SARS-CoV-2 RBD-ACE2 complexes at five selected temperatures, i.e. 200, 250, 273, 300 and 350 K. The analyses on structural flexibility and conformational distribution indicated that the structure of the SARS-CoV-2 RBD was more stable than that of the SARS-CoV RBD at all investigated temperatures. Then, molecular mechanics Poisson-Boltzmann surface area and solvated interaction energy approaches were combined to estimate the differences in binding affinity of SARS-CoV and SARS-CoV-2 RBDs to ACE2; it is found that the binding ability of ACE2 to the SARS-CoV-2 RBD was stronger than that to the SARS-CoV RBD at five temperatures, and the main reason for promoting such binding differences is electrostatic and polar interactions between RBDs and ACE2. Finally, the hotspot residues facilitating the binding of SARS-CoV and SARS-CoV-2 RBDs to ACE2, the key differential residues contributing to the difference in binding and the interaction mechanism of differential residues that exist at all investigated temperatures were analyzed and compared in depth. The current work would provide a molecular basis for better understanding of the high infectiousness of SARS-CoV-2 and offer better theoretical guidance for the design of inhibitors targeting infectious diseases caused by SARS-CoV-2.
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Affiliation(s)
- Fang-Fang Yan
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
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36
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Laine E, Grudinin S. HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps. J Phys Chem B 2021; 125:2577-2588. [PMID: 33687221 DOI: 10.1021/acs.jpcb.0c11633] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.
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Affiliation(s)
- Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, 75005 Paris, France
| | - Sergei Grudinin
- CNRS, Inria, Grenoble INP, LJK, Univ. Grenoble Alpes, 38000 Grenoble, France
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37
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Verkhivker GM, Di Paola L. Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches. J Phys Chem B 2021; 125:850-873. [PMID: 33448856 PMCID: PMC7839160 DOI: 10.1021/acs.jpcb.0c10637] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/08/2021] [Indexed: 12/13/2022]
Abstract
The rapidly growing body of structural and biochemical studies of the SARS-CoV-2 spike glycoprotein has revealed a variety of distinct functional states with radically different arrangements of the receptor-binding domain, highlighting a remarkable function-driven conformational plasticity and adaptability of the spike proteins. In this study, we examined molecular mechanisms underlying conformational and dynamic changes in the SARS-CoV-2 spike mutant trimers through the lens of dynamic analysis of allosteric interaction networks and atomistic modeling of signal transmission. Using an integrated approach that combined coarse-grained molecular simulations, protein stability analysis, and perturbation-based modeling of residue interaction networks, we examined how mutations in the regulatory regions of the SARS-CoV-2 spike protein can differentially affect dynamics and allosteric signaling in distinct functional states. The results of this study revealed key functional regions and regulatory centers that govern collective dynamics, allosteric interactions, and control signal transmission in the SARS-CoV-2 spike proteins. We found that the experimentally confirmed regulatory hotspots that dictate dynamic switching between conformational states of the SARS-CoV-2 spike protein correspond to the key hinge sites and global mediating centers of the allosteric interaction networks. The results of this study provide a novel insight into allosteric regulatory mechanisms of SARS-CoV-2 spike proteins showing that mutations at the key regulatory positions can differentially modulate distribution of states and determine topography of signal communication pathways operating through state-specific cascades of control switch points. This analysis provides a plausible strategy for allosteric probing of the conformational equilibrium and therapeutic intervention by targeting specific hotspots of allosteric interactions and communications in the SARS-CoV-2 spike proteins.
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Affiliation(s)
- Gennady M. Verkhivker
- Keck
Center for Science and Engineering, Schmid College of Science and
Technology, Chapman University, One University Drive, Orange, California 92866, United States
- Department
of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
| | - Luisa Di Paola
- Unit
of Chemical-Physics Fundamentals in Chemical Engineering, Department
of Engineering, Università Campus
Bio-Medico di Roma, via
Álvaro del Portillo 21, 00128 Rome, Italy
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38
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Meng F, Liang Z, Zhao K, Luo C. Drug design targeting active posttranslational modification protein isoforms. Med Res Rev 2020; 41:1701-1750. [PMID: 33355944 DOI: 10.1002/med.21774] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/29/2020] [Accepted: 12/03/2020] [Indexed: 12/11/2022]
Abstract
Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.
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Affiliation(s)
- Fanwang Meng
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Kehao Zhao
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, China
| | - Cheng Luo
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
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39
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Yan W, Yu C, Chen J, Zhou J, Shen B. ANCA: A Web Server for Amino Acid Networks Construction and Analysis. Front Mol Biosci 2020; 7:582702. [PMID: 33330622 PMCID: PMC7711068 DOI: 10.3389/fmolb.2020.582702] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/19/2020] [Indexed: 02/05/2023] Open
Abstract
Amino acid network (AAN) models empower us to gain insights into protein structures and functions by describing a protein 3D structure as a graph, where nodes represent residues and edges as amino acid interactions. Here, we present the ANCA, an interactive Web server for Amino Acids Network Construction and Analysis based on a single structure or a set of structures from the Protein Data Bank. The main purpose of ANCA is to provide a portal for three types of an environment-dependent residue contact energy (ERCE)-based network model, including amino acid contact energy network (AACEN), node-weighted amino acid contact energy network (NACEN), and edge-weighted amino acid contact energy network (EACEN). For comparison, the C-alpha distance-based network model is also included, which can be extended to protein–DNA/RNA complexes. Then, the analyses of different types of AANs were performed and compared from node, edge, and network levels. The network and corresponding structure can be visualized directly in the browser. The ANCA enables researchers to investigate diverse concerns in the framework of AAN, such as the interpretation of allosteric regulation and functional residues. The ANCA portal, together with an extensive help, is available at http://sysbio.suda.edu.cn/anca/.
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Affiliation(s)
- Wenying Yan
- Center for Systems Biology, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Chunjiang Yu
- School of Biotechnology, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China
| | - Jiajia Chen
- School of Chemistry, Biology and Materials Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Jianhong Zhou
- Public Library of Science, San Francisco, CA, United States
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
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40
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Wang Z, Ren J, Jin N, Liu X, Li X. Is Crocin a Potential Anti-tumor Candidate Targeting Microtubules? Computational Insights From Molecular Docking and Dynamics Simulations. Front Mol Biosci 2020; 7:586970. [PMID: 33251248 PMCID: PMC7674667 DOI: 10.3389/fmolb.2020.586970] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 10/13/2020] [Indexed: 12/27/2022] Open
Abstract
Although it is known crocin, a hydrophilic compound from the herbal plant Crocus sativus L., has promising antitumor activity, the detailed mechanism of its antitumor activity was not well understood. Recent experiments suggested tubulin as the primary target for the antitumor activity of crocin. However, due to a lack of crystal structure of tubulin bound with crocin, the exact binding mode and interaction between crocin and tubulin remains exclusive. In the present work, a computational study by integrating multiple conformation docking, molecular dynamics simulation as well as residue interaction network analysis was performed to investigate the molecular mechanism of crocin-tubulin interaction. By comparing the docking score, the most likely binding mode CRO_E1 were identified from 20 different binding modes of crocin in the vinca binding pockets. Further molecular dynamics simulation of CRO_E1 complex showed the binding of crocin is more stable than the inhibitor soblidotin and vinblastine. During the simulation course, an excessive number of hydrogen bonds were observed for the ligand crocin. The binding free energy of crocin-tubulin complex was calculated as -79.25 ± 7.24 kcal/mol, which is almost twice of the ligand soblidotin and vinblastine. By using energy decomposition, hot residues for CRO_E1 were identified as Gln11, Gln15, Thr72, Ser75, Pro173-Lys174-Val175-Ser176-Asp177, Tyr222, and Asn226 in the β-chain, and Asp245, Ala247-Leu248, Val250, Asn329, and Ile332 in the α-chain. Residue interaction network analysis also showed the importance of these hot residues in the interaction network of crocin-tubulin complex. In addition, a common residue motif Val175-Xxx176-Asp177 was discovered for all three bindings, suggesting its importance in future drug design. The study could provide valuable insights into the interaction between crocin and tubulin, and give suggestive clues for further experimental studies.
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Affiliation(s)
- Ze Wang
- Department of Pharmaceutical Sciences, Zunyi Medical University at Zhuhai Campus, Zhuhai, China
| | - Juan Ren
- Department of Pharmaceutical Sciences, Zunyi Medical University at Zhuhai Campus, Zhuhai, China
| | | | - Xingyi Liu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Xiaofei Li
- Department of Pharmaceutical Sciences, Zunyi Medical University at Zhuhai Campus, Zhuhai, China
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41
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Xiao F, Song X, Tian P, Gan M, Verkhivker GM, Hu G. Comparative Dynamics and Functional Mechanisms of the CYP17A1 Tunnels Regulated by Ligand Binding. J Chem Inf Model 2020; 60:3632-3647. [PMID: 32530640 DOI: 10.1021/acs.jcim.0c00447] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
As an important member of cytochrome P450 (CYP) enzymes, CYP17A1 is a dual-function monooxygenase with a critical role in the synthesis of many human steroid hormones, making it an attractive therapeutic target. The emerging structural information about CYP17A1 and the growing number of inhibitors for these enzymes call for a systematic strategy to delineate and classify mechanisms of ligand transport through tunnels that control catalytic activity. In this work, we applied an integrated computational strategy to different CYP17A1 systems with a panel of ligands to systematically study at the atomic level the mechanism of ligand-binding and tunneling dynamics. Atomistic simulations and binding free energy computations identify the dynamics of dominant tunnels and characterize energetic properties of critical residues responsible for ligand binding. The common transporting pathways including S, 3, and 2c tunnels were identified in CYP17A1 binding systems, while the 2c tunnel is a newly formed pathway upon ligand binding. We employed and integrated several computational approaches including the analysis of functional motions and sequence conservation, atomistic modeling of dynamic residue interaction networks, and perturbation response scanning analysis to dissect ligand tunneling mechanisms. The results revealed the hinge-binding and sliding motions as main functional modes of the tunnel dynamic, and a group of mediating residues as key regulators of tunnel conformational dynamics and allosteric communications. We have also examined and quantified the mutational effects on the tunnel composition, conformational dynamics, and long-range allosteric behavior. The results of this investigation are fully consistent with the experimental data, providing novel rationale to the experiments and offering valuable insights into the relationships between the structure and function of the channel networks and a robust atomistic model of activation mechanisms and allosteric interactions in CYP enzymes.
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Affiliation(s)
- Fei Xiao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Xingyu Song
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Peiyi Tian
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Mi Gan
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Gennady M Verkhivker
- Department of Computational and Data Sciences, Chapman University, One University Drive, Orange, California 92866, United States.,Department of Biomedical and Pharmaceutical Sciences, Chapman University Pharmacy School, 9401 Jeronimo Rd, Irvine, California 92618, United States
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
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42
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Shao Q, Gong W, Li C. A study on allosteric communication in U1A-snRNA binding interactions: network analysis combined with molecular dynamics data. Biophys Chem 2020; 264:106393. [PMID: 32653695 DOI: 10.1016/j.bpc.2020.106393] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 05/02/2020] [Accepted: 05/03/2020] [Indexed: 01/09/2023]
Abstract
The allosteric regulation during the binding interactions between small nuclear RNAs (snRNAs) and the associated protein factors is critical to the function of spliceosomes in alternative RNA splicing. Although network models combined with molecular dynamics simulations have shown to be powerful tools for the analysis of protein allostery, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems. In this work, we use a residual network model combined with a coarse-grained Gaussian network model (GNM) to investigate the binding interactions between the snRNA and the human U1A protein which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle, and to identify the residues that play an important role in the allosteric communication in U1A during this process. We also utilize the Girvan-Newman method to detect the structural organization in U1A-snRNA recognition and interactions. Our results reveal that: (Ι) not only the residues at the binding sites that are traditionally considered to play a major role in U1A-snRNA association, but those residues that are far away from the RNA binding interface participate in the U1A's allosteric signal transmission induced by the RNA binding; (Π) the structure of U1A protein is well organized with different communities acting different roles for its RNA binding and allosteric regulation. The study demonstrates that the combination of the residual network and elastic network models is an effective and efficient method which can be readily extended to the investigation of the allosteric communication for other macromolecular interaction systems.
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Affiliation(s)
- Qi Shao
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
| | - Weikang Gong
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
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43
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Verkhivker GM, Agajanian S, Hu G, Tao P. Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning. Front Mol Biosci 2020; 7:136. [PMID: 32733918 PMCID: PMC7363947 DOI: 10.3389/fmolb.2020.00136] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/08/2020] [Indexed: 12/12/2022] Open
Abstract
Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.
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Affiliation(s)
- Gennady M. Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, United States
| | - Steve Agajanian
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Peng Tao
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, TX, United States
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44
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Krieger JM, Doruker P, Scott AL, Perahia D, Bahar I. Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods. Curr Opin Struct Biol 2020; 64:34-41. [PMID: 32622329 DOI: 10.1016/j.sbi.2020.05.013] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 05/13/2020] [Accepted: 05/20/2020] [Indexed: 11/28/2022]
Abstract
With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.
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Affiliation(s)
- James M Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Pemra Doruker
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Ana Ligia Scott
- Laboratory of Bioinformatics and Computational Biology, Federal University of ABC, Santo André, SP, Brazil
| | - David Perahia
- Laboratoire de Biologie et de Pharmacologie Appliquée, Ecole Normale Superieure Paris-Saclay, UMR 8113, CNRS, 4 Avenue des Sciences, 91190 Gif-sur-Yvette, France
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA.
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45
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Crean RM, Gardner JM, Kamerlin SCL. Harnessing Conformational Plasticity to Generate Designer Enzymes. J Am Chem Soc 2020; 142:11324-11342. [PMID: 32496764 PMCID: PMC7467679 DOI: 10.1021/jacs.0c04924] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Indexed: 02/08/2023]
Abstract
Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.
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Affiliation(s)
- Rory M. Crean
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
| | - Jasmine M. Gardner
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
| | - Shina C. L. Kamerlin
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
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46
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Liang Z, Zhu Y, Long J, Ye F, Hu G. Both intra and inter-domain interactions define the intrinsic dynamics and allosteric mechanism in DNMT1s. Comput Struct Biotechnol J 2020; 18:749-764. [PMID: 32280430 PMCID: PMC7132064 DOI: 10.1016/j.csbj.2020.03.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 03/17/2020] [Accepted: 03/17/2020] [Indexed: 01/03/2023] Open
Abstract
Dynamics and allosteric potentials of the RFTS domain are proposed. Hinge sites located at the RFTS-CD interface are key regulators for inter-domain interactions. Network analysis reveals local allosteric networks and inter-domain communication pathways in DNMT1. A potential allosteric site at the TRD interface for DNMT1 is identified.
DNA methyltransferase 1 (DNMT1), a large multidomain enzyme, is believed to be involved in the passive transmission of genomic methylation patterns via methylation maintenance. Yet, the molecular mechanism of interaction networks underlying DNMT1 structures, dynamics, and its biological significance has yet to be fully characterized. In this work, we used an integrated computational strategy that combined coarse-grained and atomistic simulations with coevolution information and network modeling of the residue interactions for the systematic investigation of allosteric dynamics in DNMT1. The elastic network modeling has proposed that the high plasticity of RFTS has strengthened the correlated behaviors of DNMT1 structures through the hinge sites located at the RFTS-CD interface, which mediate the collective motions between domains. The perturbation response scanning (PRS) analysis combined with the enrichment analysis of disease mutations have further highlighted the allosteric potential of the RFTS domain. Furthermore, the long-range paths connect the intra-domain interactions through the TRD interface and catalytic interface, emphasizing some key inter-domain interactions as the bridges in the global allosteric regulation of DNMT1. The observed interplay between conserved intra-domain networks and dynamical plasticity encoded by inter-domain interactions provides insights into the intrinsic dynamics and functional evolution, as well as the design of allosteric modulators of DNMT1 based on the TRD interface.
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Affiliation(s)
- Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Yu Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Jie Long
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Fei Ye
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
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47
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Campitelli P, Modi T, Kumar S, Ozkan SB. The Role of Conformational Dynamics and Allostery in Modulating Protein Evolution. Annu Rev Biophys 2020; 49:267-288. [PMID: 32075411 DOI: 10.1146/annurev-biophys-052118-115517] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Advances in sequencing techniques and statistical methods have made it possible not only to predict sequences of ancestral proteins but also to identify thousands of mutations in the human exome, some of which are disease associated. These developments have motivated numerous theories and raised many questions regarding the fundamental principles behind protein evolution, which have been traditionally investigated horizontally using the tip of the phylogenetic tree through comparative studies of extant proteins within a family. In this article, we review a vertical comparison of the modern and resurrected ancestral proteins. We focus mainly on the dynamical properties responsible for a protein's ability to adapt new functions in response to environmental changes. Using the Dynamic Flexibility Index and the Dynamic Coupling Index to quantify the relative flexibility and dynamic coupling at a site-specific, single-amino-acid level, we provide evidence that the migration of hinges, which are often functionally critical rigid sites, is a mechanism through which proteins can rapidly evolve. Additionally, we show that disease-associated mutations in proteins often result in flexibility changes even at positions distal from mutational sites, particularly in the modulation of active site dynamics.
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Affiliation(s)
- Paul Campitelli
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona 85281, USA; , ,
| | - Tushar Modi
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona 85281, USA; , ,
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania 19122, USA; .,Department of Biology, Temple University, Philadelphia, Pennsylvania 19122, USA.,Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - S Banu Ozkan
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona 85281, USA; , ,
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48
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Sheik Amamuddy O, Veldman W, Manyumwa C, Khairallah A, Agajanian S, Oluyemi O, Verkhivker GM, Tastan Bishop Ö. Integrated Computational Approaches and Tools forAllosteric Drug Discovery. Int J Mol Sci 2020; 21:E847. [PMID: 32013012 PMCID: PMC7036869 DOI: 10.3390/ijms21030847] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 12/16/2022] Open
Abstract
Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.
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Affiliation(s)
- Olivier Sheik Amamuddy
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Wayde Veldman
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Colleen Manyumwa
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Afrah Khairallah
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
| | - Steve Agajanian
- Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA; (S.A.); (O.O.)
| | - Odeyemi Oluyemi
- Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA; (S.A.); (O.O.)
| | - Gennady M. Verkhivker
- Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA; (S.A.); (O.O.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa; (O.S.A.); (W.V.); (C.M.); (A.K.)
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49
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Analysis of allosteric communication in a multienzyme complex by ancestral sequence reconstruction. Proc Natl Acad Sci U S A 2019; 117:346-354. [PMID: 31871208 DOI: 10.1073/pnas.1912132117] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Tryptophan synthase (TS) is a heterotetrameric αββα complex. It is characterized by the channeling of the reaction intermediate indole and the mutual activation of the α-subunit TrpA and the β-subunit TrpB via a complex allosteric network. We have analyzed this allosteric network by means of ancestral sequence reconstruction (ASR), which is an in silico method to resurrect extinct ancestors of modern proteins. Previously, the sequences of TrpA and TrpB from the last bacterial common ancestor (LBCA) have been computed by means of ASR and characterized. LBCA-TS is similar to modern TS by forming a αββα complex with indole channeling taking place. However, LBCA-TrpA allosterically decreases the activity of LBCA-TrpB, whereas, for example, the modern ncTrpA from Neptuniibacter caesariensis allosterically increases the activity of ncTrpB. To identify amino acid residues that are responsible for this inversion of the allosteric effect, all 6 evolutionary TrpA and TrpB intermediates that stepwise link LBCA-TS with ncTS were characterized. Remarkably, the switching from TrpB inhibition to TrpB activation by TrpA occurred between 2 successive TS intermediates. Sequence comparison of these 2 intermediates and iterative rounds of site-directed mutagenesis allowed us to identify 4 of 413 residues from TrpB that are crucial for its allosteric activation by TrpA. The effect of our mutational studies was rationalized by a community analysis based on molecular dynamics simulations. Our findings demonstrate that ancestral sequence reconstruction can efficiently identify residues contributing to allosteric signal propagation in multienzyme complexes.
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Sanyanga TA, Nizami B, Bishop ÖT. Mechanism of Action of Non-Synonymous Single Nucleotide Variations Associated with α-Carbonic Anhydrase II Deficiency. Molecules 2019; 24:E3987. [PMID: 31690045 PMCID: PMC6864701 DOI: 10.3390/molecules24213987] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 10/18/2019] [Accepted: 10/24/2019] [Indexed: 12/12/2022] Open
Abstract
Human carbonic anhydrase II (CA-II) is a Zinc (Zn 2 + ) metalloenzyme responsible for maintenance of acid-base balance within the body through the reversible hydration of CO 2 to produce protons (H + ) and bicarbonate (BCT). Due to its importance, alterations to the amino acid sequence of the protein as a result of single nucleotide variations (nsSNVs) have detrimental effects on homeostasis. Six pathogenic CA-II nsSNVs, K18E, K18Q, H107Y, P236H, P236R and N252D were identified, and variant protein models calculated using homology modeling. The effect of each nsSNV was analyzed using motif analysis, molecular dynamics (MD) simulations, principal component (PCA) and dynamic residue network (DRN) analysis. Motif analysis identified 11 functionally important motifs in CA-II. RMSD data indicated subtle SNV effects, while PCA analysis revealed that the presence of BCT results in greater conformational sampling and free energy in proteins. DRN analysis showed variant allosteric effects, and the average betweenness centrality (BC) calculations identified Glu117 as the most important residue for communication in CA-II. The presence of BCT was associated with a reduction to Glu117 usage in all variants, suggesting implications for Zn 2 + dissociation from the CA-II active site. In addition, reductions to Glu117 usage are associated with increases in the usage of the primary and secondary Zn 2 + ligands; His94, His96, His119 and Asn243 highlighting potential compensatory mechanisms to maintain Zn 2 + within the active site. Compared to traditional MD simulation investigation, DRN analysis provided greater insights into SNV mechanism of action, indicating its importance for the study of missense mutation effects in proteins and, in broader terms, precision medicine related research.
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Affiliation(s)
- Taremekedzwa Allan Sanyanga
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.
| | - Bilal Nizami
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.
- Institute of Materials and Environmental Chemistry, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117 Budapest, Hungary.
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa.
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