1
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Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems. J Biol Phys 2022; 48:1-36. [PMID: 34822073 PMCID: PMC8866630 DOI: 10.1007/s10867-021-09586-5] [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: 05/20/2021] [Accepted: 09/07/2021] [Indexed: 10/19/2022] Open
Abstract
We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.
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2
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Chu WT, Yan Z, Chu X, Zheng X, Liu Z, Xu L, Zhang K, Wang J. Physics of biomolecular recognition and conformational dynamics. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2021; 84:126601. [PMID: 34753115 DOI: 10.1088/1361-6633/ac3800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/09/2021] [Indexed: 06/13/2023]
Abstract
Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.
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Affiliation(s)
- Wen-Ting Chu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Zhiqiang Yan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Xiakun Chu
- Department of Chemistry & Physics, State University of New York at Stony Brook, Stony Brook, NY 11794, United States of America
| | - Xiliang Zheng
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Zuojia Liu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Li Xu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Kun Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China
| | - Jin Wang
- Department of Chemistry & Physics, State University of New York at Stony Brook, Stony Brook, NY 11794, United States of America
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3
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Chen J, Qi Y, Duan Y, Duan M, Yang M. C1188D mutation abolishes specific recognition between MLL1-CXXC domain and CpG site by inducing conformational switch of flexible N-terminal. Proteins 2020; 88:1401-1412. [PMID: 32519403 DOI: 10.1002/prot.25960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 05/22/2020] [Accepted: 06/06/2020] [Indexed: 01/19/2023]
Abstract
Mixed lineage leukemia protein (MLL1 protein) recognizes the CpG site via its CXXC domain and is frequently associated with leukemia. The specific recognition is abolished by C1188D mutation, which also prevents MLL-related leukemia. In this paper, multiple molecular dynamic (MD) simulations were performed to investigate the mechanism of recognition and influences of C1188D mutation. Started from fully dissociated DNA and MLL1-CXXC domain, remarkably, the center of mass (COM) of MLL1-CXXC domain quickly concentrates on the vicinity of the CpG site in all 53 short MD simulations. Extended simulations of the wild type showed that the native complex formed in 500 ns among 4 of 53 simulations. In contrast, the C1188D mutant COM distributed broadly around the DNA and the native complex was not observed in any of the extended simulations. Simulations on the apo MLL1-CXXC domain further suggest that the wild type protein remained predominantly in an open form that closely resembles its structure in the native complex whereas C1188D mutant formed predominantly compact structures in which the N- terminal bends to D1188. This conformational switch hinders the formation of encounter complex, thus abolishes the recognition. Our study also provides clues to the study mechanism of recognition, by the CXXC domain from proteins like DNA methyltransferase and ten-eleven translocation enzymes.
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Affiliation(s)
- Jiawen Chen
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonances in Wuhan, State Key laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Yanping Qi
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonances in Wuhan, State Key laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China.,College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Yong Duan
- Department of Biomedical Engineering and UC Davis Genome Center, University of California at Davis, Davis, California, USA
| | - Mojie Duan
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonances in Wuhan, State Key laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Minghui Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonances in Wuhan, State Key laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China.,Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
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4
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Tao X, Huang Y, Wang C, Chen F, Yang L, Ling L, Che Z, Chen X. Recent developments in molecular docking technology applied in food science: a review. Int J Food Sci Technol 2019. [DOI: 10.1111/ijfs.14325] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Xuan Tao
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
| | - Yukun Huang
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
- Key Laboratory of Food Non Thermal Processing Engineering Technology Research Center of Food Non Thermal Processing Yibin Xihua University Research Institute Yibin Sichuan 644404 China
| | - Chong Wang
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
| | - Fang Chen
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
| | - Lingling Yang
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
| | - Li Ling
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
- College of Pharmacy Chengdu University of Traditional Chinese Medicine Chengdu Sichuan 611137 China
| | - Zhenming Che
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
| | - Xianggui Chen
- School of Food and Bioengineering Xihua University Chengdu Sichuan 610039 China
- Key Laboratory of Food Non Thermal Processing Engineering Technology Research Center of Food Non Thermal Processing Yibin Xihua University Research Institute Yibin Sichuan 644404 China
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5
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Deng L, Yang W, Liu H. PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees. Front Genet 2019; 10:637. [PMID: 31428122 PMCID: PMC6688581 DOI: 10.3389/fgene.2019.00637] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 06/18/2019] [Indexed: 01/24/2023] Open
Abstract
Protein-RNA interactions play essential roles in many biological aspects. Quantifying the binding affinity of protein-RNA complexes is helpful to the understanding of protein-RNA recognition mechanisms and identification of strong binding partners. Due to experimentally measured protein-RNA binding affinity data available is still limited to date, there is a pressing demand for accurate and reliable computational approaches. In this paper, we propose a computational approach, PredPRBA, which can effectively predict protein-RNA binding affinity using gradient boosted regression trees. We build a dataset of protein-RNA binding affinity that includes 103 protein-RNA complex structures manually collected from related literature. Then, we generate 37 kinds of sequence and structural features and explore the relationship between the features and protein-RNA binding affinity. We find that the binding affinity mainly depends on the structure of RNA molecules. According to the type of RNA associated with proteins composed of the protein-RNA complex, we split the 103 protein-RNA complexes into six categories. For each category, we build a gradient boosted regression tree (GBRT) model based on the generated features. We perform a comprehensive evaluation for the proposed method on the binding affinity dataset using leave-one-out cross-validation. We show that PredPRBA achieves correlations ranging from 0.723 to 0.897 among six categories, which is significantly better than other typical regression methods and the pioneer protein-RNA binding affinity predictor SPOT-Seq-RNA. In addition, a user-friendly web server has been developed to predict the binding affinity of protein-RNA complexes. The PredPRBA webserver is freely available at http://PredPRBA.denglab.org/.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China.,School of Software, Xinjiang University, Urumqi, China
| | - Wenyi Yang
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hui Liu
- Lab of Information Management, Changzhou University, Changzhou, China
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6
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Abstract
Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.
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7
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Nithin C, Ghosh P, Bujnicki JM. Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes. Genes (Basel) 2018; 9:genes9090432. [PMID: 30149645 PMCID: PMC6162694 DOI: 10.3390/genes9090432] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 07/26/2018] [Accepted: 08/21/2018] [Indexed: 12/29/2022] Open
Abstract
RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.
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Affiliation(s)
- Chandran Nithin
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland.
| | - Pritha Ghosh
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland.
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland.
- Bioinformatics Laboratory, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, ul. Umultowska 89, PL-61-614 Poznan, Poland.
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8
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Yan Z, Wang J. SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity. Nucleic Acids Res 2017; 45:e110. [PMID: 28431169 PMCID: PMC5499587 DOI: 10.1093/nar/gkx255] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 04/05/2017] [Indexed: 01/10/2023] Open
Abstract
Nucleic acids have been widely recognized as potential targets in drug discovery and aptamer selection. Quantifying the interactions between small molecules and nucleic acids is critical to discover lead compounds and design novel aptamers. Scoring function is normally employed to quantify the interactions in structure-based virtual screening. However, the predictive power of nucleic acid–ligand scoring functions is still a challenge compared to other types of biomolecular recognition. With the rapid growth of experimentally determined nucleic acid–ligand complex structures, in this work, we develop a knowledge-based scoring function of nucleic acid–ligand interactions, namely SPA-LN. SPA-LN is optimized by maximizing both the affinity and specificity of native complex structures. The development strategy is different from those of previous nucleic acid–ligand scoring functions which focus on the affinity only in the optimization. The native conformation is stabilized while non-native conformations are destabilized by our optimization, making the funnel-like binding energy landscape more biased toward the native state. The performance of SPA-LN validates the development strategy and provides a relatively more accurate way to score the nucleic acid–ligand interactions.
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Affiliation(s)
- Zhiqiang Yan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Jin Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China.,Department of Chemistry & Physics, State University of New York at Stony Brook, Stony Brook, NY 11794-3400, USA
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9
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Nithin C, Mukherjee S, Bahadur RP. A non-redundant protein-RNA docking benchmark version 2.0. Proteins 2016; 85:256-267. [PMID: 27862282 DOI: 10.1002/prot.25211] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 10/27/2016] [Accepted: 11/08/2016] [Indexed: 12/23/2022]
Abstract
We present an updated version of the protein-RNA docking benchmark, which we first published four years back. The non-redundant protein-RNA docking benchmark version 2.0 consists of 126 test cases, a threefold increase in number compared to its previous version. The present version consists of 21 unbound-unbound cases, of which, in 12 cases, the unbound RNAs are taken from another complex. It also consists of 95 unbound-bound cases where only the protein is available in the unbound state. Besides, we introduce 10 new bound-unbound cases where only the RNA is found in the unbound state. Based on the degree of conformational change of the interface residues upon complex formation the benchmark is classified into 72 rigid-body cases, 25 semiflexible cases and 19 full flexible cases. It also covers a wide range of conformational flexibility including small side chain movement to large domain swapping in protein structures as well as flipping and restacking in RNA bases. This benchmark should provide the docking community with more test cases for evaluating rigid-body as well as flexible docking algorithms. Besides, it will also facilitate the development of new algorithms that require large number of training set. The protein-RNA docking benchmark version 2.0 can be freely downloaded from http://www.csb.iitkgp.ernet.in/applications/PRDBv2. Proteins 2017; 85:256-267. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, 721302, India
| | - Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, 721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, 721302, India
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10
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Zhang Q, Wang C, Wan M, Wu Y, Ma Q. Streptococcus pneumoniae Genome-wide Identification and Characterization of BOX Element-binding Domains. Mol Inform 2016; 34:742-52. [PMID: 27491035 DOI: 10.1002/minf.201500044] [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: 05/24/2015] [Indexed: 11/11/2022]
Abstract
The BOX elements are short repetitive DNA sequences that distribute randomly in intergenic regions of the Streptococcus pneumoniae genome. The function and origin of such elements are still unknown, but they were found to modulate expression of neighboring genes. Evidences suggested that the modulation's mechanism can be fulfilled by sequence-specific interaction of BOX elements with transcription factor family proteins. However, the type and function of these BOX-binding proteins still remain largely unexplored to date. In the current study we described a synthetic protocol to investigate the recognition and interaction between a highly conserved site of BOX elements and the DNA-binding domains of a variety of putative transcription factors in the pneumococcal genome. With the protocol we were able to predict those high-affinity domain binders of the conserved BOX DNA site (BOX DNA) in a high-throughput manner, and analyzed sequence-specific interaction in the domainDNA recognition at molecular level. Consequently, a number of putative transcription factor domains with both high affinity and specificity for the BOX DNA were identified, from which the helix-turn-helix (HTH) motif of a small heat shock factor was selected as a case study and tested for its binding capability toward the double-stranded BOX DNA using fluorescence anisotropy analysis. As might be expected, a relatively high affinity was detected for the interaction of HTH motif with BOX DNA with dissociation constant at nanomolar level. Molecular dynamics simulation, atomic structure examination and binding energy analysis revealed a complicated network of intensive nonbonded interactions across the complex interface, which confers both stability and specificity for the complex architecture.
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Affiliation(s)
- Qiao Zhang
- Institute of Respiratory Diseases, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China
| | - Changzheng Wang
- Institute of Respiratory Diseases, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China
| | - Min Wan
- Institute of Respiratory Diseases, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China
| | - Yin Wu
- Institute of Respiratory Diseases, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China
| | - Qianli Ma
- Institute of Respiratory Diseases, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China
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11
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Yan Z, Wang J. Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks. J Comput Aided Mol Des 2016; 30:219-27. [DOI: 10.1007/s10822-016-9897-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 01/28/2016] [Indexed: 01/04/2023]
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12
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Qin W, Zhao G, Carson M, Jia C, Lu H. Knowledge-based three-body potential for transcription factor binding site prediction. IET Syst Biol 2016; 10:23-9. [PMID: 26816396 DOI: 10.1049/iet-syb.2014.0066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
A structure-based statistical potential is developed for transcription factor binding site (TFBS) prediction. Besides the direct contact between amino acids from TFs and DNA bases, the authors also considered the influence of the neighbouring base. This three-body potential showed better discriminate powers than the two-body potential. They validate the performance of the potential in TFBS identification, binding energy prediction and binding mutation prediction.
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Affiliation(s)
- Wenyi Qin
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Guijun Zhao
- Key Laboratory of Molecular Embryology, Ministry of Health & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai 200040, People's Republic of China
| | - Matthew Carson
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Caiyan Jia
- School of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, People's Republic of China
| | - Hui Lu
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.
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13
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Yan Z, Wang J. Optimizing the affinity and specificity of ligand binding with the inclusion of solvation effect. Proteins 2015; 83:1632-42. [PMID: 26111900 DOI: 10.1002/prot.24848] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 06/03/2015] [Accepted: 06/21/2015] [Indexed: 01/08/2023]
Abstract
Solvation effect is an important factor for protein-ligand binding in aqueous water. Previous scoring function of protein-ligand interactions rarely incorporates the solvation model into the quantification of protein-ligand interactions, mainly due to the immense computational cost, especially in the structure-based virtual screening, and nontransferable application of independently optimized atomic solvation parameters. In order to overcome these barriers, we effectively combine knowledge-based atom-pair potentials and the atomic solvation energy of charge-independent implicit solvent model in the optimization of binding affinity and specificity. The resulting scoring functions with optimized atomic solvation parameters is named as specificity and affinity with solvation effect (SPA-SE). The performance of SPA-SE is evaluated and compared to 20 other scoring functions, as well as SPA. The comparative results show that SPA-SE outperforms all other scoring functions in binding affinity prediction and "native" pose identification. Our optimization validates that solvation effect is an important regulator to the stability and specificity of protein-ligand binding. The development strategy of SPA-SE sets an example for other scoring function to account for the solvation effect in biomolecular recognitions.
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Affiliation(s)
- Zhiqiang Yan
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun, Jilin, 130022, China
| | - Jin Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences Changchun, Jilin, 130022, China.,Department of Chemistry & Physics, State University of New York at Stony Brook, Stony Brook, New York, 11794-3400, USA
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14
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The universal statistical distributions of the affinity, equilibrium constants, kinetics and specificity in biomolecular recognition. PLoS Comput Biol 2015; 11:e1004212. [PMID: 25885453 PMCID: PMC4401658 DOI: 10.1371/journal.pcbi.1004212] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 02/24/2015] [Indexed: 01/01/2023] Open
Abstract
We uncovered the universal statistical laws for the biomolecular recognition/binding process. We quantified the statistical energy landscapes for binding, from which we can characterize the distributions of the binding free energy (affinity), the equilibrium constants, the kinetics and the specificity by exploring the different ligands binding with a particular receptor. The results of the analytical studies are confirmed by the microscopic flexible docking simulations. The distribution of binding affinity is Gaussian around the mean and becomes exponential near the tail. The equilibrium constants of the binding follow a log-normal distribution around the mean and a power law distribution in the tail. The intrinsic specificity for biomolecular recognition measures the degree of discrimination of native versus non-native binding and the optimization of which becomes the maximization of the ratio of the free energy gap between the native state and the average of non-native states versus the roughness measured by the variance of the free energy landscape around its mean. The intrinsic specificity obeys a Gaussian distribution near the mean and an exponential distribution near the tail. Furthermore, the kinetics of binding follows a log-normal distribution near the mean and a power law distribution at the tail. Our study provides new insights into the statistical nature of thermodynamics, kinetics and function from different ligands binding with a specific receptor or equivalently specific ligand binding with different receptors. The elucidation of distributions of the kinetics and free energy has guiding roles in studying biomolecular recognition and function through small-molecule evolution and chemical genetics. Uncovering the principles and underlying mechanisms of biomolecular recognition and molecular binding process is crucial for understanding the function and evolution, yet challenging. We meet the challenge by quantifying the statistical natures of the relevant physical variables of biomolecular recognition using the analytical model combined with microscopic flexible docking simulation methods. We uncovered the universal statistical laws obeyed by the affinity, equilibrium constant, intrinsic specificity and kinetics for biomolecular recognition. The general statistical laws based on energy landscape theory can serve as a conceptual framework for molecular recognition in biological repertoires. They can be applied to molecular selection, in vitro evolution process, high throughput screening and virtual screening for drug discovery. The statistical laws in combinations with experiments provide quantitative signatures of a specific ligand binding to a specific receptor, these resultant laws as a guideline will contribute to drug design against a specific target. Our developed statistical methodology is general and applicable for all other biomolecular recognitions.
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15
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Yang Y, Li G, Zhao D, Yu H, Zheng X, Peng X, Zhang X, Fu T, Hu X, Niu M, Ji X, Zou L, Wang J. Computational discovery and experimental verification of tyrosine kinase inhibitor pazopanib for the reversal of memory and cognitive deficits in rat model neurodegeneration. Chem Sci 2015; 6:2812-2821. [PMID: 28706670 PMCID: PMC5489033 DOI: 10.1039/c4sc03416c] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Accepted: 01/12/2015] [Indexed: 01/10/2023] Open
Abstract
Pazopanib, a tyrosine kinase inhibitor marketed for cancer treatment, abrogates the course of neurodegeneration.
Cognition and memory impairment are hallmarks of the pathological cascade of various neurodegenerative disorders. Herein, we developed a novel computational strategy with two-dimensional virtual screening for not only affinity but also specificity. We integrated the two-dimensional virtual screening with ligand screening for 3D shape, electrostatic similarity and local binding site similarity to find existing drugs that may reduce the signs of cognitive deficits. For the first time, we found that pazopanib, a tyrosine kinase inhibitor marketed for cancer treatment, inhibits acetylcholinesterase (AchE) activities at sub-micromolar concentration. We evaluated and compared the effects of intragastrically-administered pazopanib with donepezil, a marketed AchE inhibitor, in cognitive and behavioral assays including the novel object recognition test, Y maze and Morris water maze test. Surprisingly, we found that pazopanib can restore memory loss and cognitive dysfunction to a similar extent as donepezil in a dosage of 15 mg kg–1, only one fifth of the equivalent clinical dosage for cancer treatment. Furthermore, we demonstrated that pazopanib dramatically enhances the hippocampal Ach levels and increases the expression of the synaptic marker SYP. These findings suggest that pazopanib may become a viable treatment option for memory and cognitive deficits with a good safety profile in humans.
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Affiliation(s)
- Yongliang Yang
- Center for Molecular Medicine , School of Life Science and Biotechnology , Dalian University of Technology , Dalian , 116023 , P. R. China .
| | - Guohui Li
- Laboratory of Molecular Modeling and Design , State Key Laboratory of Molecular Reaction Dynamics , Dalian Institute of Chemical Physics , Chinese Academy of Sciences , 457 Zhongshan Rd. , Dalian 116023 , P. R. China .
| | - Dongyu Zhao
- Center for Molecular Medicine , School of Life Science and Biotechnology , Dalian University of Technology , Dalian , 116023 , P. R. China .
| | - Haoyang Yu
- Department of Life Science and Biopharmaceutics , Shenyang Pharmaceutical University , Shenyang 110016 , P. R. China .
| | - Xiliang Zheng
- State Key Laboratory of Electroanalytical Chemistry , Changchun Institute of Applied Chemistry , Chinese Academy of Sciences , Changchun , Jilin , P. R. China
| | - Xiangda Peng
- Laboratory of Molecular Modeling and Design , State Key Laboratory of Molecular Reaction Dynamics , Dalian Institute of Chemical Physics , Chinese Academy of Sciences , 457 Zhongshan Rd. , Dalian 116023 , P. R. China .
| | - Xiaoe Zhang
- Center for Molecular Medicine , School of Life Science and Biotechnology , Dalian University of Technology , Dalian , 116023 , P. R. China .
| | - Ting Fu
- Laboratory of Molecular Modeling and Design , State Key Laboratory of Molecular Reaction Dynamics , Dalian Institute of Chemical Physics , Chinese Academy of Sciences , 457 Zhongshan Rd. , Dalian 116023 , P. R. China .
| | - Xiaoqing Hu
- Center for Molecular Medicine , School of Life Science and Biotechnology , Dalian University of Technology , Dalian , 116023 , P. R. China .
| | - Mingshan Niu
- Center for Molecular Medicine , School of Life Science and Biotechnology , Dalian University of Technology , Dalian , 116023 , P. R. China .
| | - Xuefei Ji
- Department of Life Science and Biopharmaceutics , Shenyang Pharmaceutical University , Shenyang 110016 , P. R. China .
| | - Libo Zou
- Department of Life Science and Biopharmaceutics , Shenyang Pharmaceutical University , Shenyang 110016 , P. R. China .
| | - Jin Wang
- State Key Laboratory of Electroanalytical Chemistry , Changchun Institute of Applied Chemistry , Chinese Academy of Sciences , Changchun , Jilin , P. R. China.,Department of Chemistry and Physics , State University of New York , Stony Brook , New York , USA .
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Khairutdinov B, Ermakova E, Sitnitsky A, Stoikov I, Zuev Y. Supramolecular complex formed by DNA oligonucleotide and thiacalix[4]arene. NMR-spectroscopy and molecular docking. J Mol Struct 2014. [DOI: 10.1016/j.molstruc.2014.05.073] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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