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Wang J, Do HN, Koirala K, Miao Y. Predicting Biomolecular Binding Kinetics: A Review. J Chem Theory Comput 2023; 19:2135-2148. [PMID: 36989090 DOI: 10.1021/acs.jctc.2c01085] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Hung N Do
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Kushal Koirala
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
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2
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Liu H, Su M, Lin HX, Wang R, Li Y. Public Data Set of Protein-Ligand Dissociation Kinetic Constants for Quantitative Structure-Kinetics Relationship Studies. ACS OMEGA 2022; 7:18985-18996. [PMID: 35694511 PMCID: PMC9178723 DOI: 10.1021/acsomega.2c02156] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/13/2022] [Indexed: 06/01/2023]
Abstract
Protein-ligand binding affinity reflects the equilibrium thermodynamics of the protein-ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure-kinetics relationship (QSKR) aim at predicting protein-ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein-ligand complexes with experimental dissociation rate constants (k off), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein-ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (k off) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein-ligand atom pair descriptors for predicting k off values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).
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Affiliation(s)
- Huisi Liu
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shangda Road, Shanghai 200444, People's Republic of China
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, People's Republic of China
| | - Minyi Su
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, People's Republic of China
| | - Hai-Xia Lin
- Department of Chemistry, College of Sciences, Shanghai University, 99 Shangda Road, Shanghai 200444, People's Republic of China
| | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
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3
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Zhou T, Wang H, Zeng C, Zhao Y. RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources. BMC Bioinformatics 2021; 22:428. [PMID: 34496744 PMCID: PMC8424408 DOI: 10.1186/s12859-021-04349-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/27/2021] [Indexed: 11/13/2022] Open
Abstract
Background RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation. Results In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design. Conclusion RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04349-4.
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Affiliation(s)
- Ting Zhou
- Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan, 430079, China
| | - Huiwen Wang
- Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan, 430079, China
| | - Chen Zeng
- Department of Physics, George Washington University, Washington, DC, 20052, USA
| | - Yunjie Zhao
- Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan, 430079, China.
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4
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Emamjomeh A, Choobineh D, Hajieghrari B, MahdiNezhad N, Khodavirdipour A. DNA-protein interaction: identification, prediction and data analysis. Mol Biol Rep 2019; 46:3571-3596. [PMID: 30915687 DOI: 10.1007/s11033-019-04763-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 03/14/2019] [Indexed: 12/30/2022]
Abstract
Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA-protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA-protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.
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Affiliation(s)
- Abbasali Emamjomeh
- Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Plant Breeding and Biotechnology (PBB), University of Zabol, Zabol, 98615-538, Iran.
| | - Darush Choobineh
- Agricultural Biotechnology, Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Behzad Hajieghrari
- Department of Agricultural Biotechnology, College of Agriculture, Jahrom University, Jahrom, 74135-111, Iran.
| | - Nafiseh MahdiNezhad
- Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Plant Breeding and Biotechnology (PBB), University of Zabol, Zabol, 98615-538, Iran
| | - Amir Khodavirdipour
- Division of Human Genetics, Department of Anatomy, St. John's hospital, Bangalore, India
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Shafaghati L, Razaghi-Moghadam Z, Mohammadnejad J. A Systems Biology Approach to Understanding Alcoholic Liver Disease Molecular Mechanism: The Development of Static and Dynamic Models. Bull Math Biol 2017; 79:2450-2473. [PMID: 28849551 DOI: 10.1007/s11538-017-0336-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 08/18/2017] [Indexed: 12/11/2022]
Abstract
Alcoholic liver disease (ALD) is a complex disease characterized by damages to the liver and is the consequence of excessive alcohol consumption over years. Since this disease is associated with several pathway failures, pathway reconstruction and network analysis are likely to explicit the molecular basis of the disease. To this aim, in this paper, a network medicine approach was employed to integrate interactome (protein-protein interaction and signaling pathways) and transcriptome data to reconstruct both a static network of ALD and a dynamic model for it. Several data sources were exploited to assemble a set of ALD-associated genes which further was used for network reconstruction. Moreover, a comprehensive literature mining reveals that there are four signaling pathways with crosstalk (TLR4, NF- [Formula: see text]B, MAPK and Apoptosis) which play a major role in ALD. These four pathways were exploited to reconstruct a dynamic model of ALD. The results assure that these two models are consistent with a number of experimental observations. The static network of ALD and its dynamic model are the first models provided for ALD which offer potentially valuable information for researchers in this field.
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Affiliation(s)
- Leila Shafaghati
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | | | - Javad Mohammadnejad
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
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6
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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7
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Gurung AB, Bhattacharjee A, Ajmal Ali M, Al-Hemaid F, Lee J. Binding of small molecules at interface of protein-protein complex - A newer approach to rational drug design. Saudi J Biol Sci 2016; 24:379-388. [PMID: 28149177 PMCID: PMC5272936 DOI: 10.1016/j.sjbs.2016.01.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 11/03/2015] [Accepted: 01/03/2016] [Indexed: 01/07/2023] Open
Abstract
Protein–protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein–protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein–protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein–protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein–protein interaction with the objective of normalizing such interactions.
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Affiliation(s)
- A B Gurung
- Department of Biotechnology and Bioinformatics, North Eastern Hill University, Shillong 793022, Meghalaya, India
| | - A Bhattacharjee
- Department of Biotechnology and Bioinformatics, North Eastern Hill University, Shillong 793022, Meghalaya, India
| | - M Ajmal Ali
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - F Al-Hemaid
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Joongku Lee
- Department of Environment and Forest Resources, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
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8
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Kim WY, Kurmar S. Sensible method for updating motif instances in an increased biological network. Methods 2015; 83:71-9. [PMID: 25869675 DOI: 10.1016/j.ymeth.2015.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Revised: 04/04/2015] [Accepted: 04/06/2015] [Indexed: 11/20/2022] Open
Abstract
A network motif is defined as an over-represented subgraph pattern in a network. Network motif based techniques have been widely applied in analyses of biological networks such as transcription regulation networks (TRNs), protein-protein interaction networks (PPIs), and metabolic networks. The detection of network motifs involves the computationally expensive enumeration of subgraphs, NP-complete graph isomorphism testing, and significance testing through the generation of many random graphs to determine the statistical uniqueness of a given subgraph. These computational obstacles make network motif analysis unfeasible for many real-world applications. We observe that the fast growth of biotechnology has led to the rapid accretion of molecules (vertices) and interactions (edges) to existing biological network databases. Even with a small percentage of additions, revised networks can have a large number of differing motif instances. Currently, no existing algorithms recalculate motif instances in 'updated' networks in a practical manner. In this paper, we introduce a sensible method for efficiently recalculating motif instances by performing motif enumeration from only updated vertices and edges. Preliminary experimental results indicate that our method greatly reduces computational time by eliminating the repeated enumeration of overlapped subgraph instances detected in earlier versions of the network. The software program implementing this algorithm, defined as SUNMI (Sensible Update of Network Motif Instances), is currently a stand-alone java program and we plan to upgrade it as a web-interactive program that will be available through http://faculty.washington.edu/kimw6/research.htm in near future. Meanwhile it is recommended to contact authors to obtain the stand-alone SUNMI program.
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Affiliation(s)
- W Y Kim
- Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics, University of Washington Bothell, Bothell, WA 98011-8246, United States.
| | - S Kurmar
- Computing and Software Systems, School of Science, Technology, Engineering, and Mathematics, University of Washington Bothell, Bothell, WA 98011-8246, United States.
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9
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Casado-Vela J, Fuentes M, Franco-Zorrilla JM. Screening of Protein–Protein and Protein–DNA Interactions Using Microarrays. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 95:231-81. [DOI: 10.1016/b978-0-12-800453-1.00008-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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10
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Perumal RC, Selvaraj A, Ravichandran S, Kumar GR. Computational kinetic studies of pyruvate metabolism in Carboxydothermus hydrogenoformans Z-2901 for improved hydrogen production. BIOTECHNOL BIOPROC E 2012. [DOI: 10.1007/s12257-011-0396-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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11
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Jones S. Computational and Structural Characterisation of Protein Associations. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2012; 747:42-54. [DOI: 10.1007/978-1-4614-3229-6_3] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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12
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Costa R, Rocha I, Ferreira E, Machado D. Critical perspective on the consequences of the limited availability of kinetic data in metabolic dynamic modelling. IET Syst Biol 2011; 5:157-63. [DOI: 10.1049/iet-syb.2009.0058] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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13
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Edelman LB, Eddy JA, Price ND. In silico models of cancer. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2010; 2:438-459. [PMID: 20836040 PMCID: PMC3157287 DOI: 10.1002/wsbm.75] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.
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Affiliation(s)
- Lucas B. Edelman
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - James A. Eddy
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - Nathan D. Price
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, Center for Biophysics and Computational Biology, University of Illinois, Urbana-Champaign
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14
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Costa RS, Machado D, Rocha I, Ferreira EC. Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations. Biosystems 2010; 100:150-7. [DOI: 10.1016/j.biosystems.2010.03.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2009] [Revised: 03/01/2010] [Accepted: 03/04/2010] [Indexed: 11/26/2022]
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15
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Edelman LB, Eddy JA, Price ND. In silico models of cancer. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2010; 2. [PMID: 20836040 PMCID: PMC3157287 DOI: 10.1002/wsbm.75 10.1002/wsbm.75] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.
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Affiliation(s)
- Lucas B. Edelman
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - James A. Eddy
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - Nathan D. Price
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, Center for Biophysics and Computational Biology, University of Illinois, Urbana-Champaign
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16
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Sutch BT, Chambers EJ, Bayramyan MZ, Gallaher TK, Haworth IS. Similarity of Protein-RNA Interfaces Based on Motif Analysis. J Chem Inf Model 2009; 49:2139-46. [DOI: 10.1021/ci900154a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Brian T. Sutch
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Eric J. Chambers
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Melina Z. Bayramyan
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Timothy K. Gallaher
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
| | - Ian S. Haworth
- Department of Pharmacology & Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089-9121
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17
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Kumar P, Han BC, Shi Z, Jia J, Wang YP, Zhang YT, Liang L, Liu QF, Ji ZL, Chen YZ. Update of KDBI: Kinetic Data of Bio-molecular Interaction database. Nucleic Acids Res 2009; 37:D636-41. [PMID: 18971255 PMCID: PMC2686478 DOI: 10.1093/nar/gkn839] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Knowledge of the kinetics of biomolecular interactions is important for facilitating the study of cellular processes and underlying molecular events, and is essential for quantitative study and simulation of biological systems. Kinetic Data of Bio-molecular Interaction database (KDBI) has been developed to provide information about experimentally determined kinetic data of protein-protein, protein-nucleic acid, protein-ligand, nucleic acid-ligand binding or reaction events described in the literature. To accommodate increasing demand for studying and simulating biological systems, numerous improvements and updates have been made to KDBI, including new ways to access data by pathway and molecule names, data file in System Biology Markup Language format, more efficient search engine, access to published parameter sets of simulation models of 63 pathways, and 2.3-fold increase of data (19,263 entries of 10,532 distinctive biomolecular binding and 11,954 interaction events, involving 2635 proteins/protein complexes, 847 nucleic acids, 1603 small molecules and 45 multi-step processes). KDBI is publically available at http://bidd.nus.edu.sg/group/kdbi/kdbi.asp.
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Affiliation(s)
- Pankaj Kumar
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - B. C. Han
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - Z. Shi
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - J. Jia
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - Y. P. Wang
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - Y. T. Zhang
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - L. Liang
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - Q. F. Liu
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - Z. L. Ji
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
| | - Y. Z. Chen
- Bioinformatics and Drug Design Group, Centre for Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 and Bioinformatics Research Group, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, P. R. China
- *To whom correspondence should be addressed. Tel: +65 6516 6877; Fax: +65 6774 6756;
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Enzyme solid-state support assays: a surface plasmon resonance and mass spectrometry coupled study of immobilized insulin degrading enzyme. EUROPEAN BIOPHYSICS JOURNAL: EBJ 2008; 38:407-14. [DOI: 10.1007/s00249-008-0384-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2008] [Revised: 11/06/2008] [Accepted: 11/11/2008] [Indexed: 02/02/2023]
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19
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Lalonde S, Ehrhardt DW, Loqué D, Chen J, Rhee SY, Frommer WB. Molecular and cellular approaches for the detection of protein-protein interactions: latest techniques and current limitations. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2008; 53:610-635. [PMID: 18269572 DOI: 10.1111/j.1365-313x.2007.03332.x] [Citation(s) in RCA: 109] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from co-localization of tags, which may be limited by the optical resolution of the microscope, to fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance, provide detailed information on physical properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and Thelen (2008; this issue, pp. 597-609). In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of using to predict interaction surfaces.
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Affiliation(s)
- Sylvie Lalonde
- Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USA.
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Abstract
Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.
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Affiliation(s)
- András Szilágyi
- Center of Excellence in Bioinformatics, University at Buffalo, State University of New York, 901 Washington St, Buffalo, NY 14203, USA
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21
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Abstract
BACKGROUND The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway. RESULTS Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions. CONCLUSION Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data.
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Affiliation(s)
- Ivan Arisi
- European Brain Research Institute, Via Fosso del Fiorano 64, Roma, Italy
| | - Antonino Cattaneo
- European Brain Research Institute, Via Fosso del Fiorano 64, Roma, Italy
- Lay Line Genomics SpA, S.Raffaele Science Park, Castel Romano, Italy
- International School of Advanced Studies (SISSA/ISAS), Biophysics Dept., Via Beirut 2-4, Trieste, Italy
| | - Vittorio Rosato
- ENEA, Casaccia Research Center, Computing and Modelling Unit, Via Anguillarese 301, S.Maria di Galeria, Italy
- Ylichron Srl, c/o ENEA, Casaccia Research Center, Via Anguillarese 301, S.Maria di Galeria, Italy
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Han L, Cui J, Lin H, Ji Z, Cao Z, Li Y, Chen Y. Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. Proteomics 2006; 6:4023-37. [PMID: 16791826 DOI: 10.1002/pmic.200500938] [Citation(s) in RCA: 50] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.
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Affiliation(s)
- Lianyi Han
- Department of Computational Science, National University of Singapore, Singapore, Singapore
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23
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Abstract
The first release of Protein-protein Interactions Thermodynamic Database (PINT) contains >1500 data of several thermodynamic parameters along with sequence and structural information, experimental conditions and literature information. Each entry contains numerical data for the free energy change, dissociation constant, association constant, enthalpy change, heat capacity change and so on of the interacting proteins upon binding, which are important for understanding the mechanism of protein-protein interactions. PINT also includes the name and source of the proteins involved in binding, their Protein Information Resource, SWISS-PROT and Protein Data Bank (PDB) codes, secondary structure and solvent accessibility of residues at mutant positions, measuring methods, experimental conditions, such as buffers, ions and additives, and literature information. A WWW interface facilitates users to search data based on various conditions, feasibility to select the terms for output and different sorting options. Further, PINT is cross-linked with other related databases, PIR, SWISS-PROT, PDB and NCBI PUBMED literature database. The database is freely available at http://www.bioinfodatabase.com/pint/index.html.
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Affiliation(s)
- M D Shaji Kumar
- Department of Biochemical Engineering and Science, Kyushu Institute of Technology Iizuka 820-8502, Fukuoka, Japan.
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HarshaRani GV, Vayttaden SJ, Bhalla US. Electronic data sources for kinetic models of cell signaling. J Biochem 2005; 137:653-7. [PMID: 16002985 DOI: 10.1093/jb/mvi083] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Functional understanding of signaling pathways requires detailed information about the constituent molecules and their interactions. Simulations of signaling pathways therefore build upon a great deal of data from various sources. We first survey electronic data resources for cell signaling modeling and then based on the type of data representation the data sources are broadly classified into five groups. None of the data sources surveyed provide all required data in a ready-to-be-modeled fashion. We then put forward a "wish list" for the desired attributes for an ideal modeling centric database. Finally, we close with perspectives on how electronic data sources for cell signaling modeling have developed. We suggest that future directions in such data sources are largely model-driven and are hinged on interoperability of data sources.
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Affiliation(s)
- G V HarshaRani
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bangalore 560065, India
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