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Malik FK, Guo JT. Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses. Proteins 2022; 90:1303-1314. [PMID: 35122321 PMCID: PMC9018545 DOI: 10.1002/prot.26313] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/17/2022] [Accepted: 01/31/2022] [Indexed: 01/18/2023]
Abstract
Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.
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Affiliation(s)
- Fareeha K Malik
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.,Research Center of Modeling and Simulation, National University of Science and Technology, Islamabad, Pakistan
| | - Jun-Tao Guo
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA
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Lin M, Malik FK, Guo JT. A comparative study of protein-ssDNA interactions. NAR Genom Bioinform 2021; 3:lqab006. [PMID: 33655206 PMCID: PMC7902235 DOI: 10.1093/nargab/lqab006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/24/2020] [Accepted: 01/26/2021] [Indexed: 12/18/2022] Open
Abstract
Single-stranded DNA-binding proteins (SSBs) play crucial roles in DNA replication, recombination and repair, and serve as key players in the maintenance of genomic stability. While a number of SSBs bind single-stranded DNA (ssDNA) non-specifically, the others recognize and bind specific ssDNA sequences. The mechanisms underlying this binding discrepancy, however, are largely unknown. Here, we present a comparative study of protein-ssDNA interactions by annotating specific and non-specific SSBs and comparing structural features such as DNA-binding propensities and secondary structure types of residues in SSB-ssDNA interactions, protein-ssDNA hydrogen bonding and π-π interactions between specific and non-specific SSBs. Our results suggest that protein side chain-DNA base hydrogen bonds are the major contributors to protein-ssDNA binding specificity, while π-π interactions may mainly contribute to binding affinity. We also found the enrichment of aspartate in the specific SSBs, a key feature in specific protein-double-stranded DNA (dsDNA) interactions as reported in our previous study. In addition, no significant differences between specific and non-specific groups with respect of conformational changes upon ssDNA binding were found, suggesting that the flexibility of SSBs plays a lesser role than that of dsDNA-binding proteins in conferring binding specificity.
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Affiliation(s)
- Maoxuan Lin
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Fareeha K Malik
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Research Center of Modeling and Simulation, National University of Science and Technology, Islamabad, 44000, Pakistan
| | - Jun-tao Guo
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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3
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Price RM, Budzyński MA, Kundra S, Teves SS. Advances in visualizing transcription factor - DNA interactions. Genome 2020; 64:449-466. [PMID: 33113335 DOI: 10.1139/gen-2020-0086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
At the heart of the transcription process is the specific interaction between transcription factors (TFs) and their target DNA sequences. Decades of molecular biology research have led to unprecedented insights into how TFs access the genome to regulate transcription. In the last 20 years, advances in microscopy have enabled scientists to add imaging as a powerful tool in probing two specific aspects of TF-DNA interactions: structure and dynamics. In this review, we examine how applications of diverse imaging technologies can provide structural and dynamic information that complements insights gained from molecular biology assays. As a case study, we discuss how applications of advanced imaging techniques have reshaped our understanding of TF behavior across the cell cycle, leading to a rethinking in the field of mitotic bookmarking.
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Affiliation(s)
- Rachel M Price
- Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.,Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada
| | - Marek A Budzyński
- Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.,Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada
| | - Shivani Kundra
- Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.,Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada
| | - Sheila S Teves
- Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.,Department of Biochemistry and Molecular Biology, Life Sciences Institute, The University of British Columbia, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada
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Corona RI, Sudarshan S, Aluru S, Guo JT. An SVM-based method for assessment of transcription factor-DNA complex models. BMC Bioinformatics 2018; 19:506. [PMID: 30577740 PMCID: PMC6302363 DOI: 10.1186/s12859-018-2538-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models. Results We present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models. Conclusions A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models. Electronic supplementary material The online version of this article (10.1186/s12859-018-2538-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rosario I Corona
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Sanjana Sudarshan
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Srinivas Aluru
- School of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA, 30332, USA
| | - Jun-Tao Guo
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
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Corona RI, Guo JT. Statistical analysis of structural determinants for protein-DNA-binding specificity. Proteins 2016; 84:1147-61. [PMID: 27147539 DOI: 10.1002/prot.25061] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 04/21/2016] [Accepted: 04/28/2016] [Indexed: 12/27/2022]
Abstract
DNA-binding proteins play critical roles in biological processes including gene expression, DNA packaging and DNA repair. They bind to DNA target sequences with different degrees of binding specificity, ranging from highly specific (HS) to nonspecific (NS). Alterations of DNA-binding specificity, due to either genetic variation or somatic mutations, can lead to various diseases. In this study, a comparative analysis of protein-DNA complex structures was carried out to investigate the structural features that contribute to binding specificity. Protein-DNA complexes were grouped into three general classes based on degrees of binding specificity: HS, multispecific (MS), and NS. Our results show a clear trend of structural features among the three classes, including amino acid binding propensities, simple and complex hydrogen bonds, major/minor groove and base contacts, and DNA shape. We found that aspartate is enriched in HS DNA binding proteins and predominately binds to a cytosine through a single hydrogen bond or two consecutive cytosines through bidentate hydrogen bonds. Aromatic residues, histidine and tyrosine, are highly enriched in the HS and MS groups and may contribute to specific binding through different mechanisms. To further investigate the role of protein flexibility in specific protein-DNA recognition, we analyzed the conformational changes between the bound and unbound states of DNA-binding proteins and structural variations. The results indicate that HS and MS DNA-binding domains have larger conformational changes upon DNA-binding and larger degree of flexibility in both bound and unbound states. Proteins 2016; 84:1147-1161. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Rosario I Corona
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, Charlotte, North Carolina, 28223
| | - Jun-Tao Guo
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, Charlotte, North Carolina, 28223
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Musayev FN, Zarate-Perez F, Bishop C, Burgner JW, Escalante CR. Structural Insights into the Assembly of the Adeno-associated Virus Type 2 Rep68 Protein on the Integration Site AAVS1. J Biol Chem 2015; 290:27487-99. [PMID: 26370092 DOI: 10.1074/jbc.m115.669960] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Indexed: 11/06/2022] Open
Abstract
Adeno-associated virus (AAV) is the only eukaryotic virus with the property of establishing latency by integrating site-specifically into the human genome. The integration site known as AAVS1 is located in chromosome 19 and contains multiple GCTC repeats that are recognized by the AAV non-structural Rep proteins. These proteins are multifunctional, with an N-terminal origin-binding domain (OBD) and a helicase domain joined together by a short linker. As a first step to understand the process of site-specific integration, we proceeded to characterize the recognition and assembly of Rep68 onto the AAVS1 site. We first determined the x-ray structure of AAV-2 Rep68 OBD in complex with the AAVS1 DNA site. Specificity is achieved through the interaction of a glycine-rich loop that binds the major groove and an α-helix that interacts with a downstream minor groove on the same face of the DNA. Although the structure shows a complex with three OBD molecules bound to the AAVS1 site, we show by using analytical centrifugation and electron microscopy that the full-length Rep68 forms a heptameric complex. Moreover, we determined that a minimum of two direct repeats is required to form a stable complex and to melt DNA. Finally, we show that although the individual domains bind DNA poorly, complex assembly requires oligomerization and cooperation between its OBD, helicase, and the linker domains.
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Affiliation(s)
- Faik N Musayev
- From the Department of Medicinal Chemistry, School of Pharmacy, and
| | - Francisco Zarate-Perez
- Department of Physiology and Biophysics, School of Medicine, Virginia Commonwealth University, Richmond, Virginia 23298
| | - Clayton Bishop
- Department of Physiology and Biophysics, School of Medicine, Virginia Commonwealth University, Richmond, Virginia 23298
| | - John W Burgner
- Department of Physiology and Biophysics, School of Medicine, Virginia Commonwealth University, Richmond, Virginia 23298
| | - Carlos R Escalante
- Department of Physiology and Biophysics, School of Medicine, Virginia Commonwealth University, Richmond, Virginia 23298
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On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 94:77-120. [PMID: 24629186 DOI: 10.1016/b978-0-12-800168-4.00004-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.
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Takeda T, Corona RI, Guo JT. A knowledge-based orientation potential for transcription factor-DNA docking. Bioinformatics 2012; 29:322-30. [DOI: 10.1093/bioinformatics/bts699] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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Turner D, Kim R, Guo JT. TFinDit: transcription factor-DNA interaction data depository. BMC Bioinformatics 2012; 13:220. [PMID: 22943312 PMCID: PMC3483241 DOI: 10.1186/1471-2105-13-220] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2012] [Accepted: 08/23/2012] [Indexed: 11/28/2022] Open
Abstract
Background One of the crucial steps in regulation of gene expression is the binding of transcription factor(s) to specific DNA sequences. Knowledge of the binding affinity and specificity at a structural level between transcription factors and their target sites has important implications in our understanding of the mechanism of gene regulation. Due to their unique functions and binding specificity, there is a need for a transcription factor-specific, structure-based database and corresponding web service to facilitate structural bioinformatics studies of transcription factor-DNA interactions, such as development of knowledge-based interaction potential, transcription factor-DNA docking, binding induced conformational changes, and the thermodynamics of protein-DNA interactions. Description TFinDit is a relational database and a web search tool for studying transcription factor-DNA interactions. The database contains annotated transcription factor-DNA complex structures and related data, such as unbound protein structures, thermodynamic data, and binding sequences for the corresponding transcription factors in the complex structures. TFinDit also provides a user-friendly interface and allows users to either query individual entries or generate datasets through culling the database based on one or more search criteria. Conclusions TFinDit is a specialized structural database with annotated transcription factor-DNA complex structures and other preprocessed data. We believe that this database/web service can facilitate the development and testing of TF-DNA interaction potentials and TF-DNA docking algorithms, and the study of protein-DNA recognition mechanisms.
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Affiliation(s)
- Daniel Turner
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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Meysman P, Marchal K, Engelen K. DNA structural properties in the classification of genomic transcription regulation elements. Bioinform Biol Insights 2012; 6:155-68. [PMID: 22837642 PMCID: PMC3399529 DOI: 10.4137/bbi.s9426] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
It has been long known that DNA molecules encode information at various levels. The most basic level comprises the base sequence itself and is primarily important for the encoding of proteins and direct base recognition by DNA-binding proteins. A more elusive level consists of the local structural properties of the DNA molecule wherein the DNA sequence only plays an indirect supportive role. These properties are nevertheless an important factor in a large number of biomolecular processes and can be considered as informative signals for the presence of a variety of genomic features. Several recent studies have unequivocally shown the benefit of relying on such DNA properties for modeling and predicting genomic features as diverse as transcription start sites, transcription factor binding sites, or nucleosome occupancy. This review is meant to provide an overview of the key aspects of these DNA conformational and physicochemical properties. To illustrate their potential added value compared to relying solely on the nucleotide sequence in genomics studies, we discuss their application in research on transcription regulation mechanisms as representative cases.
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Affiliation(s)
- Pieter Meysman
- Department of Molecular and Microbial Systems, KULeuven, Kasteelpark Arenberg 20, 3001 Leuven, Belgium
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Wu J, Hong B, Takeda T, Guo JT. High performance transcription factor-DNA docking with GPU computing. Proteome Sci 2012; 10 Suppl 1:S17. [PMID: 22759575 PMCID: PMC3380734 DOI: 10.1186/1477-5956-10-s1-s17] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. More importantly, experiments show that the docking quality depends on the coverage of the conformational sampling space. It is therefore desirable to accelerate the computation of the docking algorithm, not only to reduce computing time, but also to improve docking quality. Methods In an attempt to accelerate the sampling process and to improve the docking performance, we developed a graphics processing unit (GPU)-based protein-DNA docking algorithm. The algorithm employs a potential-based energy function to describe the binding affinity of a protein-DNA pair, and integrates Monte-Carlo simulation and a simulated annealing method to search through the conformational space. Algorithmic techniques were developed to improve the computation efficiency and scalability on GPU-based high performance computing systems. Results The effectiveness of our approach is tested on a non-redundant set of 75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We demonstrated that the GPU-based docking algorithm can significantly accelerate the simulation process and thereby improving the chance of finding near-native TF-DNA complex structures. This study also suggests that further improvement in protein-DNA docking research would require efforts from two integral aspects: improvement in computation efficiency and energy function design. Conclusions We present a high performance computing approach for improving the prediction accuracy of protein-DNA docking. The GPU-based docking algorithm accelerates the search of the conformational space and thus increases the chance of finding more near-native structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem.
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Affiliation(s)
- Jiadong Wu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA.
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