Learning probabilistic protein-DNA recognition codes from DNA-binding specificities using structural mappings

Wetzel, Joshua; Zhang, Kaiqian; Singh, Mona

doi:10.1101/2022.01.31.477772

Cited by 2 publications

(1 citation statement)

References 55 publications

(128 reference statements)

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“…Proteins need to find the appropriate target rapidly, distinguishing between many similar and competing DNA sequences, and tightly bind to their "own" sites [1,2]. But despite the extensive study of protein-DNA recognition, it is still impossible to formulate universal rules of its mechanisms similar to the principle of complementarity [3,4]. A successful approach for understanding the mechanisms of protein-nucleic acid recognition, along with the results from experimental investigations, is the data analysis using molecular biological databases.…”

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confidence: 99%

ProtNA-ASA data base: new version including information about electrostatic potential of DNA minor groove

Zhytnikova,

Shestopalova

2023

Biophysical Bulletin

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Background: In the past decades, the rapid development of molecular biology has led to a generation of an unprecedented amount of biological data obtained by the scientific community. Therefore, there is a significant and unmet need to store, process, and make sense of such a vast amount of data. There are currently available a number of databases, that cover different fields of molecular biology. Objectives: In this paper, we describe Protein-Nucleic Acid Structural Database with Information on Accessible Surface Area, ProtNA-ASA, http://www.ire.kharkov.ua/ProtNA-ASA/index.php. The main aim of ProtNA-ASA is to provide quick and convenient access to structural information about DNA and protein-DNA complexes, that can be used for comprehensive study of protein-DNA recognition. Materials and Methods: ProtNA-ASA database comprise information based on X-ray or NMR structures derived from Nucleic Acids Data Bank: 973 structures of protein-DNA complexes, 129 structures of naked А- and 403 of B-DNA ones; following structural parameters for each structure: conformational DNA parameters calculated with the 3DNA/CompDNA analyzer; DNA accessible surface area calculated using the modified algorithm of Higo and Go; DNA electrostatic potential calculated with DelPhi package. Results: The recent update of ProtNA-ASA includes the electrostatic potential of the DNA minor groove since it plays an essential role in the indirect protein-DNA recognition process. The update also includes an advanced search, which serves to ease the use of the database and contribute to a more accurate structure selection. Advanced search allows finding structures by PDB/NDB ID, citation, length and sequence of a protein or DNA chain, type of structure, method of structure obtaining and resolution. All these queries can be used in different combinations with and/or statements. Conclusion: The combination of structural information and physical characteristics from the ProtNA-ASA database is particularly useful to scientists studying the indirect readout, that based on DNA deformability. The detail analyzes of protein-DNA complexes and mechanisms of protein-DNA recognition is essential for implications in understanding cellular processes, DNA metabolism, transcriptional regulation, and developing therapeutic drugs.

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confidence: 99%

ProtNA-ASA data base: new version including information about electrostatic potential of DNA minor groove

Zhytnikova,

Shestopalova

2023

Biophysical Bulletin

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Structure-based learning to model complex protein-DNA interactions and transcription-factor co-operativity incis-regulatory elements

Fornés

Meseguer

Aguirre‐Plans

et al. 2022

Preprint

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Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ~25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. Here, we introduce ModCRE, a web server implementing a structure homology-modelling approach to predict TF motifs and automatically model higher-order TF regulatory complexes. Starting from a TF sequence or structure, ModCRE predicts a set of motifs for that TF. The predicted motifs are then used to scan the DNA for occurrences of each of them, and the best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA, as well as other TFs and co-factors. Moreover, we demonstrate that incorporating high-throughput TF binding data, such as from protein binding microarrays, addresses the protein-DNA structure scarcity problem for deriving statistical potentials. In turn, these statistical potentials are proven to be capable predictors of TF motifs. We also show the conditional advantage of using ModCRE over a nearest-neighbor approach for predicting TF binding sites as well as an improvement in prediction accuracy when using a rank-enrichment selection system. Finally, as case examples, we apply ModCRE to model the interferon beta enhanceosome and the complex of SOX2 and 11 with a nucleosome.

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Learning probabilistic protein-DNA recognition codes from DNA-binding specificities using structural mappings

Cited by 2 publications

References 55 publications

ProtNA-ASA data base: new version including information about electrostatic potential of DNA minor groove

ProtNA-ASA data base: new version including information about electrostatic potential of DNA minor groove

Structure-based learning to model complex protein-DNA interactions and transcription-factor co-operativity incis-regulatory elements

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