HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid–protein interface with a case study

Mei, Long‐Can; Wang, Yuliang; Wu, Feng-Xu; Wang, Fan; Hao, Ge‐Fei; Yang, Guang‐Fu

doi:10.1093/bib/bbaa373

Cited by 10 publications

(3 citation statements)

References 59 publications

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“…The HISNAPI method predicts the impact of mutations at specific residues on the overall protein-RNA binding affinity and uses these values to identify the hotspots (Mei et al, 2021). Based on the strategy of computational alanine scanning, HISNAPI can quantitatively evaluate the changes in binding affinity between protein and RNA interactions.…”

Section: Hisnapimentioning

confidence: 99%

Computational methods for predicting hotspots at protein–RNA interfaces

2021

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Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been producedfor hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction.

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Section: Hisnapimentioning

confidence: 99%

Computational methods for predicting hotspots at protein–RNA interfaces

2021

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“…mmCSM-NA [ 9 ] adapts the well-proven graph-based signature concept based on mCSM-NA and is the first scalable method capable of quantitatively and accurately predicting the effect of multipoint mutations on nucleic acid binding affinity. HISNAPI [ 10 ] takes into account the flexibility of protein-nucleic acid complexes by sampling conformations using molecular dynamics simulation, and using empirical force field FoldX to determine the binding energy of wild-type and mutant protein-nucleic complexs. The other is based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform

Sun

et al. 2023

BMC Bioinformatics

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Background Identification of hot spots in protein–DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein–DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein–DNA features to predict hot spots, unable to make full use of the effective information in the features. Results In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model. Conclusions Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH.

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“…Protein–nucleic acid interactions (PNIs) are essential for several fundamental biological processes involving replication, transcription, and translation (Hoffman et al, 2004; Mei et al, 2021; Rigden & Fernández, 2021). With 20,500 proteins coding for genes by humans, they confirmed that 7.5% are linked to RNA metabolism through the binding and processing of RNA or forming significant parts of RNPs (Gerstberger et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Web tools support predicting protein–nucleic acid complexes stability with affinity changes

et al. 2023

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Numerous biological processes, such as transcription, replication, and translation, rely on protein–nucleic acid interactions (PNIs). Demonstrating the binding stability of protein–nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web‐based tools have been developed to attach importance to protein–nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein–nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein–nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web‐based tools will facilitate the discovery of recognition mechanisms for protein–nucleic acid binding stability.This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein‐RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA‐Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein‐RNA Interactions: Functional Implications

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HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid–protein interface with a case study

Cited by 10 publications

References 59 publications

Computational methods for predicting hotspots at protein–RNA interfaces

Computational methods for predicting hotspots at protein–RNA interfaces

Prediction of hot spots in protein–DNA binding interfaces based on discrete wavelet transform and wavelet packet transform

Web tools support predicting protein–nucleic acid complexes stability with affinity changes

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