A combinatorial scoring function for protein–RNA docking

Zhang, Zhao; Lü, Lin; Zhang, Yue; Li, Chun Hua; Wang, Cun Xin; Zhang, Xiao Yi; Tan, Jian Jun

doi:10.1002/prot.25253

Cited by 17 publications

(15 citation statements)

References 36 publications

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“…In fact, RNA rarely acts alone, and most RNAs function only in complex with specific proteins (Dawson and Bujnicki 2016). Therefore, revealing the protein-RNA specific recognitions and binding patterns is quite significant for both understanding the important processes of life and designing new drugs (Zhang et al 2017). Several experimental methods have been developed to determine RPIs (Anko and Neugebauer 2012;Campbell and Wickens 2015), such as ultraviolet crosslinking and immunoprecipitation (CLIP) (Ule et al 2003), high-throughput sequencing of CLIP cDNA library (HITS-CLIP) (Licatalosi et al 2008), photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) (Hafner et al 2010), individual nucleotide resolution CLIP (iCLIP) (Konig et al 2010), targets of RNA-binding protein identified by editing (TRIBE) (McMahon et al 2016), covalent RNA marks (Lapointe et al 2015), interactome capture (IC) (Castello et al 2012), and serial RNA interactome capture (Ser IC) (Conrad et al 2016), etc.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, only two standalone docking software packages, including NPDock (Tuszynska et al 2015) and 3dRPC , have been developed specifically for predicting protein-RNA complexes. Similar to most docking protocols, a protein-RNA docking approach includes two steps: sampling and scoring (Zhang et al 2017). Several approaches have been developed to solve the sampling issue, such as the information-driven method used in HADDOCK (Dominguez et al 2003), the fast Fourier transformation (FFT) algorithm in GRAMM (Vakser and Aflalo 1994) and FTDock (Katchalskikatzir et al 1992), the distance geometry algorithm in DOCK (Kuntz et al 1982), and the genetic algorithm in DARWIN (Taylor and Burnett 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches have been developed to solve the sampling issue, such as the information-driven method used in HADDOCK (Dominguez et al 2003), the fast Fourier transformation (FFT) algorithm in GRAMM (Vakser and Aflalo 1994) and FTDock (Katchalskikatzir et al 1992), the distance geometry algorithm in DOCK (Kuntz et al 1982), and the genetic algorithm in DARWIN (Taylor and Burnett 2000). However, there is no ideal scoring function developed specifically for protein-RNA scoring, where the scoring function is the real way to characterize the main difference between protein-protein interactions and protein-RNA interactions (Zhang et al 2017). Thus, there is an urgent need to explore a reliable scoring function specific to protein-RNA docking.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein–RNA complexes

Sun

Wang

et al. 2018

RNA

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Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GB model with = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GB model distinguished the near-native binding structures within the top 10 decoys for 117 out of the 148 protein-RNA systems (79.1%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein–RNA complexes

Sun

Wang

et al. 2018

RNA

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“…As we know, the aromatic and Arg residues have important contributions to the stacking and ion-pi interactions with RNA bases respectively, which may explain the reason of their higher conservations. Based on our previous study [29,32], the hydrophobic residues Leu, Ile and Met do not prefer to appear at protein-RNA interfaces, while interestingly they prefer to occur in the conserved interface clusters once they appear at interfaces. Considering the analysis results on hot spot residues that the hydrophobic residues have the second largest probability of being hot spot residues among the seven classes of amino acid residues, we think that the three kinds of preferred hydrophobic residues in conserved interface clusters maybe contribute an important role to protein-RNA binding free energy through their cooperative interactions with other residues in the conserved interface clusters.…”

Section: Discussionmentioning

confidence: 93%

“…Considering the analysis results on hot spot residues that the hydrophobic residues have the second largest probability of being hot spot residues among the seven classes of amino acid residues, we think that the three kinds of preferred hydrophobic residues in conserved interface clusters maybe contribute an important role to protein-RNA binding free energy through their cooperative interactions with other residues in the conserved interface clusters. Additionally, the residue-nucleotide propensity potential obtained by us [29,32] for protein-RNA interactions showed that Cys has a higher pairing preference with A and U, and Gly is relatively preferred by interfaces. For protein-RNA, protein-protein (homodimers and heterocomplexes) interactions, the common residues preferred in conserved clusters are Leu, Ile, Met, Tyr, Phe and Trp.…”

Section: Discussionmentioning

confidence: 99%

Analyses on clustering of the conserved residues at protein-RNA interfaces and its application in binding site identification

et al. 2020

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Background: The maintenance of protein structural stability requires the cooperativity among spatially neighboring residues. Previous studies have shown that conserved residues tend to occur clustered together within enzyme active sites and protein-protein/DNA interfaces. It is possible that conserved residues form one or more local clusters in protein tertiary structures as it can facilitate the formation of functional motifs. In this work, we systematically investigate the spatial distributions of conserved residues as well as hot spot ones within protein-RNA interfaces. Results: The analysis of 191 polypeptide chains from 160 complexes shows the polypeptides interacting with tRNAs evolve relatively rapidly. A statistical analysis of residues in different regions shows that the interface residues are often more conserved, while the most conserved ones are those occurring at protein interiors which maintain the stability of folded polypeptide chains. Additionally, we found that 77.8% of the interfaces have the conserved residues clustered within the entire interface regions. Appling the clustering characteristics to the identification of the real interface, there are 31.1% of cases where the real interfaces are ranked in top 10% of 1000 randomly generated surface patches. In the conserved clusters, the preferred residues are the hydrophobic (Leu, Ile, Met), aromatic (Tyr, Phe, Trp) and interestingly only one positively charged Arg residues. For the hot spot residues, 51.5% of them are situated in the conserved residue clusters, and they are largely consistent with the preferred residue types in the conserved clusters. Conclusions: The protein-RNA interface residues are often more conserved than non-interface surface ones. The conserved interface residues occur more spatially clustered relative to the entire interface residues. The high consistence of hot spot residue types and the preferred residue types in the conserved clusters has important implications for the experimental alanine scanning mutagenesis study. This work deepens the understanding of the residual organization at protein-RNA interface and is of potential applications in the identification of binding site and hot spot residues.

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An updated dataset and a structure‐based prediction model for protein–RNA binding affinity

Tong

Xie

et al. 2023

Proteins

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Understanding the process of protein–RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein–RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein–RNA complexes can help us to understand the regulation mechanism of protein–RNA interaction. Since it is time‐consuming and labor‐intensive to determine the binding affinity for protein–RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein–RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein–RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

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A combinatorial scoring function for protein–RNA docking

Cited by 17 publications

References 36 publications

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein–RNA complexes

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein–RNA complexes

Analyses on clustering of the conserved residues at protein-RNA interfaces and its application in binding site identification

An updated dataset and a structure‐based prediction model for protein–RNA binding affinity

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