Dataset Information

HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.

ABSTRACT: The sequence-based predictors of RNA-binding residues (RBRs) are trained on either structure-annotated or disorder-annotated binding regions. A recent study of predictors of protein-binding residues shows that they are plagued by high levels of cross-predictions (protein binding residues are predicted as nucleic acid binding) and that structure-trained predictors perform poorly for the disorder-annotated regions and vice versa. Consequently, we analyze a representative set of the structure and disorder trained predictors of RBRs to comprehensively assess quality of their predictions. Our empirical analysis that relies on a new and low-similarity benchmark dataset reveals that the structure-trained predictors of RBRs perform well for the structure-annotated proteins while the disorder-trained predictors provide accurate results for the disorder-annotated proteins. However, these methods work only modestly well on the opposite types of annotations, motivating the need for new solutions. Using an empirical approach, we design HybridRNAbind meta-model that generates accurate predictions and low amounts of cross-predictions when tested on data that combines structure and disorder-annotated RBRs. We release this meta-model as a convenient webserver which is available at https://www.csuligroup.com/hybridRNAbind/.

SUBMITTER: Zhang F

PROVIDER: S-EPMC10018345 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.

Zhang Fuhao F Li Min M Zhang Jian J Kurgan Lukasz L

Nucleic acids research 20230301 5

The sequence-based predictors of RNA-binding residues (RBRs) are trained on either structure-annotated or disorder-annotated binding regions. A recent study of predictors of protein-binding residues shows that they are plagued by high levels of cross-predictions (protein binding residues are predicted as nucleic acid binding) and that structure-trained predictors perform poorly for the disorder-annotated regions and vice versa. Consequently, we analyze a representative set of the structure and d ...[more]

PMID: 36629262

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Project description:BackgroundSmall molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD+ or NAD) is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP) have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD+ to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs) from amino acid sequence using bioinformatics tools.ResultsWe extracted 1556 proteins chains from 555 NAD binding proteins whose structure is available in Protein Data Bank. Then we removed all redundant protein chains and finally obtained 195 non-redundant NAD binding protein chains, where no two chains have more than 40% sequence identity. In this study all models were developed and evaluated using five-fold cross validation technique on the above dataset of 195 NAD binding proteins. While certain type of residues are preferred (e.g. Gly, Tyr, Thr, His) in NAD interaction, residues like Ala, Glu, Leu, Lys are not preferred. A support vector machine (SVM) based method has been developed using various window lengths of amino acid sequence for predicting NAD interacting residues and obtained maximum Matthew's correlation coefficient (MCC) 0.47 with accuracy 74.13% at window length 17. We also developed a SVM based method using evolutionary information in the form of position specific scoring matrix (PSSM) and obtained maximum MCC 0.75 with accuracy 87.25%.ConclusionFor the first time a sequence-based method has been developed for the prediction of NAD binding proteins and their interacting residues, in the absence of any prior structural information. The present model will aid in the understanding of NAD+ dependent mechanisms of action in the cell. To provide service to the scientific community, we have developed a user-friendly web server, which is available from URL http://www.imtech.res.in/raghava/nadbinder/.

Dataset Information

HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.

Publications

HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.

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