PRA-Pred: Structure-based prediction of protein-RNA binding affinity

PRA-MutPred: Predicting the Effect of Point Mutations in Protein-RNA Complexes Using Structural Features

Interactions between proteins and RNAs are essential for the proper functioning of cells, and mutations in these molecules may lead to diseases. These protein mutations alter the strength of interactions between the protein and RNA, generally described as binding affinity (ΔG). Hence, the affinity change upon mutation (ΔΔG) is an important parameter for understanding the effect of mutations in protein-RNA complexes. In this work, we developed a machine-learning model to predict ΔΔG values upon mutations in protein-RNA complexes. We collected experimentally determined ΔΔG values of 710 mutations in 134 protein-RNA complexes. Diverse sequence and structural features were generated from both wild-type and modeled mutant complexes, which include conservation scores, residue-based, network-based, and interface features. Further, we developed a support vector regressor model with a correlation of 0.75 and a mean absolute error of 0.84 kcal/mol in the jack-knife test. We observed that the performance of the model is dictated by structural features, such as contact potentials, atom contacts in the interface of protein-RNA complexes, and the solvent accessibility of the mutated residue. We also developed a Web server, PRA-MutPred, predicting the protein-RNA binding affinity change upon mutation, which is available in the link https://web.iitm.ac.in/bioinfo2/pramutpred/.

Protein-nucleic acid complexes: Docking and binding affinity

Protein-nucleic interactions play essential roles in several biological processes, such as gene regulation, replication, transcription, repair and packaging. The knowledge of three-dimensional structures of protein-nucleic acid complexes and their binding affinities helps to understand these functions. In this review, we focus on two major aspects namely, (i) deciphering the three-dimensional structures of protein-nucleic acid complexes and (ii) predicting their binding affinities. The first part is devoted to the state-of-the-art methods for predicting the native structures and their performances including recent CASP targets. The second part is focused on different aspects of investigating the binding affinity of protein-nucleic acid complexes: (i) databases for thermodynamic parameters to understand the binding affinity, (ii) important features determining protein-nucleic acid binding affinity, (iii) predicting the binding affinity of protein-nucleic acid complexes using sequence and structure-based parameters and (iv) change in binding affinity upon mutation. It includes the latest developments in protein-nucleic acid docking algorithms and binding affinity predictions along with a list of computational resources for understanding protein-DNA and protein-RNA interactions.

Bioinformatics Approaches for Understanding the Binding Affinity of Protein-Nucleic Acid Complexes

Protein-nucleic acid interactions are involved in various biological processes such as gene expression, replication, transcription, translation, and packaging. Understanding the recognition mechanism of the protein-nucleic acid complexes has been investigated from different perspectives, including the binding affinities of protein-DNA and protein-RNA complexes. Experimentally, protein-nucleic acid interactions are analyzed using X-ray crystallography, Isothermal Titration Calorimetry (ITC), DNA/RNA pull-down assays, DNA/RNA footprinting, and systematic evolution of ligands by exponential enrichment (SELEX). On the other hand, numerous databases and computational tools have been developed to study protein-nucleic acid complexes based on their binding sites, specific interactions between them, and binding affinity. In this chapter, we discuss various databases for protein-nucleic acid complex structures and the tools available to extract features from them. Further, we provide details on databases and prediction methods reported for exploring the binding affinity of protein-nucleic acid complexes along with important structure-based parameters, which govern the binding affinity.

MFTrans: A multi-feature transformer network for protein secondary structure prediction

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.