PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions

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/.

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.

Mutational analysis of SARS-CoV-2 ORF6-KPNA2 binding interface and identification of potent small molecule inhibitors to recuse the host immune system

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surfaced on 31 December, 2019, and was identified as the causative agent of the global COVID-19 pandemic, leading to a pneumonia-like disease. One of its accessory proteins, ORF6, has been found to play a critical role in immune evasion by interacting with KPNA2 to antagonize IFN signaling and production pathways, resulting in the inhibition of IRF3 and STAT1 nuclear translocation. Since various mutations have been observed in ORF6, therefore, a comparative binding, biophysical, and structural analysis was used to reveal how these mutations affect the virus's ability to evade the human immune system. Among the identified mutations, the V9F, V24A, W27L, and I33T, were found to have a highly destabilizing effect on the protein structure of ORF6. Additionally, the molecular docking analysis of wildtype and mutant ORF6 and KPNA2 revealed the docking score of - 53.72 kcal/mol for wildtype while, -267.90 kcal/mol, -258.41kcal/mol, -254.51 kcal/mol and -268.79 kcal/mol for V9F, V24A, W27L, and I33T respectively. As compared to the wildtype the V9F showed a stronger binding affinity with KPNA2 which is further verified by the binding free energy (-42.28 kcal/mol) calculation. Furthermore, to halt the binding interface of the ORF6-KPNA2 complex, we used a computational molecular search of potential natural products. A multi-step virtual screening of the African natural database identified the top 5 compounds with best docking scores of -6.40 kcal/mol, -6.10 kcal/mol, -6.09 kcal/mol, -6.06 kcal/mol, and -6.03 kcal/mol for tophit1-5 respectively. Subsequent all-atoms simulations of these top hits revealed consistent dynamics, indicating their stability and their potential to interact effectively with the interface residues. In conclusion, our study represents the first attempt to establish a foundation for understanding the heightened infectivity of new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.

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

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.

Determinants of DNMT2/TRDMT1 preference for substrates tRNA and DNA during the evolution

RNA methyltransferase DNMT2/TRDMT1 is the most conserved member of the DNMT family from bacteria to plants and mammals. In previous studies, we found some determinants for tRNA recognition of DNMT2/TRDMT1, but the preference mechanism of this enzyme for substrates tRNA and DNA remains to be explored. In the present study, CFT-containing target recognition domain (TRD) and target recognition extension domain (TRED) in DNMT2/TRDMT1 play a crucial role in the substrate DNA and RNA selection during the evolution. Moreover, the classical substrate tRNA for DNMT2/TRDMT1 had a characteristic sequence CUXXCAC in the anticodon loop. Position 35 was occupied by U, making cytosine-38 (C38) twist into the loop, whereas C, G or A was located at position 35, keeping the C38-flipping state. Hence, the substrate preference could be modulated by the easily flipped state of target cytosine in tRNA, as well as TRD and TRED. Additionally, DNMT2/TRDMT1 cancer mutant activity was collectively mediated by five enzymatic characteristics, which might impact gene expressions. Importantly, G155C, G155V and G155S mutations reduced enzymatic activities and showed significant associations with diseases using seven prediction methods. Altogether, these findings will assist in illustrating the substrate preference mechanism of DNMT2/TRDMT1 and provide a promising therapeutic strategy for cancer.

Assessing the in vitro Binding Affinity of Protein-RNA Interactions Using an RNA Pull-down Technique

RNA is a vital component of the cell and is involved in a diverse range of cellular processes through a variety of functions. However, many of these functions cannot be performed without interactions with proteins. There are currently several techniques used to study protein-RNA interactions, such as electrophoretic mobility shift assay, fluorescence anisotropy, and filter binding. RNA-pulldown is a technique that uses biotinylated RNA probes to capture protein-RNA complexes of interest. First, the RNA probe and a recombinant protein are incubated to allow the in vitro interaction to occur. The fraction of bound protein is then captured by a biotin pull-down using streptavidin-agarose beads, followed by elution and immunoblotting for the recombinant protein with a His-tag-reactive probe. Overall, this method does not require specialized equipment outside what is typically found in a modern molecular laboratory and easily facilitates the maintenance of an RNase-free environment. This protocol was validated in: Nucleic Acids Res (2020), DOI: 10.1093/nar/gkaa029 Graphical abstract.

Computational tools to study RNA-protein complexes

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions

Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning.

Sun et al. present PremPLI, a machine learning approach and web tool to predict how missense mutations in a drug’s target protein will affect the drug’s binding affinity. PremPLI can be applied to identify drug resistance mechanisms in cancer cells and microorganisms and develop drugs to combat drug resistance.

Computational methods for predicting hotspots at protein-RNA interfaces

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 produced for 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. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Methods > RNA Analyses In Vitro and In Silico.

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.

Protein-nucleic acid interactions play important roles in various cellular processes. Missense mutations occurring in DNA- or RNA-binding proteins (termed MPDs and MPRs, respectively) could change the binding affinities of these interactions. Previous studies have compared protein-DNA and protein-RNA interactions from multifaceted viewpoints, but less attention has been given to the similarities and specific differences between the effects of MPDs and MPRs and between the methodologies for predicting the affinity changes induced by the two mutation classes. Therefore, we systematically compared their impacts and demonstrated that MPDs and MPRs could have specific preferences for binding affinity changes. These observations motivated us to construct regression models separately for MPDs and MPRs by introducing novel energy and nonenergy descriptors. Although similar frameworks were developed to estimate these two categories of mutation effects, different descriptors were selected in the regression models and further revealed the specificity of mutation classes. The interplay between the energy and nonenergy modules effectively improved prediction performance. Our algorithm can also be adopted to disentangle mutations significantly decreasing binding affinities from other mutations.

PremPS: Predicting the impact of missense mutations on protein stability

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.