Motivation
Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) is important for functional annotation and site-directed mutagenesis. Experimental assays to sparse RBPs are precise and convincing, but also costly and time consuming. Therefore, flexible and reliable computational methods are required to recognize RNA-binding residues.
Results
In this work, we propose PST-PRNA, a novel model for predicting RNA-binding sites (PRNA) based on protein surface topography (PST). Taking full advantage of the three-dimensional (3D) structural information of protein, PST-PRNA creates representative topography images of the entire protein surface by mapping it onto a unit spherical surface. Four kinds of descriptors are encoded to represent residues on the surface. Then, the potential features are integrated and optimized by using deep learning models. We compile a comprehensive non-redundant RBP dataset to train and test PST-PRNA using 10-fold cross-validation. Numerous experiments demonstrate PST-PRNA learns successfully the latent structural information of protein surface. On the non-redundant dataset with sequence identity of 0.3, PST-PRNA achieves AUC value of 0.860 and MCC value of 0.420. Furthermore, we construct a completely independent test dataset for justification and comparison. PST-PRNA achieves AUC value of 0.913 on the independent dataset, which is superior to the other state-of-the-art methods.
Availability
The code and data are available at https://www.github.com/zpliulab/PST-PRNA. A web server is freely available at http://www.zpliulab.cn/PSTPRNA.
Supplementary information
Supplementary data are available at Bioinformatics online.
Due to strict regulations and the requirement to reduce greenhouse gas emissions, electric vehicles (BEVs) are a promising mode of transportation. The lithium battery is the most important power source for an electric vehicle, but its performance and life are greatly restricted by temperature. To ensure the safety of automobile operation and alleviate mileage anxiety, it is urgent to understand the current situation and predict the development and challenge of battery thermal management system. This work reviews the existing thermal management research in five areas, including cooling and heating methods, modeling optimization, control methods, and thermal management system integration for lithium batteries. Battery thermal management types include air-based, liquid-based, PCM-based, heatpipe-based, and direct cooling. Designing a better battery thermal management system not only needs to be optimized using algorithms on the model but also it uses intelligent algorithms for precise control to achieve safety and reduce energy consumption. This work also reviews the differences in thermal management systems between square and cylindrical batteries and summarizes the development trend of modularity in battery thermal management systems.
Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.