MOTIVATION: The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. However, considering multi-localization phenomena remains limited in existing approaches, with none considering the influence of RNA's secondary structure. RESULTS: In this study, we propose Allocator, a multi-view parallel deep learning framework that seamlessly integrates the RNA sequence-level and structure-level information, enhancing the prediction of mRNA multi-localization. The Allocator models equip four efficient feature extractors, each designed to handle different inputs. Two are tailored for sequence-based inputs, incorporating multilayer perceptron and multi-head self-attention mechanisms. The other two are specialized in processing structure-based inputs, employing graph neural networks. Benchmarking results underscore Allocator's superiority over state-of-the-art methods, showcasing its strength in revealing intricate localization associations. AVAILABILITY AND IMPLEMENTATION: The webserver of Allocator is available at http://Allocator.unimelb-biotools.cloud.edu.au; the source code and datasets are available on GitHub (https://github.com/lifuyi774/Allocator) and Zenodo (https://doi.org/10.5281/zenodo.13235798).

Protein-metal ion interactions play a central role in the onset of numerous diseases. When amino acid changes lead to missense mutations in metal-binding sites, the disrupted interaction with metal ions can compromise protein function, potentially causing severe human ailments. Identifying these disease-associated mutation sites within metal-binding regions is paramount for understanding protein function and fostering innovative drug development. While some computational methods aim to tackle this challenge, they often fall short in accuracy, commonly due to manual feature extraction and the absence of structural data. We introduce MetalPrognosis, an innovative, alignment-free solution that predicts disease-associated mutations within metal-binding sites of metalloproteins with heightened precision. Rather than relying on manual feature extraction, MetalPrognosis employs sliding window sequences as input, extracting deep semantic insights from pre-trained protein language models. These insights are then incorporated into a convolutional neural network, facilitating the derivation of intricate features. Comparative evaluations show MetalPrognosis outperforms leading methodologies like MCCNN and M-Ionic across various metalloprotein test sets. Furthermore, an ablation study reiterates the effectiveness of our model architecture.

The Type III Secretion Systems (T3SSs) play a pivotal role in host-pathogen interactions by mediating the secretion of type III secretion system effectors (T3SEs) into host cells. These T3SEs mimic host cell protein functions, influencing interactions between Gram-negative bacterial pathogens and their hosts. Identifying T3SEs is essential in biomedical research for comprehending bacterial pathogenesis and its implications on human cells. This study presents EDIFIER, a novel multi-channel model designed for accurate T3SE prediction. It incorporates a graph structural channel, utilizing graph convolutional networks (GCN) to capture protein 3D structural features and a sequence channel based on the ProteinBERT pre-trained model to extract the sequence context features of T3SEs. Rigorous benchmarking tests, including ablation studies and comparative analysis, validate that EDIFIER outperforms current state-of-the-art tools in T3SE prediction. To enhance EDIFIER's accessibility to the broader scientific community, we developed a webserver that is publicly accessible at http://edifier.unimelb-biotools.cloud.edu.au/. We anticipate EDIFIER will contribute to the field by providing reliable T3SE predictions, thereby advancing our understanding of host-pathogen dynamics.

Origins of replication sites (ORIs) are crucial genomic regions where DNA replication initiation takes place, playing pivotal roles in fundamental biological processes like cell division, gene expression regulation, and DNA integrity. Accurate identification of ORIs is essential for comprehending cell replication, gene expression, and mutation-related diseases. However, experimental approaches for ORI identification are often expensive and time-consuming, leading to the growing popularity of computational methods. In this study, we present PLANNER (DeeP LeArNiNg prEdictor for ORI), a novel approach for species-specific and cell-specific prediction of eukaryotic ORIs. PLANNER uses the multi-scale ktuple sequences as input and employs the DNABERT pre-training model with transfer learning and ensemble learning strategies to train accurate predictive models. Extensive empirical test results demonstrate that PLANNER achieved superior predictive performance compared to state-of-the-art approaches, including iOri-Euk, Stack-ORI, and ORI-Deep, within specific cell types and across different cell types. Furthermore, by incorporating an interpretable analysis mechanism, we provide insights into the learned patterns, facilitating the mapping from discovering important sequential determinants to comprehensively analysing their biological functions. To facilitate the widespread utilisation of PLANNER, we developed an online webserver and local stand-alone software, available at http://planner.unimelb-biotools.cloud.edu.au/ and https://github.com/CongWang3/PLANNER, respectively.

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.