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1. Improving protein function prediction by learning and integrating representations of protein sequences and function labels

   https://doi.org/10.1093/bioadv/vbae120  

   Boadu, Frimpong; Cheng, Jianlin (August 2024, Bioinformatics Advances)

   Zhu, Shanfeng (Ed.)

   Abstract MotivationAs fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt. ResultsWe introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained protein language model (ESM2-t48) to learn function-relevant representations of proteins from raw protein sequences and uses a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder to generate semantic representations of GO terms from their textual definition and hierarchical relationships, which are combined together to predict protein function via the cross-attention. Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of predicting rare function terms with limited annotations by facilitating annotation transfer between GO terms. Availability and implementationhttps://github.com/BioinfoMachineLearning/TransFew. 

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2. A unified multimodal model for generalizable zero-shot and supervised protein function prediction

   https://doi.org/10.1101/2025.05.09.653226  

   Boadu, Frimpong; Wang, Yanli; Cheng, Jianlin (May 2025, bioRxiv)

   Abstract Predicting protein function is a fundamental yet challenging task that requires integrating diverse biological data modalities to capture complex functional relationships. Traditional machine learning methods often rely on single modalities or combine only a limited number (typically two), without aligning them in a unified representation, thereby constraining predictive accuracy. Moreover, most existing approaches are limited to preselected subsets of Gene Ontology (GO) function terms with sufficient annotations, making the prediction of novel function terms a persistent challenge. Here, we present FunBind, a multimodal AI model that jointly learns from protein sequences, textual descriptions, domain annotations, structural features, and GO terms to enhance prediction accuracy and infer previously unseen functions. FunBind operates in two modes: (1) self-supervised pretraining using contrastive learning to align the sequence modality with other heterogeneous modalities in a unified latent space, enabling unsupervised zero-shot function prediction, and (2) supervised fine-tuning that leverages all modalities for comprehensive and accurate function classification. Our results show that FunBind’s zero-shot capabilities allow it to generalize effectively to novel function terms never encountered before, while its joint fine-tuning strategy substantially outperforms single-modality models and current state-of-the-art approaches in prediction accuracy. 

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3. Domain-PFP allows protein function prediction using function-aware domain embedding representations

   https://doi.org/10.1038/s42003-023-05476-9  

   Ibtehaz, Nabil; Kagaya, Yuki; Kihara, Daisuke (December 2023, Communications Biology)

   Abstract Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, substantially outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment. 

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4. Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons

   https://doi.org/10.1093/bioinformatics/btaa548  

   Wei, Xiaoqiong; Zhang, Chengxin; Freddolino, Peter L; Zhang, Yang; Lu, Zhiyong (May 2020, Bioinformatics)

   Abstract Motivation Many protein function databases are built on automated or semi-automated curations and can contain various annotation errors. The correction of such misannotations is critical to improving the accuracy and reliability of the databases. Results We proposed a new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of annotation rates (RAR) for the same GO term across different taxonomic groups, where those with a relatively low RAR usually correspond to incorrect annotations. As an illustration, we applied the approach to 20 commonly-studied species in two recent UniProt-GOA releases and identified 250 potential misannotations in the 2018-11-6 release, where only 25% of them were corrected in the 2019-6-3 release. Importantly, 56% of the misannotations are “Inferred from Biological aspect of Ancestor (IBA)” which is in contradiction with previous observations that attributed misannotations mainly to “Inferred from Sequence or structural Similarity (ISS)”, probably reflecting an error source shift due to the new developments of function annotation databases. The results demonstrated a simple but efficient misannotation detection approach that is useful for large-scale comparative protein function studies. Availability https://zhanglab.ccmb.med.umich.edu/RAR Supplementary information Supplementary data are available at Bioinformatics online. 

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5. CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data

   https://doi.org/10.1091/mbc.E21-05-0257  

   Velasquez, Erick F.; Garcia, Yenni A.; Ramirez, Ivan; Gholkar, Ankur A.; Torres, Jorge Z. (November 2021, Molecular Biology of the Cell)

   Kellogg, Doug (Ed.)

   The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data. 

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