Abstract
MOTIVATION: Protein function prediction is crucial in bioinformatics, driven by the growth of protein sequence data from high-throughput technologies. Traditional methods are costly and slow, underscoring the need for computational solutions. While deep learning offers powerful tools, many models lack optimization for brain development datasets, critical for neurodevelopmental disorder research. To address this, we developed RecGOBD (Recognition of Gene Ontology-related Brain Development protein function), a model tailored to predict protein functions essential to brain development. RESULT: RecGOBD targets 10 key gene ontology (GO) terms for brain development, embedding protein sequences associated with these terms. Leveraging advanced pre-trained models, it captures both sequence and structure data, aligning them with GO terms through attention mechanisms. The category attention layer enhances prediction accuracy. RecGOBD surpassed five benchmark models in AUROC, AUPR, and Fmax metrics and was further used to predict autism-related protein functions and assess mutation impacts on GO terms. These findings highlight RecGOBD's potential in advancing protein function prediction for neurodevelopmental disorders. AVAILABILITY AND IMPLEMENTATION: All Python codes associated with this study are available at https://github.com/ZL-Xia/RECGOBD.git.