Improving accuracy, coverage, and sustainability of functional protein annotation in InterPro, Pfam and FunFam using Deep Learning methods

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Proteins are macromolecules responsible for biological processes in the cell. At their most basic level, they consist of a sequence of amino acids, determined by the sequence of nucleotides (the ATGC building blocks of life) in a gene. Proteins usually fold into three-dimensional structures, allowing them to interact with other molecules and perform their functions. Recent advances in sequencing technologies have led to a substantial accumulation of protein data, and our capacity of generating new protein sequences has surpassed our ability to fully understand their functions. Therefore, it is crucial to develop computational methods that identify sequence or structural similarities between characterised and uncharacterised proteins to transfer functional information from the former to the latter.InterPro, Pfam and FunFam are world-leading, UK-based resources that group similar protein sequences together, forming protein families. Pfam is a collection of protein domain families containing functional annotations. FunFam focuses on protein structural domains that share a common function. InterPro merges information from 13 expert protein databases, including Pfam and FunFam, into a single searchable resource, and further annotates protein families.In the past few years, Artificial Intelligence methods have been successfully applied to several biological applications. For instance, DeepMind's AlphaFold has revolutionised the prediction of how protein sequences fold into three-dimensional structures. Several promising tools are being developed by our collaborators to better identify protein families using Deep Learning (DL). These methods outperform current state-of-the-art approaches in terms of accuracy, coverage and computing efficiency, thus making them more environmentally sustainable.In this ambitious project, we will improve the efficiency, accuracy, and sustainability of InterPro, Pfam and FunFam. This will be accomplished by reducing the technical debt of Pfam, established almost three decades ago, adopting DL approaches to enhance the classification of protein sequences into families, and significantly reducing the carbon footprint of sequence annotation. Finally, we will improve the annotation of agriculturally important plant pathogens, resulting in the creation of hundreds of additional InterPro and Pfam entries.