The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. Here, we report recent developments with InterPro (version 90.0) and its associated software, including updates to data content and to the website. These developments extend and enrich the information provided by InterPro, and provide a more user friendly access to the data. Additionally, we have worked on adding Pfam website features to the InterPro website, as the Pfam website will be retired in late 2022. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB. Moreover, we report the development of a card game as a method of engaging the non-scientific community. Finally, we discuss the benefits and challenges brought by the use of artificial intelligence for protein structure prediction.
Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell-type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. By use of convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. We show that model predictions for the influence of genomic variants on gene expression align well to causal variants underlying eQTLs in human populations and can be useful for generating mechanistic hypotheses to enable fine mapping of disease loci.
Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. In this paper, we explore an alternative methodology based on deep learning that learns the relationship between unaligned amino acid sequences and their functional annotations across all 17929 families of the Pfam database. Using the Pfam seed sequences we establish rigorous benchmark assessments that use both random and clustered data splits to control for potentially confounding sequence similarities between train and test sequences. Using Pfam full, we report convolutional networks that are significantly more accurate and computationally efficient than BLASTp, while learning sequence features such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. These results suggest deep learning models will be a core component of future protein function prediction tools.Predicting the function of a protein from its raw amino acid sequence is a critical step for understanding the relationship between genotype and phenotype. As the cost of DNA sequencing drops and metagenomic sequencing projects flourish, fast and efficient tools that annotate open reading frames with function will play a central role in exploiting this data [1,2]. Doing so will help identify proteins that catalyze novel reactions, design new proteins that bind specific microbial targets, or build molecules that accelerate advances in biotechnology. Current practice for functional prediction of a novel protein sequence involves alignment across a large database of annotated sequences using algorithms such as 1
The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.
Understanding the relationship between amino acid sequence and protein function is a long-standing problem in molecular biology with far-reaching scientific implications. Despite six decades of progress, state-of-the-art techniques cannot annotate 1/3 of microbial protein sequences, hampering our ability to exploit sequences collected from diverse organisms. To address this, we report a deep learning model that learns the relationship between unaligned amino acid sequences and their functional classification across all 17929 families of the Pfam database. Using the Pfam seed sequences we establish a rigorous benchmark assessment and find a dilated convolutional model that reduces the error of both BLASTp and pHMMs by a factor of nine. Using 80% of the full Pfam database we train a protein family predictor that is more accurate and over 200 times faster than BLASTp, while learning sequence features it was not trained on such as structural disorder and transmembrane helices. Our model co-locates sequences from unseen families in embedding space, allowing sequences from novel families to be accurately annotated. These results suggest deep learning models will be a core component of future protein function prediction tools.profile hidden Markov models (pHMMs) built from aligned sequence families such as those provided by Pfam [4,5].While these approaches are generally successful, at least one-third of microbial proteins cannot be annotated through alignment to characterized sequences [6,7]. Moreover, the run times of methods such as BLASTp scale nearly linearly with the size of the labelled database, which is growing exponentially [8]. Running all 17,929 Pfam HMMs against a single sequence takes a few minutes, and about 90 hours for the 54.5 million sequences in Pfam full [9][10][11]. Broad protein families require muliple HMM profiles to model their diversity [12], while more than 22% of the highly-curated families in Pfam 32.0 have no functional annotation. More generally, models that predict function from sequence are limited by pipelines that require substitution matrices, sequence alignment, and hand-tuned scoring functions.Deep learning provides an opportunity to bypass these bottlenecks and directly predict protein functional annotations from sequence data. In these frameworks, a single model learns the distribution of multiple classes simultaneously, and can be rapidly evaluated. Besides providing highly accurate models, the intermediate layers of a deep neural network trained with supervision can capture high-level structure of the data through learned representations [13]. These can be leveraged for exploratory data analysis or supervised learning on new tasks, in particular those with limited data. For example, novel classes can be identified from just a few examples through few-shot learning.This raises the question of whether deep learning can provide protein function prediction tools with broad coverage of the protein universe, as found in the 17929 families of the recent Pfam 32.0 release [14]. Recent ...
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