The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

By Naihui Zhou, Yuxiang Jiang, Timothy Bergquist, Alexandra J. Lee, B. Kacsoh, Alex Crocker, Kimberley A. Lewis, G. E. Georghiou, Huy N. Nguyen, Nafiz Hamid and 156 more. 2019

434 citations recorded by the source corpus.

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Subfields: Molecular Biology

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Citations in the corpus

Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “While models have been developed for the qualitative classification of protein sequences into function classes, such as those in the Critical Assessment of Functional Annotation (CAFA) challenge [21], in protein engineering prediction models are required to provide a more fine-grained characteriza”
FLIP: Benchmark tasks in fitness landscape inference for proteins (2021, neutral): “The Critical Assessment of Function Annotation (CAFA) [32, 33] focuses on assigning Gene Ontology (GO) classes (categorical definitions of protein functions) to proteins.”
Exploring evolution-based & -free protein language models as protein function predictors (2022, methodology): “In this paper, we focus on functional annotation prediction (classiﬁcation task) [45, 5] and ﬁtness prediction (including both regression [8, 25] and zero-shot prediction [16, 24] tasks), as shown in Figure 2.”
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL (2020, methodology): “For the selection of sample-centric metrics, we follow the evaluation procedures over the course of todate three CAFA classification challenges [37].”
CATH: increased structural coverage of functional space (2020, methodology): “Function prediction pipelines developed using FunFams are consistently ranked among the top performers for Molecular Function and Biological Process Gene Ontology terms in the Critical Assessment of Functional Annotation competition (CAFA) (12,13).”