The structural coverage of the human proteome before and after AlphaFold

Abstract

The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 47%, considering experimentally-derived or template-based homology models, elevates up to 75% when including AlphaFold predictions, reducing the fraction of dark proteome from 22% to just 7% and the number of proteins without structural information from 4.832 to just 29. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (70% of ClinVar pathogenic mutations and 74% of oncogenic mutations), AlphaFold models still provide an additional coverage of 2% to 14% of these critically important sets of biomedical genes and mutations. We also provide several examples of disease-associated proteins where AlphaFold provides critical new insights. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

“…The PDB has served as an open-access platform for structure-based drug design, evidenced by a recent analysis of 210 drugs that were approved by the US Food and Drug Administration (FDA) between 2010 and 2016: nearly 6,000 structures were available in the PDB for 88% of the drugs and/or targets, with approximately 50% of these structures deposited in the PDB >10 years prior to FDA approval of the drug (Westbrook & Burley 2019). Therefore, it is logical to assume that the increased structural coverage of the (ordered) human proteome afforded by the AFDB (Porta-Pardo et al 2021) will lead to more structure-based drug discovery. Because IDRs/IDPs are enriched in many signaling pathways (Wright & Dyson 2015), they are highly desirable drug targets (Metallo 2010); however, it is notoriously difficult to target IDRs/IDPs with pharmaceuticals, which likely is due to their absence of stable structures, large interaction networks (Teilum et al 2021), and interconversion between different structural forms (Metallo 2010).…”

Intrinsic Protein Disorder, Conditional Folding and AlphaFold2

“…AlphaFoldDB expands the impact further by allowing interested researchers to browse predictions for proteins in several major model organisms 6,7 . This wealth of information is being used to map out less studied parts of the proteome 8,9 . It has highlighted the presence of a considerable fraction of the human proteome with low AlphaFold accuracy scores that may reflect intrinsically disordered regions (IDRs) in proteins 7,10 .…”

Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues

“…With the current improved state of art computational structural predictors like RoseTTAFold and AlphaFold, we can model regions of proteins without any known homologues for about 20% of the residues in humans [90]. In this study, we modelled the disease-associated human proteins without known homologues (calculated using BLAST) using RoseTTAFold and AlphaFold.…”