Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond

• Published in: Biomolecules (Basel, Switzerland) Vol. 12; no. 10; p. 1467
• Main Authors: Bruley, Apolline, Mornon, Jean-Paul, Duprat, Elodie, Callebaut, Isabelle
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.10.2022; MDPI
• Subjects: Amino Acid Sequence; Amino acids; Amino Acids - chemistry; Analysis; Biochemistry, Molecular Biology; Biology; Biomolecules; Cluster analysis; conditional order; Furylfuramide; Genomics; hidden order; Identification and classification; Life Sciences; long foldable segments; Nucleotide sequence; Predictions; Protein Conformation; protein sequence; Proteins; Proteome - chemistry; Proteomes; pyHCA; Sequence Alignment; soluble domains; Structural Biology; Structure; France; soluble domains; conditional order; dark proteomes; intrinsically disordered domains; long foldable segments; hidden order; protein sequence; pyHCA
• ISSN: 2218-273X; 2218-273X
• DOI: 10.3390/biom12101467

AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder.