A sequence‐based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum

Abstract

Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are-and stay-fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by Alpha-Fold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.

Random, de novo, and conserved proteins: How structure and disorder predictors perform differently

“…30 A low pLDDT is considered to indicate low confidence of prediction and to correlate with higher disorder [39][40][41][42][43] and structural heterogeneity. [44][45][46][47] Accordingly, pLDDT has been used for large-scale studies of structural conformations. 39,42,43,48,49 Since de novo and random proteins are considered to be less compact, while containing secondary elements, and more disordered, 8,10,18 not only structure predictors but also appropriate disorder predictors are essential for computational analyses of their conformations.…”

AlphaFold2‐guided description ofCoBaHMA, a novel family of bacterial domains within the heavy‐metal‐associated superfamily

“…Hydrophobic cluster analysis (HCA 35,36 ) was also used to assess the foldability of the analyzed sequences. 37,38 2.3 | Calcyanin detection and classification (Supplementary Figure 1) Sequences of calcyanins were detected in our dataset using a dedicated in-house tool called pCALF (standing for python CALcyanin Finder). pCALF uses four hidden Markov model (HMM) profiles describing the C-terminal glycine zipper triplication specific of calcyanins, called the (GlyZip) 3 motif.…”

Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology

“…In the assessment by Akdel et al, around half the residues presented a low confidence (<70) score. In their recent work, Bruley et al , highlighted the possibility of “hidden order” cases, i.e., situations where low-confidence structural predictions are not related to disorder, but correspond to foldable domains that are not correctly predicted due to AF2 intrinsic limitations (such as a lack of coevolutionary information for the target sequence). In this case, one can combine AF2 predictions with an additional tool based on the residues physicochemical properties, such as their hydrophobicity in the case of the hydrophobic cluster analysis to unveil ordered segments that remain hidden from AF2.…”