AlphaFold and implications for intrinsically disordered proteins.

By Kiersten M. Ruff, Rohit V. Pappu. Journal of Molecular Biology, 2021

542 citations recorded by the source corpus.

Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structural bioinformatics was the structural annotation of over 98% of protein sequences in the human proteome. Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs). That over 30% of regions within the proteome are disordered is congruent with estimates that have been made over the past two decades, as intense efforts have been undertaken to generalize the structure-function paradigm to include the importance of conformational heterogeneity and dynamics. With structural annotations from AlphaFold in hand, there is the temptation to draw inferences regarding the "structures" of IDRs and their interactomes. Here, we offer a cautionary note regarding the misinterpretations that might ensue and highlight efforts that provide concrete understanding of sequence-ensemble-function relationships of IDRs. This perspective is intended to emphasize the importance of IDRs in sequence-function relationships (SERs) and to highlight how one might go about extracting quantitative SERs to make sense of how IDRs function.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/960aaaa85d1de9f8e2490d872165b720e3d4b565

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Citations in the corpus

The Toxoplasma glucan phosphatase TgLaforin utilizes a distinct functional mechanism that can be exploited by therapeutic inhibitors (2022, background): “Conversely, regions displaying the highest solvent uptake were also the low-confidence regions of the AlphaFold2 model, potentially revealing intrinsically disordered protein-like character of these inserts (62, 63).”
AlphaFold2 fails to predict protein fold switching (2022, methodology): “6,7 These poorly predicted conformations often have low confidence scores, 5 calculated using the pLDDT, which quantifies the fraction of predicted C α distances that lie within their expected distance intervals.”
Using AlphaFold to predict the impact of single mutations on protein stability and function (2021, background): “Evidence in favor of this point of view is the successful prediction of natively disordered protein regions by AlphaFold and the correlation between the decrease of pLDDT and propensity to be in a disordered region [25].”
AlphaFold2 models indicate that protein sequence determines both structure and dynamics (2022, background): “The pLDDT scores have been considered as a predictor for the residue disorder contents: the residues with the pLDDT scores lower than 50 may be located in the IDPR 19 .”
Intrinsic protein disorder and conditional folding in AlphaFoldDB (2022, background): “10 The relative movements of residues cannot be captured with a single static structure.”
Predicting protein flexibility with AlphaFold (2023, background): “42 When assessing whether AF2 structures are representative of experimental protein structures in solution at ambient temperatures, independent validation by NMR residual dipolar couplings showed for a small set of AF2 structures that they provide a highly accurate representation of the average…”
The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database (2022, unknown): “Despite the limitations noted in previous research (Bagdonas et al. 2021; Pak et al. 2021; Ruff and Pappu 2021), AlphaFold 2 is acknowledged to provide high-accuracy predictions of protein structures, even for sequences with relatively fewer homologous sequences ( Jumper et al. 2021).”