The AlphaFold Database of Protein Structures: A Biologist’s Guide

David, Alessia; Islam, Suhail A.; Tankhilevich, Evgeny; Sternberg, Michael J.E.

doi:10.1016/j.jmb.2021.167336

Cited by 175 publications

(149 citation statements)

References 22 publications

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“…predictions often fail for proteins whose properties are not fully apparent from solved protein structures, such as IDPs [5][6][7] .…”

Section: Discussionmentioning

confidence: 99%

“…4; 36 , deep learning approaches reveal apparent properties of experimentally determined protein structure rather than biophysical pathways 37 . Thus, it is not surprising that its predictions often fail for proteins whose properties are not fully apparent from solved protein structures, such as IDPs 5-7 .…”

Section: Discussionmentioning

confidence: 99%

“…Thus, it is not surprising that AlphaFold2 often fails to accurately predict the conformations of intrinsically disordered proteins 5–7 , whose structures are highly heterogeneous. Specifically, AlphaFold2 predictions cover 98.5% of sequences in the human proteome (https://alphafold.ebi.ac.uk/), but only 58% of residues are modelled with high confidence 5; 7 . Many low-confidence predictions correspond to intrinsically disordered proteins/regions (IDPs/IDRs), often predicted to fold into long filaments 5; 6 .…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, AlphaFold2 predictions cover 98.5% of sequences in the human proteome (https://alphafold.ebi.ac.uk/), but only 58% of residues are modelled with high confidence 5; 7 . Many low-confidence predictions correspond to intrinsically disordered proteins/regions (IDPs/IDRs), often predicted to fold into long filaments 5; 6 . Furthermore, it remains unknown how accurately high-confidence predictions capture the structures of unchararacterized proteins, especially those either with few homologs in sequence databases or with sequences dissimilar to proteins represented in the PDB.…”

Section: Introductionmentioning

confidence: 99%

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AlphaFold2 fails to predict protein fold switching

Chakravarty

Porter

2022

Preprint

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AlphaFold2 has revolutionized protein structure prediction by leveraging sequence information to rapidly model protein folds with atomic-level accuracy. Nevertheless, previous work has shown that these predictions tend to be inaccurate for structurally heterogeneous proteins. To systematically assess factors that contribute to this inaccuracy, we tested AlphaFold2's performance on 98 fold-switching proteins, which assume at least two distinct-yet-stable secondary and tertiary structures. Topological similarities were quantified between five predicted and two experimentally determined structures of each fold-switching protein. Overall, 94% of AlphaFold2 predictions captured one experimentally determined conformation but not the other. Despite these biased results, AlphaFold2's estimated confidences were moderate-to-high for 74% of fold-switching residues, a result that contrasts with overall low confidences for intrinsically disordered proteins, which are also structurally heterogeneous. To investigate factors contributing to this disparity, we quantified sequence variation within the multiple sequence alignments used to generate AlphaFold2's predictions of fold-switching and intrinsically disordered proteins. Unlike intrinsically disordered regions, whose sequence alignments show low conservation, fold-switching regions had conservation rates statistically similar to canonical single-fold proteins. Furthermore, intrinsically disordered regions had systematically lower prediction confidences than either fold-switching or single-fold proteins, regardless of sequence conservation. These results suggest that AlphaFold2's high prediction confidences for fold switchers stem from the assumption that proteins assume one dominant fold. Our results emphasize the need to look at protein structure as an ensemble and suggest that systematic examination of fold-switching sequences may reveal propensities for multiple stable secondary and tertiary structures.

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“…predictions often fail for proteins whose properties are not fully apparent from solved protein structures, such as IDPs [5][6][7] .…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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AlphaFold2 fails to predict protein fold switching

Chakravarty

Porter

2022

Preprint

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“…Thus, to predict the holo form of a TR with its cognate ligand, it may be more insightful to use traditional homology modeling (template-based) using templates that contain the ligand coordinates since more accurate deep learning algorithm models of the apo form would be missing the ligand information entirely. This complication, currently present in the most recent models released by AlphaFold [7,91,92], will likely be solved once user selection of the appropriate ligand-bound template is allowed [93,94] or docking tools are incorporated into deep learning algorithm models [95]. Additionally, sequence similarity networks are a useful tool to provide multiple sequence alignments that would inform better structural predictions, as they have been shown to define isofunctional clusters in TR families [96].…”

Section: Structural Characterization Of the Different Allosteric Statesmentioning

confidence: 99%

Bacterial Transcriptional Regulators: A Road Map for Functional, Structural, and Biophysical Characterization

Diez

Juncos

Dujovne

et al. 2022

IJMS

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The different niches through which bacteria move during their life cycle require a fast response to the many environmental queues they encounter. The sensing of these stimuli and their correct response is driven primarily by transcriptional regulators. This kind of protein is involved in sensing a wide array of chemical species, a process that ultimately leads to the regulation of gene transcription. The allosteric-coupling mechanism of sensing and regulation is a central aspect of biological systems and has become an important field of research during the last decades. In this review, we summarize the state-of-the-art techniques applied to unravel these complex mechanisms. We introduce a roadmap that may serve for experimental design, depending on the answers we seek and the initial information we have about the system of study. We also provide information on databases containing available structural information on each family of transcriptional regulators. Finally, we discuss the recent results of research about the allosteric mechanisms of sensing and regulation involving many transcriptional regulators of interest, highlighting multipronged strategies and novel experimental techniques. The aim of the experiments discussed here was to provide a better understanding at a molecular level of how bacteria adapt to the different environmental threats they face.

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