DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

Quaglia, Federica; Mészáros, Bálint; Salladini, Edoardo; Hatos, András; Pancsa, Rita; Chemes, Lucía B.; Pajkos, Mátyás; Lázár, Tamás; Peña-Díaz, Samuel; Santos, Jaime; Ács, Veronika; Farahi, Nazanin; Fichó, Erzsébet; Aspromonte, Maria Cristina; Bassot, Claudio; Chasapi, Anastasia; Davey, Norman E.; Davidović, Radoslav; Dobson, László; Elofsson, Arne; Erdős, Gábor; Gaudet, Pascale; Giglio, Michelle; Glavina, Juliana; Iserte, Javier Alonso; Iglesias, Valentín; Kálmán, Zsófia; Lambrughi, Matteo; Leonardi, Emanuela; Longhi, Sonia; Macedo-Ribeiro, Sandra; Maiani, Emiliano; Marchetti, Julia; Marino-Buslje, Cristina; Mészáros, Attila; Monzón, Alexander Miguel; Minervini, Giovanni; Nadendla, Suvarna; Nilsson, Juliet F; Novotný, Marián; Ouzounis, Christos A.; Palopoli, Nicolás; Papaleo, Elena; Pereira, Pedro José Barbosa; Pozzati, Gabriele; Promponas, Vasilis J.; Pujols, Jordi; Rocha, Alma Carolina Sanchez; Salas, Martin; Sawicki, Luciana Rodriguez; Schád, Éva; Shenoy, Aditi; Szaniszló, Tamás; Tsirigos, Konstantinos D.; Veljković, Nevena; Parisi, Gustavo; Ventura, Salvador; Dosztányi, Zsuzsanna; Tompa, Péter; Tosatto, Silvio; Piovesan, Damiano

doi:10.1093/nar/gkab1082

Cited by 133 publications

(128 citation statements)

References 42 publications

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“…To ensure that the “confident” and “very confident” scores associated with SPOT-Disorder-predicted IDRs are not the result of poor or biased disorder predictions, we extracted the pLDDT scores for IDRs in the DisProt database of experimentally validated IDRs (Quaglia et al 2021). There is a total of 932 human IDRs in DisProt, yielding over 300,000 residues that can be used as a direct comparison to the SPOT-Disorder predictions.…”

Section: Resultsmentioning

confidence: 99%

“…The first possibility would reflect a differential “folding propensity” that is inherently encoded in the amino-acid sequences of high vs. low pLDDT-scoring IDRs, whereas the latter two possibilities would influence the AlphaFold2 prediction confidence due to the depth of the MSAs (2) or sequence similarity to the structures from the PDB used in training (3) (Jumper et al 2021a,b). Given the relatively poor coverage of IDRs in the PDB (Quaglia et al 2021) and the poor positional alignability for most IDRs (Colak et al 2013; Nguyen Ba et al 2012; Zarin et al 2019, 2021), it is plausible that some combination of all three of the aforementioned possibilities could contribute to high pLDDT scoring IDRs.…”

Section: Resultsmentioning

confidence: 99%

“…We hypothesized that the amino-acid frequencies in IDR low pLDDT should reflect the sequence biases found in disordered regions, i.e. an enrichment in some charged (D, E, K), polar (Q, S, T), small (G), and helix-disrupting (P) residues (Quaglia et al 2021). Indeed, the difference between IDR low pLDDT and ordered regions (Δ ordered ) shows that IDR low pLDDT sequences are enriched in the expected residues that are known to promote disorder ( Figure 5A , Supplementary Figure 5 ).…”

Section: Resultsmentioning

confidence: 99%

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Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Alderson

Pritišanac

Moses

et al. 2022

Preprint

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Deep learning-based approaches to protein structure prediction, such as AlphaFold2 and RoseTTAFold, can now define many protein structures with atomic-level accuracy. The AlphaFold Protein Structure Database (AFDB) contains a predicted structure for nearly every protein in the human proteome, including proteins that have intrinsically disordered regions (IDRs), which do not adopt a stable structure and rapidly interconvert between conformations. Although it is generally assumed that IDRs have very low AlphaFold2 confidence scores that reflect low-confidence structural predictions, we show here that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. The amino-acid sequences of IDRs with high-confidence structures do not show significant similarity to the Protein Data Bank; instead, these IDR sequences exhibit a higher degree of positional amino-acid sequence conservation and are more enriched in charged and hydrophobic residues than IDRs with low-confidence structures. We compared the AlphaFold2 predictions to experimental NMR data for a subset of IDRs known to fold under specific conditions, finding that AlphaFold2 tends to capture the folded state structure. We note, however, that these AlphaFold2 predictions cannot detect functionally relevant structural plasticity within IDRs and cannot offer an ensemble representation of IDRs. Nevertheless, AlphaFold2 assigns high-confidence scores to about 60% of a set of 350 IDRs that have been reported to conditionally fold, suggesting that AlphaFold2 has learned to identify conditionally folded IDRs, which is unexpected, since IDRs were minimally represented in the training data. Leveraging this ability to discover IDRs that conditionally fold, we find that up to 80% of IDRs in archaea and bacteria are predicted to conditionally fold, but less than 20% of eukaryotic IDRs. Our results suggest that a large majority of IDRs in the proteomes of human and other eukaryotes would be expected to function in the absence of conditional folding.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Alderson

Pritišanac

Moses

et al. 2022

Preprint

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“…Other returning databases focus on proteins or protein regions lacking a single, conventionally folded structure. DisProt ( 41 ), the database for intrinsically disordered protein, reports interestingly on the nuts and bolts of curation, harnessing both professional and community biocurators in a manner supported by a refactored ontology and incentivised by the APICURON database ( 42 ). The FuzDB Update ( 43 ) reports on fuzzy interactions, i.e.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2022Nucleic Acids Researchdatabase issue and the online molecular biology database collection

Rigden

Fernández

2021

Nucleic Acids Research

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The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.

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“…AlphaFold2 was run on 99 IDP sequences randomly selected from the DisProt database 15 (Methods). The pLDDT scores of predicted IDPs were compared with those of the fold-switching and single-folding protein regions determined previously (Methods).…”

Section: Alphafold2 Prediction Confidences Are Significantly Higher F...mentioning

confidence: 99%

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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DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

Cited by 133 publications

References 42 publications

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

The 2022Nucleic Acids Researchdatabase issue and the online molecular biology database collection

AlphaFold2 fails to predict protein fold switching

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