Improved protein contact predictions with the MetaPSICOV2 server in CASP12

AbstractMotivationAccurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known.ResultsWe present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these 415 have not been reported before.AvailabilityDatasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/. All programs used here are freely [email protected] informationNo supplementary data

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Residue contacts predicted by evolutionary covariance extend the application ofab initiomolecular replacement to larger and more challenging protein folds

IUCrJ ◽

10.1107/s2052252516008113 ◽

2016 ◽

Vol 3 (4) ◽

pp. 259-270 ◽

Cited By ~ 11

Author(s):

Felix Simkovic ◽

Jens M. H. Thomas ◽

Ronan M. Keegan ◽

Martyn D. Winn ◽

Olga Mayans ◽

...

Keyword(s):

Ab Initio ◽

Structure Prediction ◽

Sequence Information ◽

Protein Targets ◽

Residue Contact ◽

Residue Contacts ◽

Structure Solution ◽

Improved Performance ◽

Model Ensembles ◽

Contact Predictions

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurateab initio(non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here,AMPLE, an MR pipeline that assembles search-model ensembles fromab initiostructure predictions (`decoys'), is employed to assess the value of contact-assistedab initiomodels to the crystallographer. It is demonstrated that evolutionary covariance-derived residue–residue contact predictions improve the quality ofab initiomodels and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simpleRosettadecoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

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Large-scale structure prediction by improved contact predictions and model quality assessment

Bioinformatics ◽

10.1093/bioinformatics/btx239 ◽

2017 ◽

Vol 33 (14) ◽

pp. i23-i29 ◽

Cited By ~ 23

Author(s):

Mirco Michel ◽

David Menéndez Hurtado ◽

Karolis Uziela ◽

Arne Elofsson

Keyword(s):

Quality Assessment ◽

Structure Prediction ◽

Large Scale ◽

Large Scale Structure ◽

Scale Structure ◽

Model Quality ◽

Model Quality Assessment ◽

Contact Predictions

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Inflation and Contact Predictions of Composite Tire Shell Carcass

Journal of Aircraft ◽

10.2514/3.56794 ◽

1987 ◽

Vol 24 (12) ◽

pp. 849-855 ◽

Cited By ~ 2

Author(s):

Sunil Saigal ◽

T. Y. Yang ◽

W. Soedel

Keyword(s):

Contact Predictions

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Accurate contact predictions using covariation techniques and machine learning

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.24863 ◽

2015 ◽

Vol 84 ◽

pp. 145-151 ◽

Cited By ~ 30

Author(s):

Tomasz Kosciolek ◽

David T. Jones

Keyword(s):

Machine Learning ◽

Contact Predictions

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Accurate contact-based modelling of repeat proteins predicts the structure of Curlin and SPW repeats

10.1101/809483 ◽

2019 ◽

Author(s):

Claudio Bassot ◽

Arne Elofsson

Keyword(s):

Deep Learning ◽

High Accuracy ◽

Unique Sequence ◽

Amino Acid Sequences ◽

Primary Sequence ◽

Structure Information ◽

Repeat Proteins ◽

Eukaryotic Proteomes ◽

Direct Coupling Analysis ◽

Contact Predictions

AbstractRepeat proteins are an abundant class in eukaryotic proteomes. They are involved in many eukaryotic specific functions, including signalling. For many of these families, the structure is not known. Recently, it has been shown that the structure of many protein families can be predicted by using contact predictions from direct coupling analysis and deep learning. However, their unique sequence features present in repeat proteins is a challenge for contact predictions DCA-methods. Here, we show that using the deep learning-based PconsC4 is more effective for predicting both intra and interunit contacts among a comprehensive set of repeat proteins. In a benchmark dataset of 819 repeat proteins about one third can be correctly modelled and among 51 PFAM families lacking a protein structure, we produce models of five families with estimated high accuracy.Author SummaryRepeat proteins are widespread among organisms and particularly abundant in eukaryotic proteomes. Their primary sequence present repetition in the amino acid sequences that origin structures with repeated folds/domains. Although the repeated units are easy to be recognized in primary sequence, often structure information are missing. Here we used contact prediction for predicting the structure of repeats protein directly from their primary sequences. We benchmark our method on a dataset comprehensive of all the known repeated structures. We evaluate the contact predictions and the obtained models set for different classes of proteins and different lengths of the target, and we benchmark the quality assessment of the models on repeats proteins. Finally, we applied the methods on the repeat PFAM families missing of resolved structures, five of them modelled with high accuracy.

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