Dataset Information

PiPred - a deep-learning method for prediction of ?-helices in protein sequences.

ABSTRACT: Canonical ?-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- and ion-binding sites. Given their similarity to ?-helices, the prediction of ?-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. Here, we present PiPred, a neural network-based tool for predicting ?-helices in protein sequences. By performing a rigorous benchmark we show that PiPred can detect ?-helices with a per-residue precision of 48% and sensitivity of 46%. Interestingly, some of the ?-helices mispredicted by PiPred as ?-helices exhibit a geometry characteristic of ?-helices. Also, despite being trained only with canonical ?-helices, PiPred can identify 6-residue-long ?/?-bulges. These observations suggest an even higher effective precision of the method and demonstrate that ?-helices, ?/?-bulges, and other helical deformations may impose similar constraints on sequences. PiPred is freely accessible at: https://toolkit.tuebingen.mpg.de/#/tools/quick2d . A standalone version is available for download at: https://github.com/labstructbioinf/PiPred , where we also provide the CB6133, CB513, CASP10, and CASP11 datasets, commonly used for training and validation of secondary structure prediction methods, with correctly annotated ?-helices.

SUBMITTER: Ludwiczak J

PROVIDER: S-EPMC6499831 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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PiPred - a deep-learning method for prediction of π-helices in protein sequences.

Ludwiczak Jan J Winski Aleksander A da Silva Neto Antonio Marinho AM Szczepaniak Krzysztof K Alva Vikram V Dunin-Horkawicz Stanislaw S

Scientific reports 20190503 1

Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- and ion-binding sites. Given their similarity to α-helices, the prediction of π-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. Here, we present PiPred, a neural network-based tool for ...[more]

PMID: 31053765

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Project description:MotivationAccurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment.ResultsBenchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps.AvailabilityThe software package, source code, and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM.

Dataset Information

PiPred - a deep-learning method for prediction of ?-helices in protein sequences.

Publications

PiPred - a deep-learning method for prediction of π-helices in protein sequences.

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