Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features).

Motivation: Accurate 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. Results: We 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. Availability: Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net. All programs used here are freely available.

Motivation

Residue contact prediction was revolutionized recently by the introduction of direct coupling analysis (DCA). Further improvements, in particular for small families, have been obtained by the combination of DCA and deep learning methods. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive.

Results

Here, we introduce a novel contact predictor, PconsC4, which performs on par with state of the art methods. PconsC4 is heavily optimized, does not use any external programs and therefore is significantly faster and easier to use than other methods.

Availability and implementation

PconsC4 is freely available under the GPL license from https://github.com/ElofssonLab/PconsC4. Installation is easy using the pip command and works on any system with Python 3.5 or later and a GCC compiler. It does not require a GPU nor special hardware.

Supplementary information

Supplementary data are available at Bioinformatics online.

Motivation: Proteins fold into complex structures that are crucial for their biological functions. Experimental determination of protein structures iscostly and therefore limited to a small fraction of all known proteins. Hence,different computational structure prediction methods are necessary for themodelling of the vast majority of all proteins. In most structure predictionpipelines, the last step is to select the best available model and to estimateits accuracy. This model quality estimation problem has been growing inimportance during the last decade, and progress is believed to be importantfor large scale modelling of proteins. Current machine learning models trained to estimate the protein modelquality suffer from biases in the training set: multiple models of only a fewtargets, generated by a few methods.

Results: We propose a new methodology to train deep networks that leveragesthe structure of the problem and takes advantage of some of this redundan-cies. We demonstrate its viability by reaching results comparable with anotherstate-of-the-art method, ProQ3D, trained and evaluated on the same datasets,but employing only a small subset of the input features.The proposed training strategy is applicable to other input features anddatasets, and thus can be applied to other programs.

Availability: The code is freely available for download at: github.com/ElofssonLab/ProQ4 and runs with minimal requirements: requires only one multiplesequence alignment and a collection of models and depends only on Python3, hdf5, a deep learning framework compatible with Keras, and dssp.Contact: arne@bioinfo.se

Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue‐residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus‐based methods.