Search

The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of proteins. The backbone torsion angles play a critical role in protein structure prediction, and accurately predicting the angles can considerably advance the tertiary structure prediction by accelerating efficient sampling of the large conformational space for low energy structures. Here we first time propose evolutionary signatures computed from protein sequence profiles, and a novel recurrent architecture, termed ESIDEN, that adopts a straightforward architecture of recurrent neural networks with a small number of learnable parameters. The ESIDEN can capture efficient information from both the classic and new features benefiting from different recurrent architectures in processing information. On the other hand, compared to widely used classic features, the new features, especially the Ramachandran basin potential, provide statistical and evolutionary information to improve prediction accuracy. On four widely used benchmark datasets, the ESIDEN significantly improves the accuracy in predicting the torsion angles by comparison to the best-so-far methods. As demonstrated in the present study, the predicted angles can be used as structural constraints to accurately infer protein tertiary structures. Moreover, the proposed features would pave the way to improve machine learning-based methods in protein folding and structure prediction, as well as function prediction. The source code and data are available at the website https://kornmann.bioch.ox.ac.uk/leri/resources/download.html. [ABSTRACT FROM AUTHOR]

Protein fold recognition is an important and essential step in determining tertiary structure of a protein in biological science. In this study, a model termed NiRecor is developed for recognizing protein folds based on artificial neural networks incorporated in an adaptive heterogeneous particle swarm optimizer. The main contribution of NiRecor is that it is a data-driven and highly-performing predictor without manually tuning control parameters for different data sets. In biological science, since evolutionary- and structure-based information of amino acid sequences is greatly important in determination of tertiary structure of a protein, accordingly, in NiRecor we employ two different feature sets, which involve position specific scoring matrix and secondary structure prediction matrix, to predict the structural classes of protein folds. The experimental results demonstrate the proposed method is powerful in predicting protein folds with higher precisions by improvements of 1.1 ∼7.8 percentages on three benchmark datasets by comparing with several existing predictors. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Evolutionary information contained in the amino acid sequences of proteins specifies the biological function and fold, but exactly what information contained in the protein sequence drives both of these processes? Considerable progress has been made to answer this fundamental question, but it remains challenging to explore the potential space of cooperative interactions between amino acids. Statistical analysis plays a significant role in studying such interactions and its use has expanded in recent years to studies ranging from coevolution-guided rational protein design to protein folding in silico. Results: Here we describe a computational tool named Sibe for use in studies of protein sequence, folding, and design using evolutionary coupling between amino acids as a driving factor. In this study, Sibe is used to identify positionally conserved couplings between pairwise amino acids and aid rational protein design. In this process, pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several 'blocks', which could contribute to driving protein folding and design. A human β2-adrenergic receptor (β2AR) was used to demonstrate that those 'blocks' contribute the rational design for specifying functional residues. Sibe also provides folding modules based on both the positionally conserved couplings and well-established statistical potentials for simulating protein folding in silico and predicting tertiary structure. Our results show that statistically inferences of basic evolutionary principles, such as conservations and coupled-mutations, can be used to rapidly design a diverse set of proteins and study protein folding. Conclusions: The developed software Sibe provides a computational tool for systematical analysis from protein primary to its tertiary structure using the evolutionary couplings as a driving factor. Sibe, written in C++, accounts for compatibility with the 'big data' era in biological science, and it primarily focuses on protein sequence analysis, but it is also applicable to extend to other modeling and predictions of experimental measurements [ABSTRACT FROM AUTHOR]