Rhys Heffernan

Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

Protein subcellular localization is defined as predicting the functioning location of a given protein in the cell. It is considered an important step towards protein function prediction and drug design. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve protein subcellular localization prediction performance. However, relying solely on GO, this problem remains unsolved. At the same time, the impact of other sources of features especially evolutionary-based features has not been explored adequately for this task. In this study, we aim to extract discriminative evolutionary features to tackle this problem. To do this, we propose two segmentation based feature extraction methods to explore potential local evolutionary-based information for Gram-positive and Gram-negative subcellular localizations. We will show that by applying a Support Vector Machine (SVM) classifier to our extracted features, we are able to enhance Gram-positive and Gram-negative subcellular localization prediction accuracies by up to 6.4% better than previous studies including the studies that used GO for feature extraction.

Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.

Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network

Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between Cαi−1CαiCαi+1 (θ) and a dihedral angle rotated about the CαiCαi+1 bond (τ). θ and τ angles, as the representative of structural properties of three to four amino‐acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine‐learning technique for sequence‐based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ‐τ plane close to that of native values. The average root‐mean‐square distance of 10‐residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template‐based as well as template‐free structure prediction. The deep neural network learning technique is available as an on‐line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks‐lab.org.

Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins

MOTIVATION Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cβ (HSEβ) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEβ. RESULTS This study developed a novel method for predicting both HSEα and HSEβ (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEβ (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEβ (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction. AVAILABILITY AND IMPLEMENTATION The method is available at http://sparks-lab.org CONTACT yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility

Motivation: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non‐local interactions between amino acid residues that are close in three‐dimensional structural space but far from each other in their sequence positions. All existing machine‐learning techniques relied on a sliding window of 10–20 amino acid residues to capture some ‘short to intermediate’ non‐local interactions. Here, we employed Long Short‐Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window. Results: We showed that the application of LSTM‐BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long‐range contacts (|i‐j| >19) over a previous window‐based, deep‐learning method SPIDER2. Capturing long‐range interactions allows the accuracy of three‐state secondary structure prediction to reach 84% and the correlation coefficient between predicted and actual solvent accessible surface areas to reach 0.80, plus a reduction of 5%, 10%, 5% and 10% in the mean absolute error for backbone Symbol, &psgr;, &thgr; and &tgr; angles, respectively, from SPIDER2. More significantly, 27% of 182724 40‐residue models directly constructed from predicted C&agr; atom‐based &thgr; and &tgr; have similar structures to their corresponding native structures (6Å RMSD or less), which is 3% better than models built by Symbol and &psgr; angles. We expect the method to be useful for assisting protein structure and function prediction. Symbol. No caption available. Symbol. No caption available. Availability and implementation: The method is available as a SPIDER3 server and standalone package at http://sparks‐lab.org. Contact: yaoqi.zhou@griffith.edu.au or yuedong.yang@griffith.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks.

Predicting one-dimensional structure properties has played an important role to improve prediction of protein three-dimensional structures and functions. The most commonly predicted properties are secondary structure and accessible surface area (ASA) representing local and nonlocal structural characteristics, respectively. Secondary structure prediction is further complemented by prediction of continuous main-chain torsional angles. Here we describe a newly developed method SPIDER2 that utilizes three iterations of deep learning neural networks to improve the prediction accuracy of several structural properties simultaneously. For an independent test set of 1199 proteins SPIDER2 achieves 82 % accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively. The method provides state-of-the-art, all-in-one accurate prediction of local structure and solvent accessible surface area. The method is implemented, as a webserver along with a standalone package that are available in our website: http://sparks-lab.org .

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Abstract Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82–84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88–90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.

Advancing the Accuracy of Protein Fold Recognition by Utilizing Profiles From Hidden Markov Models

Protein fold recognition is an important step towards solving protein function and tertiary structure prediction problems. Among a wide range of approaches proposed to solve this problem, pattern recognition based techniques have achieved the best results. The most effective pattern recognition-based techniques for solving this problem have been based on extracting evolutionary-based features. Most studies have relied on the Position Specific Scoring Matrix (PSSM) to extract these features. However it is known that profile-profile sequence alignment techniques can identify more remote homologs than sequence-profile approaches like PSIBLAST. In this study we use a profile-profile sequence alignment technique, namely HHblits, to extract HMM profiles. We will show that unlike previous studies, using the HMM profile to extract evolutionary information can significantly enhance the protein fold prediction accuracy. We develop a new pattern recognition based system called HMMFold which extracts HMM based evolutionary information and captures remote homology information better than previous studies. Using HMMFold we achieve up to 93.8% and 86.0% prediction accuracies when the sequential similarity rates are less than 40% and 25%, respectively. These results are up to 10% better than previously reported results for this task. Our results show significant enhancement especially for benchmarks with sequential similarity as low as 25% which highlights the effectiveness of HMMFold to address this problem and its superiority over previously proposed approaches found in the literature. The HMMFold is available online at: http://sparks-lab.org/pmwiki/download/index.php?Download =HMMFold.tar.bz2.

Protein fold recognition using HMM–HMM alignment and dynamic programming

Detecting three dimensional structures of protein sequences is a challenging task in biological sciences. For this purpose, protein fold recognition has been utilized as an intermediate step which helps in classifying a novel protein sequence into one of its folds. The process of protein fold recognition encompasses feature extraction of protein sequences and feature identification through suitable classifiers. Several feature extractors are developed to retrieve useful information from protein sequences. These features are generally extracted by constituting proteins sequential, physicochemical and evolutionary properties. The performance in terms of recognition accuracy has also been gradually improved over the last decade. However, it is yet to reach a well reasonable and accepted level. In this work, we first applied HMM-HMM alignment of protein sequence from HHblits to extract profile HMM (PHMM) matrix. Then we computed the distance between respective PHMM matrices using kernalized dynamic programming. We have recorded significant improvement in fold recognition over the state-of-the-art feature extractors. The improvement of recognition accuracy is in the range of 2.7-11.6% when experimented on three benchmark datasets from Structural Classification of Proteins.

A Short Review of Deep Learning Neural Networks in Protein Structure Prediction Problems

Determining the structure of a protein given its sequence is a challenging problem. Deep learning is a rapidly evolving field which excels at problems where there are complex relationships between input features and desired outputs. Deep Neural Networks have become popular for solving problems in protein science. Various deep neural network architectures have been proposed including deep feed-forward neural networks, recurrent neural networks and more recently neural Turing machines and memory networks. This article provides a short review of deep learning applied to protein prediction problems.