Empirical study of deep neural network architectures for protein secondary structure prediction

Protein secondary structure prediction is a sub-problem of protein structure prediction. Instead of fully recovering the whole three dimensional structure from amino acid sequence, protein secondary structure prediction only aimed at predicting the local structures such as alpha helices, beta strands and turns for each small segment of a protein. Predicted protein secondary structure can be used for improving fold recognition, ab initial protein prediction, protein motifs prediction and sequence alignment. Protein secondary structure prediction has been extensively studied with machine learning approaches. And in recent years, multiple deep neural network methods have pushed the state-of-art performance of 8-categories accuracy to around 69 percent. Deep neural networks are good at capturing the global information in the whole protein, which are widely believed to be crucial for the prediction. And due to the development of high level neural network libraries, implementing and training neural networks are becoming more and more convenient and efficient. This project focuses on empirical performance comparison of various deep neural network architectures and the effects of hyper-parameters for protein secondary structure prediction. Multiple deep neural network architectures representing the state-of-the-art for secondary structure prediction are implemented using TensorFlow, the leading deep learning platform. In addition, a software environment for performing efficient empirical studies are implemented, which includes network input and parameter control, and training, validation, and test performance monitoring. An extensive amount of experiments have been conducted using popular datasets and benchmarks and generated some useful results. For example, the experimental results show that recurrent layers are useful in improving prediction accuracy, achieving up to 5 percent improvement on 8-category accuracy. This work also shows the trade off between running speed and building speed of the model, and the trade off between running speed and accuracy. As a result, a relatively small size recurrent network have been build and achieved 69.5 percent 8-category accuracy on dataset CB513

Machine learning methods for evaluating the quality of a single protein model using energy and structural properties

Computational protein structure prediction is one of the most important problems in bioinformatics. In the process of protein three-dimensional structure prediction, assessing the quality of generated models accurately is crucial. Although many model quality assessment (QA) methods have been developed in the past years, the accuracy of the state-of-the-art single-model QA methods is still not high enough for practical applications. Although consensus QA methods performed significantly better than single-model QA methods in the CASP (Critical Assessment of protein Structure Prediction) competitions, they require a pool of models with diverse quality to perform well. In this thesis, new machine learning based methods are developed for single-model QA and top-model selection from a pool of candidates. These methods are based on a comprehensive set of model structure features, such as matching of secondary structure and solvent accessibility, as well as existing potential or energy function scores. For each model, using these features as inputs, machine learning methods are able to predict a quality score in the range of. Five state-of-the-art machine learning algorithms are implemented, trained, and tested using CASP datasets on various QA and selection tasks. Among the five algorithms, boosting and random forest achieved the best results overall. They outperform existing single-model QA methods, including DFIRE, RW and Proq2, significantly, by up to 10% in QA scores

Protein Tertiary Model Assessment Using Granular Machine Learning Techniques

The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same class as the PDB structures (correct or incorrect protein models). Different subsets of PDB (protein data bank) are considered for evaluating the prediction potential of the machine learning methods. Here we show two such machines, one using SVM (support vector machines) and another using fuzzy decision trees (FDT). First using a preliminary encoding style SVM could get around 70% in protein model quality assessment accuracy, and improved Fuzzy Decision Tree (IFDT) could reach above 80% accuracy. For the purpose of reducing computational overhead multiprocessor environment and basic feature selection method is used in machine learning algorithm using SVM. Next an enhanced scheme is introduced using new encoding style. In the new style, information like amino acid substitution matrix, polarity, secondary structure information and relative distance between alpha carbon atoms etc is collected through spatial traversing of the 3D structure to form training vectors. This guarantees that the properties of alpha carbon atoms that are close together in 3D space and thus interacting are used in vector formation. With the use of fuzzy decision tree, we obtained a training accuracy around 90%. There is significant improvement compared to previous encoding technique in prediction accuracy and execution time. This outcome motivates to continue to explore effective machine learning algorithms for accurate protein model quality assessment. Finally these machines are tested using CASP8 and CASP9 templates and compared with other CASP competitors, with promising results. We further discuss the importance of model quality assessment and other information from proteins that could be considered for the same

Improving the accuracy of protein secondary structure prediction using structural alignment

BACKGROUND: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. RESULTS: We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. CONCLUSION: By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at . For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally

Learning biophysically-motivated parameters for alpha helix prediction

Background: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological “pseudo-energies. ” Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. Results: Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Qα value of 77.6 % and an SOVα value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. Conclusions: The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here. 1 Backgroun

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd

Improving protein structure prediction by deep learning and computational optimization

Includes vitaProtein structure prediction is one of the most important scientific problems in the field of bioinformatics and computational biology. The availability of protein three-dimensional (3D) structure is crucial for studying biological and cellular functions of proteins. The importance of four major sub-problems in protein structure prediction have been clearly recognized. Those include, first, protein secondary structure prediction, second, protein fold recognition, third, protein quality assessment, and fourth, multi-domain assembly. In recent years, deep learning techniques have proved to be a highly effective machine learning method, which has brought revolutionary advances in computer vision, speech recognition and bioinformatics. In this dissertation, five contributions are described. First, DNSS2, a method for protein secondary structure prediction using one-dimensional deep convolution network. Second, DeepSF, a method of applying deep convolutional network to classify protein sequence into one of thousands known folds. Third, CNNQA & DeepRank, two deep neural network approaches to systematically evaluate the quality of predicted protein structures and select the most accurate model as the final protein structure prediction. Fourth, MULTICOM, a protein structure prediction system empowered by deep learning and protein contact prediction. Finally, SAXSDOM, a data-assisted method for protein domain assembly using small-angle X-ray scattering data. All the methods are available as software tools or web servers which are freely available to the scientific community.Includes bibliographical reference

Resdiue-residue contact driven protein structure prediction using optimization and machine learning

Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps, tools to assess the utility of predicted contacts, and methods to construct protein tertiary structures from predicted contacts, are essential to further improve ab initio structure prediction. In this dissertation, three contributions are described -- (a) DNCON2, a two-level convolutional neural network-based method for protein contact prediction, (b) ConEVA, a toolkit for contact assessment and evaluation, and (c) CONFOLD, a method of building protein 3D structures from predicted contacts and secondary structures. Additional related contributions on protein contact prediction and structure reconstruction are also described. DNCON2 and CONFOLD demonstrate state-of-the-art performance on contact prediction and structure reconstruction from scratch. All three protein structure methods are available as software or web server which are freely available to the scientific community.Includes biblographical reference