Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

Recognition of short functional motifs in protein sequences

The main goal of this study was to develop a method for computational de novo prediction of short linear motifs (SLiMs) in protein sequences that would provide advantages over existing solutions for the users. The users are typically biological laboratory researchers, who want to elucidate the function of a protein that is possibly mediated by a short motif. Such a process can be subcellular localization, secretion, post-translational modification or degradation of proteins. Conducting such studies only with experimental techniques is often associated with high costs and risks of uncertainty. Preliminary prediction of putative motifs with computational methods, them being fast and much less expensive, provides possibilities for generating hypotheses and therefore, more directed and efficient planning of experiments. To meet this goal, I have developed HH-MOTiF – a web-based tool for de novo discovery of SLiMs in a set of protein sequences. While working on the project, I have also detected patterns in sequence properties of certain SLiMs that make their de novo prediction easier. As some of these patterns are not yet described in the literature, I am sharing them in this thesis. While evaluating and comparing motif prediction results, I have identified conceptual gaps in theoretical studies, as well as existing practical solutions for comparing two sets of positional data annotating the same set of biological sequences. To close this gap and to be able to carry out in-depth performance analyses of HH-MOTiF in comparison to other predictors, I have developed a corresponding statistical method, SLALOM (for StatisticaL Analysis of Locus Overlap Method). It is currently available as a standalone command line tool

Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction.

BackgroundOne of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.ResultsWe explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.ConclusionWhen attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used

Template Based Modeling and Structural Refinement of Protein-Protein Interactions.

Determining protein structures from sequence is a fundamental problem in molecular biology, as protein structure is essential to understanding protein function. In this study, I developed one of the first fully automated pipelines for template based quaternary structure prediction starting from sequence. Two critical steps for template based modeling are identifying the correct homologous structures by threading which generates sequence to structure alignments and refining the initial threading template coordinates closer to the native conformation. I developed SPRING (single-chain-based prediction of interactions and geometries), a monomer threading to dimer template mapping program, which was compared to the dimer co-threading program, COTH, using 1838 non homologous target complex structures. SPRING’s similarity score outperformed COTH in the first place ranking of templates, correctly identifying 798 and 527 interfaces respectively. More importantly the results were found to be complementary and the programs could be combined in a consensus based threading program showing a 5.1% improvement compared to SPRING. Template based modeling requires a structural analog being present in the PDB. A full search of the PDB, using threading and structural alignment, revealed that only 48.7% of the PDB has a suitable template whereas only 39.4% of the PDB has templates that can be identified by threading. In order to circumvent this, I included intramolecular domain-domain interfaces into the PDB library to boost template recognition of protein dimers; the merging of the two classes of interfaces improved recognition of heterodimers by 40% using benchmark settings. Next the template based assembly of protein complexes pipeline, TACOS, was created. The pipeline combines threading templates and domain knowledge from the PDB into a knowledge based energy score. The energy score is integrated into a Monte Carlo sampling simulation that drives the initial template closer to the native topology. The full pipeline was benchmarked using 350 non homologous structures and compared to two state of the art programs for dimeric structure prediction: ZDOCK and MODELLER. On average, TACOS models global and interface structure have a better quality than the models generated by MODELLER and ZDOCK.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135847/1/bgovi_1.pd

Investigation into the role of sequence-driven-features and amino acid indices for the prediction of structural classes of proteins

The work undertaken within this thesis is towards the development of a representative set of sequence driven features for the prediction of structural classes of proteins. Proteins are biological molecules that make living things function, to determine the function of a protein the structure must be known because the structure dictates its physical capabilities. A protein is generally classified into one of the four main structural classes, namely all-α, all-β, α + β or α / β, which are based on the arrangements and gross content of the secondary structure elements. Current methods manually assign the structural classes to the protein by manual inspection, which is a slow process. In order to address the problem, this thesis is concerned with the development of automated prediction of structural classes of proteins and extraction of a small but robust set of sequence driven features by using the amino acid indices. The first main study undertook a comprehensive analysis of the largest collection of sequence driven features, which includes an existing set of 1479 descriptor values grouped by ten different feature groups. The results show that composition based feature groups are the most representative towards the four main structural classes, achieving a predictive accuracy of 63.87%. This finding led to the second main study, development of the generalised amino acid composition method (GAAC), where amino acid index values are used to weigh corresponding amino acids. GAAC method results in a higher accuracy of 68.02%. The third study was to refine the amino acid indices database, which resulted in the highest accuracy of 75.52%. The main contributions from this thesis are the development of four computationally extracted sequence driven feature-sets based on the underused amino acid indices. Two of these methods, GAAC and the hybrid method have shown improvement over the usage of traditional sequence driven features in the context of smaller and refined feature sizes and classification accuracy. The development of six non-redundant novel sets of the amino acid indices dataset, of which each are more representative than the original database. Finally, the construction of two large 25% and 40% homology datasets consisting over 5000 and 7000 protein samples, respectively. A public webserver has been developed located at http://www.generalised-protein-sequence-features.com, which allows biologists and bioinformaticians to extract GAAC sequence driven features from any inputted protein sequence

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction.</p> <p>Results</p> <p>SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors.</p> <p>Conclusion</p> <p>The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.</p