Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes

G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.Peer ReviewedPostprint (author's final draft

Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

Classification of protein quaternary structure by functional domain composition

BACKGROUND: The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences. RESULTS: To explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm (NNA) was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11% CONCLUSION: Compared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics

Prediction of protein-protein interactions from primary structure using a Random Forest classifier

Međusobne interakcije između proteina temelj su niza bioloških procesa, od regulacije metaboličkih puteva, specifičnosti imunoloških reakcija, replikacije DNK do sinteze proteina. Nagli razvoj visokoprotočnih metoda doveo je do velikog povećanja produkcije bioloških sekvenci, stvorivši potrebu za razvojem metoda i alata za njihovu funkcijsku analizu, te predviđanje fenotipskih svojstava, kako na molekularnoj, tako i na razini cijelog organizma. U ovom radu smo agregirali strukturalne podatke iz postojećih baza podataka, čime smo dobili skup proteinskih kvaternih struktura visoke kvalitete koji nam je omogućio primjenu metoda strojnog učenja za predviđanje interakcija između proteina. Iskoristili smo „Random Forest“ algoritam za predviđanje interakcijskih aminokiselina iz primarnih struktura proteina. Pokazali smo da, iako „Random Forest“ alogritam ima mogućnost klasifikacije visokodimenzionalnih podataka s izuzetnom točnošću, trenutno znanje o strukturalnim faktorima koji utječu na specifičnost interakcija između proteina nije na razini koja bi omogućila predviđanje interakcija na razlučivosti pojedinih aminokiselina koristeći isključivo sekvence proteina.The interaction between proteins is fundamental to a broad spectrum of biological functions, including regulation of metabolic pathways, immunological recognition, DNA replication, progression through the cell cycle, and protein synthesis. Due to the growing disparity between the amount of sequenced genomic content and functional data, there exist a pressing need for tools and methods that will enable prediction of phenotypic traits, on the molecular or organism level, based on the sequence alone. In this work we have constructed a high quality dataset of protein structures that has enabled us to use the Random Forest non-linear classificator to develop a method for prediction of interacting residues from the protein primary structure. Our results have shown that, although the Random Forest algorithm has a unique capability of accurately classifying highly dimensional data, we still have an incomplete knowledge of structural factors that determine the specificity of protein-protein interactions, thus putting an upper limit the on the usefulness of the machine learning approach in predicting protein interactions on the level of single amino-acids

Protein sequences classification by means of feature extraction with substitution matrices

<p>Abstract</p> <p>Background</p> <p>This paper deals with the preprocessing of protein sequences for supervised classification. Motif extraction is one way to address that task. It has been largely used to encode biological sequences into feature vectors to enable using well-known machine-learning classifiers which require this format. However, designing a suitable feature space, for a set of proteins, is not a trivial task. For this purpose, we propose a novel encoding method that uses amino-acid substitution matrices to define similarity between motifs during the extraction step.</p> <p>Results</p> <p>In order to demonstrate the efficiency of such approach, we compare several encoding methods using some machine learning classifiers. The experimental results showed that our encoding method outperforms other ones in terms of classification accuracy and number of generated attributes. We also compared the classifiers in term of accuracy. Results indicated that SVM generally outperforms the other classifiers with any encoding method. We showed that SVM, coupled with our encoding method, can be an efficient protein classification system. In addition, we studied the effect of the substitution matrices variation on the quality of our method and hence on the classification quality. We noticed that our method enables good classification accuracies with all the substitution matrices and that the variances of the obtained accuracies using various substitution matrices are slight. However, the number of generated features varies from a substitution matrix to another. Furthermore, the use of already published datasets allowed us to carry out a comparison with several related works.</p> <p>Conclusions</p> <p>The outcomes of our comparative experiments confirm the efficiency of our encoding method to represent protein sequences in classification tasks.</p

Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms

Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques