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In this paper, a new coding method called radical group encoding is used for protein secondary structure prediction. The radical group encoding is used to encode 20 common amino acids, it has 42 features and every feature shows the protein composition. We use the new encoding method to predict the secondary structure. Almost all amino acids can be represented by the coding, it contains the information...
In this paper, a SVM-based method is implemented for the prediction of protein-protein interactions. This model is initially trained with a set of over 69.000 pairs of protein sequences based on documented positive interactions. Then, a cross-validation method is performed for estimating the accuracy of the system, showing acceptable performances in terms of sensitivity, specificity and geometric...
Predicting of Human Leukocyte Antigen (HLA) gene can provide procedure into the human immune system. The classification of HLA genes has been developed by using various computational methods random forest based on codon usage. And ten-fold cross-validation to evaluate the models. Here, we propose methods of amino acid composition (AAC), dipeptide compositions (DPC) and p-collocated to investigate...
Antimicrobial peptides (AMPs), which is a kind of short chain protein, have a strong antimicrobial ability which have antibacterial, antifungal, antiviral effect. Over the last few decades, the research of AMPs is drawing in large scholars, many of whom have engaged in the profound study on predicting AMPs activity, particularly in the AMPs classification. According to microbiology, the minimum inhibitory...
Structural Genomics projects are producing structural data for proteins at an unprecedented speed. The functions of many of these protein structures are still unknown. To decipher the functions of these proteins and identify functional sites on their structures have become an urgent task. In this study, we developed an innovative graph method to represent protein surface based on how amino acid residues...
We use the Lempel-Ziv complexity method to investigate effects of amino acid classification on prediction of protein structural classes. First, we find that contributions of amino acid classification are differential for predicting protein structural classes and even the performances of some amino acid classification are better than that without using the amino acid classification. This inspires us...
Protein sub-cellular localization prediction involves the computational prediction of where a protein resides in a cell. It is an active area of research in bioinformatics-based prediction of protein function and genome annotation, and research finding from this area can aid the identification of drug targets. Different machine learning and data mining techniques are used to do this prediction; however,...
In this paper, we propose a protein secondary structure prediction method based on the support vector machine (SVM) with position-specific scoring matrix (PSSM) profiles and four physicochemical features, including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we...
In Bioinformatics, the prediction of protein function is considered a very important task but also difficult. Using a set of enzymes represented by Hydrolase, Isomerase, Ligase, Lyase, Transferase and Oxidoreductase classes, previously used by Dobson et al., this paper proposes a self-learning process able to predict their classes, based on their primary and secondary structures, through a Support...
In shotgun proteomics, matching peptides to tandem mass spectrometry data is a computationally expensive process that in some cases can take days using conventional software packages. Even though many existing search engines such as Sequest, Myrimatch, and X!Tandem now exploit multiple processors via threading libraries, they leave much on the table in terms of performance and don't exploit computational...
The identification and prediction of particular types of protein-protein interactions (PPIs) based on knowledge of their interacting domains is a problem that has drawn the attention of researchers in the past few years. We focus on the prediction and analysis of obligate and non-obligate complexes by using structural domains from the CATH database. Our proposed prediction model uses desolvation energies...
Prediction of protein-protein interactions are important to understand any biological processes. The structural models of the complexes resulting from these interactions are necessary to understand those processes at the molecular level. X-ray crystallography is the most popular method to determine the three dimensional structures of protein complexes. However, some of the observed interactions in...
This paper intends to implement Probabilistic Neural Network(PNN) for protein superfamily classification problem. The classification task organizes proteins into their superfamilies and helps in correct prediction of structure and function of newly discovered proteins. The two main steps for any pattern classification problem are feature selection and feature extraction. The bi-gram hashing function...
Protein-protein interactions (PPIs) play a key role in many cellular processes, such as the regulation of enzymes, signal transduction or mediating the adhesion of cells. Knowing the PPI types can help the biological scientists understand the molecular mechanism of the cell. Computational approaches for identifying PPI types can reduce the time-consuming and expensive of biological experimental methods...
The structure of a protein is closely correlated to its function. Feature dimension reduction method is one of most famous machine learning tools. Some researchers have begun to explore feature dimension reduction method for computer vision problems. Few such attempts have been made for classification of high-dimensional protein data sets. In this paper, feature dimension reduction method is employed...
STRIKE was introduced and implemented to predict protein-protein interactions where proteins interact if they contain similar substrings of amino acids. On the yeast protein interaction literature, STRIKE was shown to improve upon the existing state-of-the-art methods for protein-protein interaction prediction. Herein, we describe the parallelization of STRIKE and its multithreaded implementation...
A key step in the development of an adaptive immune response to vaccines is the binding of peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated. Several algorithms have been proposed for such binding predictions, but are limited to a small number of MHC molecules and present imperfect prediction power. We are undertaking...
We present a new computational method for predicting ligand binding sites in protein sequences. The method uses kernelbased canonical correlation analysis and linear regression to identify binding sites in protein sequences as the residues that exhibit strong correlation between the residues' evolutionary characterization at the sites and the structure based functional classification of the proteins...
We have recently found that the computation time of homology-based subcellular localization can be substantially reduced by aligning profiles up to the cleavage site positions of signal peptides, mitochondrial targeting peptides, and chloro-plast transit peptides [1]. While the method can reduce the profile alignment time by as much as 20 folds, it cannot reduce the computation time spent on creating...
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