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Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. In this paper, the...
A protein is characterized by its 3D structure, which defines its biological function. The protein structure prediction problem has real-world significance where several diseases are associated with the wrong folding of proteins. Predicting protein structures is computationally intractable. In this paper, we present an improved genetic algorithm (GA) for predicting 3D structures of proteins based...
Protein structure prediction, known as an NP-complete problem, is one of the basic problems in computational biology. To get an efficiency approach of protein structure prediction with Toy model, a new algorithm structure based on quantum-behaved particle swarm optimization (QPSO) structure is suggested, which is named as multiple-layer QPSO (MLQPSO). In this structure, population of each generation...
Secondary structure prediction is a compute-intensive task that is used in many bioinformatics applications. In this paper we have selected two of the most well-known secondary structure prediction algorithms, the Predator and the Zuker algorithm, and we present two FPGA-based systems that implement them. Also, this paper presents different schemes of data reuse and data organization of structure...
In this paper, we present a means to fold amino acid interaction networks. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. Our approach consists in exploiting the parallel between topological and structural properties. Thus, we establish a relation between the sequence and the structure relying on topological criteria. To fold this type...
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