Protein structure

Positive selection sites in tertiary structure of Leguminosae Chalcone isomerase 1

R. K. Wang, Zhan, S. F., Zhao, T. J., Zhou, X. L., and Wang, C. E., Positive selection sites in tertiary structure of Leguminosae Chalcone isomerase 1, vol. 14, pp. 1957-1967, 2015.

Isoflavonoids and the related synthesis enzyme, chalcone isomerase 1 (CHI1), are unique in the Leguminosae, with diverse biological functions. Among the Leguminosae, the soybean is an important oil, protein crop, and model plant. In this study, we aimed to detect the generation pattern of Leguminosae CHI1. Genome-wide sequence analysis of CHI in 3 Leguminosae and 3 other closely related model plants was performed; the expression levels of soybean chalcone isomerases were also analyzed.

In silico analysis of mutations occurring in the protein N-acetylgalactosamine-6-sulfatase (GALNS) and causing mucopolysaccharidosis IVA

E. R. Tamarozzi, Torrieri, E., Semighini, E. P., and Giuliatti, S., In silico analysis of mutations occurring in the protein N-acetylgalactosamine-6-sulfatase (GALNS) and causing mucopolysaccharidosis IVA, vol. 13, pp. 10025-10034, 2014.

The goals were to analyze and characterize the secondary structure, regions of intrinsic disorder and physicochemical characteristics of three classes of mutations described in the enzyme N-acetylgalactosamine-6-sulfatase that cause mucopolysaccharidosis IVA: missense mutations, insertions and deletions. All mutations were compared to wild-type enzyme, and the results showed that with 25 of 129 missense mutations secondary structure was maintained and that 104 mutations showed minor changes, such as an increase or decrease in the size of the elements.

Gene expression and molecular modeling of the HSP104 chaperone of Trypanosoma cruzi

R. A. Campos, da Silva, M. L., da Costa, G. V., Bisch, P. M., Peralta, J. M., Silva, R., Rondinelli, E., and Ürményi, T. P., Gene expression and molecular modeling of the HSP104 chaperone of Trypanosoma cruzi, vol. 11, pp. 2122-2129, 2012.

Heat shock protein (HSP) 104 is a highly conserved molecular chaperone that catalyzes protein unfolding, disaggregation and degradation under stress conditions. We characterized HSP104 gene structure and expression in Trypanosoma cruzi, a protozoan parasite that causes Chagas’ disease. The T. cruzi HSP104 is an 869 amino-acid protein encoded by a single-copy gene that has the highest sequence similarity (76%) with that of T. brucei and the lowest (23%) with that of the human protein.

A simple and efficient method for predicting protein-protein interaction sites

R. H. Higa and Tozzi, C. L., A simple and efficient method for predicting protein-protein interaction sites, vol. 7, pp. 898-909, 2008.

Computational methods for predicting protein-protein interaction sites based on structural data are characterized by an accuracy between 70 and 80%. Some experimental studies indicate that only a fraction of the residues, forming clusters in the center of the interaction site, are energetically important for binding. In addition, the analysis of amino acid composition has shown that residues located in the center of the interaction site can be better discriminated from the residues in other parts of the protein surface.

A contact map matching approach to protein structure similarity analysis

R. C. de Melo, Lopes, C. Eduardo R., Fernandes, Jr., F. A., da Silveira, C. Henrique, Santoro, M. M., Carceroni, R. L., Meira, Jr., W., and Araújo, Ade A., A contact map matching approach to protein structure similarity analysis, vol. 5, pp. 284-308, 2006.

We modeled the problem of identifying how close two proteins are structurally by measuring the dissimilarity of their contact maps. These contact maps are colored images, in which the chromatic information encodes the chemical nature of the contacts. We studied two conceptually distinct image-processing algorithms to measure the dissimilarity between these contact maps; one was a content-based image retrieval method, and the other was based on image registration.

Predicting enzyme class from protein structure using Bayesian classification

L. C. Borro, Oliveira, S. R. M., Yamagishi, M. E. B., Mancini, A. L., Jardine, J. G., Mazoni, I., Higa, R. H., Kuser, P. R., Neshich, G., and Santos, E. H. dos, Predicting enzyme class from protein structure using Bayesian classification, vol. 5, pp. 193-202, 2006.

Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.

Recognition of α-helix transmembrane domains with an amphipathy scale generated by molecular dynamics using only the primary sequence of proteins

F. M. Mazzé, Fuzo, C. A., Ciancaglini, P., and Degrève, L., Recognition of α-helix transmembrane domains with an amphipathy scale generated by molecular dynamics using only the primary sequence of proteins, vol. 6, pp. 422-433, 2007.

We recently developed an amphipathy scale, elaborated from molecular dynamics data that can be used for the identification of hydrophobic or hydrophilic regions in proteins. This amphipathy scale reflects side chain/water molecule interaction energies. We have now used this amphipathy scale to find candidates for transmembrane segments, by examining a large sample of membrane proteins with α-helix segments. The candidates were selected based on an amphipathy coefficient value range and the minimum number of residues in a segment.

On the characterization of energy networks of proteins

C. J. M. Veloso, Silveira, C. H., Melo, R. C., Ribeiro, C., Lopes, J. C. D., Santoro, M. M., and Meira, Jr., W., On the characterization of energy networks of proteins, vol. 6, pp. 799-820, 2007.

The construction of a realistic theoretical model of proteins is determinant for improving the computational simulations of their structural and functional aspects. Modeling proteins as a network of non-covalent connections between the atoms of amino acid residues has shown valuable insights into these macromolecules. The energy-related properties of protein structures are known to be very important in molecular dynamics. However, these same properties have been neglected when the protein structures are modeled as networks of atoms and amino acid residues.

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