3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites

Concu, Riccardo; Dea-Ayuela, María Auxiliadora; Pérez-Montoto, Lázaro G.; Prado-Prado, Francisco J.; Uriarte, Eugenio; Bolás‐Fernández, F.; Podda, Gianni; Pazos, Alejandro; Munteanu, Cristian R.; Ubeira, Florencio M.; González-Dı́az, Humberto

doi:10.1016/j.bbapap.2009.08.020

Cited by 57 publications

(47 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the development of alternative drugs for the treatment of the different clinical forms of leishmaniasis is a priority for controlling the disease (Croft et al 2006;Castillo et al 2010). Great efforts have been made to identify potential chemotherapeutic targets in the genome and proteome of Leishmania sp., in the hope of devising more effective treatments (Myler 2008;Concu et al 2009;Chawla and Madhubala 2010). Proteolytic enzymes or proteinases play an essential role in the parasite's survival.…”

Section: Introductionmentioning

confidence: 99%

Ddi1-like protein from Leishmania major is an active aspartyl proteinase

Perteguer

Gómez‐Puertas

Cañavate

et al. 2013

Cell Stress and Chaperones

View full text Add to dashboard Cite

Eukaryotic cells respond to DNA damage by activating damage checkpoint pathways, which arrest cell cycle progression and induce gene expression. We isolated a full-length cDNA encoding a 49-kDa protein from Leishmania major, which exhibited significant deduced amino acid sequence homology with the annotated Leishmania sp. DNA damage-inducible (Ddi1-like) protein, as well as with the Ddi1 protein from Saccharomyces cerevisiae. In contrast to the previously described Ddi1 protein, the protein from L. major displays three domains: (1) an NH2-terminal ubiquitin like; (2) a COOH terminal ubiquitinassociated; (3) a retroviral aspartyl proteinase, containing the typical D[S/T]G signature. The function of the L. major Ddi1-like recombinant protein was investigated after expression in baculovirus/insect cells and biochemical analysis, revealing preferential substrate selectivity for aspartyl proteinase A 2 family substrates, with optimal activity in acidic conditions. The proteolytic activity was inhibited by aspartyl proteinase inhibitors. Molecular modeling of the retroviral domain of the Ddi1-like Leishmania protein revealed a dimer structure that contained a double AspSer-Gly-Ala amino acid sequence motif, in an almost identical geometry to the exhibited by the homologous retroviral aspartyl protease domain of yeast Ddi1 protein. Our results indicate that the isolated Ddi1-like protein is a functional aspartyl proteinase in L. major, opening possibility to be considered as a potential target for novel antiparasitic drugs.

show abstract

Section: Introductionmentioning

confidence: 99%

Ddi1-like protein from Leishmania major is an active aspartyl proteinase

Perteguer

Gómez‐Puertas

Cañavate

et al. 2013

Cell Stress and Chaperones

View full text Add to dashboard Cite

show abstract

“…We used this approach to predict the stability of arc repressor mutants using their negentropies and the spectral moment force field for the calculation of the indices [64,76], and we predicted their activity against pharmacological targets [77,78]. We recently extended this methodology to the prediction of protein function and enzyme classification based on only the spatial structure [79][80][81][82][83][84][85][86]. Here, we extended this methodology to predicting the stability of collagen.…”

Section: March-inside-based Model For Predicting Collagen Stabilitymentioning

confidence: 95%

Review of Computer-Aided Models for Predicting Collagen Stability

Concu

Podda²,

González-Dı́az³

et al. 2011

CAD

View full text Add to dashboard Cite

Collagen is the most abundant protein in the whole human body and its instability is involved in many important diseases, such as Osteogenesis imperfecta, Ehlers-Danlos syndrome, and collagenopathy. The stability of the collagen triple helix is strictly related to its amino acid sequence, especially the main Gly-X-Y motif. Many groups have used computational methods to investigate collagen's structure and the relationship between its stability and structure. In this study, we initially reviewed the most important computational methods that have been applied in this field. We then assembled data on a large number of collagen-like peptides to build the first Markov chain model for predicting the stability of the collagen at different temperatures, simply by analyzing the amino acid sequence. We used the literature to assemble a set of 102 peptides and their relative melting temperatures were determined experimentally, indicating a great variance with the main motif of the collagen. This dataset was then split in two classes, stable and unstable, according to their melting temperatures and the dataset was then used to build artificial neural network (ANN) models to predict collagen stability. We built models to predict stability at temperatures of 38°C, 35°C, 30°C, and 25°C degrees, and all models had an accuracy between 82% and 92%. Several cross-validation procedures were performed to validate the model. This method facilitates fast and accurate predictions of collagen stability at different temperatures.

show abstract

“…The functions of proteins correlate with their threedimensional (3D) structures. Based on the information of the 3D structure of proteins, González-Díaz and colleagues developed some models and web servers to discriminate between enzymes and nonenzymes [14,15,39], predict enzyme classes [13], and recognize protein kinases [26,27]. They also developed some quantitative structureactivity relationship (QSAR)-based methods [16,24,25] to classify polygalacturonases and nonpolygalacturonases [1], discriminate dyneins from nondyneins [17], and predict RNase scores [23], achieving encouraging results.…”

Section: Introductionmentioning

confidence: 93%

Prediction of ketoacyl synthase family using reduced amino acid alphabets

Chen

Feng

Lin

2012

Journal of Industrial Microbiology and Biotechnology

View full text Add to dashboard Cite

Ketoacyl synthases are enzymes involved in fatty acid synthesis and can be classified into five families based on primary sequence similarity. Different families have different catalytic mechanisms. Developing cost-effective computational models to identify the family of ketoacyl synthases will be helpful for enzyme engineering and in knowing individual enzymes' catalytic mechanisms. In this work, a support vector machine-based method was developed to predict ketoacyl synthase family using the n-peptide composition of reduced amino acid alphabets. In jackknife cross-validation, the model based on the 2-peptide composition of a reduced amino acid alphabet of size 13 yielded the best overall accuracy of 96.44% with average accuracy of 93.36%, which is superior to other state-of-the-art methods. This result suggests that the information provided by n-peptide compositions of reduced amino acid alphabets provides efficient means for enzyme family classification and that the proposed model can be efficiently used for ketoacyl synthase family annotation.

show abstract

3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites

Cited by 57 publications

References 69 publications

Ddi1-like protein from Leishmania major is an active aspartyl proteinase

Ddi1-like protein from Leishmania major is an active aspartyl proteinase

Review of Computer-Aided Models for Predicting Collagen Stability

Prediction of ketoacyl synthase family using reduced amino acid alphabets

Contact Info

Product

Resources

About