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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
06/08/2008 |
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Data da última atualização: |
24/10/2022 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
TOGAWA, R. C.; RIBEIRO, C.; MAZONI, I.; PELLIGRINELLI, T.; JARDINE, J. G.; NESHICH, G. |
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Afiliação: |
Roberto Coiti Togawa, Embrapa Recursos Genéticos e Biotecnologia; Cristina Ribeiro, UFMG; Ivan Mazoni, Embrapa Informação Tecnógica; Thais Vital Pelligrinelli, Embrapa Informação Tecnógica; José Gilberto Jardine, Embrapa Informação Tecnógica; Goran Neshich, Embrapa Informação Tecnógica. |
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Título: |
The table of interface forming residues as the specificity indicator for serine proteases bound to different inhibitors. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON BIOINFORMATICS & COMPUTATIONAL BIOLOGY, 2008, Georgia. Las Vegas Nevada: CSREA, 2008, p. 811-817. |
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Idioma: |
Inglês |
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Notas: |
BIOCOMP 2008. WORLDCOMP'08 |
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Conteúdo: |
We propose a novel method for defining the exclusive and exhaustive table of serine proteases specificity determining interface forming residues (IFR). The IFR are obtained by "hard body docking" among 73 structurally aligned, sequence wise non redundant, serine protease structures, with 3 inhibitors: ecotine, ovomucoid third domain inhibitor and basic pancreatic trypsin inhibitors. In silico constructed complexes offered a condition for determining which residues are participating, from both enzyme and inhibitor side, in the ensemble of amino acids that upon biding loose contact with a solvent. Our focus is on offering a thorough study on how the specificity is achieved among serine proteases even though they have very little difference in their tertiary structure (specifically if the position of catalytic triad residues is considered). Presented table of serine protease specificity based on IFR position occupation show clear variations among sub families such as: trypsines, chymotrypsines, elastases and thrombines. |
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Palavras-Chave: |
Aminoácidos; Enzyme specificity; Especificidade enzimática; Hard body docking; Interface formando resíduos; Interface forming residues; Proteases de serina. |
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Thesagro: |
Enzima. |
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Thesaurus Nal: |
Enzymes; Serine proteinases. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02051nam a2200301 a 4500
001 1191302
005 2022-10-24
008 2008 bl uuuu u00u1 u #d
100 1 $aTOGAWA, R. C.
245 $aThe table of interface forming residues as the specificity indicator for serine proteases bound to different inhibitors.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON BIOINFORMATICS & COMPUTATIONAL BIOLOGY, 2008, Georgia. Las Vegas Nevada: CSREA, 2008, p. 811-817.$c2008
500 $aBIOCOMP 2008. WORLDCOMP'08
520 $aWe propose a novel method for defining the exclusive and exhaustive table of serine proteases specificity determining interface forming residues (IFR). The IFR are obtained by "hard body docking" among 73 structurally aligned, sequence wise non redundant, serine protease structures, with 3 inhibitors: ecotine, ovomucoid third domain inhibitor and basic pancreatic trypsin inhibitors. In silico constructed complexes offered a condition for determining which residues are participating, from both enzyme and inhibitor side, in the ensemble of amino acids that upon biding loose contact with a solvent. Our focus is on offering a thorough study on how the specificity is achieved among serine proteases even though they have very little difference in their tertiary structure (specifically if the position of catalytic triad residues is considered). Presented table of serine protease specificity based on IFR position occupation show clear variations among sub families such as: trypsines, chymotrypsines, elastases and thrombines.
650 $aEnzymes
650 $aSerine proteinases
650 $aEnzima
653 $aAminoácidos
653 $aEnzyme specificity
653 $aEspecificidade enzimática
653 $aHard body docking
653 $aInterface formando resíduos
653 $aInterface forming residues
653 $aProteases de serina
700 1 $aRIBEIRO, C.
700 1 $aMAZONI, I.
700 1 $aPELLIGRINELLI, T.
700 1 $aJARDINE, J. G.
700 1 $aNESHICH, G.
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Registro original: |
Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/07/2018 |
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Data da última atualização: |
05/02/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. |
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Afiliação: |
Kaio Olímpio das Graças Dias, Universidade Federal de Lavras; Salvador Alejandro Gezan, School of Forest Resources & Conservation, University of Florida, Gainesville.; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; Alireza Nazarian, School of Forest Resources & Conservation, University of Florida, Gainesville.; Luciano da Costa e Silva, JMP Division, SAS Institute Inc., Cary.; SIDNEY NETTO PARENTONI, CNPMS; PAULO EVARISTO DE O GUIMARAES, CNPMS; Carina de Oliveira Anoni, Escola Superior de Agricultura “Luiz de Queiroz”; José Maria Villela Pádua, Universidade Federal de Lavras; MARCOS DE OLIVEIRA PINTO, CNPMS; ROBERTO WILLIANS NODA, CNPMS; Carlos Alexandre Gomes Ribeiro, Universidade Federal de Viçosa; JURANDIR VIEIRA DE MAGALHAES, CNPMS; Antonio Augusto Franco Garcia, Escola Superior de Agricultura “Luiz de Queiroz”; João Cândido de Souza, Universidade Federal de Lavras; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS. |
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Título: |
Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, London, v. 121, n. 1, p. 24-37, 2018. |
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DOI: |
10.1038/s41437-018-0053-6 |
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Idioma: |
Inglês |
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Conteúdo: |
Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids. MenosBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS a... Mostrar Tudo |
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Thesagro: |
Milho; Resistência a Seca. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03081naa a2200349 a 4500
001 2093500
005 2019-02-05
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1038/s41437-018-0053-6$2DOI
100 1 $aDIAS, K. O. das G.
245 $aImproving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.$h[electronic resource]
260 $c2018
520 $aBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.
650 $aMilho
650 $aResistência a Seca
700 1 $aGEZAN, S. A.
700 1 $aGUIMARÃES, C. T.
700 1 $aNAZARIAN, A.
700 1 $aSILVA, L. da C. e
700 1 $aPARENTONI, S. N.
700 1 $aGUIMARAES, P. E. de O.
700 1 $aANONI, C. de O.
700 1 $aPÁDUA, J. M. V.
700 1 $aPINTO, M. de O.
700 1 $aNODA, R. W.
700 1 $aRIBEIRO, C. A. G.
700 1 $aMAGALHAES, J. V. de
700 1 $aGARCIA, A. A. F.
700 1 $aSOUZA, J. C. de
700 1 $aGUIMARAES, L. J. M.
700 1 $aPASTINA, M. M.
773 $tHeredity, London$gv. 121, n. 1, p. 24-37, 2018.
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Embrapa Milho e Sorgo (CNPMS) |
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