PFstats: A Network-Based Open Tool for Protein Family Analysis

Fonseca-Júnior, Néli J.; Afonso, Marcelo Querino Lima; Oliveira, Lucas Carrijo de; Bleicher, Lucas

doi:10.1089/cmb.2017.0181

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…All steps were done using the PFstats Software. 30 Correlation Network Generation and Positive Selection Analysis. Detection of correlations and anticorrelations in the MSA followed by network construction and decomposition to find coevolving residue sets using the DRCN method 21 were performed using the PFstats software.…”

Section: ■ Methodsmentioning

confidence: 99%

“…Detection of correlations and anticorrelations in the MSA followed by network construction and decomposition to find coevolving residue sets using the DRCN method 21 were performed using the PFstats software. 30 In this method, correlations between a pair of residues + MSA columns are defined by three criteria: (1) a minimum frequency shift for a given residue+MSA column in a subalignment containing all sequences possessing another residue + MSA column, (2) a minimum subalignment size so sets still represent the final filtered alignment and are not composed of tiny unrepresentative subset, (3) a minimum cumulative binomial probability score (correlation score) for the correlation between a given pair of residues + MSA columns given their frequencies in the final filtered alignments.…”

Section: ■ Methodsmentioning

confidence: 99%

“…Residue numbering in this paper followed the bovine chymotrypsin sequence when not indicated otherwise. All steps were done using the PFstats Software …”

Section: Methodsmentioning

confidence: 99%

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Coevolved Positions Represent Key Functional Properties in the Trypsin-Like Serine Proteases Protein Family

Afonso

Fonseca

Oliveira

et al. 2020

J. Chem. Inf. Model.

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Trypsin-like serine proteases are a group of homologous enzymes which exert multiple roles in both vertebrate and invertebrate organisms. Key properties of these enzymes include their activation from an inactive zymogen form to their active form by cleavage of residues in their N-terminus, the presence of a conserved catalytic triad of residues, and the existence of different patterns of substrate selectivity for residue cleavage between the various members of this protein family. In this article, we apply the decomposition of residue coevolution networks computational method to find sets of residues related to some of these key properties, especially to zymogen activation. Positive selection detection, normal modes analysis, and the calculation of thermal couplings between the bovine trypsinogen and bovine trypsin structures residues yielded further information for understanding the zymogen activation process and highlighted the importance of some of the coevolved set residues during these transitions.

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Section: ■ Methodsmentioning

confidence: 99%

Section: ■ Methodsmentioning

confidence: 99%

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Coevolved Positions Represent Key Functional Properties in the Trypsin-Like Serine Proteases Protein Family

Afonso

Fonseca

Oliveira

et al. 2020

J. Chem. Inf. Model.

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“…We performed the family-wide sequence coevolution analysis using the Decomposition of Residue Coevolution Networks (DRCN) method with PFstats software [64,65]. A multiple sequence alignment from glycosyl hydrolases (family 1) was obtained from Pfam [66], entry PF00232.…”

Section: Coevolution Analysismentioning

confidence: 99%

Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases

Mariano

Pantuza

Santos

et al. 2020

BMC Mol and Cell Biol

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Β-glucosidases are key enzymes used in second-generation biofuel production. They act in the last step of the lignocellulose saccharification, converting cellobiose in glucose. However, most of the β-glucosidases are inhibited by high glucose concentrations, which turns it a limiting step for industrial production. Thus, β-glucosidases have been targeted by several studies aiming to understand the mechanism of glucose tolerance, pH and thermal resistance for constructing more efficient enzymes. In this paper, we present a database of β-glucosidase structures, called Glutantβase. Our database includes 3842 GH1 β-glucosidase sequences collected from UniProt. We modeled the sequences by comparison and predicted important features in the 3D-structure of each enzyme. Glutantβase provides information about catalytic and conserved amino acids, residues of the coevolution network, protein secondary structure, and residues located in the channel that guides to the active site. We also analyzed the impact of beneficial mutations reported in the literature, predicted in analogous positions, for similar enzymes. We suggested these mutations based on six previously described mutants that showed high catalytic activity, glucose tolerance, or thermostability (A404V, E96K, H184F, H228T, L441F, and V174C). Then, we used molecular docking to verify the impact of the suggested mutations in the affinity of protein and ligands (substrate and product). Our results suggest that only mutations based on the H228T mutant can reduce the affinity for glucose (product) and increase affinity for cellobiose (substrate), which indicates an increment in the resistance to product inhibition and agrees with computational and experimental results previously reported in the literature. More resistant βglucosidases are essential to saccharification in industrial applications. However, thermostable and glucose-tolerant β-glucosidases are rare, and their glucose tolerance mechanisms appear to be related to multiple and complex factors. We gather here, a set of information, and made predictions aiming to provide a tool for supporting the rational design of more efficient β-glucosidases. We hope that Glutantβase can help improve second-generation biofuel production. Glutantβase is available at http://bioinfo.dcc.ufmg.br/glutantbase.

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“…Outra maneira de identificar resíduos essenciais para a função de uma proteína é a análise de coevolução de resíduos, sendo DRCN (Decomposição de Redes de Coevolução de Resíduos) um desses métodos (Bleicher, 2011). Essa ferramenta possibilita analisar famílias de proteínas representadas por alinhamentos múltiplos de sequências e obtém determinantes de sub-classes funcionais (Fonseca-Júnior et al, 2018). O alinhamento múltiplo de sequências é realizado com três ou mais sequências primárias que possuem um ancestral comum.…”

Section: Análises De Bioinformática De Conservação E Co-evoluçãounclassified

Estudo de extensões proteicas mitocôndria-específicas da protuberância central do mitorribossomo

Jorge¹

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PFstats: A Network-Based Open Tool for Protein Family Analysis

Cited by 9 publications

References 40 publications

Coevolved Positions Represent Key Functional Properties in the Trypsin-Like Serine Proteases Protein Family

Coevolved Positions Represent Key Functional Properties in the Trypsin-Like Serine Proteases Protein Family

Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases

Estudo de extensões proteicas mitocôndria-específicas da protuberância central do mitorribossomo

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