Recognition of Errors in the Refinement and Validation of Three-Dimensional Structures of AC1 Proteins of Begomovirus Strains by Using ProSA-Web

Prajapat, Rajneesh; Marwal, Avinash; Gaur, Rajarshi Kumar

doi:10.1155/2014/752656

Cited by 22 publications

(10 citation statements)

References 26 publications

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“…Model Reputation: The calpain10 (AAH07553) model showed good stereo chemical property in terms of overall G-factor value of -0.65. G-factor value indicating that geometry of the model corresponds to the probability conformation with 55.9% residues in the core region of the Ramachandran plot showing high accuracy of model predicted [24]. The number of residues in allowed and generously allowed region was 26.5% and 17.6%, respectively, and none of the residues were present in the disallowed region of the plot (Figure 1).…”

Section: Resultsmentioning

confidence: 90%

In Silico Structure Analysis of Type 2 Diabetes Associated Cysteine Protease Calpain-10 (CAPN10)

Prajapat¹,

Bhattacharya²

2016

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Calpain-10 (CAPN10) is a cysteine protease that is known to hydrolyze specific substrates significant for calcium-regulated signaling pathways and it's activated by intracellular calcium (Ca 2+). The calpain10 is known to be involved in the cellular degenerative processes that characterize several diseases such as cancer, stroke and heart attack. The role of calpain10 was recently identified and associated with diabetes mellitus type 2. In this paper, the homology modelling procedure was used to determine the 3D structure of human calpain10 (AAH07553). The μ-calpain (1QXP) of Rattus norvegicus was selected as a template for the construction of calpain10 model. Ramachandran plot of calpain10 (AAH07553) has only 55.9% residues in the most favored regions, while template μ-calpain (1QXP) has 69.3% residues in the most favored regions. The model was validated by using protein structure tools RAMPAGE and Prochek for reliability. 3D structure of calpain10 suggested its active site remains conserved among family members and the major interactions are similar to those observed for the template (1QXP).

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Section: Resultsmentioning

confidence: 90%

In Silico Structure Analysis of Type 2 Diabetes Associated Cysteine Protease Calpain-10 (CAPN10)

Prajapat¹,

Bhattacharya²

2016

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“…The DPP4 (NP_001926) model was illustrated good stereo-chemical property in terms of overall G-factor value of -0.73. The probability conformation with 88.9% residues in the favoured region of Ramachandran plot, illustrated high accuracy of model predicted [32]. The residues number in allowed and outlier region of plot were 8.0% and 19% ( Figure 2).…”

Section: Model Reputationmentioning

confidence: 79%

In-silico Structure Modeling and Docking Studies Using Dipeptidyl Peptidase 4 (DPP4) Inhibitors against Diabetes Type-2

Prajapat¹,

Bhattacharya²

2016

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Recently recognized class of oral hypoglycemic, dipeptidyl peptidase (DPP4) inhibitors could block the dipeptidyl peptidase 4 (DPP4) enzymes. DPP4 is an intrinsic membrane glycoprotein and a serine exopeptidase that plays a major role in glucose metabolism and responsible for the degradation of incretins such as GLP-1, therefore providing a useful treatment to diabetes mellitus type 2. The present work focused on the study of the structural homology modeling of dipeptidyl peptidase 4 [Homo sapiens] (NP_001926). The Ramachandran plot of DPP4 (NP_001926.2) has 88.9% residues in the most favoured region while template 2QT9 has 96.1% residues in the most favoured region. The model was validated by using protein structure tools RAMPAGE and Prochek for reliability. Docking studies were further performed to analyze the interaction mode between selected DPP4 inhibitor anagliptin derivative SKK and receptor DPP4 by using Hex 8.0.0. The in-silico analysis was useful to identify the novel inhibitor that illustrates better activity than the other reported inhibitors.

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“…ProSA web service Z-score of the predicted structure was -5.9 (Fig.5). Positive value is the indicator of error in the model (Prajapat et al, 2014). So the predicted structure of 30S ribosomal protein, chloroplast may be an appropriate model for studying the importance of the protein.…”

Section: Resultsmentioning

confidence: 99%

Structure Prediction of 30s Ribosomal S3, RNA Polymerase II Proteins of Phoenix Pusilla using Bioinformatics Tools

Bharathi¹,

Anuradha²

2020

JSR

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Proteins have different functions like defense action, structural function, transport action, intracellular signalling etc. As proteins are important in many ways its composition analysis, structure and function determination is required. Protein structure prediction is necessary for drug designing, protein engineering and prediction of binding sites. Phoenix pusilla proteins primary structure prediction was done with Expasy protoparam, secondary structure with SOSUI, tertiary structure with swiss model and transmembrane region was identified with TMHMM v 2.0. Secondary structure prediction of 30SRibosomal protein S3 and RNA polymerase II, showed that α-helix, random coil, β-turn and extended strand predominates. Transmembrane region prediction showed that both the proteins were soluble form. As RNA polymerase II of Phoenix pusilla has only 27aminoacid residues, 3D structure has to be predicted with other logarithmic tool and so 30Sribosomal protein 3D structure was predicted with Swiss model server.

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Recognition of Errors in the Refinement and Validation of Three-Dimensional Structures of AC1 Proteins of Begomovirus Strains by Using ProSA-Web

Cited by 22 publications

References 26 publications

In Silico Structure Analysis of Type 2 Diabetes Associated Cysteine Protease Calpain-10 (CAPN10)

In Silico Structure Analysis of Type 2 Diabetes Associated Cysteine Protease Calpain-10 (CAPN10)

In-silico Structure Modeling and Docking Studies Using Dipeptidyl Peptidase 4 (DPP4) Inhibitors against Diabetes Type-2

Structure Prediction of 30s Ribosomal S3, RNA Polymerase II Proteins of Phoenix Pusilla using Bioinformatics Tools

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