The PMDB Protein Model Database

The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins. The database is designed to provide access to models published in the scientific literature, together with validating experimental data. It is a relational database and it currently contains >74 000 models for ∼240 proteins. The system is accessible at and allows predictors to submit models along with related supporting evidence and users to download them through a simple and intuitive interface. Users can navigate in the database and retrieve models referring to the same target protein or to different regions of the same protein. Each model is assigned a unique identifier that allows interested users to directly access the data

CONSTRUCTION OF COMPUTATIONAL 3D STRUCTURES OF PROTEIN DRUG TARGETS OF MYCOBACTERIUM TUBERCULOSIS

Objective: This study aims in constructing a three-dimensional modeled protein structure of potential drug targets in Mycobacterium tuberculosis bacteria. Methods: The protein models were constructed using SWISS-Model online tool. The constructed protein models were submitted in online database called Protein Model Database (PMDB) for public access to the structures. Results: A total of 100 protein sequences of M. tuberculosis were retrieved from UniProt database and were subjected for sequence similarity search and homology model construction. The constructed models were subjected for Ramachandran plot analysis to validate the quality of the structures. A total of 69 structures were considered to be of significant quality and were submitted to the online database PMDB. Conclusion: These predicted structures would help greatly in identification and drug design to various strains of M. tuberculosis that are sensitive and resistant to different antibiotics. This would greatly help in drug development and personalized drug treatment against different strains of the pathogen. This database would significantly support the structure-based computational drug design applications toward personalized medicine in regard to differences in the various strains of the pathogen

Structure and function prediction of human homologue hABH5 of _E. coli_ ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases

Structure and function prediction of human homologue hABH5 of _E. coli_ ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases

Molecular modelling and Function Prediction of hABH7, human homologue of _E. coli_ ALKB7

Human homologues of ALKB protein have shown the prime role in DNA damaging drugs, used for cancer therapy. Little is known about structure and function of hABH7, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeller 9v7 to predict the 3D structure of the hABH7 protein. The tertiary structure model of hABH7, ALKBH7.B99990002.pdb was predicted and evaluated. Validation results showed 97.8% residues in favored and additional allowed regions of Ramachandran plots. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, presence of a Phe120-Gly121-Gly122 conserved pattern in the functional domain was detected. In the predicted structural model of hABH7, amino acid residues, Arginine at 57, 58, 59 and 60 along with tyrosine at 61 were predicted in RNA binding sites of the model. The predicted and validated model of human homologue hABH7 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate the research on designing appropriate inhibitors aiding in chemotherapy and cancer related diseases

STRUCTURAL AND FUNCTIONAL ANALYSIS OF AF9-MLL ONCOGENIC FUSION PROTEIN USING HOMOLOGY MODELING AND SIMULATION BASED APPROACH

Objective: AF9-MLL has been implicated in the pathogenesis of AML, New Therapeutic regimens are prerequisite for this category of hematological malignancy due to the poor prognosis. The experimental 3D structure of AF9-MLL is not available. Therefore, present study aims in developing the homology model and evaluating the best model through Energy Minimization and MD simulation. The structure further analyzed for functional Annotation.Methods: To the best of our knowledge, our study is novel in terms of predicting homology based 3D model of AF9-MLL leukemogenic fusion protein, facilitated by I-TASSER. The 3D modeled structure was subsequently optimized with MD simulation for 2 ns. Further stereo-chemical analysis and verification of the best structure so obtained were undertaken by different computational programs including PROCHECK, PROVE, Verify3D and ERRAT.Results: Homology model predicted from I-TASSER and refined by YASARA showed results with 86.5% residues in the most favorable region, 14.7% in the allowed region, 0.8% in the generously allowed region and 0.3% in the disallowed region. The RMSD between the modeled and the refined structure was found to be 2.37 Ã…. The results of ERRAT, Verify_3D, Prove and ProSA confirmed that the simulated model and energy minimized model is very good then the predicted raw model. The final structure was successfully submitted in Protein Model Database (PMDB) under ID: PM0080061.Conclusion: In this study, homology model was developed and Validated for MLL-AF9 using bio-informatics tools. These analyses validated that the simulated model is best, robust as well as reliable enough to be used for future study and the functional analysis shows the presence of CXXC domain. Eventually, these molecular and structural studies result in advancement of newer therapies.Â

Secretory Phospholipases A2 in Plants

Secreted phospholipases (sPLA2s) in plants are a growing group of enzymes that catalyze the hydrolysis of sn-2 glycerophospholipids to lysophospholipids and free fatty acids. Until today, around only 20 sPLA2s were reported from plants. This review discusses the newly acquired information on plant sPLA2s including molecular, biochemical, catalytic, and functional aspects. The comparative analysis also includes phylogenetic, evolutionary, and tridimensional structure. The observations with emphasis in Glycine max sPLA2 are compared with the available data reported for all plants sPLA2s and with those described for animals (mainly from pancreatic juice and venoms sources).Fil: Mariani, Maria Elisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Departamento de Química. Cátedra de Química Biológica; Argentina. Universidad Nacional de Córdoba. Facultad de Cs.agropecuarias. Departamento de Fundamentación Biológica. Cátedra de Química Organica; ArgentinaFil: Fidelio, Gerardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones en Química Biológica de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Centro de Investigaciones en Química Biológica de Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Biológica; Argentin

In Silico characterization of growth hormone from freshwater ornamental fishes: Sequence analysis, molecular modelling and phylogeny

The present investigation includes in Silico sequence analysis, three-dimensional (3D) structure prediction and evolutionary profile of growth hormone (GH) from 14 ornamental freshwater fishes. The analyses were performed using the sequence data of growth hormone gene (gh) and its encoded GH protein. The evolutionary analyses were performed using maximum likelihood (ML) estimate and maximum parsimony (MP) methods. Bootstrap test (1000 replicates) was performed to validate the phylogenetic tree. The tertiary structures of GH were predicted using the comparative modelling method. The suitable template for comparative modeling protein databank (PDB IDs: 1HWG A) has been selected on the basis of basic local alignment search tool (BLASTp) and fast analysis (FASTA) results. The target-template alignment, model building, loop modelling and evaluation have been performed in Modeller 9.10. The tertiary structure of GH is α-helix structure connected by loops, which forms a compressed complex maintained by two disulfide bridges. The resultant 3D models are verified by ERRAT and ProCheck programmes. After fruitful verification, the tertiary structures of GH have been deposited to protein model database (PMDB). Sequence analyses and RNA secondary structure prediction was performed by CLC genomics workbench version 4.0. The computational models of GH could be of use for further evaluation of molecular mechanism of function.Keywords: Growth hormone, in Silico, somatotropin, growth hormone gene (gh) mRNA, freshwater ornamental fis