scholarly journals Bioethanol Production From Hassawi Rice Straw Wastes Assisted by Aspergillus sp. NAS51 Cellulosic Enzyme Under SSF and Saccharification Process With in Silico Enzyme Structural Homology Modeling

2021 ◽  
Author(s):  
Ahmed Ahmed Abdelghani Hamed Abdalla Shalabi ◽  
Hala A. Ibrahim ◽  
Mohamed Khedr ◽  
Mona Shaban E. M. Badawy ◽  
Saad Alamri ◽  
...  
Keyword(s):  
Homology Modeling ◽  
Rice Straw ◽  
In Silico ◽  
Structure Prediction ◽  
3D Structure ◽  
Cellulase Activity ◽  
Enzyme Hydrolysis ◽  
Cellulolytic Activity ◽  
Renewable Energy Resources ◽  
Aspergillus Sp

Abstract With the distribution of exploitable non-renewable energy resources, the use of lignocellulosic wastes to make bioethanol and biogas has drawn great attention from researchers. In our effort to find a potent cellulase-producing fungal strain, the fungus NAS51 was isolated from a sponge collected from the Red Sea, Jeddah, among eight isolates and selected as it displayed potent cellulolytic activity. The fungus was identified morphologically and genetically by sequencing its 18SrRNA gene as Aspergillus sp. NAS51. The cellulase activity of Aspergillus sp. NAS51 was optimized and maximum enzyme production was obtained at initial pH7, temp 30oC, incubation period 11 days, moisture content 70%, urea as a nitrogen source, and K2HPO4 (2g/L). The cellulase gene has been sequenced and the protein 3D structure was generated via in silico homology modeling. Determination of binding sites and biological annotations of the constructed protein was carried out via COACH and COFACTOR based on the I-TASSER structure prediction. To reach the maximum enzyme hydrolysis, the rice straw collected from Al-Ahsa, Kingdom of Saudi Arabia was pretreated with NaOH 1.5% to remove lignin and to enhance the saccharification process by cellulase enzyme. The saccharified product was measured using HPLC, fermented by S. cerevisiae and the bioethanol yield produced from the fermentation was 0.454 ml ethanol/g fermentable sugars.

scholarly journals Homology modeling of actin protein in Leishmania donovani and in silico prediction of active drugs

2020 ◽  
Vol 2 ◽  
pp. 52-57
Author(s):  
Dimpal Rani Bansal ◽  
Hanumanthrao Chandershekar Patil ◽  
Rajesh Kumari Patil
Keyword(s):  
Homology Modeling ◽  
Binding Affinity ◽  
In Silico ◽  
Structure Prediction ◽  
Leishmania Donovani ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Data Bank ◽  
Discovery Studio ◽  
Actin Protein

Objectives: Leishmaniasis is a disease caused by leishmania parasite which is genus of trypanosome protozoa. Leishmania donovani promastigote inhibits biogenesis of phagolysosome due to the accumulation of periphagosomal F-actin. This inhibition of phagosome maturation gives favorable environment for differentiation of promastigote-to-amastigote and causes disease progression. L. donovani actin (LdACT) has been found to have unconventional biochemical behavior due to the different amino acid region in its sequence suggesting that it must have a three-dimensional (3D) structure different from eukaryotic actins making it a more specific for predication of antileishmanial drugs which is main objective of this study. Material and Methods: For carrying out this study, protein sequence was retrieved from the database SWISSPROT, analyzed by BIOEDIT software followed by primary and secondary structure prediction by PROTPARAM and SOPMA. A 3D structure of same was constructed by homology modeling using the yeast actin-human gelsolin segment 1 complex (protein data bank [PDB] ID:1yag) as a template with the help of Swiss model. The final model obtained was further accessed by PROCHECK and VERIFY 3D software which ensured the reliability of the model. This model of actin protein was further used for screening different chemical compounds with high binding affinity by GOLD and DISCOVERY STUDIO. Results: The results give information about the some inhibitors having highest binding affinity to the actin protein. Conclusion: This study will be useful for the development of pharmacophore models for in silico predication of active drugs as a part of antileishmanial drug therapy.

In silico 3D structure prediction and hydrogen peroxide binding study of wheat catalase

2013 ◽  
Vol 5 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Gopal Krishna Sahu ◽  
Bibhuti Bhusan Sahoo ◽  
Sneha Bhandari ◽  
Shruti Pandey
Keyword(s):  
Hydrogen Peroxide ◽  
In Silico ◽  
Structure Prediction ◽  
3D Structure ◽  
Binding Study ◽  
3D Structure Prediction

Protein Structure Prediction using Homology Modeling

2016 ◽  
pp. 339-359 ◽  
Author(s):  
Akanksha Gupta ◽  
Pallavi Mohanty ◽  
Sonika Bhatnagar
Keyword(s):  
Homology Modeling ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Tertiary Structure ◽  
Structural Information ◽  
Sequence Similarity ◽  
3D Structure ◽  
Comparative Modeling ◽  
Spatial Arrangement ◽  
Good Sequence

Sequence-structure deficit marks one of the critical problems in today's scenario where high-throughput sequencing has resulted in large datasets of protein sequences but their corresponding 3D structures still needs to be determined. Homology modeling, also termed as Comparative modeling refers to modeling of 3D structure of a protein by exploiting structural information from other known protein structures with good sequence similarity. Homology models contain sufficient information about the spatial arrangement of important residues in the protein and are often used in drug design for screening of large libraries by molecular docking techniques. This chapter provides a brief description about protein tertiary structure prediction and Homology modeling. The authors provide a description of the steps involved in homology modeling protocols and provide information on the various resources available for the same.

scholarly journals Pemodelan Protein dengan Homology Modeling menggunakan SWISS-MODEL

2020 ◽  
Vol 2 (2) ◽  
pp. 65-70
Author(s):  
Noer Komari ◽  
Samsul Hadi ◽  
Eko Suhartono
Keyword(s):  
Protein Structure ◽  
Homology Modeling ◽  
In Silico ◽  
3D Structure ◽  
Three Dimensional ◽  
Structure Model ◽  
3D Protein Structure ◽  
Laboratory Equipment ◽  
Model Visualization ◽  
Structure Of Proteins

The three-dimensional (3D) structure of proteins is necessary to understand the properties and functions of proteins. Determining protein structure by laboratory equipment is quite complicated and expensive. An alternative method to predict the 3D structure of proteins in the in silico method. One of the in silico methods is homology modeling. Homology modeling is done using the SWISS-MODEL server. Proteins that will be modeled in the 3D structure are proteins that do not yet have a structure in the RCSB PDB database. Protein sequences were obtained from the UniProt database with code A0A0B6VWS2. The results showed that there were two models selected, namely model-1 with the PDB code template 1q0e and model-2 with the PDB code template 3gtv. The results of sequence alignment and model visualization show that model-1 and model-2 are identical. The evaluation and assessment of model-1 on the Ramachandran Plot have a Favored area of ??97.36%, a MolProbity score of 0.79, and a QMEAN value is 1.13. Model-1 is a good 3D protein structure model.

scholarly journals Protein Structure Prediction Using Homology Modeling

2017 ◽  
pp. 877-897 ◽  
Author(s):  
Akanksha Gupta ◽  
Pallavi Mohanty ◽  
Sonika Bhatnagar
Keyword(s):  
Homology Modeling ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Tertiary Structure ◽  
Structural Information ◽  
Sequence Similarity ◽  
3D Structure ◽  
Comparative Modeling ◽  
Spatial Arrangement ◽  
Good Sequence

Sequence-structure deficit marks one of the critical problems in today's scenario where high-throughput sequencing has resulted in large datasets of protein sequences but their corresponding 3D structures still needs to be determined. Homology modeling, also termed as Comparative modeling refers to modeling of 3D structure of a protein by exploiting structural information from other known protein structures with good sequence similarity. Homology models contain sufficient information about the spatial arrangement of important residues in the protein and are often used in drug design for screening of large libraries by molecular docking techniques. This chapter provides a brief description about protein tertiary structure prediction and Homology modeling. The authors provide a description of the steps involved in homology modeling protocols and provide information on the various resources available for the same.

scholarly journals 1,4-Dihydropyridine Calcium Channel Blockers: Homology Modeling of the Receptor and Assessment of Structure Activity Relationship

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Moataz A. Shaldam ◽  
Mervat H. Elhamamsy ◽  
Eman A. Esmat ◽  
Tarek F. El-Moselhy
Keyword(s):  
Homology Modeling ◽  
Calcium Channel ◽  
Mode Of Action ◽  
Structure Prediction ◽  
3D Structure ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Receptor Model ◽  
Modeling Tools ◽  
Structure Activity

1,4-Dihydropyridine (DHP), an important class of calcium antagonist, inhibits the influx of extracellular Ca+2 through L-type voltage-dependent calcium channels. Three-dimensional (3D) structure of calcium channel as a receptor for 1,4-dihydropyridine is a step in understanding its mode of action. Protein structure prediction and modeling tools are becoming integral parts of the standard toolkit in biological and biomedical research. So, homology modeling (HM) of calcium channel alpha-1C subunit as DHP receptor model was achieved. The 3D structure of potassium channel was used as template for HM process. The resulted dihydropyridine receptor model was checked by different means to assure stereochemical quality and structural integrity of the model. This model was achieved in an attempt to understand the mode of action of DHP calcium channel antagonist and in further computer-aided drug design (CADD) analysis. Also the structure-activity relationship (SAR) of DHPs as antihypertensive and antianginal agents was reviewed, summarized, and discussed.

scholarly journals Insilico Characterization, Phylogenetic Analysis and Homology based 3D Structural Modeling of HSPA4 Heat Shock Protein Family a (Hsp70) Member 4 from Homo Sapiens (Human).

2019 ◽  
Vol 9 (1) ◽  
pp. 3086-3091
Keyword(s):  
Heat Shock ◽  
Heat Shock Protein ◽  
Homology Modeling ◽  
Structure Prediction ◽  
Homo Sapiens ◽  
3D Structure ◽  
Protein Family ◽  
Template Molecule ◽  
Hsp 70 ◽  
Homologous Proteins

HSP70 are the specialized Heat Shock Proteins that show their novelty presence inside the deepest part of Cellular Component Networks of Chaperons and involve with various heat Shock repairing up mechanisms as main Catalytic Components. They actively participate in proper alignment and resistance of all types pressures by their proper dealing with substrates and proper exposure of their hydrophobic peptide portions inside of their substrate molecules. The exchange of information cum process of information occurs between the cell components through HSP70’s low level of affinity ATP bind sites and High Affinity level of ADP binding sites. Therefore, ATP hydrolysis process plays a key role in fundamental mechanisms, in vitro and as well as in in vivo repairing mechanisms for the functioning of Chaperone like functionalities of HSP 70 proteins. In the current study analysis, Homology Modeling methods permits us for modeling the 3D structure of the protein by taking backbone of experimentally determined 3D-Structures of homologous proteins extracted in various structural formats like PDBs. We worked with HSPA4 heat shock protein family A (HSP 70) part 4 from Homo sapiens ( Human ) and conducted 3-D structure prediction using an Automated Swiss Model Generation by employing the crystal structures of Template molecule. PDB Blast and Template search for required protein were conducted with help of all the parameters with respect to Imapact and Psi Impact angles and that leads to for most noteworthy sequence identity, structural alignments and functionalities. Finally, the homology modeling were performed by using SWISS modeler and modeled proteins were fine tuned by using the Ramachandran Plot, by using PROCHECK and 3D-Check Software Programs.

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