IN SILICO ANALYSIS OF INHIBITOR AND SUBSTRATE BINDING SITE OF SERRAPEPTIDASE FROM SERRATIA MARCESCENS MTCC 8708

Objective: Serrapeptidase is a therapeutic enzyme broadly used as an anti-inflammatory drug to treat inflammatory diseases like arthritis, bronchitis, fibrocystic breast disease and sinusitis. The objective of present study is in silco analyzes of the substrate and inhibitor binding sites of serratiopeptidase, expressed from a cloned gene.Methods: The gene encoding Serrapeptidase was amplified from genomic DNA of Serratia marcescens MTCC 8707, an isolated from the flowers of summer squash plants. The gene was sequenced, the nucleotide sequence of 1464 nucleotides was submitted to Gen Bank nucleotide database and accession number GI: KP869847 obtained. The develop amino acid sequence was used to predict 3D structure using different bioinformatics tools and software's Further, CABS-dock and Swiss Dock, the docking servers were used for enzyme-substrate/inhibitor binding site analysis. The inflammatory mediators, bradykinin, and substance-P were used as substrates, whereas, EDTA and Lisinopril were used as an inhibitor for serrapeptidase. UCSF Chimera program was used for interactive visualization and analysis of docked results.Results: The docking studies show substrates bradykinin and substance-P bind near zinc binding site with minimum RMSD value and the inhibitors EDTA and lisinopril showed favorable interaction at zinc binding site of serrapeptidase with minimum free energy.Conclusion: The result of docking studies confirm that the substrate or inhibitor binds near zinc binding domain (HEXXH.) and the peptide bond of the substrate can be effectively cleaved by serrapeptidase.Keywords: Serrapeptidase, Anti-inflammation, Arthritis, Molecular docking, Drug discovery, Protein-peptide interaction, Bradykinin, Substance-

In silico analysis of inhibitor and substrate binding site of serrapeptidase from serratia marcescens MTCC 8708

Objective: Serrapeptidase is a therapeutic enzyme broadly used as an anti-inflammatory drug to treat inflammatory diseases like arthritis, bronchitis, fibrocystic breast disease and sinusitis. The objective of present study is in silco analyzes of the substrate and inhibitor binding sites of serratiopeptidase, expressed from a cloned gene. Methods: The gene encoding Serrapeptidase was amplified from genomic DNA of Serratia marcescens MTCC 8707, an isolated from the flowers of summer squash plants. The gene was sequenced, the nucleotide sequence of 1464 nucleotides was submitted to Gen Bank nucleotide database and accession number GI: KP869847 obtained. The develop amino acid sequence was used to predict 3D structure using different bioinformatics tools and software’s Further, CABS-dock and Swiss Dock, the docking servers were used for enzyme-substrate/inhibitor binding site analysis. The inflammatory mediators, bradykinin, and substance-P were used as substrates, whereas, EDTA and Lisinopril were used as an inhibitor for serrapeptidase. UCSF Chimera program was used for interactive visualization and analysis of docked results. Results: The docking studies show substrates bradykinin and substance-P bind near zinc binding site with minimum RMSD value and the inhibitors EDTA and lisinopril showed favorable interaction at zinc binding site of serrapeptidase with minimum free energy. Conclusion: The result of docking studies confirm that the substrate or inhibitor binds near zinc binding domain (HEXXH.) and the peptide bond of the substrate can be effectively cleaved by serrapeptidase

Peptide and Protein Interaction Prediction and Intervention with Computational Methods

Proteins are the most fascinating multifaceted biomacromolecules in living systems and play various important roles such as structural, sensory, catalytic, and regulatory function. Protein and peptide interactions have emerged as an important and challenging topic inbiochemistry and medicinal chemistry. Computational methods as promising tools have been utilized to predict protein and peptide interactions in order to intervene in the biochemical processes and facilitate pharmaceutical peptide design and clarify the complications. This review will introduce the computational methods which are applicable in protein and peptide interaction prediction and summarizes the most successful examples of computational methods described in the literature.HIGHLIGHTS•Highlights the importance of peptides and proteins interactions.•Summarizes the computational methods which are applicable in peptide and protein interaction prediction.•Highlights the applications of computational methods in peptides and proteins interactions

Development of Ebola Vaccine Candidate by in silico from Glikoprotein (GP) gene of Ebola Zaire Virus

Immunoinformatics is the one of bioinformatics divisions. The focus of this division is to design compounds of immune response activists or candidate vaccine in silico or computation. One type of immune response activator compound is a peptide. Peptides are small amounts of amino acid residues. The amount of amino acid residues that can activate the immune response ranges from 9-15 amino acids. Good candidate vaccine quality is shown affinity or strong bond between peptide and MHC (Major Histocompatibility Complex) is indicated by the value of energy of molecule-blocking process through low software. This research is conducted in Data and Process Laboratory of Biology Department, Faculty of Science and Technology, States of Islamic University Sunan Gunung Djati Bandung, in December 2016 until February 2017. The study amis to get candidate vaccine ebola peptide form 9 (nine) amino acid residues. The tool used in this research is the software which made based on the working principle of immune response. The software is SDS Workbench, IEDBAR, Emboss, and CABSdock. The material used in this research is the sequence information of ebola virus glycoprotein. The results show that the peptides with FLYDRLAST (Fenilanalnin, Leusin, Tyrosine, Aspartic acid, Arginine, Leusin, Alanine, Serin Treonin) are potential candidate for the ebola peptide vaccine because of their high affinity values with MHC I, indicated by molecular-binding energy which is very low ie -1870.69 Kcal / mol

Immunoinformatics Study on Early 4 Protein of Human Papillomavirus Type 16 for Cervical Cancer Vaccine Peptide Candidate

 The aims of this study were to carry out testing of the early 4 protein of type 16 HPV through immunoinformatics meth-ods in an effort to get the peptide vaccine candidate for cervical cancer. The software used are IEDB-AR, CABSdock and Accelrys Discovery Study 4.5. Based on the analysis that sequence of ami-no acid lysine, leucine, leucine, glycine, serine, threonine, tryp-tophan, proline and threonine (KLLGSTWPT) and the sequence of amino acid tyrosine, tyrosine, valine, leucine, histidine, leucine, cysteine, leucine, alanine, alanine, threonine, lysine, tyrosine, pro-line and leucine (YYVLHLCLAATKYPL) are peptide vaccine can-didate for cervical cancer from the early 4 protein of HPV type 16