In-Silico Drug Design through Protein-Ligand Interaction Studies of Snake Venom Peptides

Author Name: *Ambika Iyer
Author Email: ambikaiyer@yahoo.com

Abstract

Toxins have evolved in plants, animals and microbes multiple times as part of defensive and/or prey capture strategies. Peptide toxins are usually found in animal venom. Most venom comprises a highly complex mixture of peptides, often with diverse and selective pharmacology. The source of peptide is specifically snake venom which is usually neurotoxins. We have selected four peptides Aggretin, Crotamine, Mambalgin, Myotoxin. In-silico drug design is crucial step to reduce the number of steps in drug designing process. The present study was focused to design a drug for snake venom peptides. The peptides are causative proteins for diseases like blood clotting, necrosis, acute renal failure, and anaphylaxis. The peptide structure was retrieved from Uniprot database and multiple sequence alignment was performed on the retrieved sequences using Clustal W. Rasmol software was used for protein visualization. Pubchem database was used for screening the chemical ligands. The chemical structure of selected ligands was obtained from Dundee pro drg server. The ligands were screened based on log Pvalue for selection of better ligands which was used for docking studies. Docking was performed to find the inhibition activity against snake venom using HEX software. For the disease Coagulopathy the protein aggretin and ligand ellagic acid showed highest negative E-value -232.51, for the disease necrosis the protein crotamine and ligand sterigmatocystin solution showed E-value of -270.78, for the disease acute renal failure the protein aggretin and ligand 1LGS5JRP31-isoquioline derivative gave E-value -229.40, for the disease anaphylaxis the protein aggretin and the ligand ketotifenfumarate gave E-value -258.49. Vibrational analysis was performed to check the energy changes and it was found that the energy of docking in case of Hex after vibrational analysis was higher. Different conformational structures of aggretin were obtained and redocked with the ligand ellagic acid for the disease and the highest E-value obtained was -255.22, for the disease necrosis different conformational structures of mambalgin was used to redock and the highest negative E-value obtained was -255.25, for the disease acute renal failure and anaphylaxis the vibrational analysis was performed using the protein aggretin and the highest E-value obtained was -235.43 and -275.69 respectively. Autodocking was done using vina for different conformations of ligand. For all the four diseases the protein aggretin showed higher affinity towards the ligand. For the disease coagulopathy the highest affinity towards ligand ellagic acid of value -5.8, for the disease necrosis the highest affinity towards ligand Sterigmatocystin solutionof value -7.9, for the disease acute renal failure the highest affinity towards ligand Isoquineline derivativeof value -6.0, for the disease anaphylaxis the highest affinity towards ligand Ketotifenfumarate of value -5.6 Therefore, this in silico analysis provides rapid and potential approach for identification of drug target and designing of drug.

Keyword

Snake Venom Peptides, Protein-ligand interaction, Docking, Vibrational analysis, Autodocking

Introduction

During drug design ‘disease selection’ is a potent step. The human body consists of four main molecules; carbohydrates, lipids, nucleic acid and proteins. As carbohydrates, lipids and nucleic acid don’t have binding sites, the drug acts on protein. Drugs are either made of ligands or peptides. Toxins have evolved in plants, animals and microbes multiple times as part of defensive and/or prey capture strategies. Peptide toxins are usually found in animal venom associated with specialized Envenomation Apparatus that allows their delivery into the soft tissue of animals via subcutaneous, intramuscular or intravenous routes [Mackessy et al, 2010]. Most venom comprises a highly complex mixture of peptides, often with diverse and selective pharmacology. Despite this diversity, venom peptides seem to have evolved from a relatively small number of structural frameworks that are particularly well suited to addressing the crucial issues of potency and stability. It is this evolved biodiversity that makes venom peptides a unique source of leads and structural templates from which new therapeutic agents might be developed. Crude venoms contain a diverse array of different peptides many of which are bioactive [Saviola A.J. et al, 2013]. Snake venoms are complex mixtures of proteins, nucleotides and inorganic ions. These combinations confer a formidable array of toxic properties on the venom, the peptides and polypeptides being responsible for a variety of toxic properties. The number of venom components in venomous animals like snake, scorpion or cone snail ranges from 50-200toxins [Tan PT et al, 2003]. The source of peptide is specifically snake venom. We have selected four peptides Aggretin, Crotamine, Mambalgin, and Myotoxin for drug designing. Snake venoms are important tools in toxicology, neuroscience, and pharmacology. The venom components are highly variable and functionally complex and they offer many research opportunities [Joyce P.Y.S et al, 2004]. These peptides cause diseases like blood clotting, necrosis, anaphylaxis, acute renal failure, neurological and psychiatric disorders. Proteins within the body are always stable. But outside the body the proteins have to be studied and stabilized. To stabilize the protein the structure of the protein is to be known hence we can stabilize it by optimizing the geometry of the protein. The geometry optimization can be done by minimizing the energy of the protein molecule. For the protein to bind with the target site both have to have equal energy levels for 100% binding. Screening of the ligand is necessary to eliminate the junk data. Screening can be done by Lipinski rule. Ambiguity study of the ligand is done for the chemical properties of the ligand. Docking is done to bind the receptor to the drug molecule to attain protein-ligand binding. The ability to predict the conformations and energies involved in 3 the binding of small molecules to proteins is quite crucial in designing potential drugs that can interact favorably with their target proteins. Docking is also used to predict protein-protein complexes, which are helpful in determining the quaternary structures of intrinsically multimeric proteins as well as to gain an understanding of the protein interaction networks [Alex Mathew Jet al, 2009]. After docking the selected ligand binds with the receptor, the expected output is docking energy. Among the different docking pairs the best pair can be selected based upon the lowest energy value which is an indication of high stability. High negativity shows good peptide receptor pairing. High negativity leads to less energy leading to high stability.

Conclusion

The Phylogenetic Analysis was carried out using CLUSTAL W tool. This showed the evolutionary relationships of the different species which were selected for the study.The snake venom peptide structure was obtained from RCSB site in PDB format and the protein visualization and modeling were done using Rasmol software.The structure was analyzed and validated using SAVES server The ligands, were screened using lipinski rule for each disease from pubchem server.The Hex software gives corresponding e-values for each docking.More negative the E-value more efficient is the docking.For the disease coagulopathy, the ligand ellagic acid showed better efficiency towards the peptide aggretin.It gave an E-value of -232.51 which is the most negative among docking for all the other ligands.In the case of necrosis, sterigmatocystin solution showed higher efficiency towards the peptide crotamine, the e-value was -270.78For the disease acute renal failure the ligand 1LGS5JRP1-isoquinoline derivative showed higher efficiency towards the peptide aggretin giving the e-value -229.40In the case of anaphylaxis, ketotifen fumarate showed higher efficiency towards the peptide aggretin, the evalue was -258.49Vibrational analysis was used to find vibrational modes of the receptor and docking was conducted on the most structurally different form with the ligand.The docking energy for all the diseasesbefore was low and after the vibrational analysis was found to be the higher.In the case of coagulopathy the value was -255.22, for necrosis thee-value was –255.25, for acute renal failure the e-value was -235.43, for anaphylaxis the e-value was -275.69The docking energy for all the diseases after the vibrational analysis was found to be the higher than the regular docking, Hence it was observed that there is change in the energy value for the docking of the proteins, obtained from the vibrational analysis, with the ligand.This shows that the conformational change achieved by vibrational tool is associated with the protein binding site.Further analysis was done through autodocking. The conformations were recognized using the scoring functions to predict the affinity. Autodocking was performed to find affinity values of the ligand to receptor.Autodocking was performed using vina software. Through autodocking tools the binding site/location can be viewed and optimized.Vina produces more than one binding mode, according to vina the first binding mode is the best one. After performing autodock is was observed that the ligands showed higher affinity towards the peptide aggretin for all the four disease.Hence the results of docking was confirmed with autodocking.

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