Welcome to SAMbinder

A web server for predicting S-adenosyl-L-methionine binding sites

S-adenosyl-L-methionine (SAM) is one of the essential metabolic cofactor/intermediate which is found in almost every cellular life forms and enzymes. It plays a vital role in various metabolic and regulatory pathways, mainly involved in transfer of various groups (e.g., methyl, aminopropyl, methylene). It is a second widely studied ligand after ATP. SAM is a potential drug target for many diseases such as cancer, Alzheimer's, Parkinson, Epilepsy, Osrteoarthritis and many more. Some of the reported drug targets are Catechol O-methyltransferase, Glycine N-methyltransferase, S-adenosylmethionine synthase isoform type-1, S-adenosylmethionine decarboxylase proenzyme, Cystathionine beta-synthase.

SAMbinder is a web server developed for predicting SAM interacting residues in a protein from its primary sequence. This server has following three modules; Sequence based module;PSSM based Module; Peptide Mapping. Standalone version of SAMbinder has been developed using Python to facilitate user to predict SAM binding sites in proteins on their local machine.

Forest

SAMbinder Major Features

  • Sequence based module:This module predict SAM binding residues in a protein from its promary sequence.
  • PSSM based Module: This module utilize evolutionary information (in form of PSSM profile) of a protein to predict SAM binding residues in a protein.
  • Peptide Mapping: This server allow user to predict SAM binding residues in a protein by mapping known peptides that contains SAM interacting central residue.
  • Standalone Software: It has been developed using Python and will be freely available to users.
  • Datasets: All datasets used in this study will be available to public.
Reference: Agrawal P, Mishra G, Raghava G P S (2020) SAMbinder: A Web Server for Predicting S-Adenosyl-L-Methionine Binding Residues of a Protein From Its Amino Acid Sequence. Frontiers in Pharmacology 10:1690.

*The server has been checked in different browsers and is best viewed in Safari and Google Chrome.