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Protein-Ligand Interaction: SAMBinder
SAMbinder 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 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. More about SAM S-adenosyl-L-methionine (SAM; SAMe; AdoMet) is an important molecule which plays an important role in various cellular reactions and metabolic pathways.......
Pfeature
Pfeature is a web server for computing wide range of protein and peptides features from their amino acid sequence. Following are main menus for computing features; i) Composition-based features, ii) Binary profile of sequences, iii) evolutionary information based features, iv) structural descriptors,and v) pattern based descriptors, for a group of protein/peptide sequences. Additionally, users will also be able to generate these features for sub-parts of protein/peptide sequences. Pfeature be helpful to annotate structure, function and therapeutic properties of proteins/peptides _ Major segments of Pfeature Compositional Features This module is desisigned for computing composition based features. These features has been calssified in following five submenus/modules; i) Simple compostions like amino acid, dipeptide, tripeptide; ii) Physico-chemical properties base composition, iii) Measuring repeats and dstribution of amino acids, iv) Shannon entropy to measure complexity in sequence and v) Miscellaneous features frequently used in previous studies like autocorrelation, conjoint triad, composition transition, pseudo amino acid composition and quasi-sequence-order descriptors. Binary Profiles This module is heavily used in annoation of a protein at residue level, for example predicting secondary state of residues, surface accessibility of residues, identification of ligand interacting residues. Generally, overlapping windows are used to create all possible patterns of fixed length from a protein.......
Ensemble Learning for Regression Analyses
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