Help with peptide prediction/ peptide modeling
1
0
Entering edit mode
16 months ago
Wendy • 0

Hi, I'm a beginner in the field of bioinformatics. I need to model peptides (+-16a.a.) obtained from the in silico hydrolysis of a protein. I'm not sure which technique and software would be most suitable. I don't understand in this case if I should use techniques by ab initio, threading or homology modeling. Pepfold, deepfold, I-TASSER or others? Could anyone help?

Peptides • 448 views
ADD COMMENT
1
Entering edit mode
16 months ago
Mensur Dlakic ★ 27k

It is not clear what you want to do, and for what purpose. For example, what does +-16a.a. mean? That your peptides are of fixed length +/- 16 residues, or that they are 16 residue long?

Without knowing your purpose, I would say that modeling proteolytic peptides is useless, as they will most likely be misfolded and disordered out of their natural context. If they are part of a larger protein and you are interested in their structure in the native context, it may be better to model the whole protein and then excise peptides of interest. Without knowing why you want to do this, it is difficult to recommend the best modeling program.

ADD COMMENT
0
Entering edit mode

Thank you for your attention! Peptides are obtained from hydrolysis with enzymes from the gastrointestinal tract. Previous supplementation with this natural whole protein has shown anti-obesity activity in an animal model. The objective would be to model these peptides resulting from the hydrolysis of this protein by digestive enzymes (peptides that have around 12-18 amino acids). The aim is to further correlate the activity of these peptides after digestion with the activity displayed in vivo by performing docking/dynamics with a therapeutic target of obesity (in physiological conditions). Could you understand better?

ADD REPLY

Login before adding your answer.

Traffic: 2409 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6