Pepfold 3 peptide structure prediction

Pepfold 3 peptide structure prediction

1. PEPFOLD3

2. Content Introduction Objectives Input & Output Methodology Results Analysis Conclusion

3. PEP-FOLD3 is a de novo approach aimed at predicting peptide structures from amino acid sequences. PEP-FOLD3 makes use of a coarse grained representation: the predicted conformation of the complex must be considered as a starting point for further modeling at high resolution. Introduction

4. “ PEP-FOLD3 is the first on-line computational framework that allows the determination of a peptide conformation either free in solution, or in interaction with a protein.”

5. Objectives Available on PEPFOLD_3 is an online web based tool that is publicly available for the users. Research Outcome PEPFOLD3 offers new possibilities to refine pre-existing models and/or to generate decoys keeping rigid regions of the structure. Analyzes A probabilistic framework that can be considered as a generalization of the concept of secondary structure.

6. Input This field is to specify the amino acid sequence of the peptide. The input sequence file must be in FASTA format. The query peptide sequence must contain a string of only the 20 standard amino acids in uppercase. PEP-FOLD main output consists in models. On-line interactive visualisation and model selection facilities are however proposed. Output Input & Output

7. Conformations Generates conformations for protein-peptide complexes. Complex modeling Fold peptides in the vicinity of a protein receptor, starting from the fuzzy definition Prediction PEP-FOLD3 runs series of 100 simulations to predict the possible peptide structures. (SA) Based on structural alphabet SA letters to describe the conformations. Modeling The PEP-FOLD 3 service accepts information to bias the 3D modeling. Methodology Peptide Structure Uses a single amino acid sequence from 5 to 50 standard amino acids.

8. Here you can visualize each section of the results provided by PEPFOLD_3.

9. 1st Progress Report Incrementally provide information about job progression and errors if any 2nd Model Visualization Interactive visualization of the models generated is based on the PV javascript 3rd Clustering Report Report is primarily a table file that describes the clusters. For each model generated, up to 10 numbers are reported Result Analysis

10. PEP-FOLD is a de novo approach aimed at predicting peptide structures from amino acid sequences. This method, based on structural alphabet SA letters to describe the conformations of four consecutive residues, couples the predicted series of SA letters to a greedy algorithm and a coarse-grained force field. Conclusion

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