CS Mukhopadhyay and RK Choudhary
School of Animal Biotechnology, GADVASU, Ludhiana
Protein folding is dictated by the physical forces acting on the atoms of the protein. In general, the most accurate way of formulating the protein‐folding or structure prediction problem is in terms of an all‐atom model subject to the physical forces. Energy functions compatible with the protein representation are considered during the ab initio approach. Faster algorithms are developed to search the best‐fitting formation, in order to minimize the energy function while predicting the tertiary structure ab initio.
To predict the protein structure based on physical principles (rather than comparative homology using the previously reported structures). The structural conformations that minimize the energy function are evaluated for the structures that the protein is likely to adopt under native conditions.
To predict the tertiary structure of a peptide using an ab initio approach with the online tool RaptorX.
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Once the job has been started, the status can be seen in real time on the screen as below:
Open the specified email ID to get the results:
Click on the link provided in the mail to open the detailed result in a new window.
The whole protein sequence is partitioned into domains (here six domains), depending on the available template structures in PDB. The domains are indicated by assigning a domain number in every third row of sequence blocks.
The proportions of secondary structures (e.g., (Helix (H), Beta‐sheet (E) and Loop (L)) are given as percentages. In our study, the result shows “Secondary struct: 40%H, 10%E, 49%C”.
Three grades of solvent accessibility are designated by Buried (B: cut‐off value 10%), Medium (M: range 10–42%) and Exposed (E: cut‐off value: 42%). The present example shows: “Solvent access: 32%E, 38%M, 29%B”.
Many parameters are checked to determine the quality of the predicted structure.
The smaller the value, the better the prediction. If the P‐value is more than 10E‐3 and 10E‐4 for alpha and beta proteins respectively, the predicted sequence is to be discarded.
The higher the score, the better is the prediction.
This indicates the number of identical residues in the alignment. Here, the “u” of “uSeqId” stands for un‐normalized. The higher the value, the better it is considered to be. In general, for a peptide sequence of 200 residues, the cut‐off value of normalized uSeqId is 30%.
This estimates the modeling error from a score determined from the residues with modeling error.
This parameter is considered for binding site prediction in order to assess the quality of predicted pocket. Multiplicity higher than 40% is considered good.
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