The document discusses experimental and computational methods for protein structure prediction. Experimental methods like NMR, X-ray crystallography, and cryo-EM can accurately determine protein structure but require isolating and crystallizing the protein. Computational methods like homology modeling, ab initio modeling, and threading/folding predict structure from sequence alone and are less accurate but do not require crystallization. Computational methods work best when a template structure is available from experimental data. While experimental methods are very accurate, they are also costly and difficult for large numbers of proteins, making computational methods a useful complement despite being less accurate.

1. Protein Prediction
Experimental Computational
Predicting the structure of protein using
computational tools.

2. Why we are calling it
prediction?
भविष्यिाणी पूिवकथन पूिव-सूचना
Proteins are flexible.
Structure depends on the environmental conditions.
Binding of ligand changes the structure of the protein.
Complete protein isolation is very difficult.
It may be

3. Protein Prediction
It may be

4. Prediction
Experimental
1. NMR
2. X-Ray Crystallograogy
3. Cryo EM
Computational
1. Ab Initio
2. Threading/Folding
3. Homology Modelling
Protein extraction and crystallization is
required
Algorithms and Statistical tools
required
Atomic Level Resolution Work good when template is available.
Template is provided by experimental
data
Accurate Not so accurate
Closer to native structure Cannot be said. Depends on accuracy
Manually Tedious Computationally Tedious
Costly Not so costly

5. Experimental Prediction
1.NMR
2.X-Ray Crystallography
3. Cryo EM

6. When experimental
prediction is doing well
then what is the need of
computational prediction?

7. 1. 2015 Study- Experimental
data analysis
Super-Computers2. Computers
Theoretical Algorithm can be computed using
Protein Evolutional stable
Increased Cost3. Repetitive Experimental
No new protein
structure
Large number of
templates
available
Computational
work great
with template

8. Computational Prediction
Homology
Modelling
Ab-Initio Threading
(Folding)
1st Student 2nd Student 3rd Student
From the beginning Grab portions Template based
Computationally very
intensive
Not intensive
Large care of small
parameters
Not much care
Toughest Easiest
Sequence Alignment

10. Hydrophobicity
Hydrophilicity
Folding
pH, pI, pKa
Physiochemical
Transmembrane
Solubility
Thermal
Phylogenic
Evolution
Anfinsen’s Theory – All information in
the sequence

11. Sequence Alignment
Global Alignment Local Alignment

12. Multiple Sequence Alignment

13. Computational Prediction
Homology
Modelling
Ab-Initio Threading
(Folding)

14. Ab-Initio

15. Threading (Folding)

16. Homology Modelling

17. https://www.expasy.org/tools/
https://en.wikipedia.org/wiki/List_of_protein_structure_prediction_software
Homology
modeling
RaptorX
Phyre2
MODELLER
BHAGEERATH-H
SWISS-MODEL
Threading/fold
recognition
RaptorX
Phyre2
HHpred
Ab initio
i-TASSER
ROBETTA

19. Study Materialhttps://epgp.inflibnet.ac.in/Home/ViewSubject?catid=1174