Protein Modeling Overview

1. Lab#5: Protein Modeling:
Overview; Method; Tools; CASP
Asian University For Women
BINF 3016: Protein Modeling
(Lab - Fall 2022)
Syed Mohammad Lokman
Instructor
Asian University for Women

3. Experimental Method
● X-Ray
● NMR
● Cryo-EM
Prediction Method
1. Knowledge Based Method
a. Homology (Comparative) Modeling
b. Threading/ Fold Recognition
2. Ab Initio Method
a. Deep Learning Based
b. Physico-chemical Properties Based

4. Some Common Tools for Prediction Method:
1. Knowledge Based Method
a. Homology (Comparative) Modeling:
i. SWISS-MODEL; MODELLER
b. Threading/ Fold Recognition
i. I-TASSER; Rosetta
2. Ab Initio Method
a. Deep Learning Based
i. RoseTTAFold; AlphaFold
b. Physico-chemical Properties Based
i. CABS-flex

5. ➔ ≥ 30% Sequence Identity is Required for a Decent Model Prediction.

6. (https://swissmodel.expasy.org/interactive)

7. Input Target
Data
P07364
CHER_ECOLI

9. Template
Selection
GMQE (Global Model Quality Estimate): a quality estimate which
combines properties from the target-template alignment and the
template structure.
The GMQE is available before building an actual model and thus
helpful in selecting optimal templates for the modelling problem at
hand.
Once a model is built, the GMQE gets updated for this specific case
by also taking into account the QMEAN global score of the obtained
model in order to increase reliability of the quality estimation.
QSQE (Quaternary Structure Quality Estimate): a number between 0
and 1, reflecting the expected accuracy of the interchain contacts
for a model built based a given alignment and template.
In general a higher QSQE is "better", while a value above 0.7 can be
considered reliable to follow the predicted quaternary structure
(Oligomeric Modelling) in the modelling process.

10. Predicted
Model

12. Structure
Assessment

13. Structure
Assessment

14. Structure
Assessment

17. An example slider graphic for a
relatively poor structure.
An example slider graphic for a
relatively good structure.

18. The green, yellow, orange and red portions in the lower bar for each chain, indicating the fraction of
residues that contain outliers for 0, 1, 2, ≥3 model-only validation criteria, respectively.
A grey segment indicates residues present in the sample but not modelled in the final structure.
The numeric value for each fraction is shown below the corresponding segment. Values <5% are
indicated with a dot.
If electron density outliers were present, there is an additional red bar above the lower bar,
indicating the fraction of residues that are RSRZ outliers. The numeric value for the fraction is
indicated above this red bar.

19. A.
B.
● UniProt: P09904
HBA_PHYMC

21. Threading/ Fold Recognition:
I-TASSER

23. With this method, you predict the structure of your target protein using known
protein folds for similar proteins found in various different databases.

26. Assignment 5.1:
Build a Model of CHER_ECOLI
(UniProt:P07364) using I-TASSER

27. Deep Learning Based Method:
RoseTTAFold

29. CASP:
Critical Assessment of protein
Structure Prediction

30. https://predictioncenter.org/casp15/index.cgi

32. CASP14 Rankings: Regular targets

33. Structure Prediction
(Registration Required)
Submit
(https://robetta.bakerlab.org/submit.php)

34. ● Length >1400
● Check “RoseTTAFold”

35. Example Result

36. Assignment 5.2:
Build a Model of CHER_ECOLI
(UniProt:P07364) using RoseTTAFold

37. https://academic.oup.com/bib/article/23/5/bbac308/6658852

38. Abstract
Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although
the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is
outstanding, their performance for specific protein families has remained unexamined. This work directly
compares the performance of these novel deep learning-based protein modeling methods for GPCRs
with the most widely used template-based software—Modeller. The official AlphaFold repository and
RoseTTAFold web service were used with default settings to predict five structures of each protein sequence.
If only looking at each program’s top-scored structure, Modeller had the smallest average modeling RMSD of
2.17 Å, which is better than AlphaFold’s 5.53 Å and RoseTTAFold’s 6.28 Å, probably since Modeller already
included many known structures as templates. However, the NN-based methods (AlphaFold and
RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model,
respectively, where no good templates were available for Modeller.

39. Thank You