rna 3d structure prediction with nast

RNA 3D Structure Prediction with NAST

Xinpei Liu

刘欣培

Background

Test Simulations with NAST

Introduction to NAST

Content

1

2

3

4

System Consistency5

Effect of Secondary Structure

RNA folding vs Protein folding RNA 3D Structure Prediction Tools

• Manual

• Automatic

• Full atomic

• Coarse grained

• Physics based

• Knowledge based

Background


Nucleic Acid Simulation Toolkit (NAST)• Funded by the Simbios National Center for Biomedical Computing• A knowledge-based coarse-grained tool for modeling RNA structures. It produ

ces a diverse set of plausible 3D structures that satisfy user-provided constraints based on:

• 1. Primary sequence• 2. Known or predicted secondary structure• 3. Known or predicted tertiary contacts (optional)

Requirements:• Python 2.6.x• PyOpenMM 2.0.0 (3.0.0 won't work!)

https://simtk.org/home/nastJonikas MA, Radmer RJ, Laederach A, Das R, Pearlman S, Herschlag D, Altman RB. Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA. 2009 Feb;15(2):189-99.

Advantages

• Provide information about the likely topology

of a molecule• Provide a good starting point for higher resolution atomic models

• Be able to handle large molecules (> 76nt) • Much faster than full-atomic simulation tools

• 1,000,000 steps within 138s• Allow uncertainty in the secondary structure (within a certain level)


How to use NAST? • Primary Sequence File

• Go to http://www.rnasoft.ca/strand/ • Search for your structure and get a BPSEQ file • Use "parseBPseq.py" file in the package to generate a sequence f

ile • Secondary Structure File

• Use secondary structure prediction tool • e.g., Mcgenus• http://eole2.lsce.ipsl.fr/ipht/tt2ne/mcgenus.php

• Tertiary Contacts File (optional)• From experiments or phylogenetic analysis


PDB ID 1ZIH 389 atoms 12 residues

Test Molecule Used

Simulations 1ZIH from an Unfolded Circle State 1,000,000 steps

Definition of q value

q is a normalized measure of similarity between a reference and comparison structure:

RMSD Mean: 2.683Sd: 0.449

3

2

3.5

4

Simulations

2.5

q value (ref.: crystal structure)Mean: 0.250 Variance: 0.00686

1ZIH from an Unfolded Circle State 1,000,000 steps

q value Mean: 0.246Variance: 0.00686


Reference value:

Definition of GDT_TS Score

GDT_TS score The Global Distance Test Total Score (GDT_TS) of Ca atoms is used to assess the correctness of the predicted model. GDT_TS has been commonly used in modeling studies and in the CASP community. GDT_TS is defined as:

where N in the total number residues of a target, GDTd is the number of aligned residues whose Ca-atom distance between the native structure and predicted model is less than d A (angstrom) after superposition of the two structures; and d is 1, 2, 4, and 8 A (angstrom).

•Zemla A: LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 2003, 31: 3370-3374.

Simulations

GDT_TSMean: 57.656%Sd: 7.223%

1ZIH from an Unfolded Circle State 1,000,000 steps

Test Molecule Used PDB ID 4JF2 1829 atoms 77 residues

Simulations 4JF2 From Unfolded Circle state 1,000,000 steps

RMSD Avg.: 11.830Sd: 2.591

15

Simulations

10

q valueMean: 0.128Variance:0.000788

20

4JF2 From Unfolded Circle state 1,000,000 steps

RMSD:10.3 ± 2.3Reference value:

q valueMean: 0.125Variance: 0.000964

Simulations

GDT_TSMean: 9.620%Sd: 4.681%

4JF2 From Unfolded Circle state 1,000,000 steps

14% ± 5% (the best cluster)

Reference value

1ZIH from Crystal Structure 1,000,000 steps


Without Secondary Structure Constraints With Secondary Structure Constraints

• RMSD

32.52

3.5


10

5

8

67

9


Without Secondary Structure Constraints

With Secondary Structure Constraints

• q value (ref: crystal)



1ZIH from Crystal Structure 1,000,000 steps


RMSD mean: 22.860Sd: 4.798



• RMSD

RMSD Avg.: 11.378Sd: 1.176

4JF2 From Crystal Structure 1,000,000 steps

30

25

20

15

10

5

10

5




• q value (ref: crystal)



4JF2 From Crystal Structure 1,000,000 steps


Effect of Secondary Structure• Simulations with different percentage of wrong pairs in secondary structure

(600, 000 steps)

Mean Std.0% 6.0969 2.597115% 5.4951 1.805425% 4.4746 1.274835% 2.6558 2.0450


System Consistency 1ZIH from an Unfolded Circle State

Reference Model: resulted structure from simulation with crystal structure (1,000,000 steps)

Reference Model:Crystal Structure (1,000,000 steps)


Reference Model: resulted structure from simulation with crystal structure (1,000,000 steps)

q valueMean:0.223Variance: 0.00276

4JF2 From Unfolded Circle stateSystem Consistency

q valueMean: 0.128Variance:0.000788

Reference Model:Crystal Structure

Folding result from NAST is able to provide a basic idea of the structure for a given sequence.

Small proportion of mistakes doesn’t really influence folding result but this holds only within a certain level.

The simulation will more likely to generate a folding that is more similar to other resulted models (with the same steps), instead of crystal structure

More tests with GDT-TS may be needed.

Conclusion

Any Question or Comment?

rna 3d structure prediction with nast

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