1. Methods: The IntFOLD Server
Group 275, IntFOLD-TS
Liam McGuffin et al.
10 November 2018

2. Outline What is the IntFOLD server? Principles of methodology
How does it work? Focus on IntFOLD-TS
Inputs and Outputs
Limitations, weaknesses and future

3. What is the IntFOLD server?
Fully automated, integrated pipeline for prediction of structure and function from sequence
Integrates tools we developed for CASP9:
IntFOLD-TS (nFOLD4)
IntFOLD-QA (ModFOLD 3.0)
IntFOLD-DR (DISOclust 2.0)
IntFOLD-FN (FunFOLD 1.0)
Also IntFOLD-DP (DomFOLD 3.0)
Machine readable and graphical output
Version 1.0 available soon

4. Principles of methodology
Quality assessment is key, underpins all methods
Multiple models
Multiple templates
Alternative alignment methods
Measurement of model variation
Integration equals efficiency
(manpower, CPU power, visualisation)

5. Model Quality Assessment - ModFOLDclust2
How does it work?
Target Sequence
3D model/s (optional)
Inputs
Generate 40 new models
Model Quality Assessment - ModFOLDclust2
Top 5
Top 1
Top 1+
templates
All models and errors
All models and errors
nFOLD4
DomFOLD 3.0
FunFOLD
1.0
DISOclust 2.0
ModFOLD 3.0
Outputs TS DP FN DR QA

6. IntFOLD-TS (nFOLD4) Input Target Sequence NR nFOLD4 Sequence-profile
PSI-BLAST
Secondary structure
PSIPRED
PDB40
Sequence-structure alignments
SP3
SPARKS2
PDB70
HHsearch
COMA
Build 40 models
Single + multi templates (modeller)
Model quality assessment
ModFOLDclust2 – global and local
Add B-factors
Output
TS file with top 5

7. Inputs
Sequence
(mandatory!)
Model/s
Name
(optional)

8. Outputs: summary 1 – TS and QA
Links to graphics
Parseable results
(CASP format)
Top 5 models,
Global scores,
Local errors

9. Outputs: QA plot view x = residue number y = error (Å) Download plot
(PostScript)

10. Outputs: summary 1 – TS and QA

11. Outputs: QA model view Download model with errors in B-factor column
Interactive view
(Jmol)

12. Outputs: summary 2 – DR and DP
x = residue num
y = disorder prob
Domain prediction

13. Outputs: DP model view Download model with domain num
in B-factor column
Interactive view
(Jmol)

14. Outputs: summary 3 – FN & full QA
Ligand binding
residues
Quality assessment
for all models

15. Outputs: FN model view Download model with all probable ligands
Interactive view
(Jmol)

16. Limitations, weaknesses and future
Relatively slow , ~1 hour for results – fair queuing system implemented – no batch jobs
Template based modelling only
Function prediction - ligand binding residues only
Relies on model quality assessment – sometimes wrong!
Not all results are integrated, some things left up to user
Using ModFOLDclust2 to improve FunFOLD
Using DISOclust results to improve FunFOLD
Using FunFOLD to improve ModFOLDclust2
Better multi-template modelling using local errors

17. Acknowledgements The group: Daniel Roche (PhD student)
Stuart Tetchner (BSc student)
Funding:
Research Councils UK Academic Fellowship (LM)
University of Reading Faculty Studentship (DR)
Everyone who voted!