1. Jianlin Jack Cheng Computer Science Department University of Missouri, Columbia, USA Mexico, 2014

2. Targeted Sampling Fold Space Alignment SpaceModel Pool Sequence Space Model Generation Template & Alignment Combination

3. Internal or CASP Model Pool Combination Refinement Side Chain Tuning Massive Assessment Model Ranking

4. Samplers BLAST CSBLAST CSIBLAST PSIBLAST SAM HMMer HHSearch HHblits HHsuite MULTICOM PRC FFAS Compass MUSTER RaptorX 1. Alignment Combination Based on E- Values 2. Alignment Combination Based on Structures 3. Multiple Sequence Alignment + Structural Features 150 – 200 Models Template Library Alignment Combination Model Generation Modeller MTMG FUSION 125,000 templates (in-house) 125,000 templates (in-house) 39,000 (in-house) 39,000 (in-house) Third- party (local) Fold Sampling

5. MULTICOM (Server) MULTICOM (Human) Servers (partial list) Best (domains) nns10 BAKER-ROSETTASERVER8 IntFOLD37 Zhang-Server4 TASSER-VMT3 MULTICOM Server2 QUARK2 RBO_Aleph2 HHPred-A2 FFAS-3D2 myprotein-me2 PhyreX1 SAM-T08-server1 ZHOU-SPARKS-X1 HHPred-X1

6. Methods (blue: in-house) TypeFeatures MULTICOM-NOVELSingleStructural, physical, chemical features OPUS-PSPSCa atom contact potentials Proq2SStructural features RWplusSSide-chain orientation dependent potential ModelEva1SStructural features, contacts ModelEva2SStructural features, contacts, disorder, conservation RS_CB_SRSSDistance dependent statistical potential SELECTproSEnergy-based (h-bond, angle, electrostatics, vdw) DopeSStatistical potential DFire2SEnergy-based potential Modfoldcluster2ClusterPairwise model similarity (geometry) APOLLOCPairwise model similarity PconsCPairwise model similarity QAproC + SWeighted pairwise model similarity MULTICOM (human)ConsensusAverage ranking

7. Methods (blue: in-house) TypeAverage GDT-TS # Better# Best MULTICOM-NOVELSingle0.3862 OPUS-PSPS0.3763 Proq2S0.3972 RWplusS0.3762 ModelEva1S0.3872 ModelEva2S0.3532 RS_CB_SRSS0.343 SELECTproS0.411 DopeS0.3873 DFire2S0.3762 Modfoldcluster2Cluster0.403 APOLLOC0.4041 PconsC0.402 QAproC + S0.3782 MULTICOM (human)Consensus0.43112

8. Combine similar models or fragments 3DRefine (energy, bond, angle) + FUSION to refold unaligned loops and tails + SCRWL for side chain packing (server) Automated detection and replacement of bad models (worked in all 13 server exception cases)

9. Templates: 4IB2, 4EF1, 4OTE, 4K3F, 3UP9, 3GXA, 4GOT The best server model designated as the first model Distribution of GDT-TS Scores of MULTICOM Server Models GDT: 0.87 GDT: 0.73 GDT: 0.84 0.6 0.65 0.7 0.75 0.80 0.85 0.9 Blue: structure Gold: model GDT-TS score: 0.86

10. Blue: structure Gold: model GDT-TS score: 0.59 Server models: Zhang-Server_TS1 BAKER-ROSETTASERVER_TS4 myprotein-me_TS1 Human model is better than Zhang-Server_TS1 Distribution of GDT-TS Scores of CASP Server Models 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

11. Blue: structure Gold: model GDT-TS score: 0.63 Human model: The same GDT-TS score Better side-chain quality Server models: nns_TS1 nns_TS3 nns_TS2 FFAS-3D_TS1 Distribution of GDT-TS Scores of CASP Server Models 0.0 0.2 0.4 0.6

12. Blue: structure Gold: model GDT-TS score: ~0.22 Selected and combined models of low (average) quality Distribution of GDT-TS Scores of CASP Server Models 0.0 0.1 0.2 0.3 0.4 0.5 0.6

13. Large-scale independent sampling Large-scale quality assessment Exception handling Model combination Model refinement Model refolding Template recognition in thin, remote profile Alignment in thin, remote profile Quality assessment with few good models

14. Group Members Badri Adhikari Deb Bhattacharya Renzhi Cao Jilong Li CASP Assessors Dr. Roland Dunbrack CASP Organizers CASP Server Predictors