Contact map guided ab initio structure prediction

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1 Contact map guided ab initio structure prediction S M Golam Mortuza Postdoctoral Research Fellow I-TASSER Workshop 2017 North Carolina A&T State University, Greensboro, NC

2 Outline Ab initio structure prediction: QUARK Contact map prediction: NeBcon Contact guided ab initio structure prediction: C-QUARK Ab initio GPCR structure prediction: GPCR-AIM 3/20/2017 2

3 Ab initio structure prediction In the absence of homologous templates,i- TASSER based models are often less useful for biomedical studies due to less accuracy of the models Ab initio protein folding method assembles protein structures without using templates Ab initio structure modeling represents the most challenging problem in structure prediction 3/20/2017 3

4 QUARK: Ab initio structure prediction method Knowledge-based potentials: 3/20/2017 4

5 QUARK: Fragment generation and distance profile Xu et al., Proteins-Structure Function and Bioinformatics, 81(2), pp (2012) 3/20/2017 5

6 QUARK: Energy Function E tot = E prm + w 1 E prs + w 2 E ev + w 3 E hb + w 4 E sa + w 5 E dh + w 6 E dp + w 7 E rg + w 8 E dab + w 9 E hp + w 10 E bp 1. Backbone atomic pair-wise potential (E prm ) 7. Fragment-based distance profile (E dp ) 2. Side-chain center pair-wise potential (E prs ) 8. Radius of gyration (E rg ) 3. Excluded volume (E ev ) 4. Hydrogen bonding (E hb ) 9. Strand-helix-strand packing (E dab ) 5. Solvent accessibility (E sa ) 6. Backbone torsion potential (E dh ) 10. Helix packing (E hp ) 11. Strand packing (E bp ) 6

7 Problems with Metropolis Monte Carlo E Low Temperature p accept ~ e de/t 1. Possibility of getting trapped at local energy basin 2. Increasing T can overcome local energy barrier, but it cannot detect low-energy regions E X X High Temperature 3/20/2017 7

8 Replica Exchange Monte Carlo Initial Random Configuration Initial Random Configuration Initial Random Configuration Make Random Change Make Random Change Make Random Change Calculate de Calculate de Calculate de p accept = e de/t 1 p accept = e de/t 2 p accept = e de/t 3 Pswap i,j = e E i E j 1 t i 1 t j T 1 T 2 T 3 3/20/ T max = L T min = L

9 Benchmark Results: QUARK vs. Rosetta Data set: 51 small proteins ( AA) and 94 medium proteins ( AA) RMSD: 96/145 targets QUARK models are better than Rosetta (pvalue: 1.51X10-4 ) TM-score: 95/145 targets QUARK models are better than Rosetta (p-value: 2.87X10-7 ) Xu et al., Proteins-Structure Function and 3/20/2017 Bioinformatics, 80(7), pp (2012) 9

10 Benchmark Results: QUARK vs. Rosetta Data set 51 small proteins with ( residues) 94 medium proteins with ( residues) Methods First (best in top five) cluster center model RMSD TM-score Rosetta 10.1 (8.5) (0.393) QUARK 9.1 (7.7) (0.441) Rosetta 13.0 (11.5) (0.346) QUARK 12.5 (10.7) (0.374) Xu et al., Proteins-Structure Function and Bioinformatics, 80(7), pp (2012) 3/20/

11 Benchmark Results: QUARK vs. Rosetta Red: Native Blue: Rosetta Green: QUARK Xu et al., Proteins- Structure Function and Bioinformatics, 80(7), pp (2012) 3/20/

12 QUARK in CASP Experiments CASP9 CASP10 CASP11 Groups Z Groups Z Groups Z QUARK 31.6 QUARK 17.1 QUARK 33.5 Multicon-Refine 22.4 TASSER-VMT 13.9 RBO_Aleph 29.6 Chunk-TASSER 20.7 Pcons-net 13.7 Multicom-con 21.4 RaptorX 19.8 PMS 11.7 RaptorX-FM 17.6 Baker-Rosetta 19.0 RaptorX-Roll 11.3 myprotein-me 15.9 Jiang_Assembly 14.7 HHpred-thread 10.9 TASSER-VMT 15.8 Gws 13.9 Multicom-clust 10.6 Baker-Rosetta 15.7 BioSerf 13.6 RBO-MBS 9.1 Seok-server 15.6 SAM-T08-server 12.7 MUFold_CRF 8.8 FUSION 15.5 Seok-server 12.6 Baker-Rosetta 8.1 nns 15.4 Here, Z-score (Z) represents the significance of the structure predictions by 3/20/2017 each group compared to the average performance 12

13 QUARK modeling of T0837-D1 (128 AA) in CASP 11 QUARK fragments RMSD ~ A Assessor s comment: T0837-D1_499_1 represents the FM model with 13 biggest improvement for PDB templates in CASP11 experiment

14 Why Zhang-Server performs better than QUARK in CASP experiments?? Models built by QUARK are compared with threading templates by LOMETS The templates are then re-ranked by their similarity to the QUARK models before they are subjected to the I-TASSER structure-assembly simulations. Zhang et al., Proteins, 84, pp (2015) 3/20/

15 Limitations in current methods Fold small proteins (<150 residues) Can only fold beta-protein with simple topology R0014 CASP10 3/20/

16 Contact maps in ab initio protein structure prediction Sequence-based contact map prediction can be useful for 3D structure folding of larger size proteins that have complicated topologies Incorrectly predicted contacts can be harmful to 3D structure construction. Contact prediction should have an accuracy of at least 22% to generate a positive effect to the ab initio structure prediction 3/20/

Basic information on contact maps Residues are in contact if the distance between C α or C β atoms of the residues is < 8 Å Contact classification: Short range: Sequence separation 6-11

17 Basic information on contact maps Residues are in contact if the distance between C α or C β atoms of the residues is < 8 Å Contact classification: Short range: Sequence separation 6-11 residues Medium range: Sequence separation residues Long range: Sequence separation >24 residues TTSQKHRDFVAEPGEKPVGSLAGIGEVLGKKLEERG 1 Short range Medium range Long range 3/20/

18 Programs for predicting contact maps Machine Learning: o BETACON o SVMcon o SVMSEQ Coevolution: o PSICOV o CCMpred o mfdca o Gremlin Meta: ostructch ometapsicov opconsc2, PconsC31 3/20/

19 NeBcon (Neural network and Bayesclassifier based contact prediction) 3/20/

20 Naïve Bayes Classifier (NBC) X ij = (X ij 1, X ij 2,, X ij m) is the confidence score for the ith and jth residues to be in contact as predicted by mth contact predictor. X ij m P C X ij = P 0 X ij = = P C P(X ij C) P(X ij ) Under the naïve assumption, the confidence scores from different contact predictors are independent from each other P C X ij = P C N m=1 P X ij m C P X ij N P C N m=1 P X ij m C P 0 m=1 P X m ij 0 + P 1 m=1 P X m ij 1 N N P 0 P X m ij 0 m=1 P 0 m=1 P X m ij 0 + P 1 m=1 P X m ij 1 N N 0 =in contact 1 =not in contact 3/20/

21 Accuracy Accuracy Contact prediction accuracy comparison Accuracy of the prediction: Acc = N corr /N T N corr = # of correctly predicted contacts in the contact map N T = # of predicted contacts in the contact map easy targets Top L/5 long range 48 hard targets Top L/5 long range 3/20/

22 Contact prediction accuracy comparison (all ranges) Methods Short (6-11) Medium (12-24) Long (>24) BETACON ( ) ( ) ( ) SVMSEQ ( ) ( ) ( ) SVMcon ( ) ( ) ( ) PSICOV ( ) ( ) ( ) CCMpred ( ) ( ) ( ) FreeContact ( ) ( ) ( ) STRUCTCH ( ) ( ) ( ) MetaPSICOV ( ) ( ) ( ) NeBcon /20/

23 Contact prediction accuracy comparison (long range) Average ACC of MetaPSICOV = Average ACC of NBC = P-value= 0.03 Average ACC of NeBcon= Average ACC of NBC = P-value= /20/2017 He et al., Bioinformatics (2017) 23

$fraction of the top-l contacts at ith cell, where L is the length of$ the protein H min = 0 All contacts are accumulated in one cell H max

24 Diversity of contact maps 100 H = p i log 2 p i i p i is the fraction of the top-l contacts at ith cell, where L is the length of the protein H min = 0 All contacts are accumulated in one cell H max =6.64 (=log 2 100) All contacts are evenly distributed when L>100 3/20/2017

25 Diversity of contact maps Methods Long All BETACON (8.4*10-16 ) (6.9*10-25 ) SVMSEQ (4.9*10-7 ) (5.6*10-13 ) SVMcon (1.5*10-16 ) (1.2*10-24 ) PSICOV (6.2*10-2 ) (1.23*10-2 ) CCMpred (6.9*10-9 ) (1.1*10-6 ) FreeContact (4.5*10-10 ) (5.0*10-6 ) STRUCTCH (2.6*10-8 ) (7.7*10-17 ) MetaPSICOV (4.0*10-5 ) (9.7*10-6 ) NeBcon (6.5*10-5 ) (3.3*10-9 ) Native /20/

26 Example: diversity of contact maps He et al., Bioinformatics (2017) 3/20/

27 C-QUARK: Contact map guided ab initio structure prediction NeBcon Knowledge-based potentials: 3/20/

28 C-QUARK in CASP 12 Groups Z C-QUARK 65.1 Baker-Rosetta 60.3 GOAL 49.9 RaptorX 44.2 ToyPred_ 40.4 Multicom-Novl 19.4 Seok-server 9.2 IntFOLD4 9.1 FFAS-3D 8.4 FALCON_TOPO 6.3 Here, Z-score (Z) represents the significance of the structure predictions by each group compared to the average performance 3/20/

29 GPCR-AIM: Ab initio GPCR structure prediction 3/20/

30 References Xu, D., and Zhang, Y., "Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field," Proteins-Structure Function and Bioinformatics, 80(7), pp (2012) Xu, D., and Zhang, Y., Toward optimal fragment generation for ab initio protein structure assembly," Proteins-Structure Function and Bioinformatics, 81(2), pp (2012) Zhang et al., "Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11," Proteins, 84, pp (2015) He, B., Mortuza, S.M., Shen, H., Wang, Y., Zhang, Y. NeBcon: Protein contact map prediction using neural network training coupled with naïve Bayes classifiers. Bioinformatics (2017) (In press) He, B., Mortuza, S.M., Wang, Y., Zhang, Y. NeBcon used to improve structure prediction. (2017) (In preparation) Wu, H., Zhang, C., Zhang, Y., Assemble atomic structure of G proteincoupled receptors from primary sequences. (2017) (In preparation) 3/20/

31 Thank You!! umich.edu/quark/ umich.edu/nebcon/ umich.edu/c-quark/ umich.edu/gpcr-aim/

proteins Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field

proteins Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field proteins STRUCTURE O FUNCTION O BIOINFORMATICS Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field Dong Xu1 and Yang Zhang1,2* 1 Department