clustq: Efficient Protein Decoy Clustering Using Superposition-free Weighted Internal Distance Comparisons
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1 clustq: Efficient Protein Decoy Clustering Using Superposition-free Weighted Internal Distance Comparisons Debswapna Auburn University ACM-BCB August 31, 2018
2 What is protein decoy clustering? Clustering groups similar set of items together in clusters folding simulation Identify most populated conformational states 2
3 3
4 Clustering and protein folding landscape Energy Entropy N There are more ways to be incorrect than to be correct 4
5 Clustering based nativeness score? potential near-native basin highest average pairwise similarity O(n 2 ) pairwise comparisons to compute the average pairwise similarity score 5
6 Pairwise comparison via structural alignment structural alignement optimal structural alignment is an optimization problem similarity score TMscore, GDT, O(n 2 ) alignment based comparisons is computationally expensive 6
7 Q-score: Alignment-free comparison {rij 1 }: internal distance matrix protein 1 {rij 2 }: internal distance matrix protein 2 Qij = exp [-(rij 1 - rij 2 )^2 ] Qij ~ 1 for very good similarity Qij ~ 0 for poor similarity 7 Sussman and coworkers, 2009
8 WQ-score: Weighted internal distance comparison Qnarrow: i - j < 6 Qshort: i - j 6 and i - j < 12 Qmedium: i - j 12 and i - j < 24 sequence separations are inspired from protein contact map prediction Qlong: i - j 24 WQ-score = (1 x Qnarrow + 2 x Qshort + 4 x Qmedium + 8 x Qlong) / 15 long range interactions carry more information about protein fold 8
9 clustq: What s conceptually new? rapid clustering based consensus scoring using alignment-free average pairwise WQ-score 9
10 Results 1/4: WQ-score vs. alignment-based scores comparisons with popular alignment-based scores datsets measures Modeller set (20 proteins) Rosetta set (58 proteins) TMscore GDT-TS Pearsons correlation Spearman correlation 10
11 Modeller set very well correlated (> 0.97) with alignment-based scores 11
12 Rosetta set well correlated (~0.8) with alignment-based scores 12
13 Results 2/4: clustq vs. alignment-based consensus scoring clustering based consensus scoring with alignment-based scores "stage 2" datasets CASP11 (80 proteins) CASP12 (40 proteins) TMscore GDT-TS measures Pearsons correlation Spearman correlation 13
14 CASP11 "stage 2" set better than TMscore based clustering comparable to GDT-TS based clustering 14
15 CASP12 "stage 2" set comparable to alignment-based consensus scoring 15
16 Computational Efficiency of clustq vs. TMscore CASP11 CASP12 16
17 clustq is 5.2 times faster than alignment based consensus scoring 17
18 Results 3/4: clustq vs. top consensus based approaches top consensus scoring full datasets CASP11 (80 proteins) CASP12 (40 proteins) Pcons APOLLO measures Pearsons correlation Spearman correlation 18
19 Results 3/4: clustq vs. top consensus based approaches Pearson Spearman CASP11 CASP12 CASP11 CASP Avg. Pearson correlation w.r.t. GDT-TS Avg. Pearson correlation w.r.t. GDT-TS clustq Pcons APOLLO 0.5 clustq Pcons APOLLO consistently better performance compared to top methods 19
20 can clustq score estimate target difficulty? 20
21 Results 4/4: Computational Efficiency of clustq vs. TMscore CASP11 CASP12 21
22 if clustq_score > 0.4: easy target (homology-based) else: hard target (homology-free) 22
23 clustq online 23
24 Conclusions alignment-free weighted internal distance comparison metric well correlated with alignment-based metrics ultra-fast clustering based consensus scoring comparable or better performance could be employed for estimating target difficulty freely available to the community 24
25 Acknowledgements Rahul Alapati Auburn University 25
26 clustq 26
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