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Table 2 The performance of the global quality predictions of our three tools and the other four methods in terms of average correlation, overall correlation, average real GDT-TS score of top 1 models ranked by each method, and average loss of top 1 models ranked by each method, evaluated on 84 CASP9 single-domain targets

From: SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

 

Avg. correlation

Over. correlation

Avg. top 1

Avg. loss

Basic (85 targets)

0.737

0.737

0.588

0.082

Profile

0.708

0.658

0.589

0.080

Profile+SOV

0.696

0.681

0.594

0.075

ModelEvaluator

0.636

0.767

0.597

0.073

ProQ

0.494

0.707

0.563

0.110

ProQ2

0.662

0.787

0.607

0.066

QMEAN

0.733

0.803

0.594

0.078

  1. Basic, profile, and profile + SOV are the three single-model local QA tools (SMOQ) presented in this manuscript.
  2. The other four QA predictors are ModelEvaluator (predictor name in CASP9: MULTICOM-NOVEL), ProQ, ProQ2, and QMEAN. Top 3 QA predictors’ performances according to each metric were bolded.
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BMC Bioinformatics

ISSN: 1471-2105

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