GPU-based detection of protein cavities using Gaussian surfaces

BMC Bioinformatics

Table 2 Performance of benchmarking detection methods for holo proteins in terms of: (d) distance (FN) false negatives to PDBsum ground-truth cavity centers; (TP) true positives; (FP) false positives; (TN) true negatives; (S _v) sensitivity; (S _c) specificity; (a) accuracy; (r _d) ratio of detected ground-truth cavities; and (C _u) cumulative number of undetected ground-truth cavities

d	GaussianFinder	ConCavity	POCASA	SURFNET	PASS	GHECOM	Fpocket	3V	KVFinder
d∈[0.0,1.0]	16081	3133	5234	3668	11738	10438	14063	9174	12493
d∈]1.0,2.0]	366	239	338	2574	2100	571	432	703	719
d∈]2.0,3.0]	410	296	419	1063	281	789	406	811	813
d∈]3.0,4.0]	334	488	609	925	360	932	504	349	418
TP	17191	4156	6600	8230	14479	12730	15405	12049	14443
FP	2460	2155	2083	1806	634	1278	2151	1564	1941
TN	3231	916	2673	1559	4080	4968	3423	2476	2725
FN	440	362	214	227	207	658	391	511	553
S _v	0.975	0.919	0.969	0.973	0.986	0.951	0.975	0.959	0.963
S _c	0.568	0.298	0.562	0.463	0.866	0.795	0.614	0.612	0.584
a	0.876	0.668	0.801	0.828	0.957	0.901	0.881	0.875	0.873
r _d	0.963	0.233	0.369	0.461	0.811	0.713	0.863	0.675	0.809
C _u	659	13694	11250	9620	3371	5120	2445	5801	3407

ISSN: 1471-2105