  Motivation: Profile hidden Markov Models (pHMMs) are a popular and very
useful tool in the detection of the remote homologue protein families.
Unfortunately, their performance is not always satisfactory when proteins are
in the 'twilight zone'. We present HMMER-STRUCT, a model construction algorithm
and tool that tries to improve pHMM performance by using structural information
while training pHMMs. As a first step, HMMER-STRUCT constructs a set of pHMMs.
Each pHMM is constructed by weighting each residue in an aligned protein
according to a specific structural property of the residue. Properties used
were primary, secondary and tertiary structures, accessibility and packing.
HMMER-STRUCT then prioritizes the results by voting. Results: We used the SCOP
database to perform our experiments. Throughout, we apply leave-one-family-out
cross-validation over protein superfamilies. First, we used the MAMMOTH-mult
structural aligner to align the training set proteins. Then, we performed two
sets of experiments. In a first experiment, we compared structure weighted
models against standard pHMMs and against each other. In a second experiment,
we compared the voting model against individual pHMMs. We compare method
performance through ROC curves and through Precision/Recall curves, and assess
significance through the paired two tailed t-test. Our results show significant
performance improvements of all structurally weighted models over default
HMMER, and a significant improvement in sensitivity of the combined models over
both the original model and the structurally weighted models.
