A study of structural properties on profiles HMMs

8002 ceD 11 ]IA.sc[ 2v0102.4070:viXra BIOINFORMATICS Vol.

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8002 ceD 11 ]IA.sc[ 2v0102.4070:viXra BIOINFORMATICS Vol. 00 no. 00 2005 Pages 1–7 A study of structural properties on profile HMMs a∗ b c Juliana S Bernardes , Alberto M. R. Da´vila , V´ıtor Santos Costa , Gerson a Zaverucha a b COPPE Engenharia de Sistemas e Computac¸a˜o, UFRJ, Rio de Janeiro, Instituto Oswaldo Cruz, c Fiocruz, Rio de Janeiro, Brazil, CRACS and DCC-FCUP, Universidade do Porto, Portugal ABSTRACT basedonsequence similaritysearch(Gough et al.,2001;Park et al., Motivation: Profile hidden Markov Models (pHMMs) are a popu- 1998). lar and very useful tool in the detection of the remote homologue Profile Hidden Markov Models are probabilistic models that protein families. Unfortunately, their performance is not always are often used to represent groups of homolog sequences. These satisfactory when proteins are in the “twilight zone”. We present models have been a key tool in protein annotation, and are highly HMMER-STRUCT, a model construction algorithm and tool that tries effective for scoring similar sequences. Unfortunately, the perfor- to improve pHMM performance by using structural information while mance of pHMMs degrades for sequences in the twilight zone, training pHMMs. As a first step, HMMER-STRUCT constructs a set that is, for homologue sequences with low identity (below 30%). of pHMMs. Each pHMM is constructed by weighting each residue in This limitation has motivated a number of different approaches to an aligned protein according to a specific structural property of the increase pHMM performance. Proposals include new scoring func- residue. Properties used were primary, secondary and tertiary struc- tions, new null models (Karplus et al., 2005) and prior probability tures, accessibility and packing. HMMER-STRUCT then prioritizes (Brown et al., 1993). Researchers have also combined other infor- the results by voting. mationwithpHMMs: T-HMM(Qian et al.,2004)usesphylogenetic Results: We used the SCOP database to perform our experiments. information; HMM-STR (Bystroff et al., 2000), combines pHMMs Throughout, we apply leave-one-family-out cross-validation over pro- and support vector machines (Scho¨lkopf et al., 1999). tein superfamilies. First, we used the MAMMOTH-mult structural The observation that homologue proteins tend to preserve struc- aligner to align the training set proteins. Then, we performed two ture suggests that structural information should be extremely rele- sets of experiments. In a first experiment, we compared structure vant in detecting homologues. In fact, it has been shown that weighted models against standard pHMMs and against each other. pHMMs trained with multiple sequence alignments based on prote- In a second experiment, we compared the voting model against ins structural alignment can have better performance than pHMMs individual pHMMs. We compare method performance through ROC based on state-of-the art aligners that apply primary sequence curves and through Precision/Recall curves, and assess si

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