Optimal Hidden Markov Models for All Sequences of Known Structure
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چکیده
Hidden Markov Models (HMMs) are probably the most powerful tool for the detection of protein sequence homology [4]. Maximization of their capabilities and biological usefulness requires the correct interpretation of their scores, and sufficient coverage of the sequence variations that exist in different protein families. Using information available from the SCOP database we investigated optimal measures for these two aspects of HMMs [5]. The SCOP database is a hierarchical classification of the domains that are found in all proteins of known structure. Those evolutionary relationships of proteins which cannot be detected from sequence can usually be inferred from structure. These are listed in the SCOP database where superfamilies contain sets of proteins with evolutionary relationships derived from both sequence and structure. Searches performed using a set of HMMs produced for each member of a superfamily of homologous sequences worked more effectively than a single HMM built from an alignment of those sequences. Using the SAM-Target99 procedure (see below), HMM models were made for all PDB95 sequences, that is the 4591 sequences of proteins of known structure which have sequence identities of 95% or less. These sequences in the context of their SCOP classification form the basis of the results described below.
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تاریخ انتشار 2000