Hidden Markov models that use predicted secondary structures for fold recognition

✍ Scribed by Jeanette Hargbo; Arne Elofsson

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 112 KB
• Volume: 36
• Category: Article
• ISSN: 0887-3585
• DOI: 10.1002/(sici)1097-0134(19990701)36:1<68::aid-prot6>3.0.co;2-1

✦ Synopsis

There are many proteins that share the same fold but have no clear sequence similarity. To predict the structure of these proteins, so called ''protein fold recognition methods'' have been developed. During the last few years, improvements of protein fold recognition methods have been achieved through the use of predicted secondary structures (Rice and Eisenberg, J Mol Biol 1997;267:1026-1038), as well as by using multiple sequence alignments in the form of hidden Markov models (HMM) (Karplus et al., Proteins Suppl 1997;1:134-139). To test the performance of different fold recognition methods, we have developed a rigorous benchmark where representatives for all proteins of known structure are matched against each other. Using this benchmark, we have compared the performance of automatically-created hidden Markov models with standard-sequence-search methods. Further, we combine the use of predicted secondary structures and multiple sequence alignments into a combined method that performs better than methods that do not use this combination of information. Using only single sequences, the correct fold of a protein was detected for 10% of the test cases in our benchmark. Including multiple sequence information increased this number to 16%, and when predicted secondary structure information was included as well, the fold was correctly identified in 20% of the cases. Moreover, if the correct secondary structure was used, 27% of the proteins could be correctly matched to a fold. For comparison, blast2, fasta, and ssearch identifies the fold correctly in 13-17% of the cases. Thus, standard pairwise sequence search methods perform almost as well as hidden Markov models in our benchmark. This is probably because the automatically-created multiple sequence alignments used in this study do not contain enough diversity and because the current generation of hidden Markov models do not perform very well when built from a few sequences.