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A simple approach for the sensitive detection of distant relationships among protein families and for sequence--structure alignment via comparison of hidden Markov models based on their quasi-consensus sequences is presented. Using a previously published benchmark dataset, the approach is demonstrated to give better homology detection and yield alignments with improved accuracy in comparison to an existing state-of-the-art dynamic programming profile--profile comparison method. This method also runs significantly faster and is therefore suitable for a server covering the rapidly increasing structure database. A server based on this method is available at http://liao.cis.udel.edu/website/servers/modmod Contact:roland.dunbrack@fccc.edu; lliao@mail.eecis.udel.edu

Motivation: Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins. Results: We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84% for topology and 89% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for ∼20--30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans. Availability: http://liao.cis.udel.edu/website/servers/TMMOD/ Contact: lliao@cis.udel.edu

Summary: A public server for evaluating the accuracy of protein sequence alignment methods is presented. CASA is an implementation of the alignment accuracy benchmark presented by Sauder et al. (Proteins, 40, 6–22, 2000). The benchmark currently contains 39321 pairwise protein structure alignments produced with the CE program from SCOP domain definitions. The server produces graphical and tabular comparisons of the accuracy of a user’s input sequence alignments with other commonly used programs, such as BLAST, PSI-BLAST, Clustal W, and SAM-T99. Availability: The server is located at http://capb.dbi.udel.edu/casa. Contact: RL_ Dunbrack@fccc.edu * To whom correspondence should be addressed.