MOTIVATION: The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity measures. However, their rigid matching policies limit them to the identification of perfect MCSs. To eliminate this restriction, we introduce a new mismatch tolerant search method for identifying flexible MCSs (FMCSs) containing a user-definable number of atom and/or bond mismatches. RESULTS: The fmcsR package provides an R interface, with the time-consuming steps of the FMCS algorithm implemented in C++. It includes utilities for pairwise compound comparisons, structure similarity searching, clustering and visualization of MCSs. In comparison with an existing MCS tool, fmcsR shows better time performance over a wide range of compound sizes. When mismatching of atoms or bonds is turned on, the compute times increase as expected, and the resulting FMCSs are often substantially larger than their strict MCS counterparts. Based on extensive virtual screening (VS) tests, the flexible matching feature enhances the enrichment of active structures at the top of MCS-based similarity search results. With respect to overall and early enrichment performance, FMCS outperforms most of the seven other VS methods considered in these tests. AVAILABILITY: fmcsR is freely available for all common operating systems from the Bioconductor site (http://www.bioconductor.org/packages/devel/bioc/html/fmcsR.html). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins. RESULTS: To identify these conserved motifs efficiently, we propose a method for extracting the most information-rich regions in protein families from their profile HMMs. The method was used here to predict a comprehensive set of sub-HMMs from the Pfam domain database. Cross-validations with the PROSITE and CSA databases confirmed the efficiency of the method in predicting most of the known functionally relevant motifs and residues. At the same time, 46,768 novel conserved regions could be predicted. The data set also allowed us to link at least 461 Pfam domains of known and unknown function by their common sub-HMMs. Finally, the sub-HMM method showed very promising results as an alternative search method for identifying proteins that share only short sequence similarities. CONCLUSIONS: Sub-HMMs extend the application spectrum of profile HMMs to motif discovery. Their most interesting utility is the identification of the functionally relevant residues in proteins of known and unknown function. Additionally, sub-HMMs can be used for highly localized sequence similarity searches that focus on shorter conserved features rather than entire domains or global similarities. The motif data generated by this study is a valuable knowledge resource for characterizing protein functions in the future.