There have been numerous attempts to identify PKS in environmental samples using non-computational methods (e.g. [19]). Here, we present a computational approach based on Hidden-Markov-Model (HMM) sequence searches (as done in other PKS focused studies like [20]) followed by the construction of maximum-likelihood trees. This allows us to screen for multi-domain proteins and to estimate the potential of the different environments to serve as a source of PKS I sequences. Although the discrimination of type I PKS from type II PKS and type II FAS is simple, due to the large evolutionary distance [12] and PKS III are also a clearly separable group, a unique PKS I identification remains challenging. Reasons among others are the paralogy of type I PKS with type I fatty acid synthases [12] and with other enzymes and the fast evolution of PKS I. As PKS I proteins can be very large, it is unlikely that complete proteins are found in the highly fragmented shotgun metagenomic sequences. However, their multi-domain, repeated structure provides multiple instances of evidence to find real PKS I orthologs when searching independently with HMM of each of the eight domains introduced above. Our approach included the creation and use of domain specific HMMs to find members of the type I PKS domain in six published metagenomic data sets - Minnesota farm soil (MSF) [21], Sargasso Sea (SGS) [22], human gut (HGUT) [23], acid mine drainage (AMD) [24], enhanced biological phosphorus removal sludges (EBPRS) [25] and whale falls (bones from sunken whales) (WLF) [21]. We used the UniRef database [26] as an reference set by treating it as another sample to be able to identify biases and the status of PKS I annotation. In contrast to most other studies that cover computational PKS analysis we did not only focus on AT and KS domains but took all eight domains into account. The results of the searches were the basis for the construction of maximum- likelihood trees which allowed the more precise classification of the HMM hits into type I PKS and non-PKS I members. Results Extracting PKS I candidate sequences using Hidden Markov Models From 926 annotated type I PKS domain sequences in the PKSDB dataset [27], we generated multiple alignments and constructed eight Hidden Markov Models (one for each domain) that were searched against 6,613,204 predicted proteins in six metagenomics samples and UniRef (for details see methods). In total 22,106 candidate sequences of the eight PKS I domains were retrieved and analyzed. They range from 45 MT domain sequences to 4355 sequences of the KS domain type (for individual datasets see Table S1). For most of the domains the UniRef set has the highest total and relative (compared to the total number of analyzed proteins) number of candidate type I PKS. Refining potential PKS I sequences using maximum likelihood trees Although we did not find type II PKS sequences, due to the similarity of PKS I to FAS I and other enzymes, HMMs alone were not sufficient to discriminate PKS I proteins and related enzymes. Therefore, we applied a phylogenetic approach [28] which allowed the subsequent characterization of type I PKS subgroups. In agreement with previous knowledge the trees of the AT, DH, ER, KR, KS and PP domains show in general a consistent phylogenetic profile and contain PKS I and non-PKS I taxa (see Fig. 1 as an example, all other trees can be found in Methods S1 and S2). The main fraction of leaves in the PKS I branches is contributed by the Actinobacteria and clusters mostly together (see Table S2). Members of the Proteobacteria and other bacteria PKS I Screen in Metagenomes phyla occur in mixed groups. The fungal sequences form in most of the trees one or two groups within the PKS I branch and are closely located to sequences of other eukaryotes like Dictyostelium and animals. It was previously described that most of these animal proteins are FAS I members which are phylogenetically related to the fungal type I PKS [12,16] and also the occurrence of PKS-like sequences in animal genomes (e.g. in sea urchin for the production of pigments) has been reported [10]. Not all domains perform equally in identifying PKS I members. For example, in the TE domain tree two clades are dominated by PKS I sequences but a clear discrimination between PKS I and non-PKS I members cannot be made for the rest of the tree. For example, the MT domain tree contains only a few members as the domain occurs quite rarely in type PKS I; also due to the short length of the PP domain the results in this tree are less resolved than those of the other seven domains. The non-PKS I branches are large in some trees. In particular, in AT, KS and TE domain trees many unspecified acyltransferases, ketoacyl synthases and thioesterases respectively, were apparently not filtered out by the HMM searches. In the DH domain tree the non-PKS I sequences are predominately annotated as FAS members while ER and KR domain HMM searches seems to attract non- specified dehydrogenases and other oxidoreductases. The non-PKS I PP domain members were mainly adenylate amino acids or nonribosomal peptide synthetases (NRPS). Quality analysis of the tree-based approach and HMM searches The enormous computational requirements of the tree recon- structions made bootstrap analyses infeasible. However, the fragmented environmental sequences could strongly influence the quality and significance of the branches. We thus compared the trees with reference trees without metagenomic sequences and randomly created trees with the same amount of taxa. The Robinson-Foulds distances [29] between the test trees and the references trees were in general much smaller than the distances to random trees (see Fig. S2, Table S3 and Methods S3). Also, the log likelihood of the reference tress and trees with metagenomics samples show a much better fit to the sequence alignments and are much more similar to each other than to trees with random topologies (see Methods S3 and S4). This implies that the trees are a good representation of the phylogenetic signal in the dataset and that their topologies are not overly influenced by the inclusion of the metagenomic sequences. To support the tree-based annotation of the metagenomics sequences, the placements of all manually annotated PKS I from PKSDB were checked. They should only be found in branches of the trees that are marked as PKS I containing branches. With exception of the TE domain set which has three PKSDB sequences that are located in non-PKS I branches (see Methods S5) all sequences are placed as expected in PKS I branches. Using the trees for classification, it became apparent that the HMM bit score values are not a sufficient criterion for discriminating the type I PKS from the non-PKS I sequences. To quantify this, sequences of the HMM searches were grouped by their tree based annotation (implying that this is close to the true function). The bit score distributions of these groups were compared domain-wise and plotted as box plot (Fig. 2 for the AT domain, Fig. S1 for all domains). All domains have a higher median value for the PKS I than the non-PKS I. But for most of the domains there is a large overlap of the bit score value between these groups. Especially the many outliers with low bit scores in the type I PKS group coming from metagenomic proteins fall in the inter-quartile range of the non-PKS I group. PLoS ONE | www.plosone.org 2 October 2008 | Volume 3 | Issue 10 | e3515