HMS-S-S: a tool for the identification of sulfur metabolism-related genes and analysis of operon structures in genome and metagenome assemblies

Sulfur compounds are used in a variety of biological processes including respiration and photosynthesis. Sulfide and sulfur compounds of intermediary oxidation state can serve as electron donors for lithotrophic growth while sulfate, thiosulfate and sulfur are used as electron acceptors in anaerobic respiration. The biochemistry underlying the manifold transformations of inorganic sulfur compounds occurring in sulfur metabolizing prokaryotes is astonishingly complex and knowledge about it has immensely increased over the last years. The advent of next-generation sequencing approaches as well as the significant increase of data availability in public databases has driven focus of environmental microbiology to probing the metabolic capacity of microbial communities by analysis of this sequence data. To facilitate these analyses, we created HMS-S-S, a comprehensive equivalogous hidden Markov model (HMM)-supported tool. Protein sequences related to sulfur compound oxidation, reduction, transport and intracellular transfer are efficiently detected and related enzymes involved in dissimilatory sulfur oxidation as opposed to sulfur compound reduction can be confidently distinguished. HMM search results are coupled to corresponding genes, which allows analysis of co-occurrence, synteny and genomic neighborhood. The HMMs were validated on an annotated test dataset and by cross-validation. We also proved its performance by exploring meta-assembled genomes isolated from samples from environments with active sulfur cycling, including members of the cable bacteria, novel Acidobacteria and assemblies from a sulfur-rich glacier, and were able to replicate and extend previous reports.

Evidence for the preferential reuse of sub-domain motifs in primordial protein folds

A comparison of protein backbones makes clear that not more than approximately 1400 different folds exist, each specifying the three-dimensional topology of a protein domain. Large proteins are composed of specific domain combinations and many domains can accommodate different functions. These findings confirm that the reuse of domains is key for the evolution of multi-domain proteins. If reuse was also the driving force for domain evolution, ancestral fragments of sub-domain size exist that are shared between domains possessing significantly different topologies. For the fully automated detection of putatively ancestral motifs, we developed the algorithm Fragstatt that compares proteins pairwise to identify fragments, that is, instantiations of the same motif. To reach maximal sensitivity, Fragstatt compares sequences by means of cascaded alignments of profile Hidden Markov Models. If the fragment sequences are sufficiently similar, the program determines and scores the structural concordance of the fragments. By analyzing a comprehensive set of proteins from the CATH database, Fragstatt identified 12 532 partially overlapping and structurally similar motifs that clustered to 134 unique motifs. The dissemination of these motifs is limited: We found only two domain topologies that contain two different motifs and generally, these motifs occur in not more than 18% of the CATH topologies. Interestingly, motifs are enriched in topologies that are considered ancestral. Thus, our findings suggest that the reuse of sub-domain sized fragments was relevant in early phases of protein evolution and became less important later on.

ProtPCV: A Fixed Dimensional Numerical Representation of Protein Sequence to Significantly Reduce Sequence Search Time

Protein sequence is a wealth of experimental information which is yet to be exploited to extract information on protein homologues. Consequently, it is observed from publications that dynamic programming, heuristics and HMM profile-based alignment techniques along with the alignment free techniques do not directly utilize ordered profile of physicochemical properties of a protein to identify its homologue. Also, it is found that these works lack crucial bench-marking or validation in absence of which their incorporation in search engines may appears to be questionable. In this direction this research approach offers fixed dimensional numerical representation of protein sequences extending the concept of periodicity count value of nucleotide types (2017) to accommodate Euclidean distance as direct similarity measure between two proteins. Instead of bench-marking with BLAST and PSI-BLAST only, this new similarity measure was also compared with Needleman-Wunsch and Smith-Waterman. For enhancing the strength of comparison, this work for the first time introduces two novel benchmarking methods based on correlation of "similarity scores" and "proximity of ranked outputs from a standard sequence alignment method" between all possible pairs of search techniques including the new one presented in this paper. It is found that the novel and unique numerical representation of a protein can reduce computational complexity of protein sequence search to the tune of O(log(n)). It may also help implementation of various other similarity-based operation possible, such as clustering, phylogenetic analysis and classification of proteins on the basis of the properties used to build this numerical representation of protein.