OMAmer: tree-driven and alignment-free protein assignment to subfamilies outperforms closest sequence approaches

Assigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive.Here, we first show that in multiple animal datasets, 19 to 68% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using subfamily-informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND. OMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer.

Complete protein assignment from sets of spectra recorded overnight

A flexible and scalable approach for protein NMR is introduced that builds on rapid data collection via projection spectroscopy and analysis of the spectral input data via joint decomposition. Input data may originate from various types of spectra, depending on the ultimate goal: these may result from experiments based on triple-resonance pulse sequences, or on TOCSY or NOESY sequences, or mixtures thereof. Flexible refers to the free choice of spectra for the joint decompositions depending on the purpose: assignments, structure, dynamics, interactions. Scalable means that the approach is open to the addition of similar or different experiments, e.g. larger proteins may require a wider selection of triple-resonance based experiments. Central to the proposed approach is the mutual support among the different spectra during the spectral analysis: for example, sparser triple-resonance spectra may help decomposing (separating) spin systems in a TOCSY or identifying unique NOEs. In the example presented, backbone plus side chain assignments of ubiquitin were obtained from the combination of either two or three of the following projection experiments: a 4D HCCCONH, a 4D HNCACO and a 3D HNCACB. In all cases, TOCSY data (4D HCCCONH) proved crucial not only for the side chain assignments, but also for the sequential assignment. Even when total recording time was reduced to about 10 h, nearly complete assignments were obtained, with very few missing assignments and even fewer differences to a reference.

CLIPPER: an add-on to the Trans-Proteomic Pipeline for the automated analysis of TAILS N-terminomics data

Data analysis in proteomics is complex and with the extra challenges involved in the interpretation of data from N-terminomics experiments, this can be daunting. Therefore, we have devised a rational pipeline of steps to approach N-terminomics data analysis in a statistically-based and valid manner. We have automated these steps in CLIPPER, an add-on to the Trans-Proteomic Pipeline (TPP). Applying CLIPPER to the analysis of N-terminomics data generated by terminal amine isotopic labeling of substrates (TAILS) enables high confidence peptide to protein assignment, protein N-terminal characterization and annotation, and for protease analysis readily allows protease substrate discovery with high confidence.

Identification of an Iron-Regulated, Hemin-Binding Outer Membrane Protein in Sinorhizobium meliloti

ABSTRACT Rhizobia are soil bacteria that are able to establish symbiotic associations with leguminous hosts. In iron-limited environments these bacteria can use iron present in heme or heme compounds (hemoglobin, leghemoglobin). Here we report the presence in Sinorhizobium meliloti of an iron-regulated outer membrane protein that is able to bind hemin but not hemoglobin. Protein assignment was done by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Tryptic peptides correlated with the mass measurements obtained accounted for 54% of the translated sequence of a putative heme receptor gene present in the chromosome of S. meliloti 1021. The results which we obtained suggest that this protein (designated ShmR for S inorhizobium heme receptor) is involved in high-affinity heme-mediated iron transport.