Covariance NMR Processing and Analysis for Protein Assignment

Author(s):  
Bradley J. Harden ◽  
Dominique P. Frueh
Keyword(s):  
2003 ◽  
pp. 29-52 ◽  
Author(s):  
Brian Whitehead ◽  
C. Jeremy Craven ◽  
Jonathan P. Waltho

2007 ◽  
Vol 119 (7) ◽  
pp. 1097-1100 ◽  
Author(s):  
Guido Pintacuda ◽  
Nicolas Giraud ◽  
Roberta Pierattelli ◽  
Anja Böckmann ◽  
Ivano Bertini ◽  
...  

2020 ◽  
Author(s):  
Victor Rossier ◽  
Alex Warwick Vesztrocy ◽  
Marc Robinson-Rechavi ◽  
Christophe Dessimoz

AbstractAssigning 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.


2002 ◽  
Vol 68 (12) ◽  
pp. 5877-5881 ◽  
Author(s):  
Federico Battistoni ◽  
Raúl Platero ◽  
Rosario Duran ◽  
Carlos Cerveñansky ◽  
Julio Battistoni ◽  
...  

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.


2012 ◽  
Vol 393 (12) ◽  
pp. 1477-1483 ◽  
Author(s):  
Ulrich auf dem Keller ◽  
Christopher M. Overall

Abstract 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.


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