181,489 research outputs found
Computational identification and analysis of protein short linear motifs
No full textShort linear motifs (SLiMs) in proteins can act as targets for proteolytic cleavage, sites of post-translational modification, determinants of sub-cellular localization, and mediators of protein-protein interactions. Computational discovery of SLiMs involves assembling a group of proteins postulated to share a potential motif, masking out residues less likely to contain such a motif, down-weighting shared motifs arising through common evolutionary descent, and calculation of statistical probabilities allowing for the multiple testing of all possible motifs. Much of the challenge for motif discovery lies in the assembly and masking of datasets of proteins likely to share motifs, since the motifs are typically short (between 3 and 10 amino acids in length), so that potential signals can be easily swamped by the noise of stochastically recurring motifs. Focusing on disordered regions of proteins, where SLiMs are predominantly found, and masking out non-conserved residues can reduce the level of noise but more work is required to improve the quality of high-throughput experimental datasets (e.g. of physical protein interactions) as input for computational discovery
GASP: gapped ancestral sequence prediction for proteins
No full textBackground: the prediction of ancestral protein sequences from multiple sequence alignments is useful for many bioinformatics analyses. Predicting ancestral sequences is not a simple procedure and relies on accurate alignments and phylogenies. Several algorithms exist based on Maximum Parsimony or Maximum Likelihood methods but many current implementations are unable to process residues with gaps, which may represent insertion/deletion (indel) events or sequence fragments.Results: here we present a new algorithm, GASP (Gapped Ancestral Sequence Prediction), for predicting ancestral sequences from phylogenetic trees and the corresponding multiple sequence alignments. Alignments may be of any size and contain gaps. GASP first assigns the positions of gaps in the phylogeny before using a likelihood-based approach centred on amino acid substitution matrices to assign ancestral amino acids. Important outgroup information is used by first working down from the tips of the tree to the root, using descendant data only to assign probabilities, and then working back up from the root to the tips using descendant and outgroup data to make predictions. GASP was tested on a number of simulated datasets based on real phylogenies. Prediction accuracy for ungapped data was similar to three alternative algorithms tested, with GASP performing better in some cases and worse in others. Adding simple insertions and deletions to the simulated data did not have a detrimental effect on GASP accuracy.Conclusions: GASP (Gapped Ancestral Sequence Prediction) will predict ancestral sequences from multiple protein alignments of any size. Although not as accurate in all cases as some of the more sophisticated maximum likelihood approaches, it can process a wide range of input phylogenies and will predict ancestral sequences for gapped and ungapped residues alik
Shields, C W, 408098
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/416650Surname: SHIELDS. Given Name(s) or Initials: C W. Military Service Number or Last Known Location: 408098. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 48459.238803
Item: [2016.0049.48911] "Shields, C W, 408098
Estimation and efficient computation of the true probability of recurrence of short linear protein sequence motifs in unrelated proteins.
No full textBackground: large datasets of protein interactions provide a rich resource for the discovery of Short Linear Motifs (SLiMs) that recur in unrelated proteins. However, existing methods for estimating the probability of motif recurrence may be biased by the size and composition of the search dataset, such that p-value estimates from different datasets, or from motifs containing different numbers of non-wildcard positions, are not strictly comparable. Here, we develop more exact methods and explore the potential biases of computationally efficient approximations. Results: a widely used heuristic for the calculation of motif over-representation approximates motif probability by assuming that all proteins have the same length and composition. We introduce pv, which calculates the probability exactly. Secondly, the recently introduced SLiMFinder statistic Sig, accounts for multiple testing (across all possible motifs) in motif discovery. However, it approximates the probability of all other possible motifs, occurring with a score of p or less, as being equal to p. Here, we show that the exhaustive calculation of the probability of all possible motif occurrences that are as rare or rarer than the motif of interest, Sig', may be carried out efficiently by grouping motifs of a common probability (i.e. those which have permuted orders of the same residues). Sig'v, which corrects both approximations, is shown to be uniformly distributed in a random dataset when searching for non-ambiguous motifs, indicating that it is a robust significance measure. Conclusions: a method is presented to compute exactly the true probability of a non-ambiguous short protein sequence motif, and the utility of an approximate approach for novel motif discovery across a large number of datasets is demonstrated
Surgeon J. Hayes Shields autograph letter to Surgeon C. C. Cox
No full textShield reports that he has received the receipt for medical supplies.https://library.udel.edu/static/purl.php?mss041
Letter from James Magoffin, Gordy, E. H., and V. C., Land Office, St. Stephens, Alabama, to Honorable J. A. Shields, Commissioner, Alabama, January 2, 1846
No full text
Shielding performances of finite extension shields against transient magnetic fields
No full textFinite extension shields are analyzed against transient magnetic fields produced by lightning phenomena. The shielding performances of finite width PEC shields are calculated by the FEM for eddy current problems and by the FDTD method for wave propagation problems. The analysis is also carried out for penetrable shields by imposing impedance network boundary conditions (INBCs) in the numerical methods. The results obtained for PEC and penetrable shields are compared against current line and plane wave field sources
SLiMFinder: a probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins
No full textBackground. Short linear motifs (SLiMs) in proteins are functional microdomains of fundamental importance in many
biological systems. SLiMs typically consist of a 3 to 10 amino acid stretch of the primary protein sequence, of which as few as
two sites may be important for activity, making identification of novel SLiMs extremely difficult. In particular, it can be very
difficult to distinguish a randomly recurring ‘‘motif’’ from a truly over-represented one. Incorporating ambiguous amino acid
positions and/or variable-length wildcard spacers between defined residues further complicates the matter. Methodology/
Principal Findings. In this paper we present two algorithms. SLiMBuild identifies convergently evolved, short motifs in
a dataset of proteins. Motifs are built by combining dimers into longer patterns, retaining only those motifs occurring in
a sufficient number of unrelated proteins. Motifs with fixed amino acid positions are identified and then combined to
incorporate amino acid ambiguity and variable-length wildcard spacers. The algorithm is computationally efficient compared
to alternatives, particularly when datasets include homologous proteins, and provides great flexibility in the nature of motifs
returned. The SLiMChance algorithm estimates the probability of returned motifs arising by chance, correcting for the size and
composition of the dataset, and assigns a significance value to each motif. These algorithms are implemented in a software
package, SLiMFinder. SLiMFinder default settings identify known SLiMs with 100% specificity, and have a low false discovery
rate on random test data. Conclusions/Significance. The efficiency of SLiMBuild and low false discovery rate of SLiMChance
make SLiMFinder highly suited to high throughput motif discovery and individual high quality analyses alike. Examples of such
analyses on real biological data, and how SLiMFinder results can help direct future discoveries, are provided. SLiMFinder is
freely available for download under a GNU license from http://bioinformatics.ucd.ie/shields/software/slimfinder/
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