Quantitative N- or C-Terminal Labelling of Proteins with Unactivated Peptides by Use of Sortases and a d-Aminopeptidase

Quantitative and selective labelling of proteins is widely used in both academic and industrial laboratories, and catalytic labelling of proteins using transpeptidases, such as sortases, has proved to be a popular strategy for such selective modification. A major challenge for this class of enzymes is that the majority of procedures require an excess of the labelling reagent or, alternatively, activated substrates rather than simple commercially sourced peptides. We report the use of a coupled enzyme strategy which enables quantitative N- and C-terminal labelling of proteins using unactivated labelling peptides. The use of an aminopeptidase in conjunction with a transpeptidase allows sequence-specific degradation of the peptide by-product, shifting the equilibrium to favor product formation, which greatly enhances the reaction efficiency. Subsequent optimisation of the reaction allows N-terminal labelling of proteins using essentially equimolar ratios of peptide label to protein and C-terminal labelling with only a small excess. Minimizing the amount of substrate required for quantitative labelling has the potential to improve industrial processes and facilitate the use of transpeptidation as a method for protein labelling

Data-driven response generation in social media

We present a data-driven approach to generating responses to Twitter status posts, based on phrase-based Statistical Machine Translation. We find that mapping conversational stimuli onto responses is more difficult than translating between languages, due to the wider range of possible responses, the larger fraction of unaligned words/phrases, and the presence of large phrase pairs whose alignment cannot be further decomposed. After addressing these challenges, we compare approaches based on SMT and Information Retrieval in a human evaluation. We show that SMT outperforms IR on this task, and its output is preferred over actual human responses in 15% of cases. As far as we are aware, this is the first work to investigate the use of phrase-based SMT to directly translate a linguistic stimulus into an appropriate response. \ua9 2011 Association for Computational Linguistics.Peer reviewed: YesNRC publication: Ye

Quantitative N‐ or C‐Terminal Labelling of Proteins with Unactivated Peptides by Use of Sortases and a d‐Aminopeptidase

Quantitative and selective labelling of proteins is widely used in both academic and industrial laboratories, and catalytic labelling of proteins using transpeptidases such as sortases has proved to be a popular strategy for selective modification. A major challenge for this class of enzymes is that the majority of procedures require an excess of the labelling reagent or, alternatively, activated substrates rather than simple commercially-sourced peptides. We report the use of a coupled enzyme strategy which enables quantitative N- and C-terminal labelling of proteins using un-activated labelling peptides. The use of an aminopeptidase in conjunction with a transpeptidase allows sequence-specific degradation of the peptide by-product, shifting the equilibriums to favor product formation which greatly enhances the reaction efficiency. Subsequent optimization of the reaction allows N‑terminal labelling of proteins using essentially equimolar ratios of peptide label to protein and C-terminal labelling with only a small excess. Minimizing the amount of substrate required for quantitative labelling has the potential to improve industrial processes and facilitate the use of transpeptidation as a method for protein labelling

In pursuit of QALY weights for relatives: empirical estimates in relatives caring for older people

Economics, Relatives, Caregivers, QALY weight,