Peroxisomes are small subcellular compartments that utilize proteins manufactured in the cytoplas... more Peroxisomes are small subcellular compartments that utilize proteins manufactured in the cytoplasm. Proteins use one of two peroxisomal import pathways. This paper presents a simple evolutionary search for a motif that describes the signal used by one of the two pathways: PTS2. The evolved motif has a discriminative accuracy exceeding previously manually curated motifs and can be used to screen genomic data for putative peroxisomal proteins.
Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment.... more Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.
Peroxisomes are small subcellular compartments that utilize proteins manufactured in the cytoplas... more Peroxisomes are small subcellular compartments that utilize proteins manufactured in the cytoplasm. Proteins use one of two peroxisomal import pathways. This paper presents a simple evolutionary search for a motif that describes the signal used by one of the two pathways: PTS2. The evolved motif has a discriminative accuracy exceeding previously manually curated motifs and can be used to screen genomic data for putative peroxisomal proteins.
Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment.... more Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.
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Papers by John Hawkins