From Precursor to Final Peptides: A Statistical Sequence-Based Approach to Predicting Prohormone Processing

Amanda B. Hummon; Norman P. Hummon; Rebecca W. Corbin; Lingjun Li; Ferdinand S. Vilim; Klaudiusz R. Weiss; Jonathan V Sweedler

doi:10.1021/pr034046d

From Precursor to Final Peptides: A Statistical Sequence-Based Approach to Predicting Prohormone Processing

Amanda B. Hummon, Norman P. Hummon, Rebecca W. Corbin, Lingjun Li, Ferdinand S. Vilim, Klaudiusz R. Weiss, Jonathan V Sweedler

Research output: Contribution to journal › Article › peer-review

Abstract

Predicting the final neuropeptide products from neuropeptides genes has been problematic because of the large number of enzymes responsible for their processing. The basic processing of 22 Aplysia californica prohormones representing 750 cleavage sites have been analyzed and statistically modeled using binary logistic regression analyses. Two models are presented that predict cleavage probabilities at basic residues based on prohormone sequence. The complex model has a correct classification rate of 97%, a sensitivity of 97%, and a specificity of 96% when tested on the Aplysia dataset.

Original language	English (US)
Pages (from-to)	650-656
Number of pages	7
Journal	Journal of Proteome Research
Volume	2
Issue number	6
DOIs	https://doi.org/10.1021/pr034046d
State	Published - Nov 2003

Keywords

MALDI MS
Neuropeptides
Prediction algorithms
Proteolytic processing

ASJC Scopus subject areas

Genetics
Biotechnology
Biochemistry

Online availability

10.1021/pr034046d

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title = "From Precursor to Final Peptides: A Statistical Sequence-Based Approach to Predicting Prohormone Processing",

abstract = "Predicting the final neuropeptide products from neuropeptides genes has been problematic because of the large number of enzymes responsible for their processing. The basic processing of 22 Aplysia californica prohormones representing 750 cleavage sites have been analyzed and statistically modeled using binary logistic regression analyses. Two models are presented that predict cleavage probabilities at basic residues based on prohormone sequence. The complex model has a correct classification rate of 97%, a sensitivity of 97%, and a specificity of 96% when tested on the Aplysia dataset.",

keywords = "MALDI MS, Neuropeptides, Prediction algorithms, Proteolytic processing",

author = "Hummon, {Amanda B.} and Hummon, {Norman P.} and Corbin, {Rebecca W.} and Lingjun Li and Vilim, {Ferdinand S.} and Weiss, {Klaudiusz R.} and Sweedler, {Jonathan V}",

year = "2003",

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AU - Hummon, Amanda B.

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AU - Corbin, Rebecca W.

AU - Li, Lingjun

AU - Vilim, Ferdinand S.

AU - Weiss, Klaudiusz R.

AU - Sweedler, Jonathan V

PY - 2003/11

Y1 - 2003/11

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AB - Predicting the final neuropeptide products from neuropeptides genes has been problematic because of the large number of enzymes responsible for their processing. The basic processing of 22 Aplysia californica prohormones representing 750 cleavage sites have been analyzed and statistically modeled using binary logistic regression analyses. Two models are presented that predict cleavage probabilities at basic residues based on prohormone sequence. The complex model has a correct classification rate of 97%, a sensitivity of 97%, and a specificity of 96% when tested on the Aplysia dataset.

KW - MALDI MS

KW - Neuropeptides

KW - Prediction algorithms

KW - Proteolytic processing

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From Precursor to Final Peptides: A Statistical Sequence-Based Approach to Predicting Prohormone Processing

Abstract

Keywords

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