Literature DB >> 7858149

"Simulated molecular evolution" or computer-generated artifacts?

F Darius, R Rojas.   

Abstract

1. The authors define a function with value 1 for the positive examples and 0 for the negative ones. They fit a continuous function but do not deal at all with the error margin of the fit, which is almost as large as the function values they compute. 2. The term "quality" for the value of the fitted function gives the impression that some biological significance is associated with values of the fitted function strictly between 0 and 1, but there is no justification for this kind of interpretation and finding the point where the fit achieves its maximum does not make sense. 3. By neglecting the error margin the authors try to optimize the fitted function using differences in the second, third, fourth, and even fifth decimal place which have no statistical significance. 4. Even if such a fit could profit from more data points, the authors should first prove that the region of interest has some kind of smoothness, that is, that a continuous fit makes any sense at all. 5. "Simulated molecular evolution" is a misnomer. We are dealing here with random search. Since the margin of error is so large, the fitted function does not provide statistically significant information about the points in search space where strings with cleavage sites could be found. This implies that the method is a highly unreliable stochastic search in the space of strings, even if the neural network is capable of learning some simple correlations. 6. Classical statistical methods are for these kind of problems with so few data points clearly superior to the neural networks used as a "black box" by the authors, which in the way they are structured provide a model with an error margin as large as the numbers being computed.7. And finally, even if someone would provide us with a function which separates strings with cleavage sites from strings without them perfectly, so-called simulated molecular evolution would not be better than random selection.Since a perfect fit would only produce exactly ones or zeros,starting a search in a region of space where all strings in the neighborhood get the value zero would not provide any kind of directional information for new iterations. We would just skip from one point to the other in a typical random walk manner.

Mesh:

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Year:  1994        PMID: 7858149      PMCID: PMC1225587          DOI: 10.1016/S0006-3495(94)80695-2

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  3 in total

1.  The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site.

Authors:  G Schneider; P Wrede
Journal:  Biophys J       Date:  1994-02       Impact factor: 4.033

2.  The inverse protein folding question and simulated molecular evolution.

Authors:  L B Ellis
Journal:  Biophys J       Date:  1994-02       Impact factor: 4.033

3.  Development of artificial neural filters for pattern recognition in protein sequences.

Authors:  G Schneider; P Wrede
Journal:  J Mol Evol       Date:  1993-06       Impact factor: 2.395

  3 in total
  2 in total

1.  pkaPS: prediction of protein kinase A phosphorylation sites with the simplified kinase-substrate binding model.

Authors:  Georg Neuberger; Georg Schneider; Frank Eisenhaber
Journal:  Biol Direct       Date:  2007-01-12       Impact factor: 4.540

Review 2.  A Brief History of Protein Sorting Prediction.

Authors:  Henrik Nielsen; Konstantinos D Tsirigos; Søren Brunak; Gunnar von Heijne
Journal:  Protein J       Date:  2019-06       Impact factor: 2.371

  2 in total

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