Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Influence of Multiple Sequence Alignment Depth on Potts Statistical
  Models of Protein Covariation   Potts statistical models have become a popular and promising way to analyze
mutational covariation in protein Multiple Sequence Alignments (MSAs) in order
to understand protein structure, function and fitness. But the statistical
limitations of these models, which can have millions of parameters and are fit
to MSAs of only thousands or hundreds of effective sequences using a procedure
known as inverse Ising inference, are incompletely understood. In this work we
predict how model quality degrades as a function of the number of sequences
$N$, sequence length $L$, amino-acid alphabet size $q$, and the degree of
conservation of the MSA, in different applications of the Potts models: In
"fitness" predictions of individual protein sequences, in predictions of the
effects of single-point mutations, in "double mutant cycle" predictions of
epistasis, and in 3-d contact prediction in protein structure. We show how as
MSA depth $N$ decreases an "overfitting" effect occurs such that sequences in
the training MSA have overestimated fitness, and we predict the magnitude of
this effect and discuss how regularization can help correct for it, use a
regularization procedure motivated by statistical analysis of the effects of
finite sampling. We find that as $N$ decreases the quality of point-mutation
effect predictions degrade least, fitness and epistasis predictions degrade
more rapidly, and contact predictions are most affected. However, overfitting
becomes negligible for MSA depths of more than a few thousand effective
sequences, as often used in practice, and regularization becomes less
necessary. We discuss the implications of these results for users of Potts
covariation analysis.

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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: cs