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Protein sequence sampling and prediction from structural data

Gabriel A. Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P. Dunne, Leonardo Alvarez
doi: https://doi.org/10.1101/2021.09.06.459171
Gabriel A. Orellana
1Protera Biosciences, Av. Santa Maria 2810, Providencia, Santiago, Chile
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Javier Caceres-Delpiano
1Protera Biosciences, Av. Santa Maria 2810, Providencia, Santiago, Chile
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Roberto Ibañez
2Protera Biosciences, 176 Avenue Charles de Gaulle, Neuilly Sur Seine Cedex, Paris, France
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Michael P. Dunne
2Protera Biosciences, 176 Avenue Charles de Gaulle, Neuilly Sur Seine Cedex, Paris, France
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Leonardo Alvarez
2Protera Biosciences, 176 Avenue Charles de Gaulle, Neuilly Sur Seine Cedex, Paris, France
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  • For correspondence: leonardo@proterabio.com
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Abstract

The increasing integration between protein engineering and machine learning has led to many interesting results. A problem still to solve is to evaluate the likelihood that a sequence will fold into a target structure. This problem can be also viewed as sequence prediction from a known structure.

In the current work, we propose improvements in the recent architecture of Geometric Vector Perceptrons [1] in order to optimize the sampling of sequences from a known backbone structure. The proposed model differs from the original in that there is: (i) no updating in the vectorial embedding, only in the scalar one, (ii) only one layer of decoding. The first aspect improves the accuracy of the model and reduces the use of memory, the second allows for training of the model with several tasks without incurring data leakage.

We treat the trained classifier as an Energy-Based Model and sample sequences by sampling amino acids in a non-autoreggresive manner in the empty positions of the sequence using energy-guided criteria and followed by random mutation optimization. We improve the median identity of samples from 40.2% to 44.7%.

An additional question worth investigating is whether sampled and original sequences fold into similar structures independent of their identity. We chose proteins in our test set whose sampled sequences show low identity (under 30%) but for which our model predicted favorable energies. We used AlphaFold [2, 3] and observed that the predicted structures for sampled sequences highly resemble the predicted structures for original sequences, with an average TM-score of 0.84.

Competing Interest Statement

Provisional patent applications have been filed based on the results presented here.

Footnotes

  • gorellana{at}proterabio.com, jcaceres{at}proterabio.com, ribanez{at}proterabio.com, mdunne{at}proterabio.com, leonardo{at}proterabio.com

  • Michael P. Dunne was added as author. We added an illustration of how the energy of a sequence is calculated. We added stats on the performance of the two sampling schemes (AR and non-AR). Selection of proteins to be evaluated by structure prediction was changed, choosing the lowest energies on an individual sequence basis instead of average of sequences per protein.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 22, 2021.
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Protein sequence sampling and prediction from structural data
Gabriel A. Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P. Dunne, Leonardo Alvarez
bioRxiv 2021.09.06.459171; doi: https://doi.org/10.1101/2021.09.06.459171
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Protein sequence sampling and prediction from structural data
Gabriel A. Orellana, Javier Caceres-Delpiano, Roberto Ibañez, Michael P. Dunne, Leonardo Alvarez
bioRxiv 2021.09.06.459171; doi: https://doi.org/10.1101/2021.09.06.459171

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