scholarly journals A neural network model for the prediction of membrane-spanning amino acid sequences

1994 ◽  
Vol 3 (9) ◽  
pp. 1597-1601 ◽  
Author(s):  
Reinhard Lohmann ◽  
Gisbert Schneider ◽  
Dirk Behrens ◽  
Paul Wrede
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Network Model ◽  
Neural Network Model ◽  
Amino Acid Sequences ◽  
Membrane Spanning

scholarly journals Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes

Biomolecules ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 67 ◽  
Author(s):  
Óscar Álvarez-Machancoses ◽  
Enrique J. De Andrés-Galiana ◽  
Juan Luis Fernández-Martínez ◽  
Andrzej Kloczkowski
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Network Model ◽  
Neural Network Model ◽  
Pearson Correlation ◽  
Single Point ◽  
Point Mutations ◽  
Cumulative Distribution ◽  
Multiple Point ◽  
Protein Mutations

Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab experiments to get a better understanding of protein mutations is costly and time consuming, and because of huge number of possible mutations the need of computational methods that could accurately predict effects of amino acid mutations is of greatest importance. In this research, we present a robust methodology to predict the energy changes of a proteins upon mutations. The proposed prediction scheme is based on two step algorithm that is a Holdout Random Sampler followed by a neural network model for regression. The Holdout Random Sampler is utilized to analysis the energy change, the corresponding uncertainty, and to obtain a set of admissible energy changes, expressed as a cumulative distribution function. These values are further utilized to train a simple neural network model that can predict the energy changes. Results were blindly tested (validated) against experimental energy changes, giving Pearson correlation coefficients of 0.66 for Single Point Mutations and 0.77 for Multiple Point Mutations. These results confirm the successfulness of our method, since it outperforms majority of previous studies in this field.

scholarly journals Large-scale design and refinement of stable proteins using sequence-only models

2021 ◽  
Author(s):  
Jedediah M. Singer ◽  
Scott Novotney ◽  
Devin Strickland ◽  
Hugh K. Haddox ◽  
Nicholas Leiby ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Amino Acid Sequences ◽  
Novel Proteins ◽  
Wide Range ◽  
The Stability ◽  
Scale Design ◽  
Future Work

AbstractEngineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

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