Efficient generative modeling of protein sequences using simple autoregressive models
By J. Trinquier, Guido Uguzzoni, Andrea Pagnani, Francesco Zamponi, Martin Weigt. 2021
95 citations recorded by the source corpus.
Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 102 and 103). Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Within these models, we can easily estimate both the probability of a given sequence, and, using the model’s entropy, the size of the functional sequence space related to a specific protein family. In the example of response regulators, we find a huge number of ca. 1068 possible sequences, which nevertheless constitute only the astronomically small fraction 10−80 of all amino-acid sequences of the same length. These findings illustrate the potential and the difficulty in exploring sequence space via generative sequence models. Deep learning is a powerful tool for the design of novel protein sequences, yet can be computationally very inefficient. Here the authors propose using simple forecasting models to efficiently generate a large number of novel protein structures.
Subfields: Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Joe G. Greener
- David T. Jones
- Lewis Moffat
- Shaun M. Kandathil
- Yang Zhang
- Badri Adhikari
- Robin Pearce
- Jianlin Cheng
Citations in the corpus
- Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021, background): “…variational autoencoders (Greener et al., 2018; Hawkins-Hooker et al., 2021), deep exploration networks (Linder et al., 2020), graph neural networks (Strokach et al., 2020), recurrent neural networks (Alley et al., 2019) and autoregressive models (Shin et al., 2021; Trinquier et al., 2021).”