Fitness landscape, a key concept in evolutionary biology, is utilized to map how different genetic variations affect an organism’s survival and reproduction capabilities. This concept forms the foundation of understanding evolutionary processes, as well as protein engineering advancements. However, mapping the fitness landscape is an arduous task due to the huge number of potential genotypes for every protein.
The task calls for studying the fitness of an array of genotypes for a detailed fitness landscape map. Given the expansive number of potential genotypes for any specific protein, employing traditional methods makes this task almost impossible, requiring contemporary techniques to predict and analyze these vast landscapes.
Various studies on fitness landscapes have employed experimental methods, which though informational, have limitations since genotypes are high dimensional and genetic components interact in a non-linear and complex way while determining an organism’s fitness.
A solution to these issues was introduced by a researcher from the University of Zurich, who suggested the use of deep learning. Models like multilayer perceptrons, recurrent neural networks, and transformers, have been used to foresee the fitness of genotypes using experimental data. The models use a subset of genotypes with known fitness values for training and use this information to predict the fitness of a larger set. Research indicates that certain training methods, like random and uniform sampling, significantly improve the model’s accuracy.
The use of deep learning models has proven highly effective, with some elucidating over 90% of fitness variance in the data. Notably, it’s possible to achieve high prediction accuracy with relatively small training samples, reducing reliance on large experimental data sets. The efficient use of the sampling strategy assisted in enhancing deep learning models.
Conclusively, research into fitness landscape studies showed promising advancement thanks to deep learning models, offering a more scalable and efficient solution to traditional methods. The findings also emphasize the importance of sampling strategies in optimizing the performance of these models. This is likely to pave the way for a paradigm shift in fitness landscapes studies and promote further research into evolutionary biology and protein engineering.
This research was conducted and published by the University of Zurich. Follow us on our platforms for recent developments and engagements: Twitter, ML SubReddit, Facebook Community, Discord Channel, LinkedIn Group and our Telegram Channel. You can also check out our newsletter.
