The game of chess and searches in protein sequence space

By W. Mandecki. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Kevin Kaichuang Yang
2. Zachary Wu
3. Frances H. Arnold
4. Philip A. Romero
5. Andreas Krause
6. Yunan Luo
7. Carlos Guestrin
8. Niv Buchbinder

Citations in the corpus

• Exploring protein fitness landscapes by directed evolution (2009, neutral): “Searching in this space for billions of years for solutions to survival, nature has explored only an infinitesimal fraction of the possible protein”
• Navigating the protein fitness landscape with Gaussian processes (2012, background): “First, the space of possible protein sequences is incomprehensibly large and will never be searched exhaustively by any means, naturally, in the laboratory, or computationally (2, 3).”
• Machine-learning-guided directed evolution for protein engineering (2018, background): “Furthermore, the space of possible proteins is too large to search exhaustively naturally, in the laboratory, or computationally 3 .”
• Machine learning in protein engineering (2018, background): “The space of possible protein sequences is too large to be searched exhaustively naturally, in the laboratory, or computationally (Mandecki, 1998).”