Navigating the protein fitness landscape with Gaussian processes

By Philip A. Romero, Andreas Krause, Frances H. Arnold. 2012

366 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “To reduce the experimental effort, machine learning (ML) methods have been proposed to assist directed evolution and successfully engineered multiple important functional proteins [15–20] .”
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design (2020, background): “In a pioneering study, Romero et al. (2013) demonstrate the use of BO for protein engineering.”
Machine learning in protein engineering (2018, methodology): “Given structural information, the identity of pairs of amino acids within a certain distance in the structure can also be one-hot encoded (Romero et al., 2013b; Bedbrook et al., 2017).”
Model-based methods for continuous and discrete global optimization (2017, background): “, in the engineering domain [66, 67, 68, 69], bioinformatics [70], or data science [71].”
Machine-learning-guided directed evolution for protein engineering (2018, methodology): “GPs have been used to predict thermostability 11,32,43 , substrates for enzymatic reactions 44 , fluorescence 45 , membrane localization 12 , and channelrhodopsin photoproperties 13 .”
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design (2019, methodology): “GP optimization for protein engineering GPUCB has been used to find improved protein sequences when sequences can be queried directly (Romero et al., 2013; Bedbrook et al., 2017).”
Simulation optimization: a review of algorithms and applications (2014, background): “Protein engineering [168], cardiovascular surgery [202], breast cancer epidemiology [55], bioprocess control [197, 165], ECG analysis [67], medical image analysis [136] Engineering Welded beam design [209], solid waste management [210], pollution source identiﬁcation [14], chemical supply chains…”
Deep Learning in Protein Structural Modeling and Design (2020, neutral): “The amino acid sequence, like other sequential data, is typically converted into one-hot encoding-based representation (each residue is represented with one high bit to identify the amino acid type and all the others low) that can be directly used in many sequence-based DL techniques.(73,74) However”
Dissecting enzyme function with microfluidic-based deep mutational scanning (2015, methodology): “When combined with methods from statistics and machine learning, protein design rules can be extracted and applied in an automated manner (39).”