An exciting but challenging road ahead for computational enzyme design

By David Baker. 2010

182 citations recorded by the source corpus.

Subfields: Molecular Biology, Organic Chemistry, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

ProDCoNN: Protein design using a convolutional neural network (2019, background): “3,4,7,12 The design will usually start from a known structure, which will be kept in the process, and modify part of the sequence to achieve a new function.”
Computational protein design: a review (2017, background): “The potential applications can be divided into three main categories: in chemistry(e.g enzyme design [12, 35 – 37]), in material sciences [38 – 40], and medicine.”
Machine learning in protein engineering (2018, methodology): “Generative models of the distribution of functional proteins would enable these large jumps and provide an attractive alternative to de novo design methods (Baker, 2010).”
Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity (2015, background): “However, changes in the protein backbones to accommodate changes in amino acid sequence [25] are the rule rather than the exception, and a key reason for failed designs is that they do not adopt the required precise geometry of an engineered functional site [6,18].”
Computational Design of Ligand Binding Proteins with High Affinity and Selectivity (2013, background): “The lack of accuracy in programming protein–small-molecule interactions alsocontributes to low catalytic efficiencies ofcomputationally designed enzymes 9–14 .”
Protein design and variant prediction using autoregressive generative models (2021, background): “…20 years, success in protein engineering has emerged from two distinct approaches, directed evolution 1,2 and knowledge-based force-ﬁ eld modeling 3,4 . and generating biomolecules with known functions is now a major goal of biotechnology and biomedicine, propelled by our ability to synthesize…”
Emerging themes in the computational design of novel enzymes and protein–protein interfaces (2013, background): “Such improvements will be crucial to obtain the next generation of highly active computationally designed catalysts and bridge the currently large efﬁciency gap between designed and natural enzymes [42].”
Navigating the protein fitness landscape with Gaussian processes (2012, background): “A major challenge for computational protein engineering is ﬁ nding models that accurately describe the mapping from sequence to function (10).”