AIMC Topic: Protein Engineering

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CLADE 2.0: Evolution-Driven Cluster Learning-Assisted Directed Evolution.

Journal of chemical information and modeling
Directed evolution, a revolutionary biotechnology in protein engineering, optimizes protein fitness by searching an astronomical mutational space via expensive experiments. The cluster learning-assisted directed evolution (CLADE) efficiently explores...

Robust deep learning-based protein sequence design using ProteinMPNN.

Science (New York, N.Y.)
Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based pro...

Hallucinating symmetric protein assemblies.

Science (New York, N.Y.)
Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here, we use deep network hallucination to generate a wide range of symmetric protein homo-...

Scaffolding protein functional sites using deep learning.

Science (New York, N.Y.)
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without n...

Machine learning-aided engineering of hydrolases for PET depolymerization.

Nature
Plastic waste poses an ecological challenge and enzymatic degradation offers one, potentially green and scalable, route for polyesters waste recycling. Poly(ethylene terephthalate) (PET) accounts for 12% of global solid waste, and a circular carbon e...

Machine learning to navigate fitness landscapes for protein engineering.

Current opinion in biotechnology
Machine learning (ML) is revolutionizing our ability to understand and predict the complex relationships between protein sequence, structure, and function. Predictive sequence-function models are enabling protein engineers to efficiently search the s...

Accurate positioning of functional residues with robotics-inspired computational protein design.

Proceedings of the National Academy of Sciences of the United States of America
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geom...

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.

Chemical communications (Cambridge, England)
Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and evolution campaigns inclu...

Protein sequence design with a learned potential.

Nature communications
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network mo...

Therapeutic enzyme engineering using a generative neural network.

Scientific reports
Enhancing the potency of mRNA therapeutics is an important objective for treating rare diseases, since it may enable lower and less-frequent dosing. Enzyme engineering can increase potency of mRNA therapeutics by improving the expression, half-life, ...