AIMC Topic: Protein Engineering

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Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline.

Scientific reports Jun 24, 2024
As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purificat...
Escherichia coli Robotics Reproducibility of Results Protein Engineering Polyethylene Terephthalates High-Throughput Screening Assays Hydrolases
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A systematic analysis of regression models for protein engineering.

PLoS computational biology May 3, 2024
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process...
Algorithms Computational Biology Regression Analysis Proteins Protein Engineering Machine Learning
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Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions.

mAbs Apr 26, 2024
The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These buildin...
Single-Domain Antibodies Machine Learning Humans Antibodies, Bispecific Animals Protein Engineering Immunoglobulin Heavy Chains
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Generative artificial intelligence for de novo protein design.

Current opinion in structural biology Apr 24, 2024
Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forwar...
Protein Conformation Protein Engineering Proteins Models, Molecular Artificial Intelligence
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Computational scoring and experimental evaluation of enzymes generated by neural networks.

Nature biotechnology Apr 23, 2024
In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to ass...
Enzymes Amino Acid Sequence Protein Engineering Computational Biology Neural Networks, Computer Algorithms
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Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Science (New York, N.Y.) Apr 19, 2024
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases w...
DNA Deep Learning Models, Molecular Crystallography Proteins Amino Acids Protein Engineering
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Protein Engineering with Lightweight Graph Denoising Neural Networks.

Journal of chemical information and modeling Apr 17, 2024
Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establish...
Protein Conformation Protein Engineering Models, Molecular Mutation Neural Networks, Computer Deep Learning Proteins
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GraphKM: machine and deep learning for K prediction of wildtype and mutant enzymes.

BMC bioinformatics Mar 28, 2024
Michaelis constant (K) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of K are difficult and time-consuming, prediction of...
Deep Learning Protein Engineering Electric Power Supplies Neural Networks, Computer Language
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Security challenges by AI-assisted protein design : The ability to design proteins in silico could pose a new threat for biosecurity and biosafety.

EMBO reports Mar 26, 2024
Scientists and security experts are concerned that the increasing power of AI-assisted protein design and synthesis could be abused by various actors for terrorist or criminal purposes. [Image: see text]
Artificial Intelligence Computer Simulation Protein Engineering Humans Proteins Security Measures
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Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proceedings of the National Academy of Sciences of the United States of America Mar 7, 2024
Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objectiv...
Machine Learning Proteins Mutation Protein Engineering Gene Library
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