AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Protein Engineering

Showing 31 to 40 of 112 articles

Clear Filters

Advancing microbial production through artificial intelligence-aided biology.

Biotechnology advances
Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer mic...

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

Scientific reports
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...

Machine learning-assisted substrate binding pocket engineering based on structural information.

Briefings in bioinformatics
Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-li...

Neural network extrapolation to distant regions of the protein fitness landscape.

Nature communications
Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-functi...

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.

Nature communications
The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (...

Context-aware geometric deep learning for protein sequence design.

Nature communications
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning appr...

EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.

Journal of chemical information and modeling
Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enha...

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.

Cell research
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we ...

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Journal of chemical information and modeling
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited scr...

Navigating the landscape of enzyme design: from molecular simulations to machine learning.

Chemical Society reviews
Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is c...