Deep Mutational Scanning & Directed Evolution
Mapping protein fitness landscapes with deep mutational scanning and pushing function forward with directed evolution, one NGS-quantified round at a time.
Working on a deep mutational scanning & directed evolution project? See our deep mutational scanning services.
deep mutational scanning services →Deep Mutational Scanning for Antibody Affinity Maturation
How deep mutational scanning maps an antibody's affinity landscape and reveals which residues to optimize without breaking developability.
Engineering Enzyme Thermostability: A CRO Playbook
Engineering thermostable enzymes for industrial biocatalysis: directed evolution, consensus design, and DMS-guided strategies that hold up at 70°C.
Industrial Enzyme Engineering: A CRO Playbook for Biocatalysis
How CROs engineer enzymes for industrial biocatalysis: substrate scope, organic solvent tolerance, regiospecificity, and process compatibility.
Engineering pH-Dependent Antibodies on Yeast Surface Display: A 640-Clone Case Study
A technical walkthrough of a real pH-dependent antibody engineering campaign — 640-clone yeast display library, six FACS sorts, convergent hotspot residues, and quantitative enrichment-score ranking.
In Vivo Mutagenesis for AI Training Data
How in vivo DNA mutagenesis systems like CRISPR-guided base editors and error-prone polymerases generate the large, unbiased protein variant datasets that machine learning models need. Practical comparison with synthetic library approaches for AI-driven protein engineering.
Deep Mutational Scanning: Mapping Protein Fitness Landscapes
Deep mutational scanning (DMS) combines saturation variant libraries, functional selection, and NGS to measure the functional consequences of thousands of mutations in one experiment — producing a complete map of a protein's fitness landscape.
Leveraging AI and Deep Mutational Scanning to Engineer Novel Enzymes
A comprehensive guide to combining deep mutational scanning with machine learning for enzyme engineering, covering variant library generation, functional selection, data analysis, and the iterative AI-DMS cycle.
Directed Evolution: A Technical Guide for Protein Stability and Function
Directed evolution uses iterative cycles of mutagenesis and selection to engineer proteins for stability, activity, and specificity — without requiring structural knowledge. A technical guide to the diversity generation cycle, screening methods, and integration with AI-guided design.
Library Design Decisions That Determine Screening Campaign Success
How diversity calculations, codon strategy, transformation efficiency, and NGS-based QC directly determine whether your screening campaign produces leads or wastes months of work.
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