Deep mutational scanning services
Map the fitness landscape of your protein. Every single amino acid substitution at every position, scored for function in a single pooled experiment.
Discuss your project →Comprehensive variant fitness landscapes from a single experiment
Deep mutational scanning (DMS) systematically measures the functional impact of thousands of protein variants in parallel. A saturation mutagenesis library — typically covering all 19 non-wild-type amino acid substitutions at every position in the target region — is constructed, introduced into a display or selection system, and subjected to functional pressure.
Pre- and post-selection populations are sequenced by NGS, and the enrichment or depletion of each variant quantifies its fitness relative to wild type. The result is a complete mutational landscape: a position-by-substitution matrix that reveals which residues are essential, which tolerate mutation, and which substitutions improve function.
What you receive from a DMS experiment
Fitness score matrix
A position-by-amino-acid matrix of log-enrichment ratios. Each cell quantifies how a specific substitution at a specific position affects the selected phenotype relative to wild type. Scores are normalized and reproducible across replicates.
Heatmap visualization
Publication-ready heatmaps showing gain-of-function, loss-of-function, and neutral mutations across the entire scanned region. Immediately identifies functional hotspots, conserved positions, and mutation-tolerant loops.
Beneficial variant list
Rank-ordered list of substitutions that improve the selected phenotype — binding affinity, thermostability, expression level, or catalytic activity depending on your selection pressure. Ready for combinatorial optimization.
Combinatorial design guidance
Beneficial single mutations are candidates for combination. DMS data de-risks combinatorial library design by identifying which positions tolerate simultaneous substitution, guiding the next round of optimization.
DMS applications in protein engineering
Affinity maturation
Identify every substitution that improves binding at every position in a binder or antibody. Combine top hits to achieve multi-log improvements in Kd without random screening.
Stability engineering
Map thermostability contributions across your protein. Identify stabilizing mutations for formulation development, extended shelf life, or higher expression yields.
Epitope mapping
Determine which target residues are critical for binder interaction. Loss-of-binding mutations on the target surface map the functional epitope at single-residue resolution.
Variant effect prediction benchmarking
Generate ground-truth fitness data to benchmark computational variant effect predictors (ESM, EVE, AlphaMissense). Essential for calibrating in silico models against experimental reality.
Training data for AI protein models
DMS datasets are among the highest-value training inputs for machine learning models that predict variant effects, protein fitness, and structure-function relationships. Systematic, quantitative, and covering complete sequence space, DMS data provides the ground truth that computational models need to generalize.
Enzyme optimization
Score every substitution for catalytic activity, substrate specificity, or product selectivity using activity-coupled selection. Identify positions that decouple activity from stability.
Functional scoring methods for DMS
The phenotype you measure determines the mutations you find. We match the selection system to your engineering objective.
Display-based binding selection
Variants displayed on yeast or mammalian cells, sorted by FACS for target binding. Enrichment ratios quantify the binding fitness of each substitution. Multi-concentration sorting provides apparent affinity rankings across the entire library.
Surface expression as stability proxy
Surface expression level on display platforms correlates with thermodynamic stability. Sorting for high expression enriches stabilizing mutations and depletes destabilizing ones. A rapid, functional proxy for thermal stability measurements.
Activity-based functional selection
Variants selected for enzymatic or biological activity rather than binding. Intracellular selection links protein function to cell survival or reporter output, while microfluidic droplet compartmentalization enables single-variant catalytic readouts. Examples include recombinase activity in cells and polymerase activity via droplet PCR.
Map your protein's fitness landscape
Send us your target protein and the phenotype you want to optimize. We will design a DMS campaign and deliver a complete mutational landscape.
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