Our computational protein evolution service integrates machine learning,
molecular dynamics, and sequence–structure modeling to accelerate the
evolution of proteins with enhanced or novel functions—without extensive
wet-lab screening. Using advanced algorithms inspired by natural evolution
(mutation, recombination, and selection), we simulate and predict beneficial
mutations that improve activity, stability, expression, and binding affinity.
This approach enables rapid in silico protein optimization, guiding
experimental efforts with high precision and efficiency.
Background
Protein design and evolution are two complementary strategies at the core of
modern biotechnology and pharmaceutical innovation. They aim to create or
optimize proteins — enzymes, antibodies, receptors, or structural scaffolds —
for desired biological functions or improved performance in therapeutic,
industrial, or research applications. While rational protein design uses
structural and computational insights to introduce targeted modifications,
computational directed evolution mimics the process of natural selection to
identify beneficial variants through iterative mutation and screening.
Together, they form a powerful, iterative loop of "Design → Build → Test →
Learn", accelerating protein discovery and optimization.
Our Service
Using machine learning, molecular modeling, and evolutionary algorithms, we
can explore vast sequence spaces in silico—predicting mutations that
enhance activity, stability, binding, and solubility—without requiring
extensive wet-lab screening.
Services
Description
Computational Protein Design
Structure-Based Engineering
Design proteins from atomic structures using tools such as Rosetta,
FoldX, and ProteinMPNN.
Binding and Interface Optimization
Refine protein–protein, protein–ligand, or antibody–antigen interfaces
through docking and affinity prediction.
Stability and Solubility Enhancement
Identify mutations that improve thermostability or reduce
aggregation.
De Novo Design
Build entirely new protein scaffolds with predefined folds or binding
functions.
Computational Evolution Pipeline
Virtual Mutagenesis and Variant Generation
Simulate random and targeted mutations based on sequence conservation
and structural constraints.
Fitness Landscape Modeling
Predict how individual or combined mutations affect function and folding
energy.
Machine Learning–Guided Selection
Use AI models trained on experimental data to identify beneficial
variants.
Iterative Optimization
Combine design predictions with simulated selection pressure for
progressive improvement.
Molecular Dynamics and Energy Profiling
Simulate conformational flexibility and protein stability under
physiological or stressed conditions.
Evaluate structural robustness and mutation impacts using free-energy
and RMSD analysis.
Applications
We combine structural biology insight with cutting-edge algorithms to deliver
faster, smarter, and cost-efficient protein evolution—empowering discovery and
development across pharmaceutical, industrial, and synthetic biology
applications.
Field
Use Cases
Therapeutic Protein Design
Optimize antibodies, cytokines, and enzymes for enhanced efficacy and
stability.
Enzyme Engineering
Improve catalytic activity, substrate scope, and solvent tolerance.
Protein–Protein Interaction Design
Engineer tighter binding interfaces for immune or signaling targets.
Industrial Biocatalysts
Develop robust enzymes for biofuel, food, and detergent industries.
Synthetic Biology
Create artificial proteins and circuits for metabolic or regulatory
functions.
Sample Requirements
Clear Objectives: Specify the desired properties, functions, or applications
of the protein. For example, if the aim is to design a protein for targeted
drug delivery, details such as the target molecule, release kinetics, and
biodistribution requirements should be provided.
Available Data: Share any existing relevant data, such as known protein
structures, sequences of similar proteins, or experimental results related
to the target protein's function.
Results Delivery
Ranked list of top candidate mutations and variants.
3D structural models and stability/interaction predictions.
Evolutionary pathway and fitness landscape report.
Comprehensive summary of computational methods and validation suggestions.
Our Advantages
Reduces experimental burden by preselecting high-fitness variants.
Integrates AI, MD, and bioinformatics for accuracy and scalability.
Customizable for enzymes, antibodies, or structural proteins.
Seamlessly integrates with experimental directed evolution or
high-throughput screening workflows.
Enables rational + data-driven design synergy.
For research use only. Not intended for any clinical use.
