Optimize your protein design projects with structure-based modeling
Bridge the gap between traditional wet lab approaches and in silico protein optimization with Schrödinger’s Protein Design Services. Schrödinger scientists will leverage our expertise and unique computationally-driven protein mutation workflow which integrates our differentiated technologies, including FEP+, to propel your discovery program.
Available Services
Affinity engineering:
Enhance or decrease binding affinity of a protein to its target
Selectivity engineering:
Tune preferential binding of a protein to one target over another
Cross-reactivity engineering:
Extending breadth of binding across multiple targets
pH-dependent binding:
Engineer pH-dependent association or dissociation between two proteins
Stability engineering:
Enhance physical stability of a protein-based biologic
Developability assessment:
Optimize properties like solubility, chemical stability, and physical stability
Best suited for:
Companies and Teams who…
Want to reduce protein optimization cost and speed up time-to-results
Want to utilize a computational, structure-based approach to identify better quality candidates
Want to leverage Schrödinger’s advanced physics-based methods and expertise
Projects…
With experimentally determined high resolution 3D structure of the input protein for stability prediction or a protein-protein complex for affinity, selectivity, cross reactivity, and pH-dependent binding prediction
Where optimizing multiple parameters is necessary
Propel your discovery program with unrivaled technologies and expertise
Reduce the cost and time of your protein optimization efforts from months to weeks by discarding irrelevant mutations early, as well as quickly generating new ideas and follow-up designs
Identify better quality candidates faster through simultaneous optimization of multiple parameters to facilitate more rapid testing and triaging of ideas
Benefit from the full impact of Schrödinger technologies at scale. Services include all computing, licensing, and service hours to perform comprehensive protein engineering
Complement your unique project knowledge with our computational expertise, built upon more than 30 years of R&D
Case Study
Challenge
Improving protein affinity is a key determinant in designing biologics with better efficacy, developability, and safety profiles. Traditional approaches like site-directed mutagenesis require the production, purification, and testing of each variant which can be both time consuming and costly, only to yield around 5-10% of mutations with improved affinity.
Result
On a data set of 702 mutations, 18 mutations at 39 sites, Schrödinger’s Protein Engineering Workflow was able to identify 21 of 33 (64%) of the affinity improving mutations in an experimental panel 1/8 of the size of the full set, in less than 4 weeks. Leveraging FEP+ for protein affinity improvement enables scientists to identify important mutations faster while reducing experimental costs.
Scientifically-validated solutions for structure-based drug discovery
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Robust prediction of relative binding energies for protein–protein complex mutations using free energy perturbation calculations
Sampson JM, et al. J Mol Biol. 2024, 436(16), 168640.
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Accurate prediction of protein thermodynamic stability changes upon residue mutation using free Energy perturbation
Scarabelli G, et al. J Mol Biol. 2022, 434(2), 167375.
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Relative binding affinity prediction of charge-changing sequence mutations with FEP in protein–protein interfaces
Clark AJ, et al. J Mol Biol. 2019, 431(7), 1481-1493.
Software and services to meet your organizational needs
Software Platform
Deploy digital drug discovery workflows using a comprehensive and user-friendly platform for molecular modeling, design, and collaboration.
Modeling Services
Leverage Schrödinger’s computational expertise and technology at scale to advance your projects through key stages in the drug discovery process.
Support & Training
Access expert support, educational materials, and training resources designed for both novice and experienced users.