At Adaptyv Bio, we make it easier for researchers to test their protein designs by providing fast and reliable experimental validation. In this case study, we highlight how Microsoft Research used our automated lab to validate proteins generated by EvoDiff, their novel sequence-first protein design model, in just a few weeks.
For a deeper dive into EvoDiff, including its methodology and broader applications, you can check out the EvoDiff paper.
Want to experimentally validate your own proteins? Start configuring your first experiment here!
What is EvoDiff?
EvoDiff is a generative model developed by Microsoft Research that operates directly in sequence space, bypassing the need to generate 3D structural models as part of the design process. This makes it fundamentally different from structure-first approaches like RFdiffusion, which rely on predicting and optimizing 3D protein structures before converting them into sequences.
By focusing on sequences, EvoDiff can explore regions of the design space that structure-based models may struggle to reach. This includes intrinsically disordered regions (IDRs), which lack a stable 3D structure but can still be highly functional, and other unconventional protein scaffolds.
For example, where RFdiffusion would need to design a specific structural fold and then identify a sequence to stabilize that fold, EvoDiff directly generates sequences optimized for the desired function, skipping the intermediate structural step. This gives it more flexibility, particularly for targets where structural constraints are less critical or poorly understood.
EvoDiff can be tailored to different design scenarios via conditioning:
- Filling Gaps: If you have part of a protein sequence, EvoDiff can fill in the missing regions while optimizing for specific properties, like binding affinity or solubility.
- Scaffolding Functional Motifs: The model can generate sequences that incorporate known binding motifs into diverse protein backbones.
- Mimicking Evolutionary Patterns: Using evolutionary alignments, EvoDiff can design sequences that resemble naturally occurring proteins, potentially improving stability and functionality.
This versatility makes EvoDiff a promising tool for both exploratory and application-driven protein design projects.
Protecting the ‘guardian of the genome’
MDM2 is a protein present in most human cells, where it plays a crucial role in regulating cell growth and survival. Its primary function is to control the activity of p53, another protein which often referred to as the “guardian of the genome.” p53 protects against cancer by halting cell division or triggering cell death when it detects DNA damage. MDM2 binds to p53 and signals for its degradation, effectively silencing this protective function when it is no longer needed.
In many cancers, MDM2 is overproduced, disrupting the balance and allowing cells to grow unchecked. This makes MDM2 an important target for cancer therapies. In this study, EvoDiff was used to design proteins that bind to MDM2. Competitive binding to MDM2 via a synthetic protein could help restore p53’s tumor-suppressing role.
However, designing molecules to bind MDM2 is challenging because you need to precisely target the p53-binding pocket to block the interaction.
EvoDiff employs a strategy known as “motif scaffolding,” where the binding motif, derived from p53, is preserved, but its stability and binding efficiency are enhanced by a scaffold designed by the model. This scaffold is optimized to hold the motif in the ideal conformation for binding to MDM2, improving the chances of effectively blocking the MDM2-p53 interaction. Importantly, with EvoDiff’s sequence-based design framework, this motif scaffolding occurs entirely in sequence space – without any information about the MDM2 target or the structure of the p53 binding peptide.
Validating the protein designs in the lab
We used our high-throughput Affinity Characterization workflow to validate EvoDiff’s designs. This streamlined process provides fast, high-quality data with minimal manual intervention.
- DNA Synthesis & Protein Expression: We designed and optimized DNA constructs for the EvoDiff designs and produced the proteins in a high-efficiency protein expression system.
- Binding Analysis: Using biolayer interferometry (BLI), we measured how well each protein bound to MDM2, calculating dissociation constants (KDs).
- Data Analysis: We classified binders, analyzed binding strength, and generated detailed kinetic profiles to evaluate performance.
After the team from Microsoft Research uploaded their protein designs to our platform, the entire workflow from designing the DNA sequences to uploading the final results took just about 3 weeks thanks to our automated wet lab.
Results & Takeaways
Strong Binders
Out of 24 tested EvoDiff designs, 8 designs showed KD values below 50 nM for full-length MDM2, with the top performers reaching 25.9 nM and 40.2 nM, respectively.
Fragment vs. Full-Length Performance
When tested against the MDM2 fragment (containing just the p53-binding domain), most binders showed reduced affinity compared to the full-length protein, with KD values for the fragment often falling into the micromolar range. The full-length protein likely provides additional interactions that stabilize binding, making it critical to validate designs across multiple contexts.
Evolutionary Conditioning Advantage
Designs generated with evolutionary conditioning (using multiple sequence alignments, or MSAs) consistently outperformed unconditioned designs. These binders demonstrated stronger affinities and greater reliability across full-length and fragment tests. Conditioning with evolutionary data seems to enhance design quality by leveraging patterns that have been naturally optimized over millions of years.
Design your own experiment
Ready to test your own designs? Use our Experiment Configurator to validate your sequences and take your protein engineering projects to the next level!
We’d like to thank everyone that contributed to EvoDiff: Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Neil Tenenholtz, Robert Strome, Alan M. Moses, Alex X. Lu, Nicolò Fusi, Ava P. Amini, Kevin K. Yang. Check out their paper here!
