PRODIGY: protein-based molecular design for next-generation therapy

About the project

Proteins are at the heart of every human disease, and are thus targets of most therapeutics, but the development of therapeutic molecules is a difficult challenge.

With the emergence of new machine learning (ML) or generative artificial intelligence (GenAI) methods, enabled by big data, we are at the cusp of one of the most substantial transformations in biomedicine of the last 50 years. Combined with recent breakthroughs in structural biology, in particular cryo-EM, ML-based computational models will enable the design of novel proteins, peptides and small molecule drugs with potentially transformative therapeutic potential for a wide range of applications.

Indeed, GenAI approaches have begun to impact society in various domains such as image and text generation, and are becoming a transformative toolbox in molecular design and drug discovery. Despite these impressive advances, GenAI is however still in its infancy and facing many obstacles, in particular in domains where data is limited, and the problems are complex, such as the design of novel molecular matter acting within a biological context. Many fundamental challenges in ML and GenAI approaches remain unsolved, such as imposing empirical priors, data representation, sampling performance, data availability or explainability, and systematic research efforts are paramount.

Within the NCCR PRODIGY (Protein-based Molecular Design for Next-Generation Therapy), we will set up a collaborative network that combines: excellence in fundamental research in ML methods, computational design of small-molecules, peptides and proteins, structural biology, chemical biology, high-throughput biology powered by omics approaches, to innovate on GenAI approaches for the design of novel therapeutic modalities

Leadership team

Bruno Correia

Computational protein design, intersecting biochemistry, structural biology, immunology, and computer science.

Paola Picotti

Molecular ‘omics, with a focus on mass spectrometry-based chemical and structural proteomics.

Beat Fierz

Protein chemistry and biophysics, to reveal molecular mechanisms in the fields of epigenetics genome regulation and cytoskeleton control.

Nicolas Thomä

Chemical and structural biology of gene regulation and the ubiquitin system, development of molecular glues.

Sereina Riniker

Development of methods and software for classical molecular dynamics simulations and cheminformatics, and their application to gain insights into challenging biological and chemical questions.

Matteo Dal Peraro

Multiscale models and dynamic integrative modeling to investigate the assembly and function of molecular assemblies mimicking conditions of the cellular environment.
 

Our Partners

 

What's new with PRODIGY?

BindCraft: One-shot design of functional protein binders

The lab of Bruno Correia, with Martin Pacesa (EPFL) and Sergey Ovchinnikov (MIT) present BindCraft, a breakthrough computational algorithm for accurate design of protein protein interactions in a one-shot approach. Experimental evaluation on challenging targets yielded binder affinities in the nanomolar and extremely high success rates. The program is freely available to the public and has already been widely adopted. Today out in Nature: https://www.nature.com/article... 

Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation

A study led by Maria Brbić (EPFL) and Mor Nitzan (Hebrew University of Jerusalem) present Systema. Systema helps to evaluate perturbation response prediction methods by focusing on perturbation-specific effects rather than systematic variation. By focusing on true effects and varied gene panels, Systema guides better models. The study has been published in Nature Biotechnology (https://doi.org/10.1038/s41587...).

First Swiss Symposium on Protein Design in Academia and Industry

The aim of the event, organized by Prof. Dr. Abdullah Kahraman, Group Leader, Data Science in Life Sciences at the University of Applied Sciences, FNHW Muttenz, and headlined by PRODIGY's own Prof. Bruno Correia, is to promote exchange between the academic world and the biotech and pharmaceutical industry in Basel and beyond.

New class of inhibitors against SARS‐CoV‐2

Scientists at the ZHAW, led by Rainer Rield identified a compound that blocks a key protein (PLpro) the COVID-19 virus needs to replicate. By improving this compound, they developed two stronger versions that effectively inhibit the virus in lab tests. These new molecules could help guide the development of future antiviral drugs against COVID-19.

Selective CBP/EP300 Bromodomain Inhibitors

In a study led by Cristina Nevado, UZH, researchers designed and developed a new class of molecules that block inflammation by targeting specific proteins (CBP/EP300) involved in immune signaling. These inhibitors reduce inflammatory cytokines like TNF-α in lab and animal models, showing promise for treating autoimmune diseases such as rheumatoid arthritis and Crohn’s disease without harming healthy cells.

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Selected recent publications

1.
One-shot design of functional protein binders with BindCraft.
Nature 1–10 (2025). doi: 10.1038/s41586-025-09429-6
2.
Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation.
Nature Biotechnology (2025). doi: 10.1038/s41587-025-02777-8
3.
Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.
Journal of Chemical Theory and Computation 21, 7450–7459 (2025). doi: 10.1021/acs.jctc.5c00728
4.
Neural SHAKE: geometric constraints in neural differential equations.
Journal of Cheminformatics 17, 115 (2025). doi: 10.1186/s13321-025-01053-w
5.
Vinyl cyclopropanes as a unifying platform for enantioselective remote difunctionalization of alkenes.
Nature Communications 16, 6958 (2025). doi: 10.1038/s41467-025-61363-3
6.
Highly parallel optimisation of chemical reactions through automation and machine intelligence.
Nature Communications 16, 6464 (2025). doi: 10.1038/s41467-025-61803-0
7.
Exploring New Nanopore Sensors from the Aerolysin Family.
Small 2501219 (2025). doi: 10.1002/smll.202501219
8.
Efficient Multistate Free-Energy Calculations with QM/MM Accuracy Using Replica-Exchange Enveloping Distribution Sampling.
The Journal of Physical Chemistry B 129, 5948–5960 (2025). doi: 10.1021/acs.jpcb.5c02086
9.
Selective CBP/EP300 Bromodomain Inhibitors: Novel Epigenetic Tools to Counter TNF-α-Driven Inflammation.
JACS Au jacsau.5c00085 (2025). doi: 10.1021/jacsau.5c00085
10.
ProtMamba: a homology-aware but alignment-free protein state space model.
Bioinformatics 41, btaf348 (2025). doi: 10.1093/bioinformatics/btaf348
11.
Phage Display Selection against a Mixture of Protein Targets.
ACS Chemical Biology acschembio.5c00121 (2025). doi: 10.1021/acschembio.5c00121
12.
Single-molecule analysis reveals the mechanism of chromatin ubiquitylation by variant PRC1 complexes.
Science Advances 11, eadt7013 (2025). doi: 10.1126/sciadv.adt7013
13.
A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists.
Nature Chemistry (2025). doi: 10.1038/s41557-025-01815-x
14.
An approach to characterize mechanisms of action of anti-amyloidogenic compounds in vitro and in situ.
npj Parkinson’s Disease 11, 122 (2025). doi: 10.1038/s41531-025-00966-5
15.
A tissue-specific atlas of protein–protein associations enables prioritization of candidate disease genes.
Nature Biotechnology (2025). doi: 10.1038/s41587-025-02659-z
16.
Regulation of the cGAS-STING Pathway.
Annual Review of Immunology 43, 667–692 (2025). doi: 10.1146/annurev-immunol-101721-032910
17.
A multi-modal transformer for predicting global minimum adsorption energy.
Nature Communications 16, 3232 (2025). doi: 10.1038/s41467-025-58499-7
18.
Interrogating the anti- Insertion of Alkynes into Gold(III).
JACS Au 5, 1439–1447 (2025). doi: 10.1021/jacsau.5c00056
19.
Accessible homeostatic gastric organoids reveal secondary cell type-specific host-pathogen interactions in Helicobacter pylori infections.
Nature Communications 16, 2767 (2025). doi: 10.1038/s41467-025-57131-y
20.
Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.
Journal of the American Chemical Society 147, 6835–6856 (2025). doi: 10.1021/jacs.4c17015
21.
MARBLE: interpretable representations of neural population dynamics using geometric deep learning.
Nature Methods 1–9 (2025). doi: 10.1038/s41592-024-02582-2
22.
Minimal shuttle vectors for Saccharomyces cerevisiae.
Synthetic Biology 10, ysaf010 (2025). doi: 10.1093/synbio/ysaf010
23.
Aerolysin Nanopore Structures Revealed at High Resolution in a Lipid Environment.
Journal of the American Chemical Society 147, 4984–4992 (2025). doi: 10.1021/jacs.4c14288
24.
A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes.
Journal of the American Chemical Society jacs.4c17283 (2025). doi: 10.1021/jacs.4c17283
25.
Rapid and sensitive protein complex alignment with Foldseek-Multimer.
Nature Methods (2025). doi: 10.1038/s41592-025-02593-7
26.
Structural basis of SIRT7 nucleosome engagement and substrate specificity.
Nature Communications 16, 1328 (2025). doi: 10.1038/s41467-025-56529-y
27.
Novel strategies to manage CAR-T cell toxicity.
Nature Reviews Drug Discovery (2025). doi: 10.1038/s41573-024-01100-5
28.
Phase separation of a microtubule plus-end tracking protein into a fluid fractal network.
Nature Communications 16, 1165 (2025). doi: 10.1038/s41467-025-56468-8
29.
Computational design of highly signalling-active membrane receptors through solvent-mediated allosteric networks.
Nature Chemistry (2025). doi: 10.1038/s41557-024-01719-2
30.
Targeting protein–ligand neosurfaces with a generalizable deep learning tool.
Nature (2025). doi: 10.1038/s41586-024-08435-4
31.
Phosphorylation of a nuclear condensate regulates cohesion and mRNA retention.
Nature Communications 16, 390 (2025). doi: 10.1038/s41467-024-55469-3
32.
Interpreting and comparing neural activity across systems by geometric deep learning.
Nature Methods 22, 467–468 (2025). doi: 10.1038/s41592-024-02581-3
33.
Phthalimide derivatives as a new class of papain‐like protease inhibitors in SARS‐CoV‐2.
Archiv der Pharmazie 358, e2400714 (2025). doi: 10.1002/ardp.202400714
34.
Primed for degradation: How weak protein interactions enable molecular glue degraders.
Current Opinion in Structural Biology 92, 103052 (2025). doi: 10.1016/j.sbi.2025.103052
35.
A biodegradable suction patch for sustainable transbuccal peptide delivery.
Journal of Controlled Release 384, 113947 (2025). doi: 10.1016/j.jconrel.2025.113947
36.
Structural determinants of co-translational protein complex assembly.
Cell 188, 764–777.e22 (2025). doi: 10.1016/j.cell.2024.11.013
37.
Bulk Measurement of Membrane Permeability for Random Cyclic Peptides in Living Cells to Guide Drug Development.
Angewandte Chemie International Edition e202500493 (2025). doi: 10.1002/anie.202500493
 

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