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Protein ENGineering (PENG)

Solicitation ID: DARPA-PS-26-129

Agency: DARPA/DSO

Type: Solicitation
Deadline: 2026-10-19T16:00:00 ET
Eligibility: Maybe (Team Member/MIPR)
Funding: Up to $150,000 per year for an optional FDRTA award and up to $160,000 per student for an optional IIP award. Proposers must include a Rough Order of Magnitude (ROM) cost estimate for the main effort.
Doc Type: PS
Clearance: CUI
Program Manager: PENG@darpa.mil
Last Updated: 2026-08-07 16:03
Analyzed File: DARPA-PS-26-129.pdf

Eligibility Reasoning

For this solicitation, DARPA will not establish new contractual agreements for the participation of FFRDCs... DARPA, under this solicitation, will not award separate contracts to FFRDCs as prime or subawardees but will instead leverage their existing sponsors' agreements... Proposals that include a... FFRDC... as a subcontractor may also be deemed non-conforming unless... cost proposals must exclude their funding, as DARPA will not fund them through the prime.

Note: This is an AI-generated summary...

Summary

The Defense Advanced Research Projects Agency (DARPA) Protein ENGineering (PENG) program aims to create a universal, target-agnostic platform for the precise, programmable editing of endogenous proteins. The goal is to move beyond permanent genomic modifications (like CRISPR) to achieve transient, tunable, and reversible control directly at the protein level. The 42-month, multi-phase program requires performers to integrate three core technologies: 1) AI-driven Programmable Targeting to bind virtually any protein, 2) Generalizable Chemistry (e.g., engineered inteins, co-translational editing) for universal modification, and 3) Multiplexing Capability for simultaneous, non-interfering edits. The program will progress from foundational validation in cell culture (Phase 1A), to a capability demonstration on DARPA-selected targets (Phase 1B), and finally to integration and validation in complex systems like organoids (Phase 2). The ultimate deliverable is a prototype platform that can execute on-demand, multi-site functional edits on proteins in complex biological models, meeting specific metrics for efficiency, reusability, and complexity. The solicitation explicitly excludes approaches based on permanent genomic editing, traditional pharmacology, and in silico modeling without experimental validation.

Overall Technical Areas

Proteome EngineeringGenerative AI for ProteinsComputational Structural BiologySynthetic Biology PlatformsProtein Splicing & LigationCo-translational Protein ModificationMultiplexed Editing SystemsOrganoid & 3D Cellular ModelsDe Novo Protein DesignProtein BiochemistryQuantitative ProteomicsEngineered Intein ChemistryAnimal Model Validation

Probable LBNL Areas

BSACSAESALD· Organoid Culture

Focus Areas

Example Priorities:
  • Bypassing the historical inability to address specific folded domains by utilizing generative deep learning models and structural computational chemistry tools.
  • The computational design of de novo protein binders capable of docking editing machinery to virtually any epitope on a mature, native target protein.
  • Structural AI models now enable the computational design and in silico modeling of editing machinery onto virtually any mature target with near-atomic precision.
Specific Technical Skills:
Generative AIDeep LearningStructural BiologyComputational ChemistryProtein Binder DesignMolecular DockingIn Silico ModelingEpitope MappingBiophysics
Potential PIs for this Area:
NameScoreOrganization
Billy Poon0.75BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Structural Biology
Daniel Minor0.75BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Structural Biology
Mauro Del Ben0.75CSA | AMCR | Computer Science · Computational Chemistry
Line Kristensen0.75BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Epitope Mapping
Satyarth Praveen0.74CSA | SciData | Data Science Applications · Deep Learning
Dorothee Liebschner0.74BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Molecular Docking
Carlos Bustamante0.73BSA | Molecular Biophysics & Integrated Bioimaging | Bioenergetics Dept · Biophysics
Maciej Haranczyk0.73CSA | AMCR | Computer Science · In Silico Modeling
Banumathi Sankaran0.72BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Protein Binder Design
Talita Perciano Costa Leite0.71CSA | SciData | Data Science Research · Generative AI
Example Priorities:
  • Transitioning away from single use point-solution chemistry toward universal mechanisms for protein modification.
  • Engineering generalized intein chassis capable of logic-gated chemistry triggered by specific cellular environments.
  • Utilizing co-translational editing to intercept and modify nascent peptide chains at the ribosomal exit tunnel, bypassing the thermodynamic and steric barriers of fully folded proteins.
  • Integrate novel splicing, ligation, and co-translational techniques to directly recognize, modify, and rewrite endogenous proteins within complex biological architectures.
Specific Technical Skills:
Protein EngineeringSynthetic BiologyIntein EngineeringCo-translational ModificationProtein LigationProtein SplicingBiochemistryEnzyme DesignRibosome Profiling
Potential PIs for this Area:
NameScoreOrganization
John Dueber0.74BSA | Biological Systems & Engineering | Biodesign Dept · Synthetic Biology
Jay Keasling0.74BSA | Biological Systems & Engineering | Biodesign Dept · Synthetic Biology
David Carruthers0.74BSA | Biological Systems & Engineering | Biodesign Dept · Intein Engineering
Banumathi Sankaran0.73BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Protein Engineering
Paul Adams0.72LD | LD | Biosciences · Protein Engineering
Hans Carlson0.72BSA | Env. Genomics & Sys. Biology | Comparative and Funct Genomics · Biochemistry
Adam Deutschbauer0.71BSA | Env. Genomics & Sys. Biology | Comparative and Funct Genomics · Ribosome Profiling
John Conboy0.71BSA | Biological Systems & Engineering | Department of BioEngineering and BioMedical Sciences · Protein Splicing
Miaw-Sheue Tsai0.71BSA | Biological Systems & Engineering | Department of BioEngineering and BioMedical Sciences · Protein Ligation
Corie Ralston0.70ESA | MF | MF Biological Facility · Co-translational Modification
Example Priorities:
  • Deploying multiple, orthogonal, non-interfering editing modules simultaneously.
  • Achieve precise, multi-site reprogramming of biological machines in real-time without destabilizing the mature protein structure or inducing cellular toxicity.
  • This integrated platform will be validated in high-fidelity cellular models (e.g., organoids), to provide an integrated proof-of-concept at the tissue level.
  • The prototype platform should demonstrate robust adaptability and rapid programmability against diverse, previously unannounced protein targets and achieve multiple point-specific, measurable and functional outcomes in complex tissue models and animal models.
Specific Technical Skills:
Systems BiologyCell BiologyTissue EngineeringOrganoid CultureOrthogonal ChemistryFunctional AssaysQuantitative Mass SpectrometryCellular Toxicity AssaysAnimal ModelsPlatform Integration
Potential PIs for this Area:
NameScoreOrganization
Christopher Petzold0.73BSA | Biological Systems & Engineering | Biodesign Dept · Quantitative Mass Spectrometry
Jamie Inman0.73BSA | Biological Systems & Engineering | Department of BioEngineering and BioMedical Sciences · Systems Biology
John Hartwig0.73ESA | CSD | Catalysis · Orthogonal Chemistry
William Riehl0.72BSA | Env. Genomics & Sys. Biology | BioSystems Data Science · Systems Biology
Sunita Ho0.72ESA | ALS | Users · Tissue Engineering
Jennifer Rosenbluth0.71 · Organoid Culture
Carolyn Larabell0.71BSA | Molecular Biophysics & Integrated Bioimaging | Bioenergetics Dept · Cell Biology
Natalia Molchanova0.70ESA | MF | MF Biological Facility · Functional Assays
Aris Polyzos0.70BSA | Molecular Biophysics & Integrated Bioimaging | Cellular & Tissue Image · Cellular Toxicity Assays
Angelos Ioannou0.70CSA | AMCR | Computer Science · Platform Integration

Potential LBNL PIs (Overall)

Note on PI Matching: These suggestions are generated through an AI-driven semantic analysis of LBNL staff profiles.

NameScoreOrganization
John Dueber0.78BSA | Biological Systems & Engineering | Biodesign Dept · Synthetic Biology Platforms
Oleg Sobolev0.78BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Computational Structural Biology
Mohammad Kaazem Pur Mofrad0.78BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Generative AI for Proteins
Banumathi Sankaran0.77BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · De Novo Protein Design
Christopher Petzold0.77BSA | Biological Systems & Engineering | Biodesign Dept · Proteome Engineering
Susan Marqusee0.74BSA | Molecular Biophysics & Integrated Bioimaging | Structural Biology Dept · Protein Biochemistry
Robert Haushalter0.74BSA | Biological Systems & Engineering | Biodesign Dept · Engineered Intein Chemistry
Markus de Raad0.73BSA | Env. Genomics & Sys. Biology | Molecular Ecosystems Bio · Quantitative Proteomics
Jennifer Rosenbluth0.73 · Organoid & 3D Cellular Models
Michal Kosicki0.72BSA | DOE Joint Genome Institute | JGI Technology Dept · Multiplexed Editing Systems

Fundamental Research Exemption (FRE)

As of the date of publication of this solicitation, the Government expects that program goals as described herein may be met by proposed efforts for fundamental research and non-fundamental research. Some proposed research may present a high likelihood of disclosing performance characteristics of military systems or manufacturing technologies that are unique and critical to defense. Based on the anticipated type of proposer (e.g., university or industry) and the nature of the solicited work, the Government expects that some awards will include restrictions on the resultant research that will require the awardee to seek DARPA permission before publishing any information or results relative to the program.