ONHS Report: DARPA-PA-26-10

HUBBLE DRAFT Program Announcement (PA)

Solicitation ID: DARPA-PA-26-10_DRAFT

Agency: DARPA/BTO

Type: Presolicitation

Deadline: 2026-10-02T15:00:00 ET

Eligibility: Maybe (Conditional)

Funding: N/A

Doc Type: BAA

Clearance: CUI

Program Manager: Dr. Abhishek Singharoy

Last Updated: 2026-09-25 16:05

Analyzed File: DRAFT_HUBBLE PA.pdf

Eligibility Reasoning

UARCs and FFRDCs including National Labs... are highly discouraged from proposing against this solicitation as awards to UARCs or FFRDCs will only be made by exception. UARCs and FFRDCs interested in this solicitation, either as a prime or a subcontractor, should contact the Agency Point of Contact (POC) listed in the Overview section prior to the proposal (or abstract) due date to discuss potential participation as part of the government team or eligibility as a technical performer.

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

Summary

The Defense Advanced Research Projects Agency (DARPA) High-throughput Unmasking of Biological Interactomes with Binding Landscape Emulators (HUBBLE) program aims to develop an in-silico capability to map the entire human protein-protein interactome (PPI). The program's central technical approach involves creating low-dimensional, physics-based 'signatures' of PPIs by parameterizing their 'diffusive binding landscape' to dramatically reduce computational complexity. The ultimate goal is to generate a 'hypercatalog' of over 10 million unique, context-aware PPIs. The utility of this capability will be demonstrated through its application to Traumatic Brain Injury (TBI), with the objective of identifying novel diagnostic, prognostic, and therapeutic targets. The 30-month program is structured in two phases: Phase 1 (12 months) will focus on developing the initial capability and creating a TBI-focused interactome of approximately 100,000 PPIs. Phase 2 (18 months) will scale this effort to achieve the 10 million PPI hypercatalog, enhance prediction accuracy for various interaction parameters (e.g., binding energy, residence time), and model diverse interaction types from dimers to biocondensates across numerous cellular contexts. Performer models will be validated against government-provided experimental data.

Overall Technical Areas

In-Silico Protein Interaction MappingHigh-Throughput Computational BiologyPPI Binding Landscape ModelingBiophysical Interaction SignaturesThermodynamic and Kinetic ModelingComputational Dimensionality ReductionStatistical Learning for InteractomesAI for Protein Interaction PredictionContext-Aware Interactome ModelingTraumatic Brain Injury (TBI) ProteomicsComputational Biomarker DiscoveryMulti-protein Complex SimulationBiomolecular Condensate Modeling

Probable LBNL Areas

BSACSAEESAESALD· In Vitro/In Vivo Validation

Focus Areas

Example Priorities:

HUBBLE posits that a physics-based signature governs PPIs: the complex dynamics of interaction events can be effectively described by the topology of its 'diffusive binding landscape'.
An in-silico approach for decreasing the dimensionality of PPI energy landscapes.
The essential 'signature' of this landscape, and thus the interaction itself, can be parameterized by two key sets of topological features (Figure 1): Metastable Minima and Transition Pathways.
Simulation methods leverage different dimensionality-reduction methods to simulate interactions and diffusions.

Specific Technical Skills:

Computational BiophysicsMolecular Dynamics SimulationStatistical MechanicsFree Energy CalculationDimensionality ReductionStructural BiologyHigh-Performance ComputingBiophysical ModelingProtein Kinematics

Potential PIs for this Area:

Name	Score	Organization
Andy DeGiovanni	0.78	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Structural Biology
Susan Marqusee	0.77	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Structural Biology
Mohammad Kaazem Pur Mofrad	0.77	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Protein Kinematics
Lisa Claus	0.76	CSA \| NERSC \| Sci Eng & Workflows Department · High-Performance Computing
Banumathi Sankaran	0.76	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Computational Biophysics
Michal Hammel	0.75	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Biophysical Modeling
Ahmad Omar	0.75	ESA \| MSD \| Material Physics · Molecular Dynamics Simulation
Piotr Zarzycki	0.74	EESA \| EG \| Geochemistry Dept · Free Energy Calculation
Horst Simon	0.74	CSA \| Computing \| CSDO Divisional Office · Dimensionality Reduction
Yizhi Shen	0.73	CSA \| AMCR \| Applied Mathematics · Statistical Mechanics

Example Priorities:

Ultimately, HUBBLE seeks to construct a hypercatalog of >10 million interactions, creating a near-complete PPI atlas...
Performers will achieve a PPI hypercatalog of 10 million unique interactions modeled over >50 unique cellular contexts.
A scalable strategy for modeling PPIs across the entire human proteome.
Phase 2 of the program will focus on scaling and increasing the throughput of Phase 1 strategies by increasing compute resources, leveraging AI strategies and implementing high-throughput PPI experimentation.

Specific Technical Skills:

Machine LearningHigh-Throughput ComputingAlgorithm DevelopmentStatistical LearningLarge-Scale Data ManagementComputational ProteomicsBioinformaticsSystems Biology

Potential PIs for this Area:

Name	Score	Organization
Georgios Pavlopoulos	0.77	BSA \| DOE Joint Genome Institute \| JGI Science Dept · Bioinformatics
Zhong Wang	0.77	BSA \| DOE Joint Genome Institute \| Data Science & Informatics · Bioinformatics
Sharon Greenblum	0.76	BSA \| DOE Joint Genome Institute \| JGI Technology Dept · Systems Biology
Angelos Ioannou	0.76	CSA \| AMCR \| Computer Science · High-Throughput Computing
Devarshi Ghoshal	0.76	CSA \| SciData \| Data Science Applications · Large-Scale Data Management
Cees De Laat	0.75	CSA \| ESNET \| Tech Adv and Engagement · Large-Scale Data Management
Talita Perciano Costa Leite	0.74	CSA \| SciData \| Data Science Research · Machine Learning
Steven Brenner	0.74	BSA \| Env. Genomics & Sys. Biology \| BioSystems Data Science · Computational Proteomics
Michael Mahoney	0.74	CSA \| SciData \| Data Science Research · Statistical Learning
Oscar Antepara	0.74	CSA \| AMCR \| Computer Science · Algorithm Development

Example Priorities:

A strategy for modeling how contextual perturbations to the system alter the energy landscapes of a PPI. (e.g. PTM changes, unique cell types, small molecule interventions, environmental contexts)
By capturing context-aware interactomes, HUBBLE will model how a healthy, nominal brain operates and compare it directly to an injured brain subjected to simulated tactical blast conditions.
Dynamic perturbations of PPI Signatures can distinguish between healthy and injured states.
Performers are expected to capture stable dimers, transient interactions, multiprotein complexes, and biocondensate/ domain formation by end of performance.

Specific Technical Skills:

Systems BiologyPerturbation AnalysisBiochemical Pathway ModelingPost-Translational Modification AnalysisMulti-scale ModelingProtein Complex ModelingBiocondensate SimulationPharmacodynamics

Potential PIs for this Area:

Name	Score	Organization
Sharon Greenblum	0.76	BSA \| DOE Joint Genome Institute \| JGI Technology Dept · Systems Biology
Mohammad Kaazem Pur Mofrad	0.76	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Protein Complex Modeling
Jamie Inman	0.75	BSA \| Biological Systems & Engineering \| Department of BioEngineering and BioMedical Sciences · Systems Biology
Ahmad Omar	0.75	ESA \| MSD \| Material Physics · Biocondensate Simulation
Dorothee Liebschner	0.75	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Protein Complex Modeling
Hector Garcia Martin	0.74	BSA \| Biological Systems & Engineering \| Process Engr & Analytics · Biochemical Pathway Modeling
Banumathi Sankaran	0.74	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Pharmacodynamics
Ishan Srivastava	0.74	CSA \| AMCR \| Applied Mathematics · Multi-scale Modeling
James Demmel	0.73	CSA \| AMCR \| Applied Mathematics · Perturbation Analysis
Christopher Petzold	0.72	BSA \| Biological Systems & Engineering \| Biodesign Dept · Post-Translational Modification Analysis

Example Priorities:

Utility of this capability will be demonstrated in identifying novel diagnostic and therapeutic targets within the interaction space underlying conditions including traumatic brain injury.
HUBBLE’s hypothesis is that TBI biochemical injury patterns can be defined and targeted by shifting the focus from static, isolated biomarkers to dynamic Protein-Protein Interactions (or PPIs).
HUBBLE will provide the molecular-level fingerprints needed to deliver precise mild TBI diagnostics, accurate long-term prognoses, and targeted medical countermeasures...
If specific causal drivers are identified in the PPI space for mTBI by the end of Phase 2, these targets will be evaluated for therapeutic potential in animal models and enter pre-clinical development.

Specific Technical Skills:

Translational MedicineBiomarker DiscoveryComputational Drug DiscoveryNeurobiologyPathophysiologyIn Vitro/In Vivo ValidationMolecular DiagnosticsTherapeutic Target IdentificationNeurotrauma Research

Potential PIs for this Area:

Name	Score	Organization
Maciej Haranczyk	0.74	CSA \| AMCR \| Computer Science · Computational Drug Discovery
Jian-Hua Mao	0.74	BSA \| Biological Systems & Engineering \| Department of BioEngineering and BioMedical Sciences · Biomarker Discovery
Ehud Isacoff	0.74	BSA \| Molecular Biophysics & Integrated Bioimaging \| Cellular & Tissue Image · Neurobiology
Karthik Shekhar	0.74	BSA \| Biological Systems & Engineering \| Biodesign Dept · Neurobiology
Kevin Fan	0.73	LD \| LD \| Strategic Partnerships · Neurotrauma Research
Jennifer Rosenbluth	0.73	· In Vitro/In Vivo Validation
Georgios Pavlopoulos	0.73	BSA \| DOE Joint Genome Institute \| JGI Science Dept · Biomarker Discovery
Oleg Sobolev	0.73	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Computational Drug Discovery
James Fraser	0.72	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Therapeutic Target Identification
Deepika Awasthi	0.71	BSA \| Biological Systems & Engineering \| Biodesign Dept · Pathophysiology

Potential LBNL PIs (Overall)

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

Name	Score	Organization
Georgios Pavlopoulos	0.77	BSA \| DOE Joint Genome Institute \| JGI Science Dept · Computational Biomarker Discovery
Mohammad Kaazem Pur Mofrad	0.76	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Multi-protein Complex Simulation
Piotr Zarzycki	0.76	EESA \| EG \| Geochemistry Dept · Thermodynamic and Kinetic Modeling
Kevin G Knauss	0.76	EESA \| EG \| Geochemistry Dept · Thermodynamic and Kinetic Modeling
Banumathi Sankaran	0.75	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Biophysical Interaction Signatures
Zhe Bai	0.74	CSA \| AMCR \| Computer Science · Computational Dimensionality Reduction
Nikos Kyrpides	0.74	BSA \| DOE Joint Genome Institute \| JGI Science Dept · High-Throughput Computational Biology
Ahmad Omar	0.74	ESA \| MSD \| Material Physics · Biomolecular Condensate Modeling
Steven Brenner	0.74	BSA \| Env. Genomics & Sys. Biology \| BioSystems Data Science · PPI Binding Landscape Modeling
Corie Ralston	0.73	ESA \| MF \| MF Biological Facility · Traumatic Brain Injury (TBI) Proteomics

Fundamental Research Exemption (FRE)

All DARPA-awarded Procurement Contracts and Other Transactions, including those involving Fundamental Research, at a minimum require prime performers and subcontractors to demonstrate compliance with CMMC Level 1, which focuses on the protection of FCI and consists of the security requirements that correspond to the 15 basic safeguarding requirements specified in 48 CFR 52.204-21.