Final Project Ideas

FINAL PROJECT IDEAS

• GXM UPTAKE INHIBITOR (s.neoformans, c.gatti)

• EARLY LIVER DAMAGE BIOSENSOR

• BIOSENSOR FOR TOXICOLOGY

GXM UPTAKE INHIBITOR (GXM SHIELD)

Concept: A dual-therapy approach using in silico designed proteins to Shield liver receptors and a non-Fc Sponge to neutralize and redirect GXM to renal clearance.

Aim 1: The Liver Shield (Receptor Antagonism)

Goal: Block the “portals” (CD14, SR-A1, TLR4) that capture GXM into the liver/spleen.

• Step 1: Interface Mapping: Use AlphaFold 3 to map the hydrophobic pockets of human CD14 (residues 1–152) and the trimeric collagenous domain of SR-A1.

• Step 2: Design: Generate small, high-affinity protein “plugs” that mimic GXM but lack its toxic signaling.

• Step 3: Sequence Refinement: Use ProteinMPNN to ensure these binders are highly soluble and stable at physiological pH (7.4).

Primary Toolset: AlphaFold 3, RFdiffusion, ProteinMPNN, PyMOL (visualization).

Aim 2: The GXM Sponge (Non-Fc Sequestration)

Goal: Create a high-affinity “scavenger” protein that binds circulating GXM without triggering liver uptake.

• Step 1: Epitope Modeling: Model the M2 hexasaccharide motif of GXM (focusing on mannose backbone).
• Step 2: Binder Generation: Design a scaffold based on the known GXM-binding peptide sequence.

Primary Toolset: BindCraft, AlphaFold 3 (Carbohydrate module), Rosetta (stability testing).

Aim 3: Systems Modeling & Clearance Simulation

Goal: Quantify the therapeutic window and “redirection” efficiency.

• Step 1: Pharmacokinetic (PK) Modeling
• Step 2: Competitive Binding Simulation. Simulate the “Shield” occupying 90% of Kupffer cell receptors before the “Sponge” is released.
• Step 3: Clearance Prediction. Calculate the rate of Renal Clearance vs. Liver Accumulation for the Sponge-GXM complex.

Primary Toolset: PK-Sim, MATLAB (SimBiology), R (mrgsolve).

Experimental Validation (Wet Lab)

1. Production: Synthesize the Aim 1 & 2 proteins via E. coli expression or mRNA-LNP delivery.
2. Binding: Confirm the Sponge’s affinity for purified GXM.
3. Competition: Perform a Flow Cytometry assay on human HepG2 or Kupffer cells to prove the Shield prevents GXM uptake.

• CD14 Structural Domains: Structural basis of CD14-Lipid Interaction
• AlphaFold 3 Capabilities: Isomorphic Labs Whitepaper
• GXM Sequestration Dynamics: The Casadevall Lab Papers
• https://pubs.rsc.org/en/content/articlelanding/2020/sc/d0sc01249a
• https://pmc.ncbi.nlm.nih.gov/articles/PMC1307074/
• https://pmc.ncbi.nlm.nih.gov/articles/PMC1346664/#:~:text=There%20are%20several%20direct%20and,to%20the%20pathogenesis%20of%20cryptococcosis.

Group Final Project: L Protein Design

https://pages.htgaa.org/2026a/jamilet-ordonez-villacis/labs/week-05-lab-protein-design-part-ii/index.html

Individual Final Project

EARLY LIVER DAMAGE BIOSENSOR

Abstract:

Liver disease is a major global health issue, and early detection of liver damage remains challenging due to the need for centralized laboratory testing and reliance on single-biomarker diagnostics. This project addresses the need for a rapid, low-cost, and accessible diagnostic tool for early and accurate detection of liver injury. The overall objective is to develop a paper-based, cell-free biosensor capable of detecting and grading liver damage from a finger-prick blood sample.

The project hypothesizes that a multiplex biosensing system integrating synthetic gene circuits can accurately distinguish between different levels of liver damage by measuring multiple circulating biomarkers simultaneously, specifically miR-122 and Keratin-18 (K18). These biomarkers reflect early hepatocyte stress and cell death, respectively, enabling more robust disease classification than single-marker approaches.

To achieve this objective, the system will use freeze-dried cell-free transcription-translation (TX-TL) systems embedded in paper-based microfluidic devices. Each biomarker will be detected through engineered genetic circuits such as RNA-responsive toehold switches or aptamer-based recognition systems, producing a measurable colorimetric or fluorescent output. Signals from multiple detection zones will be integrated into a computational scoring model that assigns a liver damage grade from 0 (healthy) to 4 (critical damage).

This approach is expected to demonstrate that synthetic biology-based biosensors can enable rapid, multiplex, point-of-care diagnostics for liver disease using minimal equipment and sample volume.

AIMS