Beyond Mutations: Inside Immuto’s Quest To Decode Disease Structure

Genomics and proteomics have given researchers powerful ways to understand which genes and proteins are present in disease, but a) they focus on proteins that are altered or mutated, which represent only a small subset of disease biology, and b) they provide limited insight into how those proteins behave in their native environments. It’s critical to look beyond mutation or overexpression when studying disease omics – cells with identical sequences can display profoundly different surface architectures that drive immune evasion, uncontrolled signaling or therapeutic resistance. In many cancers and immune disorders, wild-type proteins can adopt altered shapes or complexes that change their function and make them uniquely druggable.

Immuto recognized that the “shape” of a protein can be as disease-specific as a mutation, yet this structural information has been inaccessible with conventional tools. 

Our platform, which we call Rover, was built to fill that gap. By measuring how proteins fold and present disease-specific epitopes, we can identify targets that were previously invisible to conventional methods. This approach enables the discovery of a new class of therapeutic targets that exist only in the disease state, expanding what is possible in precision drug discovery.

Our discovery process begins with clinically sourced, patient-derived samples that accurately reflect the biology of native disease. Using our proprietary workflow, which incorporates labeling of intact proteins in living models of disease and downstream mass spectrometry for data output, we analyze the living cell surface to detect changes in protein structure, modification and dynamics. We then integrate these empirical data with AI and molecular modeling to map conformations that are present only in diseased cells. The result is a detailed blueprint of the surfaceome at the level of structure, not just composition.

Once we have mapped the conformational landscape and pinpointed disease-specific targets, we transition those findings into our Radar platform to design and refine therapeutic antibodies. The platform screens and optimizes antibodies against conformational epitopes that are exposed only in the diseased state. We use structural constraints derived from the initial proteomic data to guide AI-driven structure prediction, conformational modeling and paratope optimization for affinity maturation. This end-to-end process enables us to generate fully validated, conformation-selective antibodies that can be advanced as antibody-drug conjugates (ADCs), multispecifics or cell therapies.

Most structural biology techniques were designed for purified proteins under controlled conditions, not for complex, heterogeneous, living samples from patients. Methods like X-ray crystallography and cryo electron microscopy require crystallization or freezing, which removes the native microenvironment that influences conformation. Even high-throughput proteomic and footprinting techniques tend to perturb the proteins they aim to measure, leading to artifacts rather than true native-state data.

We developed proprietary techniques that allow us to capture dynamic conformational states in living cells and models of disease. By preserving native context, we can finally read structural differences as they exist inside the disease itself.

Mutation-focused approaches limit discovery to a small fraction of patients whose cancers carry specific genetic alterations. Furthermore, much of the pathology of disease is driven by wild-type pathways that are specifically activated in disease, although they are currently difficult to target due to their ubiquity and non-specificity to disease.

To overcome the non-specificity and blunt approach of interfering with delicate signaling pathways, structural surfaceomics identifies disease-specific conformations of wild-type proteins that arise from signaling, stress or metabolic changes, rather than from mutations. This greatly broadens the potential patient population and opens the door to therapies that target fundamental features of disease biology rather than rare genetic events.

Targeting conformation also delivers a new level of selectivity. A protein that is wild-type in sequence but structurally “misfolded” or activated in a tumor can be targeted without affecting its normal form in healthy tissues. This structural discrimination allows higher dosing, improved safety margins and more consistent efficacy across diverse patient groups. It is a powerful way to make existing biology druggable.

Our technology is platform-based, not disease-restricted. Any condition where the disease drives structural remodeling of surface proteins can benefit. We are already exploring inflammatory and autoimmune diseases, where protein activation and immune cell signaling depend on conformation rather than mutation. These are areas where conventional genomic screens have struggled to identify clear therapeutic entry points.

Other opportunities include neurodegeneration and fibrotic diseases, where stress and aggregation alter surface structures in tissue-specific ways. Because structural surfaceomics can detect these conformational fingerprints directly from patient-derived material, it provides an unbiased way to uncover new, druggable targets across a wide range of pathologies.

Conformational targeting addresses specificity at its most fundamental level. Instead of recognizing a sequence or expression pattern shared by healthy and diseased cells, we design binders that engage only a structure that exists in the disease state. If that structural epitope is not exposed on the surface of normal cells, the drug simply cannot bind, even when the same protein is present elsewhere in the body.

Our Radar antibody discovery workflow reinforces that precision. Every antibody is screened directly in a cellular context against both the disease conformation and its normal counterpart. Structural constraints guide AI-powered optimization for affinity and counter-screening to eliminate cross-reactivity. This process dramatically reduces on-target, off-disease toxicity and allows developers to dose more aggressively while maintaining a wide therapeutic window.

Genomics and transcriptomics are now routine, while structure-based target discovery is completely untapped since we previously lacked the tools and capabilities to observe the architecture of proteins in living, diseased cells. After more than a decade of foundational research, Immuto has developed a suite of technologies that finally make it possible to probe the natural biology of proteins and, for the first time, uncover how proteins change their structures in diseased environments.   Our platform automates and scales this process, linking structural insights directly to therapeutic design. We are now demonstrating how rapidly our approach moves from patient samples to validated antibodies.