Toward a Mechanistic Atlas of Protein Sequences (MAPS): Interpreting ESM-C embeddings to explain missense variant pathogenicity

Jaanak Prashar

Introduction

As genome sequencing becomes increasingly accessible, it has become a valuable source of clinical information. A sequencing run returns the positions where a patient's genome diverges from the reference. Most differences are harmless, a normal part of human variation, but occasionally one is largely responsible for a patient's illness.

Sorting the harmful from the harmless is the problem of variant effect prediction (VEP). It is hard because many variants are rare, and the same change can be catastrophic in one context and benign in another. It also reaches beyond disease risk to molecular consequences — whether a change disrupts a splice site, perturbs a regulatory signal, or alters how a protein folds. When these questions can be answered, a lab can assign the variant a clinical significance, helping clinicians reach a diagnosis, anticipate a condition's course, choose a treatment, or, just as valuably, rule a suspect variant out.

When the evidence is too thin, the variant is instead classified as one of uncertain significance (VUS). Labs deposit these calls — pathogenic, likely pathogenic, benign, likely benign, or uncertain — with their supporting evidence in ClinVar, the public archive of observed variants and their interpreted significance, and the source of every clinical label in this study. But ClinVar grows by accretion, and the uncertain pile grows fastest: a large fraction of missense entries are VUS, real and observed in patients but too thinly supported to classify. Each is a patient and a clinician left without an answer, sometimes for years. A good computational predictor promises to triage that backlog — flagging the VUS most likely to be pathogenic for follow-up, and reassuring those most likely benign.

In prior work, we introduced EVEE (Evo 2 Covariance Probing), showing that information learned inside a DNA foundation model, Evo 2, is a powerful tool for VEP on ClinVar. Covariance probing, a technique we developed, reads pathogenicity from Evo 2 embeddings across variants of both known and uncertain significance, and yields a mechanistic hypothesis by showing how a variant disrupts a panel of probes for functional genomic information.

However, a DNA model cannot fully capture the protein-structural consequences of a coding change. For the large class of variants that act by altering a protein, there is a further layer of "why": is the mutated residue buried in the core or exposed on the surface, does it line an active site or a binding pocket, is it a cysteine locked into a structural disulfide or a residue anchoring a transmembrane helix? These are the terms a structural biologist reaches for, and they live most naturally in a model that has learned proteins rather than DNA. We therefore extend covariance probing to ESM-C (Evolutionary Scale Modeling Cambrian), a bidirectional transformer trained on billions of protein sequences to predict amino acids from their surrounding context. Because evolution preserves only mutations compatible with a protein's structure and function, this objective forces ESM-C's representations to encode structural protein information. It is this latent knowledge that we probe for variant effects.

ESM-C covariance probing outperforms zero-shot and previous-generation baselines and is competitive with state-of-the-art approaches like AlphaMissense and Evo 2 (EVEE), and its functional information adds mechanistic explanations on top of EVEE. To explore these, we introduce the Mechanistic Atlas of Protein Sequences (MAPS), a viewer of the variant-effect mechanisms ESM-C uncovers. Grounding VEP in a protein language model complements genome-level approaches like EVEE. In future work, combining mechanistic insight across the layers of the central dogma — starting with DNA and protein — is a promising path to fully understanding variant effects.

Results

Predicting pathogenicity from ESM-C embeddings using covariance pooling

To predict pathogenicity, we train probes over covariance pooled embeddings from a frozen ESM-C model. Covariance pooling aggregates a model's per-residue embeddings into a single representation that captures how embedding features co-occur, rather than simply averaging them. The change in this representation from wild-type to mutant is what we probe for pathogenicity. We read probes off layer 80, which a per-layer sweep identifies as the depth where the pathogenicity signal peaks. For training, we used ~50,000 ClinVar variants confidently labelled pathogenic or benign, and evaluated on homology-aware splits so that train and test never share a protein family. We found that ESM-C covariance pooling (test AUROC 0.949) outperforms the zero-shot and ESM2 baselines and is competitive with AlphaMissense (0.943) and Evo 2 covariance (0.940).

Mechanisms of pathogenicity can be explained by interpreting ESM-C embeddings

We've found that ESM-C embeddings carry a strong pathogenicity signal. Can we also extract the biological properties associated with pathogenicity? To test this, we train linear probes for several per-residue annotations spanning structural, topological, functional, and evolutionary features, along with stability and InterPro domains.

To see how these properties are disrupted by a variant, we embed both the wild type and mutant sequences and probe the difference. We collect the shift in each decoded property to form a "disruption profile" for each variant.

Case studies demonstrate how ESM-C reveals pathogenicity mechanisms that can't be found in genomics models

Finally, we return to the original motivation of our work to test whether variant prediction from protein models does provide additional mechanistic information beyond genomic models. In several cases, ESM-C probing provided accurate information on variant mechanisms not captured by Evo 2 probing.

Discussion

In this work, we introduce a mechanistic atlas of functional and structural disruptions that missense variants can plausibly cause in proteins.

Where Evo 2 probing (EVEE) characterizes variant effects at the DNA level, MAPS serves as a complementary view that explains them at the protein level. As our case studies show, ESM-C often captures information beyond the reach of a genome language model.

Several directions could build on this work. Our interpretations are bounded by the annotations available to probe; future work could improve extraction of known annotations or develop feature-discovery methods that surface new ones. Combining genomic and protein language models, along with protein folding models, is another promising avenue, and likely necessary to fully characterize variant effects.

Related Works

Several prior works have explored how protein language models can score how mutations affect function and disease risk. Typically, this is done by reading a variant's effect from sequence likelihoods in a zero-shot manner (Meier et al., 2021; Brandes et al., 2023). Related approaches model evolutionary distributions directly (Frazer et al., 2021) or fold in structural context, as in AlphaMissense (Cheng et al., 2023). Additional signals from sequence prediction models with structure- and energy-based methods can also improve performance in variant effect predictions (Guo et al. 2026). Here, we extend this line of work by applying our covariance pooling method on the latest model in the ESM family.

Another related thread of research focuses on mechanistically explaining what biological information is encoded in protein language models. Many of these methods use sparse autoencoders to decompose dense protein sequence embeddings into interpretable features that map onto known biology — amino acid identity, secondary structure, tertiary contacts, folds, and functional sites (Simon and Zou, 2025; Gujral et al., 2025; Silberg et al., 2025; Candido et al., 2026). Our work differs in that we are interested in interpreting features to characterize clinical disruption. To this end, we strongly believe that integrating information from multiple biological modalities of the central dogma can enhance our understanding of variant disruption.

Our work is most related to that of Fang et al. 2026, where the authors use SAE features for zero-shot variant effect prediction and mechanistic explanations. A key difference is that our pathogenicity predictors are separate from our annotation predictors. In addition, SAEs are unsupervised feature discoverers that use autointerpreted labels, while we take a supervised approach with known, ground-truth annotations. Future work comparing or combining these two approaches may provide a more cohesive mechanistic explanation for pathogenicity.