Cross-Domain Protein Engineering

• Cross-domain generalization is achieved by curating nonredundant datasets and implementing hard splits, ensuring reliable prediction across diverse protein families.
• SE(3)-equivariant/invariant models and multi-modal alignment techniques enhance model robustness by encoding 3D structure and integrating cross-modal data.
• Meta-learning and domain adaptation strategies accelerate swift adaptation to new tasks, significantly improving mutational prediction and therapeutic design.

Cross-domain generalization in protein engineering refers to the capacity of computational models, especially those based on machine learning, to accurately predict or design protein properties, functions, or interfaces beyond the distribution encountered in their training data. This property is fundamental to practical protein engineering scenarios, where model deployment requires robust operation across diverse protein families, experimental setups, or functional requirements. Achieving such generalizability involves methodological and architectural innovations spanning dataset curation, inductive bias, cross-modal integration, domain adaptation, and benchmarking under explicit distribution shifts.

1. Dataset Stratification and Redundancy Control

A primary challenge in cross-domain generalization arises from overfitting to redundant or highly similar samples within benchmark datasets. This leads to models performing well on in-distribution (ID) splits but failing on truly novel (out-of-distribution, OOD) examples. The construction of PPIRef (Bushuiev et al., 2023) epitomizes rigorous dataset stratification, beginning from approximately 837,241 PPI interfaces (PPIRef800K). By applying resolution, method, and buried surface area filters, the set is refined to 300K. The key innovation lies in the removal of near-duplicates using the iDist algorithm—SE(3)-invariant vector embeddings assessed by Euclidean distance—to obtain a highly non-redundant set (PPIRef50K) of ~45,553 interfaces.

Removing redundancy and constructing "hard splits" (e.g., grouping test folds by independent interfaces, not just sequences) directly mitigates data leakage and ensures that models trained on PPIRef see a diversity of binding patterns. This enables valid assessment of a model’s ability to generalize to novel protein–binder variants as encountered in real applications. Models trained on nonredundant datasets such as PPIRef are less prone to memorizing artifacts, a prerequisite for true cross-domain prediction and design.

2. Equivariant and Invariant Geometric Modeling

The architecture of learning models underpins generalization across biophysical domains. SE(3)-equivariant or invariant models, such as PPIformer (Bushuiev et al., 2023) and those employing graph-based or point cloud formalisms (García-Vinuesa et al., 19 Jun 2025), encode 3D structure independent of global rotations or translations. In these approaches, residue-level graphs are constructed with nodes carrying features such as alpha-carbon (X), amino acid types (F₀), and virtual beta-carbon vectors (F₁). Model blocks (e.g., Equiformer) update node features while respecting the symmetries of 3D space, guaranteeing that learned geometric representations are coordinate system independent:

$$f(G, X, E, F_0, F_1) = f(G, X R + 1 t^T, E, F_0, F_1 R)$$

Such symmetry-respecting inductive biases are crucial for generalization: they allow pre-trained embeddings to capture biophysically meaningful patterns that extend across folds, families, and functional domains. Loss formulations are adapted accordingly, with masked modeling losses incorporating label smoothing, amino acid-frequency reweighting, and, in fine-tuning, a log odds formulation directly motivated by thermodynamic antisymmetry.

3. Domain-Adaptive Pretraining and Multi-Modal Alignment

Generalization often benefits from leveraging signals across multiple biological modalities. Recent frameworks (e.g., PAAG (Yuan et al., 18 Apr 2024)) introduce multi-modal learning that fuses high-level textual annotations from databases (UniProtKB, domain/protein function, etc.) with protein sequences. PAAG utilizes dual encoders (text: SciBERT; sequence: ProtBERT/ESM2), aligning them through global and domain-level contrastive losses:

• Annotation-Domain Contrastive (ADC) loss (InfoNCE for domain and annotation alignment)
• Annotation-Protein Matching (APM) and Contrastive (APC) global objectives

This alignment steers the generative process to produce sequences that realize specific domain functions, even when these functions are not explicitly represented in evolutionary or structural data. Experimentally, PAAG achieves much higher success rates for domain-specific design (e.g., zinc-finger: 24.7% vs. 4.7% for baselines; immunoglobulin: 54.3% vs. 22.0%).

4. Out-of-Distribution Robustness and Benchmarking

Explicit evaluation under OOD conditions is required to demonstrate cross-domain generalization. Antibody DomainBed (Tagasovska et al., 15 Jul 2024) adapts the DomainBed framework to model the distribution shifts arising from active antibody design cycles (e.g., varying generative models, antigen targets, assay protocols):

• Domains/environments correspond to distinct “design rounds,” with varying noise, targets (e.g., SARS-CoV-2, HER2), and label generation protocols.
• Standard empirical risk minimization (ERM) is extended to include domain generalization (DG) algorithms: IRM, CORAL, Fish, etc., which promote learning of features invariant to the specific domain.
• Ensemble strategies—both output aggregation and weight averages—further increase reliability.

Analysis of feature representations reveals that OOD robustness is enhanced when DG penalties and ensembling are applied: models attend more to functionally relevant (paratope/epitope) regions, reducing the risk of overfitting to spurious domain-specific signals.

5. Cross-Domain Transfer via Retrieval-Augmented and Generative Models

Modern frameworks increasingly integrate retrieval of prior structural knowledge—spanning diverse binder domains—with generative modeling to effect cross-domain transfer. RADiAnce (Zhang et al., 12 Oct 2025) unifies retrieval and generation within a contrastive latent space:

• Both binding sites and interfaces are encoded by a variational autoencoder; latent representations are aligned by a retrieval loss, such that similar interaction patterns cluster.
• At inference, relevant templates (from peptides, antibodies, protein fragments) are retrieved via fast similarity search and integrated into a conditional latent diffusion process.
• Cross-attention within the diffusion model fuses these prompts, enabling effective transfer of interaction motifs across domains.

Empirically, inclusion of cross-domain structures in the retrieval phase enhances the generation of functional binders, with improvements reported in Amino Acid Recovery, RMSD, ΔΔG, and interaction site match. The method directly demonstrates that knowledge from peptides or antibodies can be leveraged to design functional interfaces in the other domain.

6. Meta-Learning and Few-Shot Generalization for Mutational Prediction

In mutation effect prediction, cross-domain generalization is framed as rapid adaptation across diverse proteins and measurement conditions. Applying Model-Agnostic Meta-Learning (MAML) (Badrinarayanan et al., 23 Oct 2025), a transformer is trained with meta-parameters $\theta$ optimized so that few gradient steps suffice to adapt to new tasks:

• Inner loop: computes gradient updates for a specific task (support set), updating $\theta$ to $\theta'_\tau$.
• Outer loop: updates the original parameters $\theta$ to minimize loss on query sets after adaptation.

A novel mutation encoding—using separator tokens to split the sequence around the mutated site—addresses transformers' intrinsic limitations with positional context (e.g., avoiding [UNK] tokens for mutation indices). This strategy results in major improvements: a 29% increase in accuracy for functional fitness with 65% reduced training time, and 94% better accuracy for solubility with 55% faster training compared to conventional fine-tuning.

The episodic and context-driven training structure inherent in meta-learning allows robust transfer across protein families and measurement setups, supporting early-stage industrial and precision bioengineering applications.

7. Implications for Foundation Model Development and Translational Protein Engineering

Unified architectural and data-centric advances described above collectively establish the foundation for protein engineering models capable of robust, cross-domain generalization. The integration of diverse, nonredundant datasets; SE(3)-equivariant models; cross-modal and contrastive alignment strategies; retrieval-augmented diffusion frameworks; and meta-learning for task-rapid adaptation are central elements.

Such advances translate to direct improvements in real-world applications:

• Reliable prediction of mutation effects for therapeutic protein design, with case studies showing successful SARS-CoV-2 antibody optimizations and improved thrombolytic staphylokinase mutants (Bushuiev et al., 2023).
• High-throughput screening capabilities with speed improvements over force-field methods.
• Design of novel or multi-domain proteins guided by text, structural, or prior interface knowledge.
• Transfer of knowledge across protein, peptide, and antibody domains for improved interface generation and drug discovery (Zhang et al., 12 Oct 2025).

These capabilities lower the risk of spurious correlations, enhance sample efficiency, and enable a future in which protein engineering leverages foundation models that generalize as effectively as those in natural language processing, accelerating innovation in biotechnology and biomedicine.