The review was conducted using a PRISMA-guided systematic literature review methodology, following the Kitchenham guidelines for reproducible evidence synthesis. The authors searched three academic databases, Web of Science, PubMed, and IEEE Xplore, on 2 March 2026, using structured Boolean queries built around large language model terms, protein language model terms such as transformer, BERT, and ESM, and protein structure prediction concepts. The initial search identified 284 records; after deduplication, title and abstract screening, and full-text evaluation against predefined inclusion criteria, the final corpus settled at 38 studies spanning 2020 to 2025. Two reviewers conducted screening with adjudication by a third author, and the complete data extraction sheet has been released as supplementary material to allow independent verification of the entire process.

At the heart of the review lies a technical distinction that is often blurred in popular coverage. Protein language models, or PLMs, are transformer-based models pretrained on protein sequence or structure-augmented sequence data, exemplified by systems such as ESM, ProtBERT, and ProtTrans. General-purpose large language models, by contrast, are trained primarily on natural-language corpora and appear in this literature only when adapted to biological tasks such as literature mining or annotation. The review’s corpus is dominated by dedicated PLMs, which account for 19 of the 38 studies, or 50 percent, followed by general-purpose transformer adaptations at 18.4 percent, with the remainder comprising review and perspective articles. This concentration signals that domain-specific sequence representation learning has become the field’s dominant paradigm.

The technical machinery behind these models is elegant. Transformers employ self-attention mechanisms that capture long-range dependencies within sequential data, and when amino acid sequences are treated as tokens, the models learn contextual relationships between residues directly from massive protein sequence databases. The resulting embeddings encode latent biochemical, evolutionary, and structural information, capturing evolutionary conservation patterns and functional motifs without explicit supervision. Yet the review is careful to note a crucial caveat: sequence-only transformers do not explicitly encode the physicochemical and geometric constraints that govern protein folding, and high-dimensional embedding similarity should not be overinterpreted as evidence of biological realism, particularly for dynamic or poorly characterised protein systems.

To evaluate the field systematically, the authors propose a Biological-Readiness Taxonomy organised around six complementary dimensions: the source of biological knowledge, the representation mechanism, structural reasoning capacity, the biological prediction tasks addressed, validation maturity, and interpretability. Rather than classifying studies by model family, the taxonomy asks whether a model can support biologically meaningful structural inference. The framework is operationalised as an explicit, reproducible scoring rubric, with each dimension scored on an ordinal scale and a composite biological readiness score reported for every study in the corpus. The authors draw an analogy to Technology Readiness Level frameworks used in applied science, and to arguments that AlphaFold predictions constitute testable hypotheses rather than validated structures, but their instrument is domain-specific and multi-dimensional rather than generic.

Applying this framework reveals a clear evolutionary trajectory. The literature has shifted decisively from sequence-only representation learning toward hybrid and multimodal frameworks that integrate evolutionary information from multiple sequence alignments, structural priors, graph-based reasoning, and other biological signals. The review’s quantitative comparison of nine representative foundation-scale models, including AlphaFold2, RoseTTAFold, ESMFold, OmegaFold, AlphaFold-Multimer, AlphaFold3, RoseTTAFold All-Atom, Chai-1, and Boltz-1, traces a progression from single-chain tertiary structure prediction toward comprehensive biomolecular interaction modelling encompassing protein complexes, nucleic acids, and ligands. Earlier methods predicted the shape of individual proteins; the newest systems attempt to model the entire molecular choreography of the cell.

But the validation maturity analysis delivers the review’s most striking numbers. Among the nine representative models, benchmark evaluation remains the predominant validation strategy, accounting for 88.9 percent, or eight of nine models. Only one, AlphaFold3, extended evaluation beyond conventional benchmarking by explicitly comparing predictions against experimentally determined structures. None of the nine reported independent external dataset validation or prospective wet-laboratory validation. The authors are careful to frame this as an observation within an illustrative subset rather than a statistically representative estimate for the entire field, but the pattern is consistent with the corpus-wide taxonomy scores: research priorities remain heavily weighted toward benchmark accuracy, embedding quality, and computational performance, while interpretability, mechanistic biological reasoning, and experimental verification receive comparatively little attention.

The review identifies several systemic challenges that recur across many independent studies. Benchmark dependence is prominent: standardised datasets such as CASP and the Protein Data Bank enable reproducible comparison but draw on well-characterised protein families with extensive homologous sequence information, potentially yielding overly optimistic assessments of robustness on structurally heterogeneous proteins, intrinsically disordered regions, and dynamic conformational states. Transformer architectures also carry quadratic computational complexity with respect to sequence length, limiting scalability to large proteins and proteome-wide applications. Interpretability remains perhaps the most persistent weakness: most models achieve high predictive performance while the reasoning behind their predictions stays opaque, and the few explainability efforts that exist typically offer model-level information such as attention weights rather than biologically valid, experimentally testable explanations.

These limitations matter far beyond academic benchmarking. Accurate protein structure prediction underpins drug discovery, therapeutic protein engineering, biomarker identification, disease diagnosis, and personalised medicine, and the review’s clinical discussion makes clear that the documented gaps in biological interpretability, external validation, and experimental verification would need to close before language-model-based structural predictions could be routinely trusted in therapeutic contexts. The authors also acknowledge the limitations of their own work with unusual candour: the search was restricted to English-language studies in three databases, taxonomy scoring was performed by a single author without inter-rater reliability statistics, and the taxonomy itself remains an unvalidated instrument intended as a starting point for discussion rather than a settled standard.

The review’s ultimate message is a call to rebalance the field’s priorities. Future progress, the authors argue, should be evaluated not only by benchmark accuracy but also by biological validation, structural reasoning capability, interpretability, and translational applicability. They point toward biologically grounded modelling frameworks that integrate structural constraints, evolutionary information, physicochemical knowledge, and multimodal evidence, alongside continued advances in computational efficiency, lightweight foundation models, and explainable AI. The Biological-Readiness Taxonomy offers a practical instrument for tracking that progress, giving researchers and funders a structured way to distinguish models that merely top the leaderboards from those genuinely ready to accelerate discovery in the laboratory. For a field that has reshaped structural biology in half a decade, the next frontier may not be predicting structures better, but proving that the predictions can be believed.

Subject of Research: Systematic review of protein language models for protein structure prediction and a proposed biological readiness taxonomy

Article Title: A systematic review of protein language models for protein structure prediction and a proposed biological readiness taxonomy

Article References: Al-Quraishi, T., Zaboon, W. A., Abawajy, J., & Nowrozy, R. (2026). A systematic review of protein language models for protein structure prediction and a proposed biological readiness taxonomy. Discover Artificial Intelligence, 6(1), Article 1338. https://doi.org/10.1007/s44163-026-02333-6

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02333-6

Keywords: protein language models, protein structure prediction, AlphaFold, transformers, biological validation, interpretability, systematic review, benchmarking, multimodal learning, drug discovery, computational biology, taxonomy