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

Abstract

Protein structure prediction (PSP) has become a major application of artificial intelligence in computational biology, and recent advances in transformer-based large language models (LLMs) and protein language models (PLMs) have substantially improved sequence representation learning and structural inference. Despite rapid methodological progress, existing reviews remain fragmented and provide limited systematic evaluation of methodological trends, biological applicability, and validation maturity. This systematic review synthesises 38 eligible studies published between 2020 and 2025 using a PRISMA-guided methodology and introduces a Biological-Readiness Taxonomy for evaluating LLM- and PLM-based PSP approaches. The proposed framework systematically classifies existing methods according to biological knowledge source, representation mechanism, structural reasoning capability, biological prediction task, validation maturity, and interpretability. The evidence synthesis reveals a shift from sequence-only protein language models (PLMs) toward hybrid and multimodal frameworks integrate structural, evolutionary, and biological information. Although benchmark performance has improved substantially, most representative models continue to rely primarily on computational evaluation, with comparatively limited biological validation, interpretability, and experimental verification. Overall, this review presents a unified synthesis combining taxonomy-driven classification, structured comparative analysis, and critical evaluation of biological readiness for protein language models (PLMs). The proposed framework offers a structured basis for comparing existing approaches and identifies future research priorities centred on biological validation, explainability, and translational applicability.