Protein Language Models (PLMs)

Updated 13 July 2025

Protein Language Models (PLMs) are deep neural architectures that treat protein sequences as a language using techniques like Transformer encoders and decoders.
They convert evolutionary and statistical sequence patterns into actionable insights for structure prediction, function annotation, and protein design.
PLMs leverage large-scale datasets and diverse pretraining objectives, fueling rapid advancements in computational biology and biotechnological applications.

Protein LLMs (PLMs) are deep neural architectures, primarily based on the Transformer framework, that model protein sequences as a "language" and aim to capture the sequence-structure-function paradigm in proteins. These models have rapidly advanced computational protein science, demonstrating profound impacts in structure prediction, function annotation, protein engineering, and more. PLMs are now foundational in translating the statistical and evolutionary patterns of protein sequences into predictive and generative tasks that power both basic research and biotechnology.

1. Foundations and Model Architectures

PLMs treat protein sequences as a linear arrangement of tokens, analogous to words in NLP. The earliest models leveraged word embedding methods such as ProtVec, which applied the word2vec framework to k-mers in protein sequences (2502.06881). This evolved rapidly to contextualized sequence encoders using recurrent neural networks (RNNs), notably mLSTM (as in UniRep) and ELMo-inspired models (SeqVec). The introduction of the Transformer architecture ("Attention is All You Need") precipitated a paradigm shift; most mainstream PLMs now use either:

Encoder-only (BERT-style): Contextual embeddings for each residue, used for per-residue tasks (2206.06583, 2502.06881).
Decoder-only (GPT-style): Autoregressive models such as ProGen and ProtGPT2 for sequence generation (2303.16452, 2504.04453).
Encoder–decoder (T5-style): For sequence-to-sequence applications, e.g., predicting complementary chains (2502.06881).

Key architectural decisions include the choice of pretraining objective (masked LLMing, next-token prediction), the depth and width of networks (e.g., ESM-1b with 33 layers and 650M parameters, Prot42-L with 1.1B parameters and 24 layers (2504.04453)), and variant-specific design such as using MSAs as input (MSA-Transformer).

Positional Encoding and Sequence Length

Transformers lack inherent order awareness; thus, positional encodings—either absolute (sinusoidal or learnable) or relative (such as RoPE [Rotary Positional Encoding])—are added to input token embeddings (2502.06881). Innovations allow for the modeling of sequences up to 8,192 residues (Prot42 (2504.04453)), greatly exceeding early models that truncated at 1,024 residues.

2. Pretraining Data, Objectives, and Training Strategies

PLMs rely on large-scale protein databases: UniRef (50/90/100), Swiss-Prot, TrEMBL, BFD, and metagenomic datasets, sometimes in excess of 50 million sequences (2206.06583, 2502.06881, 2504.04453). Standard objectives involve:

Masked LLMing (MLM): Randomly mask amino acids and train the model to reconstruct them (2206.06583, 2505.20052). This objective, sometimes using dynamic masking strategies, is effective in encoder-based models.
Autoregressive Next-Token Prediction: Used in decoder-only architectures for sequence generation (2303.16452, 2504.04453).
Multi-task Pretraining: Recent advances, such as Ankh3, combine MLM with protein sequence completion tasks to enhance model generalizability (2505.20052).
Contrastive Learning and Structure Alignment: Dual-task frameworks can incorporate structural information from graph neural networks (pGNNs) using contrastive objectives, as in Structure-Aligned PLMs (2505.16896).

Efforts are ongoing to improve compute efficiency. Scaling laws adapted from NLP demonstrate that, within a fixed compute budget, model size should scale sublinearly and token count superlinearly, with diminishing returns in loss after about a single dataset pass (2406.07249).

3. Representation Learning and Downstream Applications

The contextual embeddings learned by PLMs are rich in evolutionary, biophysical, and sometimes functional information. Core downstream applications include:

Structure Prediction: PLMs underpin structure predictors (e.g., Evoformer in AlphaFold, ESMFold) (2206.06583), often excelling at secondary structure and contact map inference (e.g., Evoformer achieves Precision@L ~ 94.6% for contacts).
Function Prediction: LM embeddings are used directly or finetuned for functional annotation tasks, e.g., metal ion binding, antibiotic resistance, and enzyme function (2206.06583, 2412.13519). Evolution-free models (e.g., ESM-1b) can outperform MSA-dependent models in many functional settings.
Protein Engineering and Design: Generative variants are applied to design novel functional proteins (2302.01649, 2303.16452, 2504.04453). Approaches like LM-Design implant lightweight structural adapters for structure-informed sequence generation, while ProtFIM uses fill-in-middle objectives for flexible engineering tasks.
Mutation Effect and Variant Prediction: Protein delta networks (MutaPLM (2410.22949)) and inference-only dropout (2506.14793) improve mutation effect predictions and enable human-readable explanations.

A selection of typical benchmarks:

Application Type	Example Benchmark	Evaluation Metric
Structure	CAMEO, CASP, SCOP, CATH	pTM, sc-TM, contact P@L
Function	TAPE, CAFA, ProteinGym	Classification accuracy, ρ
Protein Design	CATH, GB1, FLIP	Sequence recovery, fitness

4. Integration of Structural and Functional Modalities

PLMs historically focused on sequence, but recent developments incorporate explicit structural or biophysical knowledge. Notable models and methodologies:

Structure-Informed Models: Evoformer in AlphaFold is trained with both MSA and structural supervision (distogram losses), excelling at structure-centric tasks but generally trailing sequence-only models in function prediction (2206.06583).
Multimodal and Diffusion-based Models: DPLM-2 unifies the modeling of sequence and structure using a discrete diffusion process and a lookup-free quantizer for 3D coordinates, enabling joint sequence-structure generation and conditional motif scaffolding (2410.13782).
Meta-learning and Contextual Adaptation: Metalic leverages meta-learning over fitness prediction tasks, achieving state-of-the-art results with far fewer parameters through in-context learning and preference-based loss formulations (2410.08355).
Structure-Aligned Pretraining: Models like SaESM2 infuse inter-protein and intra-protein structural knowledge via contrastive alignment with protein GNNs and structure token prediction heads, delivering significant increases in contact map and mutation effect prediction accuracy (2505.16896).

5. Evaluation Metrics, Benchmarks, and Limitations

Metrics for PLM evaluation are comprehensive and application-specific:

Pretraining Metrics: Perplexity, cross-entropy loss (often exhibiting power-law scaling with model/data size) (2406.07249, 2502.06881).
Structure: Contact precision, TM-score, RMSD, sc-TM.
Function and Fitness: Pearson or Spearman correlation with activity assays, accuracy, F1, ROC/auROC.
Design: Sequence recovery rate, functional validation in synthetic experiments.
Representation Analysis: Interpretability methods (e.g., neuron labeling (2507.06458)) now provide natural language explanations of what features are encoded at each model layer or neuron.

Limitations and challenges identified include:

Compute and Scaling: Most widely-used PLMs may not be compute-optimal; single-pass training is shown to match or outperform much larger, multiply-trained models (2406.07249).
Data Quality and Diversity: Reliance on MSAs can limit application in orphan proteins; data imbalance (e.g., underrepresentation of β-sheet structures) hampers performance on some structural motifs (2303.16452).
Interpretability: Biophysical meaning of internal representations is often opaque, though automated neuron labeling provides a scalable solution (2507.06458).
Annotation and Generalization: Many datasets carry noisy annotation. Long proteins (30–30,000+ residues) and multi-domain architectures are challenging for standard PLMs but addressed in recent long-context variants like Prot42 (2504.04453).

6. Advancements in Protein Design and Engineering

PLMs now underpin rapid, sequence-only binder generation (e.g., Prot42 (2504.04453)), explainable mutation prioritization with cross-modal natural language supervision (MutaPLM (2410.22949)), and RL-guided optimization (DPO_pLM (2412.12979); RL frameworks for sequence design (2407.03154)). The field sees experimental validation successes, including identification of nanomolar EGFR binders using PLM-based RL within hours (2412.12979).

Meta-learning and multi-task approaches (Ankh3 (2505.20052)) offer improved generalization to new protein families and robust prediction of structure-function relationships, promising new directions in protein engineering efficiency. Open-source integration—in frameworks like DeepChem—democratizes access, enabling protein design even for users lacking high-end compute resources (2412.13519).

7. Future Directions and Outlook

Emerging areas include:

Multi-modal and Instruction-Tuned Models: Strong momentum exists toward combining sequence, structure, and textual instructions in a unified generative or predictive framework (2501.10282, 2410.13782).
Scaling Laws and Efficiency: Systematic exploration of model/data scaling for maximal efficiency and minimal environmental footprint (2406.07249).
Explainability and Generative Steering: Automated neuron labeling enables generative steering of protein properties for controlled design (e.g., biochemical and structural trait targeting) (2507.06458).
Benchmark and Tool Standardization: Community benchmarks (e.g., ProteinGym, FLIP, TAPE) and tools for visualization and statistical analysis (PyMOL, TM-align, Foldseek, t-SNE/UMAP) are integral to evaluation and interpretation (2502.06881).
Limitations and Opportunities: Critical issues persist in dataset bias, transferability, annotation quality, and achieving foldable, biologically viable designs, motivating work in robust evaluation and safety protocols (2410.22949).

In summary, PLMs have redefined computational approaches in protein sequence-structure-function analysis. Through advances in architecture, pretraining, integration of structural knowledge, and new interpretability methods, these models are central to the next generation of protein design, functional annotation, and mechanistic understanding in molecular biology.