Expanding the scope of protein language modeling to protein-protein interactions with MSA Pairformer.

Hybrid Approach to Protein–Protein Complex Affinity Prediction Based on Language Models and Molecular Dynamics

HyBind-NN is developed, a multimodal graph neural network that integrates protein language models (PLMs) with 3D structural and dynamic datasets to predict protein–protein and protein–peptide affinity, and it is demonstrated that combining ESM-2 sequence embeddings with precise 3D Voronoi spatial geometry enables accurate affinity predictions across diverse structural datasets.

Decoding viral protein sequences by large language models

Abstract Large language models (LLMs) for biological sequences are transforming computational biology, enabling a nuanced understanding of protein and nucleotide sequence data. Recent models, including ESM2, ESM3, AlphaGenome, Evo-1, and Evo-2, adapt natural language processing principles to the biological domain by learning high-dimensional hidden representations that capture evolutionary constraints, structural patterns, and functional motifs. This mini-review summarizes recent developments in devising and applying such models, emphasizing viral protein analysis. We highlight studies that have leveraged sequence-based LLMs in the protein domain (i.e. protein language models, or PLMs) for important application tasks such as viral protein annotation, variant effect prediction, and immune escape characterization. Additionally, we present a benchmark evaluation of these state-of-the-art protein language models to evaluate their core ability to capture evolutionary relationships between viral protein sequences. By discussing the opportunities and challenges of PLMs, the review outlines a road map for the potential application of LLMs in empowering virology research and pathogen surveillance.

The Words of Proteins: Motif-Level Language Modeling for Interpretable Protein Generation

What are the fundamental units of protein sequences? Most protein language models treat amino acids as tokens, yet biological functions are not encoded at the single-residue level. Instead, they emerge from combinations of residues that form functional units that corresponds to conserved sequence motifs. Just like how modern language models work at the level of learned sub-word units instead of characters, we argue that explicitly modeling at the functional motif level provides both mechanistic insight into sequence-function relationships and interpretable control over protein generation. We demonstrate this framework on metalloproteins, where similar coordination chemistry is shared at the structure level yet how sequence controls metal specificity remains underexplored. We develop a three-step workflow: (1) construction of a dictionary of functional motifs; (2) prediction of the next motif and inter-motif gaps; and (3) sequence infilling given the predicted anchor motifs. Motif analysis confirms that the extraction captures known metal-coordination chemistry. Compared to random masking, motif-guided generation improves Conserved Domain Database annotation rates with greater target-family enrichment. Compared to generation approaches that use BPE tokenization, our approach achieves more specific hits on metalloprotein families and substantially reduces off-target annotations. Generation output directly mirrors dictionary composition, demonstrating that motif vocabularies provide explicit control over generation scope. AlphaFold 3 structure prediction with explicit metal ions confirms plausible coordination geometry, validating functional binding sites in generated sequences. Together, functional motif modeling enables interpretable, controllable protein generation, an important step toward compositional design of novel protein functions.

Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling

Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation. Recent advances in deep learning have transformed the field from multiple sequence alignment (MSA)-driven monomer folding into broader frameworks capable of modeling protein complexes and increasingly heterogeneous molecular systems. Existing reviews have summarized this progress from the perspectives of representative models, application domains, and protein design. Building on these efforts, this review focuses on the methodological evolution of the field itself. It examines recent developments through three closely related dimensions: representations and data, architectures and learning strategies, and confidence and evaluation. Within this perspective, the field is organized into four methodological phases and three cross-cutting transitions: from explicit evolutionary coupling features and early contact prediction to learned sequence representations in AlphaFold2, RoseTTAFold, and ESMFold; from protein-only monomer folding to increasingly integrated modeling of heterogeneous molecular systems in AlphaFold-Multimer, RoseTTAFoldNA, and AlphaFold3; and, more recently, from prediction-oriented structure inference to design-oriented generative modeling in RFdiffusion and related frameworks. This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.

PLMView: collaborative protein language model representations for fast and scalable specialized protein function inference

The functional classification of protein sequences remains a major bottleneck in biology. Although protein language model (PLM)-based approaches have substantially improved broad protein function prediction, most protein sequences still lack precise annotation at the level of specialized functions—the fine-grained molecular roles that define specificity within protein families. We present PLMView, an unsupervised framework for fine-grained protein function classification directly from sequence. PLMView reframes protein function inference as a relational problem: instead of embedding sequences in isolation, it positions them within a collaborative functional space defined by comparisons with PLM embeddings of anchor sequences, thereby capturing subtle sequence–function relationships. Without requiring labeled data, family-specific training, or PLM fine-tuning, PLMView accurately distinguishes specialized functions among homologous proteins and highlights residues likely to determine functional specificity. The method achieves high precision while remaining computationally efficient, classifying approximately 10,000 sequences with 1,000 anchors in under 40 minutes; compared with pooled-embedding approaches and, in challenging cases, Sequence Similarity Networks, PLMView provides finer and more biologically coherent functional resolution, while achieving more than 10-fold speed-up over SSN reconstruction on datasets of this scale. Applications to thioredoxins, visual opsins, and Tara Oceans environmental diatom cold-shock proteins show that PLMView can move from interpretable residue-level determinants in well-studied protein families to large-scale environmental functional discovery, linking molecular specialization to ecological distribution and transcriptional deployment across the global ocean.

Prot-ΔΔG: Prediction of protein–protein binding affinity changes upon mutations with pre-training strategies