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1. Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations

   https://doi.org/10.1038/s41467-024-49780-2  

   Diaz, Daniel J; Gong, Chengyue; Ouyang-Zhang, Jeffrey; Loy, James M; Wells, Jordan; Yang, David; Ellington, Andrew D; Dimakis, Alexandros G; Klivans, Adam R (December 2024, Nature Communications)

   Abstract Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies. 

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2. Analysis of AlphaFold2 for Modeling Structures of Wildtype and Variant Protein Sequences

   https://doi.org/10.29007/5g4v  

   Kabir, Anowarul; Inan, Toki; Shehu, Amarda (March 2022, EPiC Series in Computing)

   ResNet and, more recently, AlphaFold2 have demonstrated that deep neural networks can now predict a tertiary structure of a given protein amino-acid sequence with high accuracy. This seminal development will allow molecular biology researchers to advance various studies linking sequence, structure, and function. Many studies will undoubtedly focus on the impact of sequence mutations on stability, fold, and function. In this paper, we evaluate the ability of AlphaFold2 to predict accurate tertiary structures of wildtype and mutated sequences of protein molecules. We do so on a benchmark dataset in mutation modeling studies. Our empirical evaluation utilizes global and local structure analyses and yields several interesting observations. It shows, for instance, that AlphaFold2 performs similarly on wildtype and variant sequences. The placement of the main chain of a protein molecule is highly accurate. However, while AlphaFold2 reports similar confidence in its predictions over wildtype and variant sequences, its performance on placements of the side chains suffers in comparison to main-chain predictions. The analysis overall supports the premise that AlphaFold2-predicted structures can be utilized in further downstream tasks, but that further refinement of these structures may be necessary. 

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3. Biophysics-based protein language models for protein engineering

   https://doi.org/10.1038/s41592-025-02776-2  

   Gelman, Sam; Johnson, Bryce; Freschlin, Chase R; Sharma, Arnav; D’Costa, Sameer; Peters, John; Gitter, Anthony; Romero, Philip A (September 2025, Nature Methods)

   Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose mutational effect transfer learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics. We fine-tune METL on experimental sequence–function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL’s ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering. 

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4. Topological machine learning for protein–nucleic acid binding affinity changes upon mutation

   https://doi.org/10.1088/2632-2153/ae1714  

   Liu, Xiang; Wee, Junjie; Wei, Guo-Wei (November 2025, Machine Learning: Science and Technology)

   Abstract Understanding how protein mutations affect protein–nucleic acid binding is critical for unraveling disease mechanisms and advancing therapies. Current experimental approaches are laborious, and computational methods remain limited in accuracy. To address this challenge, we propose a novel topological machine learning model (TopoML) combining persistent Laplacian (from topological data analysis) with multi-perspective features: physicochemical properties, topological structures, and protein Transformer-derived sequence embeddings. This integrative framework captures robust representations of protein–nucleic acid binding interactions. To validate the proposed method, we employ two datasets, a protein–DNA dataset with 596 single-point amino acid mutations, and a protein–RNA dataset with 710 single-point amino acid mutations. We show that the proposed TopoML model outperforms state-of-the-art methods in predicting mutation-induced binding affinity changes for protein–DNA and protein–RNA complexes. 

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5. CAP: Commutative algebra prediction of protein-nucleic acid binding affinities

   https://doi.org/10.1088/2632-2153/ae29bc  

   Zia, Mushal; Suwayyid, Faisal; Hozumi, Yuta; Wee, JunJie; Feng, Hongsong; Wei, Guo-Wei (December 2025, Machine Learning: Science and Technology)

   Abstract An accurate prediction of protein-nucleic acid binding affinity is vital for deciphering genomic processes, yet existing approaches often struggle in reconciling high accuracy with interpretability and computational efficiency. In this study, we introduce commutative algebra prediction (CAP) framework, which couples persistent Stanley–Reisner theory with advanced sequence embedding for predicting protein-nucleic acid binding affinities. CAP encodes proteins through transformer-learned embeddings that retain long-range evolutionary context, and represents DNA and RNA withk-mer algebra embeddings derived from persistent facet ideals, which capture fine-scale nucleotide geometry. We demonstrate that CAP surpasses the SVSBI protein-nucleic acid benchmark and, in a further test, maintains reasonable performance on newly curated protein-RNA and protein-nucleic acid datasets. Leveraging only primary sequences, CAP generalizes to any protein-nucleic acid pair with minimal preprocessing, enabling genome-scale analyses without 3D structural data and promising faster virtual screening for drug discovery and protein engineering. 

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