MOTIVATION: Protein-protein interactions (PPIs) govern virtually all cellular processes, and a single mutation within a PPI can significantly impact protein functionality, potentially leading to diseases. While numerous approaches have emerged to predict changes in the free energy of binding due to mutations (ΔΔGbind), most lack precision. Recently, protein language models (PLMs) have shown powerful predictive capabilities by leveraging both sequence and structural data from protein complexes, yet they have not been optimized specifically for ΔΔGbind prediction. RESULTS: We developed an approach, ProBASS (Protein Binding Affinity from Structure and Sequence), to predict the effects of mutations on ΔΔGbind using two most advanced PLMs, ESM2 and ESM-IF1, which incorporate sequence and structural features, respectively. We first generated embeddings for each PPI mutant from the two PLMs and then fine-tuned ProBASS by training on a large dataset of experimental ΔΔGbind values. When training and testing were done on the same PPI, ProBASS achieved correlations with experimental ΔΔGbind values of 0.83 ± 0.05 and 0.69 ± 0.04 for single and double mutations, respectively. Additionally, when evaluated on a dataset of 2,325 single mutations across 131 PPIs, ProBASS reached a correlation of 0.81 ± 0.02, substantially outperforming other PLMs in predictive accuracy. Our results demonstrate that refining pre-trained PLMs with extensive ΔΔGbind datasets across multiple PPIs is a successful approach for creating a precise and broadly applicable ΔΔGbind prediction model, facilitating future protein engineering and design studies. ProBASS's accuracy could be further improved through training as more experimental data becomes available. AVAILABILITY AND IMPLEMENTATION: ProBASS is available at: https://colab.research.google.com/github/sagagugit/ProBASS/blob/main/ProBASS.ipynb.

Although the concept of using brain waves, e.g. Electroencephalogram (EEG), for personal identification has been validated in several studies, some unanswered practical and theoretical questions prevent this technology from further development for commercialization. Based on a well-designed personal identification experiment using EEG recordings, this study addressed three of these questions, which are (1) feasibility of using portable EEG equipment, (2) necessity for controlling factors influencing EEG, (3) the optimal set of features. With our understanding of the answers to these questions, the EEG-based personal identification system we built achieved an average accuracy of 97.5% on a dataset with 40 subjects. Results of this study provided supporting evidence that EEG-based personal identification from proof-of-concept to system implementation is promising.