ESM-PsyPred: Leveraging Protein Language Models for Accurate Prediction of Psychrophilic Proteins.

Psychrophilic proteins, which maintain high activity and stability in low-temperature environments, hold significant potential for industrial and ecological research. However, existing predictive tools predominantly focus on thermophilic proteins, while psychrophilic protein prediction models remain constrained by data scarcity and subtle sequence variations, resulting in suboptimal performance. To overcome these barriers, this study introduces ESM-PsyPred, a computational framework that integrates the evolutionary-scale protein language model ESM-2 with a support vector machine (SVM). By extracting high-dimensional semantic features from protein sequences via ESM-2 and employing an SVM classifier, the model achieves independent test accuracies of 88.9% and 83.9% in binary (psychrophilic vs. mesophilic) and ternary (psychrophilic, mesophilic, thermophilic) classification tasks, respectively, significantly outperforming existing methods. Visualization analyses demonstrate the model's ability to identify critical cold-adaptation signatures. Furthermore, the construction of high-quality datasets, PMTTer and PNPBin, alongside cross-dataset validation, underscores the framework's robust generalization capabilities. The open-source availability of the code (accessible at https://github.com/tust-lamee/ESM-PsyPred ) establishes ESM-PsyPred as an efficient tool for the rational design and industrial development of cold-adapted proteins.