SOPPCL: Sequence-based Order Parameter Prediction with Contrastive Learning

Protein dynamics are pivotal to biological function, and elucidating these dynamic properties is essential for understanding their behavior in cellular processes. Nuclear magnetic resonance (NMR) spectroscopy quantifies residue-specific motional freedoms through order parameters (S²), thereby providing critical insights into local structural flexibility and conformational dynamics. Nevertheless, accurate prediction of NMR order parameters remains a critical challenge in structural biology. Traditional approaches often rely on experimental structures or suffer from limited accuracy when using sequence-based methods. To address this challenge, we introduce SOPPCL, a novel deep learning framework that significantly improves sequence-based prediction of order parameters by integrating advanced protein sequence representations and contrastive learning. Our method leverages the ESM-2 protein language model to capture high-dimensional semantic features from amino acid sequences and employs HHblits-derived HMM profiles to encode evolutionary information. To further enhance feature discriminability, SOPPCL introduces a contrastive learning module that aligns and optimizes the fused representations of ESM-2 and HMM features by maximizing mutual information between positive pairs while minimizing similarity among negative pairs. Subsequently, the refined features are fed into a regression network to predict order parameter values. Evaluation on a benchmark dataset containing 10 proteins shows that SOPPCL achieves a Pearson correlation coefficient of 0.845 and a root mean square error of 0.132, surpassing sequence-only baselines such as DynaMine (PCC=0.464, RMSE=0.178) by 82% and 26% relative improvements, respectively. By eliminating reliance on structural data, SOPPCL provides a powerful computational approach for investigating protein dynamics directly from sequence information, with potential applications in NMR-assisted drug design and functional annotation. Altogether, our study highlights the synergistic benefits of integrating protein language models, evolutionary information, and contrastive learning for advancing the prediction of biomolecular properties.