Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protein language models and graph neural networks. These models can capture intrinsic patterns from protein sequences and structures through masking and task-related losses. However, the learned protein representations are usually not well optimized, leading to performance degradation due to limited data, difficulty adapting to new tasks, etc. To address this, we propose a new \underline{d}eep \underline{m}anifold \underline{t}ransformation approach for universal \underline{p}rotein \underline{r}epresentation \underline{l}earning (DMTPRL). It employs manifold learning strategies to improve the quality and adaptability of the learned embeddings. Specifically, we apply a novel manifold learning loss during training based on the graph inter-node similarity. Our proposed DMTPRL method outperforms state-of-the-art baselines on diverse downstream tasks across popular datasets. This validates our approach for learning universal and robust protein representations. We promise to release the code after acceptance.
Enhancing Protein Representation Learning via Manifold Restore Mixing
Software College, Northeastern University（东北大学软件学院）；Tianjin University（天津大学）；School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计）
Learning Harmonic Molecular Representations on Riemannian Manifold
ByteDance Research（字节跳动研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））
Implementation of The Double-Centroid Reduced Representation of Proteins and its Application to the Prediction of Ligand Binding Sites and Protein-Protein Interaction Partners Using FORTRAN 77/90 Language
KTH Royal Institute of Technology（皇家理工学院）；University of Amsterdam（阿姆斯特丹大学）；University of Cambridge（剑桥大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Delft University of Technology（代尔夫特理工大学）
Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction
Department of Physics, via Celoria 16, 20133 Milano, Italy（米兰大学物理系）；Department of Physics and INFN, via Celoria 16, 20133 Milano, Italy（米兰大学物理系与意大利国家核物理研究院）
