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 deep manifold transformation approach for universal protein representation learning (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 internode 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.