The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides open access to macromolecular structures and offers an extensive structural keyword search infrastructure. However, the current RCSB PDB APIs do not support natural-language semantic search. To overcome this challenge, we propose RAD-PROTEIN, a novel multi-source knowledge-enriched Retrieval-Augmented Protein structure search system that answers user natural-language queries by jointly leveraging heterogeneous structural literature and metadata. In addition, we introduce the first benchmark for text-based protein-structure search evaluation, comprising 454 expert-verified queries. RAD-PROTEIN substantially outperforms baselines, achieving up to +19.8% Recall@1 and +16.1% in MRR with hybrid retrieval and multi-source knowledge enrichment. Comprehensive ablation studies further confirm the robustness of our proposed approach. Finally, we develop an interpretable interface to return top-ranked structures with LLM-generated explanations, enabling efficient human verification and downstream scientific analysis. Code: https://github.com/Cassie07/RAD_PROTEIN