In this paper, we propose a novel MSA (Multiple Sequence Alignment) design model, PLAME, to improve the structure prediction accuracy of low-similarity proteins and orphan proteins. Unlike existing methods, PLAME enhances the evolutionary information by utilizing the evolutionary embedding of pre-trained protein language models, and improves the generation quality through the conservation-diversity loss function. In addition, we propose a new MSA selection method that effectively selects high-quality MSAs and a new sequence quality assessment metric to evaluate MSA quality. On the AlphaFold2 benchmark for low-similarity and orphan proteins, PLAME achieves state-of-the-art performance with consistent performance improvements even in AlphaFold3. We verify the effectiveness of the MSA selection method through ablation studies, and provide insights into the relationship between the prediction quality of AlphaFold and MSA properties through extensive case studies on various protein types. Finally, we show that PLAME can serve as an adapter to achieve AlphaFold2-level accuracy at the inference speed of ESMFold.