  We introduce a pioneering methodology for boosting large language models in
the domain of protein representation learning. Our primary contribution lies in
the refinement process for correlating the over-reliance on co-evolution
knowledge, in a way that networks are trained to distill invaluable insights
from negative samples, constituted by protein pairs sourced from disparate
categories. By capitalizing on this novel approach, our technique steers the
training of transformer-based models within the attention score space. This
advanced strategy not only amplifies performance but also reflects the nuanced
biological behaviors exhibited by proteins, offering aligned evidence with
traditional biological mechanisms such as protein-protein interaction. We
experimentally observed improved performance on various tasks over datasets, on
top of several well-established large protein models. This innovative paradigm
opens up promising horizons for further progress in the realms of protein
research and computational biology.
