PurposeSequenced　Protein-Protein　Interaction　(PPI)　prediction　represents　a　pivotal　area　of　study　in　biology,　playing　a　crucial　role　in　elucidating　the　mechanistic　underpinnings　of　diseases　and　facilitating　the　design　of　novel　therapeutic　interventions.　Conventional　methods　for　extracting　features　through　experimental　processes　have　proven　to　be　both　costly　and　exceedingly　complex.　In　light　of　these　challenges,　the　scientific　community　has　turned　to　computational　approaches,　particularly　those　grounded　in　deep　learning　methodologies.　Despite　the　progress　achieved　by　current　deep　learning　technologies,　their　effectiveness　diminishes　when　applied　to　larger,　unfamiliar　datasets.ResultsIn　this　study,　the　paper　introduces　a　novel　deep　learning　framework,　termed　DL-PPI,　for　predicting　PPIs　based　on　sequence　data.　The　proposed　framework　comprises　two　key　components　aimed　at　improving　the　accuracy　of　feature　extraction　from　individual　protein　sequences　and　capturing　relationships　between　proteins　in　unfamiliar　datasets.　1.　Protein　Node　Feature　Extraction　Module:　To　enhance　the　accuracy　of　feature　extraction　from　individual　protein　sequences　and　facilitate　the　understanding　of　relationships　between　proteins　in　unknown　datasets,　the　paper　devised　a　novel　protein　node　feature　extraction　module　utilizing　the　Inception　method.　This　module　efficiently　captures　relevant　patterns　and　representations　within　protein　sequences,　enabling　more　informative　feature　extraction.　2.　Feature-Relational　Reasoning　Network　(FRN):　In　the　Global　Feature　Extraction　module　of　our　model,　the　paper　developed　a　novel　FRN　that　leveraged　Graph　Neural　Networks　to　determine　interactions　between　pairs　of　input　proteins.　The　FRN　effectively　captures　the　underlying　relational　information　between　proteins,　contributing　to　improved　PPI　predictions.　DL-PPI　framework　demonstrates　state-of-the-art　performance　in　the　realm　of　sequence-based　PPI　prediction.