Javed Aman will present his FPO " Towards Scalable Inference of Protein-Protein Interfaces: Reasoning with structural, functional, and sequence data " on Monday, August 25, 2025 at 3:00 PM in Jadwin 111. The members of Javed ’s committee are as follows: Examiners: Mona Singh (Adviser), Ellen Zhong , Stanislav Shvartsman , Martin Wühr Readers: Olga Troyanskaya , Yuri Pritykin Everyone is invited to attend his talk. Abstract follows below: Protein–protein interactions (PPIs) are central to virtually all biological processes. Understanding how these interactions are mediated, including identifying which residues form the interface, is critical for studying disease mechanisms and designing targeted therapeutics. However, identifying and characterizing PPI interface residues at the proteome scale remains a major challenge due to limitations in experimental coverage and throughput. This dissertation presents three computational approaches for inferring or annotating protein–protein interfaces across different biological and data scales. The first, protein-protein interacDome (PPI-DOME), uses structural data from X-ray crystallography and cryoelectron microscopy to build domain–domain interaction profiles. These profiles identify which positions within domains mediate interactions and enable efficient screening of millions of candidate interactions in seconds, providing a scalable alternative to structure-based computational modeling methods. The second project, Kinase activity and inference dashboard (KINAID), focuses on identifying transient interactions in phosphorylation cascades. It combines orthology mapping, experimentally-derived position weight matrices capturing the specificities of human kinases, and experimentally observed phosphosites to annotate kinase–substrate relationships across over 2,000 kinases from ten model organisms. KINAID enables rapid analysis of high-throughput phosphoproteomic data. The third project, Comparable embeddings from sequence networks (CESN), addresses the need for high-quality, sequence-level representations, or embeddings, suitable for downstream machine learning models. CESN leverages amino acid embeddings generated by protein language models and learns representations that preserve amino acid-level detail across the entire sequence, overcoming the limitations of standard pooling strategies such as mean or CLS token pooling. The CESN approach provides a foundation for machine learning models that predict protein properties or interactions between pairs of proteins. Together, the methods introduced in this thesis contribute to scalable, interface-centric reasoning by leveraging structural information, functional information, and representation learning. They o er complementary strategies for advancing our understanding of how protein interfaces govern molecular interaction, function, and regulation across diverse biological systems.