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Prediction and characterization of protein–protein interfaces that bind intrinsically disordered protein regions

University of British Columbia

Intrinsically disordered protein regions (IDRs) constitute a significant portion of our proteome but have traditionally received less attention than folded domains, making IDRs a focus of ongoing research. These protein regions that are not folded prior to binding have functional importance, contradicting the protein structure–function paradigm. One mechanism through which IDRs function is by forming interactions with protein partners through interaction-mediating elements, including molecular recognition features (MoRFs). Computational biologists have developed many protein-sequence-based methods for predicting IDRs and MoRFs and have applied them in proteome-wide studies, leading to the recognition of their significant roles in regulatory and signaling pathways, housekeeping proteins, and interaction network hubs. IDRs’ involvement in these processes made them attractive targets for research and therapy. However, the folded (globular) proteins interacting with IDRs have received less attention. We developed a structure-based protein interface predictor for binding sites of IDRs named IDRBind, which incorporated features specific to MoRF binding sites with ideas from existing globular protein interface predictors. IDRBind was developed using machine learning and was trained on MoRF–globular complex structures. It consists of two gradient boosted trees models that are combined using a conditional random fields (CRF) model. The structural data used for the development of IDRBind was also useful for characterizing and comparing IDR and globular interactions. In this thesis, I will cover the development and benchmarking of IDRBind and examine the properties of MoRF interactions with comparisons to those of globular proteins and peptides. IDRBind exhibits high performance on predicting both MoRF and peptide binding sites. Our analysis also revealed that MoRF binding sites are positioned between those of peptide and globular proteins on multiple measured properties, in agreement with the performance trends of IDRBind. The differentiating characteristics of IDR-mediated interactions were further investigated by comparing the localization patterns of mutations. Despite the flexibility of IDRs, the interaction surfaces of the IDR complex structures are just as enriched in disease-associated mutations as globular interactions. Their prominent roles in disease, especially in cancer, as well as attributes that favor drug targeting, make IDR interactions a fascinating topic for research.

Text

2020-12-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/72797

Biochemistry and Molecular Biology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/