Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein–Protein Binding Affinity

Protein–protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein–protein complex formation and defining the structure–function relationship. Despite advancements in understanding protein–protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein–protein interface. We found that the numbers of specific amino acids at the protein–protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein–Protein Binding Affinity

Protein–protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein–protein complex formation and defining the structure–function relationship. Despite advancements in understanding protein–protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein–protein interface. We found that the numbers of specific amino acids at the protein–protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry

Dual Imaging Single Vesicle Surface Protein Profiling and Early Cancer Detection

Single vesicle molecular profiling has the potential to transform cancer detection and monitoring by precisely probing cancer-associated extracellular vesicles (EVs) in the presence of normal EVs in body fluids, but it is challenging due to the small EV size, low abundance of antigens on individual vesicles, and a complex biological matrix. Here, we report a facile dual imaging single vesicle technology (DISVT) for surface protein profiling of individual EVs and quantification of target-specific EV subtypes based on direct molecular capture of EVs from diluted biofluids, dual EV-protein fluorescence-light scattering imaging, and fast image analysis using Bash scripts, Python, and ImageJ. Plasmonic gold nanoparticles (AuNPs) were used to label and detect targeted surface protein markers on individual EVs with dark-field light scattering imaging at the single particle level. Monte Carlo calculations estimated that the AuNPs could detect EVs down to 40 nm in diameter. Using the DISVT, we profiled surface protein markers of interest across individual EVs derived from several breast cancer cell lines, which reflected the parental cells. Studies with plasma EVs from healthy donors and breast cancer patients revealed that the DISVT, but not the traditional bulk enzyme-linked immunosorbent assay, detected human epidermal growth factor receptor 2 (HER2)-positive breast cancer at an early stage. The DISVT also precisely differentiated HER2-positive breast cancer from HER2-negative breast cancer. We additionally showed that the amount of tumor-associated EVs was tripled in locally advanced patients compared to that in early-stage patients. These studies suggest that single EV surface protein profiling with DISVT can provide a facile and high-sensitivity method for early cancer detection and quantitative monitoring