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.

Multiplexed Surface Protein Detection and Cancer Classification Using Gap-Enhanced Magnetic-Plasmonic Core-Shell Raman Nanotags and Machine Learning Algorithm.

Cancer is the second leading cause of death attributed to disease worldwide. Current standard detection methods often rely on a single cancer marker, which can lead to inaccurate results, including false negatives, and an inability to detect multiple cancers simultaneously. Here, we developed a multiplex method that can effectively detect and classify surface proteins associated with three distinct types of breast cancer by utilizing gap-enhanced Raman scattering nanotags and machine learning algorithm. We synthesized anisotropic magnetic core-gold shell gap-enhanced Raman nanotags incorporating three different Raman reporters. These multicolor Raman nanotags were employed to distinguish specific surface protein markers in breast cancer cells. The acquired signals were deconvoluted and analyzed using classical least-squares regression to generate a surface protein profile and characterize the breast cancer cells. Furthermore, computational data obtained via finite-difference time-domain and discrete dipole approximation showed the amplification of the electric fields within the gap region due to plasmonic coupling between the two gold layers. Finally, a random forest classifier achieved an impressive classification and profiling accuracy of 93.9%, enabling effective distinguishing between the three different types of breast cancer cell lines in a mixed solution. With the combination of immunomagnetic multiplex target specificity and separation, gap-enhancement Raman nanotags, and machine learning, our method provides an accurate and integrated platform to profile and classify different cancer cells, giving implications for identification of the origin of circulating tumor cells in the blood system.

Nanomaterials for Molecular Detection and Analysis of Extracellular Vesicles.

Extracellular vesicles (EVs) have emerged as a novel resource of biomarkers for cancer and certain other diseases. Probing EVs in body fluids has become of major interest in the past decade in the development of a new-generation liquid biopsy for cancer diagnosis and monitoring. However, sensitive and specific molecular detection and analysis are challenging, due to the small size of EVs, low amount of antigens on individual EVs, and the complex biofluid matrix. Nanomaterials have been widely used in the technological development of protein and nucleic acid-based EV detection and analysis, owing to the unique structure and functional properties of materials at the nanometer scale. In this review, we summarize various nanomaterial-based analytical technologies for molecular EV detection and analysis. We discuss these technologies based on the major types of nanomaterials, including plasmonic, fluorescent, magnetic, organic, carbon-based, and certain other nanostructures. For each type of nanomaterial, functional properties are briefly described, followed by the applications of the nanomaterials for EV biomarker detection, profiling, and analysis in terms of detection mechanisms.

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.

Gold Nanorod-Assisted Photothermal Therapy and Improvement Strategies.

Noble metal nanoparticles have been sought after in cancer nanomedicine during the past two decades, owing to the unique localized surface plasmon resonance that induces strong absorption and scattering properties of the nanoparticles. A popular application of noble metal nanoparticles is photothermal therapy, which destroys cancer cells by heat generated by laser irradiation of the nanoparticles. Gold nanorods have stood out as one of the major types of noble metal nanoparticles for photothermal therapy due to the facile tuning of their optical properties in the tissue penetrative near infrared region, strong photothermal conversion efficiency, and long blood circulation half-life after surface modification with stealthy polymers. In this review, we will summarize the optical properties of gold nanorods and their applications in photothermal therapy. We will also discuss the recent strategies to improve gold nanorod-assisted photothermal therapy through combination with chemotherapy and photodynamic therapy.