Direct calculation of relative binding affinities between antibodies and antigens is a long-sought goal. However, despite substantial efforts, no generally applicable computational method has been described. Here, we describe a systematic free energy perturbation (FEP) protocol and calculate the binding affinities between the gp120 envelope glycoprotein of HIV-1 and three broadly neutralizing antibodies (bNAbs) of the VRC01 class. The protocol has been adapted from successful studies of small molecules to address the challenges associated with modeling protein–protein interactions. Specifically, we built homology models of the three antibody–gp120 complexes, extended the sampling times for large bulky residues, incorporated the modeling of glycans on the surface of gp120, and utilized continuum solvent-based loop prediction protocols to improve sampling. We present three experimental surface plasmon resonance data sets, in which antibody residues in the antibody/gp120 interface were systematically mutated to alanine. The RMS error in the large set (55 total cases) of FEP tests as compared to these experiments, 0.68 kcal/mol, is near experimental accuracy, and it compares favorably with the results obtained from a simpler, empirical methodology. The correlation coefficient for the combined data set including residues with glycan contacts, R2 = 0.49, should be sufficient to guide the choice of residues for antibody optimization projects, assuming that this level of accuracy can be realized in prospective prediction. More generally, these results are encouraging with regard to the possibility of using an FEP approach to calculate the magnitude of protein–protein binding affinities.

Protein metabolism, consisting of both synthesis and degradation, is highly complex, playing an indispensable regulatory role throughout physiological and pathological processes. Over recent decades, extensive efforts, using approaches such as autoradiography, mass spectrometry, and fluorescence microscopy, have been devoted to the study of protein metabolism. However, noninvasive and global visualization of protein metabolism has proven to be highly challenging, especially in live systems. Recently, stimulated Raman scattering (SRS) microscopy coupled with metabolic labeling of deuterated amino acids (D-AAs) was demonstrated for use in imaging newly synthesized proteins in cultured cell lines. Herein, we significantly generalize this notion to develop a comprehensive labeling and imaging platform for live visualization of complex protein metabolism, including synthesis, degradation, and pulse–chase analysis of two temporally defined populations. First, the deuterium labeling efficiency was optimized, allowing time-lapse imaging of protein synthesis dynamics within individual live cells with high spatial–temporal resolution. Second, by tracking the methyl group (CH3) distribution attributed to pre-existing proteins, this platform also enables us to map protein degradation inside live cells. Third, using two subsets of structurally and spectroscopically distinct D-AAs, we achieved two-color pulse–chase imaging, as demonstrated by observing aggregate formation of mutant hungtingtin proteins. Finally, going beyond simple cell lines, we demonstrated the imaging ability of protein synthesis in brain tissues, zebrafish, and mice in vivo. Hence, the presented labeling and imaging platform would be a valuable tool to study complex protein metabolism with high sensitivity, resolution, and biocompatibility for a broad spectrum of systems ranging from cells to model animals and possibly to humans.

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Protein degradation is a regulatory process essential to cell viability, and its dysfunction is implicated in many diseases such as aging and neurodegeneration. Here we report stimulated Raman scattering microscopy coupled with metabolic labeling of 13C-phenylalanine to visualize protein degradation in living cells with subcellular resolution. We choose the ring breathing modes of endogenous 12C-phenylalanine and incorporated 13C-phenylalanine as protein markers for the original and nascent proteome, respectively, and quantified the decay of the former through ratio maps. We demonstrated time dependent imaging of proteomic degradation in mammalian cells under steady-state condition and various perturbations including oxidative stress, cell differentiation, and huntingtin protein aggregation.