A λ-Dynamics Investigation of Insulin Wakayama and Other A3 Variant Binding Affinities to the Insulin Receptor.

Insulin Wakayama is a clinical insulin variant where a conserved valine at the third residue on insulin's A chain (ValA3) is replaced with a leucine (LeuA3), weakening insulin receptor (IR) binding by 140-500-fold. This severe impact on binding from a subtle modification has posed an intriguing problem for decades. Although experimental investigations of natural and unnatural A3 mutations have highlighted the sensitivity of insulin-IR binding at this site, atomistic explanations of these binding trends have remained elusive. We investigate this problem computationally using λ-dynamics free energy calculations to model structural changes in response to perturbations of the ValA3 side chain and to calculate associated relative changes in binding free energy (ΔΔGbind). The Wakayama LeuA3 mutation and seven other A3 substitutions were studied in this work. The calculated ΔΔGbind results showed high agreement compared to experimental binding potencies with a Pearson correlation of 0.88 and a mean unsigned error of 0.68 kcal/mol. Extensive structural analyses of λ-dynamics trajectories revealed that critical interactions were disrupted between insulin and the insulin receptor as a result of the A3 mutations. This investigation also quantifies the effect that adding an A3 Cδ atom or losing an A3 Cγ atom has on insulin's binding affinity to the IR. Thus, λ-dynamics was able to successfully model the effects of mutations to insulin's A3 side chain on its protein-protein interactions with the IR and shed new light on a decades-old mystery: the exquisite sensitivity of hormone-receptor binding to a subtle modification of an invariant insulin residue.

A λ-dynamics investigation of insulin Wakayama and other A3 variant binding affinities to the insulin receptor.

Insulin Wakayama is a clinical insulin variant where a conserved valine at the third residue on insulin's A chain (ValA3) is replaced with a leucine (LeuA3), impairing insulin receptor (IR) binding by 140-500 fold. This severe impact on binding from such a subtle modification has posed an intriguing problem for decades. Although experimental investigations of natural and unnatural A3 mutations have highlighted the sensitivity of insulin-IR binding to minor changes at this site, an atomistic explanation of these binding trends has remained elusive. We investigate this problem computationally using λ-dynamics free energy calculations to model structural changes in response to perturbations of the ValA3 side chain and to calculate associated relative changes in binding free energy (ΔΔGbind). The Wakayama LeuA3 mutation and seven other A3 substitutions were studied in this work. The calculated ΔΔGbind results showed high agreement compared to experimental binding potencies with a Pearson correlation of 0.88 and a mean unsigned error of 0.68 kcal/mol. Extensive structural analyses of λ-dynamics trajectories revealed that critical interactions were disrupted between insulin and the insulin receptor as a result of the A3 mutations. This investigation also quantifies the effect that adding an A3 Cδ atom or losing an A3 Cγ atom has on insulin's binding affinity to the IR. Thus, λ-dynamics was able to successfully model the effects of subtle modifications to insulin's A3 side chain on its protein-protein interactions with the IR and shed new light on a decades-old mystery: the exquisite sensitivity of hormone-receptor binding to a subtle modification of an invariant insulin residue.

Multiplexed Signal Ion Emission Reactive Release Amplification (SIERRA) Assay for the Culture-Free Detection of Gram-Negative and Gram-Positive Bacteria and Antimicrobial Resistance Genes.

The global prevalence of antibiotic-resistant bacteria has increased the risk of dangerous infections, requiring rapid diagnosis and treatment. The standard method for diagnosis of bacterial infections remains dependent on slow culture-based methods, carried out in central laboratories, not easily extensible to rapid identification of organisms, and thus not optimal for timely treatments at the point-of-care (POC). Here, we demonstrate rapid detection of bacteria by combining electrochemical immunoassays (EC-IA) for pathogen identification with confirmatory quantitative mass spectral immunoassays (MS-IA) based on signal ion emission reactive release amplification (SIERRA) nanoparticles with unique mass labels. This diagnostic method uses compatible reagents for all involved assays and standard fluidics for automatic sample preparation at POC. EC-IA, based on alkaline phosphatase-conjugated pathogen-specific antibodies, quantified down to 104 bacteria per sample when testing Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa lysates. EC-IA quantitation was also obtained for wound samples. The MS-IA using nanoparticles against S. aureus, E. coli, Klebsiella pneumoniae, and P. aeruginosa allowed selective quantitation of ∼105 bacteria per sample. This method preserves bacterial cells allowing extraction and amplification of 16S ribosomal RNA genes and antibiotic resistance genes, as was demonstrated through identification and quantitation of two strains of E. coli, resistant and nonresistant due to ß-lactamase cefotaximase genes. Finally, the combined immunoassays were compared against culture using remnant deidentified patient urine samples. The sensitivities for these immunoassays were 83, 95, and 92% for the prediction of S. aureus, P. aeruginosa, and E. coli or K. pneumoniae positive culture, respectively, while specificities were 85, 92, and 97%. The diagnostic platform presented here with fluidics and combined immunoassays allows for pathogen isolation within 5 min and identification in as little as 15 min to 1 h, to help guide the decision for additional testing, optimally only on positive samples, such as multiplexed or resistance gene assays (6 h).

Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome.

Temperature-sensitive (TS) missense mutants have been foundational for characterization of essential gene function. However, an unbiased approach for analysis of biochemical and biophysical changes in TS missense mutants within the context of their functional proteomes is lacking. We applied MS-based thermal proteome profiling (TPP) to investigate the proteome-wide effects of missense mutations in an application that we refer to as mutant thermal proteome profiling (mTPP). This study characterized global impacts of temperature sensitivity-inducing missense mutations in two different subunits of the 26S proteasome. The majority of alterations identified by RNA-Seq and global proteomics were similar between the mutants, which could suggest that a similar functional disruption is occurring in both missense variants. Results from mTPP, however, provide unique insights into the mechanisms that contribute to the TS phenotype in each mutant, revealing distinct changes that were not obtained using only steady-state transcriptome and proteome analyses. Computationally, multisite λ-dynamics simulations add clear support for mTPP experimental findings. This work shows that mTPP is a precise approach to measure changes in missense mutant-containing proteomes without the requirement for large amounts of starting material, specific antibodies against proteins of interest, and/or genetic manipulation of the biological system. Although experiments were performed under permissive conditions, mTPP provided insights into the underlying protein stability changes that cause dramatic cellular phenotypes observed at nonpermissive temperatures. Overall, mTPP provides unique mechanistic insights into missense mutation dysfunction and connection of genotype to phenotype in a rapid, nonbiased fashion.