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Manual proteomic sample preparation methods limit sample throughput and often lead to poor data quality when thousands of samples must be analyzed. Automated liquid handler systems are increasingly used to overcome these issues for many of the sample preparation steps. Here, we detail a step-by-step protocol to prepare samples for bottom-up proteomic analysis for Gram-negative bacterial and fungal cells. The full modular protocol consists of three optimized protocols to: (A) lyse Gram-negative bacteria and fungal cells; (B) quantify the amount of protein extracted; and (C) normalize the amount of protein and set up tryptic digestion. These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers, and allow flexibility for future protocol development. By using this workflow 50 micrograms of protein from 96 samples can be prepared for tryptic digestion in under an hour. We validate these protocols by analyzing 47 Pseudomonas putida and Rhodosporidium toruloides samples and show that this modular workflow provides robust, reproducible proteomic samples for high-throughput applications. The expected results from these protocols are 94 peptide samples from Gram-negative bacterial and fungal cells prepared for bottom-up quantitative proteomic analysis without the need for desalting column cleanup and with protein relative quantity variance (CV%) below 15%., Competing Interests: The authors have declared that no competing interests exist.

Mass spectrometry-based quantitative proteomic analysis has proven valuable for clinical and biotechnology-related research and development. Improvements in sensitivity, resolution, and robustness of mass analyzers have also added value. However, manual sample preparation protocols are often a bottleneck for sample throughput and can lead to poor reproducibility, especially for applications where thousands of samples per month must be analyzed. To alleviate these issues, we developed a "cells-to-peptides" automated workflow for Gram-negative bacteria and fungi that includes cell lysis, protein precipitation, resuspension, quantification, normalization, and tryptic digestion. The workflow takes 2 h to process 96 samples from cell pellets to the initiation of the tryptic digestion step and can process 384 samples in parallel. We measured the efficiency of protein extraction from various amounts of cell biomass and optimized the process for standard liquid chromatography-mass spectrometry systems. The automated workflow was tested by preparing 96 Escherichia coli samples and quantifying over 600 peptides that resulted in a median coefficient of variation of 15.8%. Similar technical variance was observed for three other organisms as measured by highly multiplexed LC-MRM-MS acquisition methods. These results show that this automated sample preparation workflow provides robust, reproducible proteomic samples for high-throughput applications.

Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution., (© 2018 Wiley Periodicals, Inc.)

Tethering peptides and proteins to abiotic surfaces has the potential to create biomolecule-functionalized surfaces with useful properties. Commonly used methods of immobilization lack control over the orientation in which biological molecules are covalently or physically bound to the surface, leading to sub-optimal materials. Here we use an engineered beta-galactosidase that can be chemically immobilized on a surface with a well-defined orientation through unique surface-accessible cysteine residues. A combined study using sum frequency generation (SFG) vibrational spectroscopy and coarse grained molecular dynamics (MD) simulations was performed to determine the effects of enzyme immobilization site and abiotic surface chemistry on enzyme surface orientation, surface coverage, and catalytic activity. Two beta-galactosidase variants that were immobilized through cysteine introduced at positions 227 and 308 were studied. In both cases, when the abiotic surface was made more hydrophilic, the enzyme surface coverage decreased, but the activity increased. MD simulations indicated that this is due to the weakened interactions between the immobilized enzyme and the more hydrophilic surface. These studies provide improved understanding of how enzyme-surface interactions can be optimized to maximize the catalytic activity of surface tethered enzymes.

Surface-immobilized enzymes are important for a wide range of technological applications, including industrial catalysis, drug delivery, medical diagnosis, and biosensors; however, our understanding of how enzymes and proteins interact with abiological surfaces on the molecular level remains extremely limited. We have compared the structure, activity, and thermal stability of two variants of a β-galactosidase attached to a chemically well-defined maleimide-terminated self-assembled monolayer surface through a unique cysteinyl residue. In one case the enzyme is attached through an α helix and in the other case through an adjacent loop. Both enzymes exhibit similar specific activities and adopt similar orientations with respect to the surface normal, as determined by sum-frequency generation and attenuated total reflectance FT-IR spectroscopies. Surprisingly, however, the loop-tethered enzyme exhibits a thermal stability 10 °C lower than the helix-tethered enzyme and 13 °C lower than the enzyme in free solution. Using coarse-grain models, molecular dynamics simulations of the thermal unfolding of the surface-tethered enzymes were able to reproduce these differences in stability. Thus, revealing that tethering through the more flexible loop position provides more opportunity for surface residues on the protein to interact with the surface and undergo surface-induced unfolding. These observations point to the importance of the location of the attachment point in determining the performance of surface-supported biocatalysts and suggest strategies for optimizing their activity and thermal stability through molecular simulations.