Improved Sample Preparation Method for Protein and Peptide Identification from Human Hair

A fast and sensitive direct extraction (DE) method developed in our group can efficiently extract proteins in 30 min from a 5 cm-long hair strand. Previously, we coupled DE to downstream analysis using gel electrophoresis followed by in-gel digestion, which can be time-consuming. In searching for a better alternative, we found that a combination of DE with a bead-based method (SP3) can lead to significant improvements in protein discovery in human hair. Since SP3 is designed for general applications, we optimized it to process hair proteins following DE and compared it to several other in-solution digestion methods. Of particular concern are genetically variant peptides (GVPs), which can be used for human identification in forensic analysis. Here, we demonstrated improved GVP discovery with the DE and SP3 workflow, which was 3 times faster than the previous in-gel digestion method and required significantly less instrument time depending on the number of gel slices processed. Additionally, it led to an increased number of identified proteins and GVPs. Among the tested in-solution digestion methods, DE combined with SP3 showed the highest sequence coverage, with higher abundances of the identified peptides. This provides a significantly enhanced means for identifying proteins and GVPs in human hair.

Identifying Anaerobic Bacteria Using MALDI-TOF Mass Spectrometry: A Four-Year Experience

Because of the special culture requirements of anaerobic bacteria, their low growth-rate and the difficulties to isolate them, MALDI-TOF MS has become a reliable identification tool for these microorganisms due to the little amount of bacteria required and the accuracy of MALDI-TOF MS identifications. In this study, the performance of MALDI-TOF MS for the identification of anaerobic isolates during a 4-year period is described. Biomass from colonies grown on Brucella agar was directly smeared onto the MALDI-TOF target plate and submitted to on-plate protein extraction with 1μl of 100% formic acid. Sequencing analysis of the 16S rRNA gene was used as a reference method for the identification of isolates unreliably or not identified by MALDI-TOF MS. Overall, 95.7% of the isolates were identified to the species level using the updated V6 database vs 93.8% with previous databases lacking some anaerobic species; 68.5% of the total were reliably identified with high-confidence score values (≥2.0) and 95.0% with low-confidence values (score value ≥1.7). Besides, no differences between Gram-positive and Gram-negative isolates were detected beyond a slight decrease of correct species assignment for gram positive cocci (94.1% vs 95.7% globally). MALDI-TOF MS has demonstrated its usefulness for the identification of anaerobes, with high correlation with phenotypic and conventional methods. Over the study period, only 2.1% of the isolates could not be reliably identified and required molecular methods for a final identification. Therefore, MALDI-TOF MS provided reliable identification of anaerobic isolates, allowing clinicians to streamline the most appropriate antibiotic therapy and manage patients accordingly.

Novel Descriptors and Digital Signal Processing- Based Method for Protein Sequence Activity Relationship Study

The work aiming to unravel the correlation between protein sequence and function in the absence of structural information can be highly rewarding. We present a new way of considering descriptors from the amino acids index database for modeling and predicting the fitness value of a polypeptide chain. This approach includes the following steps: (i) Calculating Q elementary numerical sequences (Ele_SEQ) depending on the encoding of the amino acid residues, (ii) determining an extended numerical sequence (Ext_SEQ) by concatenating the Q elementary numerical sequences, wherein at least one elementary numerical sequence is a protein spectrum obtained by applying fast Fourier transformation (FFT), and (iii) predicting a value of fitness for polypeptide variants (train and/or validation set). These new descriptors were tested on four sets of proteins of different lengths (GLP-2, TNF alpha, cytochrome P450, and epoxide hydrolase) and activities (cAMP activation, binding affinity, thermostability and enantioselectivity). We show that the use of multiple physicochemical descriptors coupled with the implementation of the FFT, taking into account the interactions between residues of amino acids within the protein sequence, could lead to very significant improvement in the quality of models and predictions. The choice of the descriptor or of the combination of descriptors and/or FFT is dependent on the couple protein/fitness. This approach can provide potential users with value added to existing mutant libraries where screening efforts have so far been unsuccessful in finding improved polypeptide mutants for useful applications.

One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators

Enzymes are biological catalysts with many industrial applications, but natural enzymes are usually unsuitable for industrial processes because they are not optimized for the process conditions. The properties of enzymes can be improved by directed evolution, which involves multiple rounds of mutagenesis and screening. By using mathematical models to predict the structure-activity relationship of an enzyme, and by defining the optimal combination of mutations in silico, we can significantly reduce the number of bench experiments needed, and hence the time and investment required to develop an optimized product. Here, we applied our innovative sequence-activity relationship methodology (innov'SAR) to improve glucose oxidase activity in the presence of different mediators across a range of pH values. Using this machine learning approach, a predictive model was developed and the optimal combination of mutations was determined, leading to a glucose oxidase mutant (P1) with greater specificity for the mediators ferrocene-methanol (12-fold) and nitrosoaniline (8-fold), compared to the wild-type enzyme, and better performance in three pH-adjusted buffers. The k_cat /K_M ratio of P1 increased by up to 121 folds compared to the wild type enzyme at pH 5.5 in the presence of ferrocene methanol.