Proteomic profiling of the endogenous peptides of MRSA and MSSA

Isovanillic acid protects mice against Staphylococcus aureus by targeting vWbp and Coa

Aim: Our primary objective was to investigate the protective effects and mechanisms of isovanillic acid in mice infected with Staphylococcus aureus Newman. Methods: In vitro coagulation assays were used to validate vWbp and Coa as inhibitory targets of isovanillic acid. The binding mechanism of isovanillic acid to vWbp and Coa was investigated using molecular docking and point mutagenesis. Importantly, a lethal pneumonia mouse model was used to assess the effect of isovanillic acid on survival and pathological injury in mice. Results & Conclusion: Isovanillic acid reduced the virulence of S. aureus by directly binding to inhibit the clotting activity of vWbp and Coa, thereby reducing lung histopathological damage and improving the survival rate in mice with pneumonia.

Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective

Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".

DeepSCP: utilizing deep learning to boost single-cell proteome coverage

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target–decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.