Mass Spectrometry (MS/MS) and Proteomics: A High-Performance Bioanalytical Technique for The Peptide Drug Discovery

Abstract:: Drug discovery research focuses on Rational Drug Design (RDD) concepts and the major obstacles in the drug discovery process are lack of target specificity and selectivity. The realization of higher target selectivity of peptide drugs promoted the peptide research. Rapid growth in the genomics along with recombinant DNA (rDNA) technology and gene expression studies stimulated the peptide research. The promising use of peptide therapeutics demands sensitive and selective quantification methods. Protein sequencing and proteomic investigations can be successfully accomplished through Mass Spectroscopy (MS) based methods. Mass spectroscopy-based soft ionization methods namely, electrospray ionization (ESI) and Matrix-Assisted Laser Desorption/Ionization (MALDI) offers high-throughput sequencing provide the characterization (sequence and structure) of intact proteins/peptides. The advent of tandem Mass Spectrometry (MS/MS) along with data acquisition methods are the basis for the evolution in peptide therapeutics research. The evolution of data science, helped in developing computational proteomics, which assists in the quantitative determination of protein samples. This review narrates the role of mass spectrometry in the peptide drug discovery in particular the sequence characterization along with latest developments, such as computational proteomics.

Profiling of post-translational modifications by chemical and computational proteomics

We summarized the recent developments of chemical and computational proteomic strategies to delineate the global landscapes of cellular functional PTMs and provided outlooks on the future directions of the field.

Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, the bioinformatics analysis is becoming an increasingly complex and convoluted process involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are targeted and design for single desktop application limiting the scalability and reproducibility of the data analysis. In this paper we overview the key steps of metabolomic and proteomics data processing including main tools and software use to perform the data analysis. We discuss the combination of software containers with workflows environments for large scale metabolomics and proteomics analysis. Finally, we introduced to the proteomics and metabolomics communities a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflows environments: Galaxy and Nextflow.