Human body-fluid proteome: quantitative profiling and computational prediction

Abstract

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein–protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

Forensic proteomics

“…Advances in proteomics also extend to bioinformatic analysis. The use of machine learning in creating and differentiating proteomic profiles is a promising and powerful area of future investigation [373][374][375].…”

SecProCT: In Silico Prediction of Human Secretory Proteins Based on Capsule Network and Transformer

“…At present, there have been many studies conducted to predict which proteins are located on the cell surface or secreted into the extracellular environment [5][6][7]. The methods proposed in these studies are mostly based on annotation information such as the amino acid compositions of protein, protein domains and protein functions.…”

Sample preparation and fractionation techniques for intact proteins for mass spectrometric analysis

“…Despite acceptance in the pharmaceutical industry, the adoption of top‐down proteomics by clinical laboratories is occurring at a slower pace [24,25]. Also further research focused on the data‐driven discovery and machine‐learning models of disease protein markers are emerging in this field [26].…”