Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Abstract

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.

Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery

“…Interpretable machine learning is a valuable approach to widen our understanding as well as to discover novel biomarker candidates that might be difficult to detect by the conventional workflow, i.e., using “bottom up” proteomics, because of their low abundance in CSF. Utilizing information gained from prediction tools as the one presented here is a rapid, effortless and cost-effective approach to support biomarker candidate identification and selection …”

“…Utilizing information gained from prediction tools as the one presented here is a rapid, effortless and cost-effective approach to support biomarker candidate identification and selection. 12 Here, we built a CSF-specific protein secretion prediction model, using a curated CSF proteome data set and the brain elevated HPA proteome, which provides valuable insights for fluid biomarker research. Our predictor is able to distinguish between CSF secreted and non-CSF secreted proteins, with an AUC between 0.81 and 0.89 depending on the stringency used to define the CSF proteome.…”

Unleashing the power of colloidal gold immunochromatographic assays for plant virus diagnostics

“…Tools like BLAST can be used to compare sequences and assess potential cross-reactivity. If the 3D structure of the antigen is known or can be predicted, molecular modeling tools can help identify surface-exposed regions that are likely to be antigenic [ [62] , [63] ].…”

Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias

“…The implementation of fluid biomarkers for clinical research is often a slow and tedious process, one of the major hurdles being the translation of novel biomarker candidates into sensitive immunoassays. Thus, the efficient generation of highly specific novel antibodies is an essential first step towards the development of high-throughput antibody-based assays ( 104 ). The advent of advanced bioinformatics and artificial intelligence tools has simplified this process as it is now possible to sequence millions of proteins simultaneously, as well as predict their structures ( 105 , 106 ).…”