Liu receives NIH R01 grant to develop new computational methods for proteoform identification
October 9, 2020
Xiaowen Liu, associate professor of bioinformatics at the IU School of Informatics and Computing at IUPUI, has received a 4-year, $1.26M award from the National Institutes of Health (NIH) for his project entitled, “Computational tools for proteoform identification by top-down data independent acquisition mass spectrometry.” This is the second time Liu has received a highly competitive and prestigious R01 grant from the NIH. He will collaborate with faculty from The Ohio State University, Michigan State University, and the Indiana University School of Medicine on the project.
Proteomics, the study of proteins expressed by an organism, tissue, or cell, includes the exploration of proteoforms—protein products with various primary structure alterations resulting from biological processes such as gene mutations, alternative splicing, and post-translational modifications. These functional variants are implicated in many types of diseases, from Alzheimer’s disease to cancer, and understanding them is critical to developing new targeted therapies and treatments.
Top-down mass spectrometry (MS) is the method of choice for identifying proteoforms. Data dependent acquisition (DDA) and data independent acquisition (DIA) are two main approaches in top-down MS. Top-down DDA MS produces a relatively low number of proteoform identifications and misses those in low abundance. Top-down DIA MS can increase that number but is seldom used due to high complexity of data and lack of efficient data analysis tools.
Liu’s project will develop new computational methods and is expected to yield the first open source software suite for high-throughput, top-down DIA-MS data analysis.
Liu says, “Because of the complexity of top-down spectra, software tools designed for interpreting bottom-up MS data cannot be directly used for proteoform identification by top-down DIA-MS. We will design new dynamic programming algorithms and machine learning models for solving the problem.”
It is anticipated that the proposed tools—predictive models and algorithms—will be routinely used by researchers in the top-down proteomics community to facilitate identification and understanding of proteoforms and their functions. The software will make significant contribution to the annotation of gene and protein sequence databases and the development of proteoform databases.
Sarath Janga, interim chair of the Department of Biohealth Informatics, notes that “although several groups have built tools for analyzing RNA sequencing data, relatively fewer efforts have been laid out to develop robust software to accurately identify and map protein forms in humans and other model systems. Hence, Dr. Liu’s efforts at building these much needed tools would significantly advance our understanding and annotation of the human proteome to facilitate development of therapies directed at specific proteoforms.”
About the NIH R01 Grant
The Research Project Grant (R01) is the original and historically oldest grant mechanism used by NIH. The R01 provides support for health-related research and development based on the mission of the NIH. Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM118470. Total program or project costs of $1,260,000 are financed with federal money. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.