Complex Proteoform Identification and Quantification Using Top-down Mass Spectrometry

Abstract

Proteoforms are the molecular forms in which the protein product of an individual gene can be found, including changes due to genetic variations, alternatively spliced RNA transcripts and post-translational modifications. Proteoform identification and quantification is the first step for unraveling their functions, discovering molecular signatures for disease diagnosis, and pinpointing potential drug targets. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has emerged as the preferred method for the identification and quantification of complex proteoforms. To overcome the combinatorial explosion of possible proteoforms, the proteoform mass graph and spectrum mass graph are used to represent the protein database and the spectrum, respectively. The problem becomes identifying and quantifying proteoforms between the proteoform mass graph and the spectrum mass graph. The main important issue to solve the problem is to handle mass errors of peaks in the input spectrum. In previous methods, an error tolerance value is used to handle the mass differences between the matched consecutive nodes/peaks in protein mass graph and spectrum mass graph. However, such a way to handle mass error can not guarantee that the mass difference between any pairs of nodes in the alignment is approximately the same for both protein mass graph and spectrum mass graph. It may lead to large error accumulation if positive (or negative) errors occur consecutively for a large number of consecutive matched node pairs.

In this dissertation, we propose several novel algorithms for proteoform identification and quantification by top-down mass spectrometry to handel this issue. First, we propose a new model named TopMGRefine to handle the mass errors of peaks based on the formulation of the protein mass graph and spectrum mass graph for proteoform identification. This method allows each peak in the input spectrum to have a pre-defined error range. In the alignment of protein mass graph and spectrum mass graph, we need to give a correction of the mass for each matched peak within the pre-defined error range. After the correction, we impose that the mass between any two (not necessarily consecutive) matched nodes in the protein mass graph is identical to that of the corresponding two matched peaks in the spectrum mass graph. Intuitively, this kind of alignment is more accurate. We design an algorithm to find an maximum number of matched node and peak pairs in the two (protein and spectrum) mass graphs under the new constraint. The obtained alignment can show matched node and peak pairs as well as the corrected positions of peaks. The algorithm works well for moderate size input instances and takes very long time as well as huge size memory for large input size instances. Therefore, we propose an algorithm to do diagonal alignment. The diagonal alignment algorithm can solve large input size instances in reasonable time. Furthermore, we re-calculate the peak correction ranges and delete the range overlaps between adjacent peaks and produce a program package TopMGFast. The newly designed algorithm is more efficient and requires less space and running time so that we are able to compute global optimal alignments for the two input mass graphs in a reasonable time. Third, we propose a new model to handle the mass errors of peaks for proteoform quantification. This method can identify and quantify two or more proteoforms with their corresponding relative abundances. Last, as all the proteoform identification and quantification problem in this issue are based on graph and tree, I have also studied the problem constrained graph searching on trees. We give some faster algorithms for the edge searching problem and faster searching problem.

All the proposed algorithms in the dissertation have been organized into the website https://github.com/Zeirdo for complex proteoform identification and quantification using Top-down tandem mass spectrometry.

Date of Award	27 Sept 2024
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Lusheng WANG (Supervisor)