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Machine Learning methods in shotgun proteomics

Time: Tue 2023-06-13 14.00

Location: Air & Fire, Science for Life Laboratory, Tomtebodavägen 23A, 17121 Solna

Video link:

Language: English

Subject area: Biotechnology

Doctoral student: Patrick Truong , Genteknologi, Science for Life Laboratory, SciLifeLab, Käll Research Lab

Opponent: Professor Peter Nilsson, Affinitets-proteomik, Science for Life Laboratory, SciLifeLab

Supervisor: Professor Lukas Käll, Genteknologi, Science for Life Laboratory, SciLifeLab, SeRC - Swedish e-Science Research Centre

QC 2023-05-22


As high-throughput biology experiments generate increasing amounts of data, the field is naturally turning to data-driven methods for the analysis and extraction of novel insights. These insights into biological systems are crucial for understanding disease progression, drug targets, treatment development, and diagnostics methods, ultimately leading to improving human health and well-being, as well as, deeper insight into cellular biology. Biological data sources such as the genome, transcriptome, proteome, metabolome, and metagenome provide critical information about biological system structure, function, and dynamics. The focus of this licentiate thesis is on proteomics, the study of proteins, which is a natural starting point for understanding biological functions as proteins are crucial functional components of cells. Proteins play a crucial role in enzymatic reactions, structural support, transport, storage, cell signaling, and immune system function. In addition, proteomics has vast data repositories and technical and methodological improvements are continually being made to yield even more data. However, generating proteomic data involves multiple steps, which are prone to errors, making sophisticated models essential to handle technical and biological artifacts and account for uncertainty in the data. In this licentiate thesis, the use of machine learning and probabilistic methods to extract information from mass-spectrometry-based proteomic data is investigated. The thesis starts with an introduction to proteomics, including a basic biological background, followed by a description of how massspectrometry-based proteomics experiments are performed, and challenges in proteomic data analysis. The statistics of proteomic data analysis are also explored, and state-of-the-art software and tools related to each step of the proteomics data analysis pipeline are presented. The thesis concludes with a discussion of future work and the presentation of two original research works. The first research work focuses on adapting Triqler, a probabilistic graphical model for protein quantification developed for data-dependent acquisition (DDA) data, to data-independent acquisition (DIA) data. Challenges in this study included verifying that DIA data conformed with the model used in Triqler, addressing benchmarking issues, and modifying the missing value model used by Triqler to adapt for DIA data. The study showed that DIA data conformed with the properties required by Triqler, implemented a protein inference harmonization strategy, and modified the missing value model to adapt for DIA data. The study concluded by showing that Triqler outperformed current protein quantification techniques. The second research work focused on developing a novel deep-learning based MS2-intensity predictor by incorporating the self-attention mechanism called transformer into Prosit, an established Recurrent Neural Networks (RNN) based deep learning framework for MS2 spectrum intensity prediction. RNNs are a type of neural network that can efficiently process sequential data by capturing information from previous steps, in a sequential manner. The transformer self-attention mechanism allows a model to focus on different parts of its input sequence during processing independently, enabling it to capture dependencies and relationships between elements more effectively. The transformers therefore remedy some of the drawbacks of RNNs, as such, we hypothesized that the implementation of MS2-intensity predictor using transformers rather than RNN would improve its performance. Hence, Prosit-transformer was developed, and the study showed that the model training time and the similarity between the predicted MS2 spectrum and the observed spectrum improved. These original research works address various challenges in computational proteomics and contribute to the development of data-driven life science.