/N-Glycosylation-Markov-Models

Cleaned-up Version

Primary LanguageMATLAB

Toward rational glycoengineering: Markov models for N-Glycosylation

Introduction

Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders the controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modeling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout.

Requirement

The pipeline is based on our previously published work. MATLAB ver. 2019b or above is required to ensure proper execution of all functions. Operators and Elementary Operations, Global Optimization, Parallel Computing, Statistics and Machine Learning, Econometrics, Curve Fitting, and System Identification, Graph and Network Algorithms Toolboxes. All third-party functions were cited as comments at the beginnings of corresponding code scripts. Minimal 32 GB RAM and two 4-core CPU(s) are highly recommended to ensure efficient running and fitting processes. Using the minimally required hardware, users should be able to run 3-4 MATLAB sessions for the fitting step (Step 3). RAM is usually the bottleneck for running more MATLAB sessions.

Pipeline Overview

Please read through this section before you start the process. You may also refer to the specific sections as your proceed. The basic pipeline of model fitting and visualization include the following steps:

  • Step 0: Data preparation

    • Raw glycomics data and annotations (if available) need to be gathered and processed prior to fitting. Glycomics data with glycan structures/glycan compositions but no m/z values can also be used. If you do not have the model-compatible glycan compositions but only the glycan structures, Sup1_Get_compositions_from_linearcodes.m in the Supplemental Steps folder can be used to obtain corresponding glycan compositions from user-provided linear codes. Annotations of high-intensity or branching glycoforms are highly recommended.
    • Data.xlsx in the Data folder is the only input required to run the basic pipeline (Figure 1). Please refer to Step0_Data_Preprocessing.pptx for details on how to organize your data.
    • Example glycoprofiles (used in our previous work) were included in the example Data.xlsx

  • Step 1: Load Preprocessed Data

    • This step reads the data from Data.xlsx and stores the dataset as a structure variable DataSet, which is used for fitting & analyses.
    • Directly run the script Step1_Load_Preprocessed_Data.m to obtain Data.mat, a MATLAB file containing DataSet and stored in Data folder. The existing Data.mat was generated for the example glycoprofiles used for fitting demonstration.

  • Step 2: Generate a generic N-glycosylation Network

    • This step generates a N-glcosylation synthetic network by recursively modifying the high-mannose structure (Man9) using reaction rules as specified in our previous work. The size of the network can be controlled based on the scope of glycan structures considered or an arbitrary complexity level. The network is organized as an adjacency matrix for constructing Markov models.
    • Directly run the script Step2_Create_Genric_Glycan_Network.m to obtain GenericNetwork.mat, a MATLAB file containing GenericNetwork and stored in Data folder. GenericNetwork contains all information regarding the generic model used for fitting.

  • For practicality, the complexity level of the network should not exceed 23.

  • Step 3_1/2: Fit a generic Markov model to WT/other glycoprofiles

    • This step generates fitted Markov models for each specified glycoprofile. Using the default setting, the time required to obtain models for all the glycoprofile is approximately 1.5 hr/optimization x number of models fitted for each profile x number of profiles/total MATLAB sessions. Please make sure that CPU/memory utilization is not consistently at 100% when the optimization is running. Please refer to MathWork for more details on parallel processing.
    • Fitting the WT glycoprofile first is required when the model steric parameters (Change Log Jul-26-2022) are considered for model fitting (running Step3_1_Fit_Markov_Models_with_WT_Glycoprofile.m first and Step3_2_Fit_Markov_Models_with_other_Glycoprofiles.m second). If steric parameters are not considered, either scripts can be used. To obtain more reliable steric factors, users are suggested to fit as many wildtype models as pratically possible.
    • Please empty or relocate the content in the folder Data/OptimizationResults if you wish to use your own fitted models.
    • Before running either script, specify the variables ProfSel (model names selected to be fitted), StericFlag (true or false, whether to consider the steric impact), and UseWTStericFlag (true or false, whether to use the steric impact learnt from the wild type models). Refer to comments in the script for more details.
    • For debug, the evaluation of the Pattern Search Algorithm is plotted in real time and printed to the command window. (Figure 1).
    • Upon completion, running either script will start the fitting process and store the fitted model parameters in OptimizationResults. For consistent organization when running multiple MATLAB sessions, the OptimizationResults from each session will be stored in yourPreferredName_#d.mat files (where #d is a number) in the folder Data/OptimizationResults/. Only one yourPreferredName should be used for all glycoprofiles in the same study to ensure our customized file loaders can load all relevant files. OptimizationResults_Steric_WT_#d.mat and OptimizationResults_Steric_others_#d.mat were generated with the example dataset.

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Figure 1: (Top) Values of objective function at each optimization iteration with Pattern Search Algorithm. (Bottom) The currently best set of model parameters (transition probabilities associated with reaction types) that minimizes the objective function.

  • Step 4: Visualize predicted glycoprofiles from fitted Markov models
    • Step4_Visualize_Fitted_Glycoprofiles.m quantifies a variety of model features (e.g. pseudo-fluxes, pseudo-concentration) and produces visuals by simulating the generic models with the fitted parameters from Step 3. ProfSel should be specified first to selected the glycoprofiles for visualization. A few example visuals are shown in Figure 2 & Figure 3.

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Figure 2: Visualization of model features from models fitted with the wildtype N-glycoprofile. (A) The model fluxes through each of the reaction types. (B) The transition probabilities (model parameters) associated with each of the reaction types, including the steric factors. (C) Comparison between the experimental glycoprofile and the predicted glycoprofiles produced from the fitted Markov models in terms of realtive intensities (MS signals). (D) The relative ratios (shades of color) of major glycoforms at each m/z values predicted by the fitted models. Red glycans means it is the most abundant glycoform at a specific m/z value.

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Figure 3 Visualization of the network topology of the from models fitted with the wildtype N-glycoprofile. Each node represents a glycan and each edge represents a glycosyltransferase/glycosidase reaction (reaction types). (A) The active reactions and intermediate glycans (blue edges and blue nodes) leading to the production of major glycoforms (red nodes), in the context of a generic network (grey edges and nodes). (B) The major fluxes (red edges) leading to the productiong of major glycoforms (red nodes), in the context of the active network (as show in (A)).

Please refer to the comments in the MATLAB scripts for more details on:

  • Individual steps (functions) and the variables (including their formats) required to run the pipelines
  • Customizing fitting parameters or visualization
  • Accessing fitted model parameters and features for additional analyses

Change Log

  • Jul-30-2022

    • Removed the branch restriction for iGnT to account for major glycoforms with observed LacNAc elongation on branch 1 & 2 in glycoprofiles such as Mgat2 knockout.

  • Jul-26-2022

    • Updated consturction, fitting, and visualization functions to make them compatible when users choose to consider the impact of steric interactions of N-glycan antenna on model parameters (transition probabilities):
      • Rxns considered included a3SiaT, b4GalT, iGnT, GnTIV, and GnTV.
      • Quantified impact of steric interactions to each considered rxn as a parameter.

  • Jul-20-2022

    • Removed constraint of fucosylation (a6FucT) to include all theoretically possible glycans (CreateGlycanRxnList.mat).
    • Restricted the maximal length of LacNAc chain in the function CreateGlycanRxnList.

Reference

  1. A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering
  2. GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data

Additional Info

For additional questions, please reach out to Systems Biology and Cell Engineering – Lewis Lab at the University of California, San Diego.