LipidCCS Help

1. Overview of MetCCS Predictor

Important Notes:

MetCCS Predictor provides the predicted CCS values derived from nitrogen buffer gas, denoted as Ω_N2. Currently, the prediction of CCS values in helium gas (Ω_He) or in other buffer gases are not supported.
MetCCS Predictor is applicable primarily for metabolites (<1000 Da). However, we found it may also applicable for other small molecules like synthetic drugs, pesticides, nature products, and so on. But we believe the software is NOT applicable for peptide and protein.

MetCCS Predictor is developed to predict CCS values for metabolites in ion mobility − mass spectrometry (IM-MS), and it allows everyone to predict CCS values of metabolites of interest. We find it can also be applicable for predicting of CCS values of other small chemical compounds like drugs, pesticides and so on. Users can simply import 14 common molecular descriptors of one metabolite to calculate its CCS values within seconds. The software employs a support vector regression (SVR) based machine-learning algorithm for prediction, and the general principle has been published on Analytical Chemistry (2016).¹ We experimentally measured CCS values (Ω_N2) of ∼400 metabolites in nitrogen buffer gas and used these values as training data to optimize the prediction method. Prediction precision of this method has been validated with a median relative error (MRE) of ~3%. Since CCS values of metabolites in the training data set were all acquired in nitrogen buffer gas, the predicted CCS values are all nitrogen CCS values.

In addition to prediction function, MetCCS Predictor also includes search and match functions. The database search function facilitates users to search CCS values of metabolites in MetCCS database using known HMDB ID, SMILES or InChI Identifier. Metabolite match function is designed for users to identify unknown metabolites using experimentally measured m/z and CCS values.

2. Metabolite CCS value prediction

The workflow for MetCCS Prediction is divided into five steps: (1) import molecular descriptors, (2) check data quality, (3) impute missing values (if necessary), (4) predict CCS values, and (5) export results (Figure 1).

Figure 1. Schematic illustration of the workflow of MetCCS Predictor.

2.1) Import Molecular Descriptors

14 common molecular descriptors of one metabolite (or chemical compound) are used to predict its CCS values by MetCCS predictor. These descriptors and their suggested ranges are listed in the Table 1. All descriptors are calculated by cheminformatic software like ChemAxon and ALOGPS using the molecular structure. Human Metabolome Database (HMBD) also provides the values of molecular descriptors. Users can directly input molecular descriptors into the textbox or import as a CSV file.

Table 1. List of molecular descriptors for CCS prediction and their suggested ranges

No.	Name	Definition	Source	Range
1	Exact_Mass	The exact mass of compound	Molecular formula	[100, 1000]
2	Formal_Charge	The formal charge	ChemAxon	[-2, 1]
3	Physiological_Charge	The physiological charge	ChemAxon	[-8, 4]
4	logP_ALOGPS	The octanol/water partition coefficient	ALOGPS	[-4, 10]
5	logP_ChemAxon	The octanol/water partition coefficient	ChemAxon	[-11, 12]
6	logS	The aqueous solubility	ALOGPS	[-8, 1]
7	pKa_Strongest_Acidic	The acid dissociation constant	ChemAxon	[-7, 20]
8	pKa_Strongest_Basic	The basic dissociation constant	ChemAxon	[-10, 12]
9	Hydrogen_Acceptor_Count	The sum of the acceptor atoms	ChemAxon	[0, 21]
10	Hydrogen_Donor_Count	The sum of the donor atoms	ChemAxon	[0, 14]
11	Polar_Surface_Area	The sum of the all polar atoms surface in a molecule	ChemAxon	[0, 400]
12	Rotatable_Bond_Count	The number of rotatable bonds in the molecule	ChemAxon	[0, 43]
13	Refractivity	Molar refractivity	ChemAxon	[0, 252]
14	Polarizability	Molecular polarizability	ChemAxon	[0, 105]

The units of descriptors: Exact mass (Da); Polar Surface Area (Å²); Refractivity (m³·mol^-1); Polarizability (Å³).

2.2) Check Data Quality

As shown in Figure 2a, the software first checks the quality of the imported molecular descriptors, including availability of exact mass and the number of missing values. "Exact mass" is the mandatorily required molecular descriptor for the prediction. Therefore, the value of "Exact_mass" must be imported, otherwise, MetCCS Predictor returns error information. The number of missing values (denoted as "NA") is also checked, and the maximum tolerance of missing values is set as 7. If the number of missing descriptors is less than 7, warning information is given out.

Figure 2. Schematic illustration of checking data quality (a) and imputing missing values (b).

2.3) Impute Missing Values

Before the prediction of CCS values, missing values for molecular descriptors are imputed using K-Nearest Neighbor algorithm (KNN).² All imported descriptors are first integrated with dataset of HMDB (www.hmdb.ca), and 10 of the most similar metabolites in terms of molecular descriptors across all metabolites in HMDB are chosen. The weighted average of these 10 metabolites is calculated and replaces the corresponding missing value based on their similarity. Then, all descriptors are saved and transferred to the prediction model.

2.4) Predict CCS Values

The method of CCS prediction was introduced in our previous publication. Briefly, MetCCS Predictor employs SVR algorithm to implicitly map molecular descriptors of metabolites into a high-dimensional feature space using a kernel function, and to construct a hyperplane in that space to perform the high-dimensional regression between molecular descriptors and CCS values in the training dataset. For more detailed information, please refer to our publication.

2.5) Export Results

The prediction results of metabolites are listed in Table 2. For each metabolite, CCS values for 5 ion adducts are predicted, such as [M+H]⁺, [M+Na]⁺ and [M+H-H₂O]⁺ in positive mode, and [M-H]^- and [M+Na-2H]^- in negative mode.

Figure 3. The example table for CCS prediction result. You can click table menu on the top right of table to output your match results.

Explanations for Status:

Error 1: Exact_Mass is missing or invalid.
Error 2: The number of missing values for molecular descriptors exceeds the limit (larger than 7).
Warning: Missing values are found for molecular descriptors, which are imputed using k-Nearest Neighbor (KNN) algorithm before prediction.

3. MetCCS Database Search

Users can readily search CCS values of metabolites in the MetCCS database. This function allows users to directly search database in three different ways: HMDB ID, SMILES or InCHI identifiers. It also supports batch search function with a maximum of 100 query lines per request. The MetCCS database contains 35,203 metabolites with exact mass between 60 and 1000 Da, accounting for 176,015 CCS values for 5 different adducts. If a metabolite is not included in the database, a hint message will give out.

4. Metabolite Match

This function is designed for users to identify unknown metabolites using experimentally measured m/z and CCS values. Users are required to input both of the m/z and CCS values of an unknown metabolite and define a proper tolerance for the m/z and CCS value measurements together with polarity information. The web server will return metabolite candidates in the MetCCS database within the defined tolerances. For a metabolite with m/z 332.0746 Da and CCS value 168 Å², we define the tolerance of m/z and CCS values as 15 ppm and 3% respectively. After metabolite match, this metabolite is identified as dAMP. The m/z accuracy is 4 ppm and CCS relative error is 1.3%. In addition, we also provide HMDB ID of this metabolite, and user can click the HMDB ID to link, and get more information about the metabolite in HMDB website. Users also can click grid menu to download match results as CSV file. The result of this example is shown in Figure 4.

Figure 4. The metabolite match example: a metabolite with m/z 332.0746 and CCS 168 Å² was matched with the MetCCS database, and identified as dAMP.

The equations to calculate delta m/z and delta CCS values are shown as Eq. 1 and 2.

Eq. 1
Eq. 2

5. Change Log

Feb 17th, 2017: Added MetCCS Database Search and Metabolite Match

Nov 9th, 2016: Added batch mode

Oct 24th, 2016: Created this site

6. Reference

(1) Zhou, Z.; Shen, X.; Tu, J.; Zhu, Z. J. Anal. Chem. 2016, 88, 11084-11091.

(2) Hastie, T.; Tibshirani, R.; Narasimhan, B.; Chu, G.; R package.

Help: LipidCCS Calculate Interface and Results

Change Log

Contents

1. Overview of MetCCS Predictor

2. Metabolite CCS value prediction

Figure 1. Schematic illustration of the workflow of MetCCS Predictor.

2.1) Import Molecular Descriptors

Table 1. List of molecular descriptors for CCS prediction and their suggested ranges

2.2) Check Data Quality

Figure 2. Schematic illustration of checking data quality (a) and imputing missing values (b).

2.3) Impute Missing Values

2.4) Predict CCS Values

2.5) Export Results

Figure 3. The example table for CCS prediction result. You can click table menu on the top right of table to output your match results.

3. MetCCS Database Search

4. Metabolite Match

Figure 4. The metabolite match example: a metabolite with m/z 332.0746 and CCS 168 Å² was matched with the MetCCS database, and identified as dAMP.

5. Change Log

6. Reference

Contents

Figure 1. Schematic illustration of the workflow of MetCCS Predictor.

Table 1. List of molecular descriptors for CCS prediction and their suggested ranges

Figure 2. Schematic illustration of checking data quality (a) and imputing missing values (b).

Figure 3. The example table for CCS prediction result. You can click table menu on the top right of table to output your match results.

Figure 4. The metabolite match example: a metabolite with m/z 332.0746 and CCS 168 Å2 was matched with the MetCCS database, and identified as dAMP.

Figure 4. The metabolite match example: a metabolite with m/z 332.0746 and CCS 168 Å² was matched with the MetCCS database, and identified as dAMP.