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CandidateSearch

Proof-of-concept implementation of a search engine that uses CandidateVectorSearch to identify the best peptide candidates for a given mass spectrum. CandidateSearch is also the computational backend of the non-cleavable crosslink search in MS Annika. CandidateSearch creates the vector encodings of peptides and spectra that are needed for the sparse matrix search of CandidateVectorSearch.

CandidateSearch can identify peptide candidates from a given mass spectrum without any precursor ion/mass information and no previous knowledge about potential fixed or variable modifications. CandidateSearch can also identify peptidoform candidates if a set of fixed and variable modifications is provided. The aim of CandidateSearch is to reduce the search space for a given identification task by filtering out unlikely peptide or peptidoform candidates. It is NOT meant to be a standalone search engine for peptide/peptidoform identification.

A simplified break down of the CandidateSearch algorithm is given in the following:

  • Read the given MS2 spectra from the mgf file.
  • Generate the encoding vectors for each spectrum.
  • Transform spectrum encoding vectors into the representation needed for CandidateVectorSearch.
  • Read the given fasta file.
  • Digest the proteins of the fasta file into peptides.
  • [Optional] Generate decoy peptides and peptidoforms.
  • Calculate theoretical ion m/z values for all peptides.
  • Generate the encoding vectors for each peptide.
  • Transform the peptide encoding vectors into the representation needed for CandidateVectorSearch.
  • Run CandidateVectorSearch.
  • Process the results of CandidateVectorSearch.
  • Create a csv file that maps every spectrum (scan number) to a list of the best n peptide candidates.
  • Done!

Usage

Running CandidateSearch requires three files:

  • An mgf file containing MS2 spectra.
    We highly recommend to deisotope and deconvolute spectra before search!
  • A fasta file containing sample proteins.
  • A settings file containing parameters for digestion, ion calculation and search (see below for an explanation of the settings file).

The CandidateSearch executable can then be run like this:

CandidateSearch.exe spectra.mgf database.fasta settings.txt

Example files that can be used to test CandidateSearch can be found in /data.

Settings

The settings file accepts the following parameters:

  • MAX_CLEAVAGES: The maximum number of allowed missed cleavages during digestion. (integer, default = 2)
  • MIN_PEP_LENGTH: The minimum length of a peptide to be considered for search. (integer, default = 5)
  • MAX_PEP_LENGTH: The maximum length of a peptide to be considered for search. (integer, default = 30)
  • MAX_PRECURSOR_CHARGE: The maximum considered precursor ion charge. (integer, default = 4)
  • MAX_FRAGMENT_CHARGE: The maximum considered fragment ion charge. (string, default = +1)
  • MAX_NEUTRAL_LOSSES: The maximum number of neutral losses considered during ion calculation. (integer, default = 1)
  • MAX_NEUTRAL_LOSS_MODS: The maximum number of neutral loss modifications considered during ion calculation. (integer, default = 2)
  • FIXED_MODIFICATIONS: Fixed modifications that should be considered during search given as (char)amino_acid:(double)modification_mass. An example would be carbamidomethylation of cysteine, which would be denoted as C:57.021464;. Several fixed modifications can be provided. (string, default = None)
  • VARIABLE_MODIFICATIONS: Variable modifications that should be considered during search given as (char)amino_acid:(double)modification_mass. An example would be oxidation of methionine, which would be denoted as M:15.994915;. Several variable modifications can be provided. If no modifications are given, CandidateSearch will return the best scoring unmodified peptidoforms for a given spectrum. (string, default = None)
  • DECOY_SEARCH: Whether decoy search should be performed or not. Accepts true or false. (bool, default = true)
  • TOP_N: The number of best candidates that should be returned by the search. (integer, default = 1000)
  • TOLERANCE: Tolerance used for matching theoretical ions to experimental peaks. Given in Dalton. (double, default = 0.02)
  • NORMALIZE: Whether or not CandidateVectorSearch scores should be normalized before selecting the best n candidates. Accepts true or false. (bool, default = false)
  • USE_GAUSSIAN: Whether or not experimental peaks should be modelled as gaussian distributions with mu = (m/z) and sigma = (tolerance/3). Accepts true or false. (bool, default = true)
  • MODE: Search approach used by CandidateVectorSearch. One of the following (default = CPU_SMi32):
    • CPU_DVi32: Sparse int matrix - dense int vector search on the CPU.
    • CPU_DVf32: Sparse float matrix - dense float vector search on the CPU.
    • CPU_DMi32: Sparse int matrix - dense int matrix search on the CPU.
    • CPU_DMf32: Sparse float matrix - dense float matrix search on the CPU.
    • CPU_SVi32: Sparse int matrix - sparse int vector search on the CPU.
    • CPU_SVf32: Sparse float matrix - sparse float vector search on the CPU.
    • CPU_SMi32: Sparse int matrix - sparse int matrix search on the CPU.
    • CPU_SMf32: Sparse float matrix - sparse float matrix search on the CPU.
    • GPU_DVf32: Sparse float matrix - dense float vector search on the GPU (see requirements).
    • GPU_DMf32: Sparse float matrix - dense float matrix search on the GPU (see requirements).
    • GPU_SMf32: Sparse float matrix - sparse float matrix search on the GPU (see requirements).

For the last five parameters you might additionally want to check the documentation of CandidateVectorSearch to get a better understanding of their meaning.

An empty settings.txt file is a valid configuration for search (default parameters will be used), however not providing a settings file at all is not valid.

An example settings.txt file is provided here.

Additionally its contents are listed below, which should help in understanding the formatting:

## DIGESTIONS PARAMETERS
MAX_CLEAVAGES = 2
MIN_PEP_LENGTH = 5
MAX_PEP_LENGTH = 30

## ION CALCULATION PARAMETERS
MAX_PRECURSOR_CHARGE = 4
MAX_FRAGMENT_CHARGE = +1
MAX_NEUTRAL_LOSSES = 1
MAX_NEUTRAL_LOSS_MODS = 2
#FIXED_MODIFICATIONS = None
FIXED_MODIFICATIONS = C:57.021464;
#VARIABLE_MODIFICATIONS = None
VARIABLE_MODIFICATIONS = M:15.994915;
#VARIABLE_MODIFICATIONS = M:15.994915;K:284.173607;

## SEARCH PARAMETERS
DECOY_SEARCH = true

## VECTOR SEARCH PARAMETERS
TOP_N = 1000
TOLERANCE = 0.02
NORMALIZE = false
USE_GAUSSIAN = true
MODE = CPU_SMi32

Documentation

The code of this search engine is fully documented within the .cs code files. A good entry point is the main function of CandidateSearch which is implemented here. Documentation generated by Doxygen is also available here: https://hgb-bin-proteomics.github.io/CandidateSearch/

Requirements

  • .NET may be required on Windows systems.
  • [Optional] Using GPU based approaches requires a CUDA capable GPU and CUDA version == 12.2.0 (download here). Other CUDA versions may or may not produce the desired results (see this issue).

Downloads

Compiled DLLs and and executables are available in the exe folder or in Releases.

We supply compiled executables and DLLs for:

  • Windows 10/11 (x86, 64-bit)
  • Ubuntu 22.04 (x86, 64-bit)
  • macOS 14.4 (arm, 64-bit)

For other operating systems/architectures please compile the source code yourself! You will also need to compile CandidateVectorSearch!

Limitations

This a proof-of-concept implementation that shows the applicability of our CandidateVectorSearch approach and not a fully fledged search engine, therefore this implementation comes with a few limitations:

  • We currently only have implemented tryptic digestion.
    • You can implement your own digestion here.
  • We currently have not implemented support for N- or C-terminal modifications.
  • We currently have only implemented support for one possible modification per amino acid.
  • We only support spectra in centroid mode (we can't really do anything with spectra in profile mode).
  • We only support databases up to a size of 12 500 000 peptides/peptidoforms, beyond that we can't guarantee that the matrix can be allocated anymore.
    • Consider splitting your fasta into smaller chunks and searching them separately if the generated database size exceeds 12 500 000.
  • The limitations of CandidateVectorSearch also apply here.

Results

Example results of CandidateSearch and results analysis are given in tests. An extensive report is given in results.md.

Results on a HeLa dataset

Figure 1: Identifying peptide candidates and peptidoform candidates with CandidateSearch [v1.0.0] in a HeLa dataset using the human swissprot database. The considered ground truth was an MS Amanda search validated with Percolator. For every high-confidence PSM we checked if the identified peptide/peptidoform was among the top 50/100/500/1000 hits of CandidateSearch. We reach almost 100% coverage within the first 1000 hits of CandidateSearch (for reference: the whole database contained ~4 200 000 peptides or ~10 500 000 peptidoforms).

Benchmarks

Benchmarks of the different algorithms can be found in benchmarks.md.

benchmark_hpc_1A

Figure 2: Int32-based sparse matrix * dense matrix search using Eigen generally yields the fastest computation time on modern CPUs.

Known Issues

List of known issues

Citing

If you are using [parts of] CandidateSearch please cite:

MS Annika 3.0 (publication wip)

License

Contact

micha.birklbauer@fh-hagenberg.at