 Methodology
 Open access
 Published:
A graphbased filtering method for topdown mass spectral identification
BMC Genomics volume 19, Article number: 666 (2018)
Abstract
Background
Database search has been the main approach for proteoform identification by topdown tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains posttranslational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.
Results
In this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for topdown mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli topdown mass spectrometry data set and compared the performances of the SGM filtering method and two tagbased filtering methods on a data set of MCF7 cells.
Conclusions
Experimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrummatches compared with the tagbased methods in topdown mass spectrometry data analysis.
Background
Topdown mass spectrometry (MS) is an important technology for identifying proteoforms with primary sequence alterations, such as posttranslational modifications (PTMs) and mutations [1], because it provides “a bird’s eye” view of whole proteoforms. Reliable identification of protein alterations plays an important role in understanding biological mechanisms underlying diseases [2].
Represented by tools such as ProsightPC [3, 4] and TopPIC [5], database search [6, 7] is the dominant approach for topdown MSbased proteoform identification, in which topdown tandem mass (MS/MS) spectra are searched against a protein sequence database to identify the best matches between the spectra and protein sequences in the database. Maping a query spectrum generated from a proteoform with multiple alterations to its corresponding database protein sequence without alterations is a challenging computational problem.
Methods for identifying proteoforms with alterations are classified into two approaches. The first approach is to use reported protein alterations in the literature to build an extended proteoform database [4, 8]. For example, phosphorylation, acetylation, ubiquitylation and sumoylation sites have been reported in the tumor protein p53 [9], and the knowledge can be used to build a proteoform database of the protein. Using an extended database simplifies database search if query spectra are generated from proteoforms with known alterations in the database. However, there are its limitations in extended database approach. Many PTM sites of proteins are unknown because the proteins have not been extensively studied. Even if all PTM sites of a protein are known, the number of combinations of these PTM sites is an exponential function of the number of sites because each PTM site may or may not be present in a proteoform. As a result, it is impractical to construct a complete extended protein database.
The second approach uses spectral alignment algorithms [5–7, 10–12] to align topdown MS/MS spectra from modified proteoforms against unmodified database protein sequences. Although spectral alignment is effective for single protein spectrum matches, it is very time consuming to align thousands of query spectra to against thousands of protein sequences in a large database. Therefore, efficient protein sequence filtering algorithms are critical for accelerating database search in proteomelevel proteomics research [6].
For a gene or a transcript, most protein databases, such as RefSeq [13] and UniProt [14], contain only one unmodified reference sequence. Given a query MS/MS spectrum and a database of unmodified protein sequences, the object of a protein sequence filtering algorithm is to quickly filter out most unmatched protein sequences in the database while keeping the target one that produced the query spectrum. Fragment masses of the query spectrum is more important than the precursor mass for protein sequence filtration because the precursor mass does not match the molecular mass of its corresponding database protein sequence when the target proteoform contains alterations.
In bottomup and topdown MS [15–18], many filtering methods have been proposed. A popular filtering approach is to use sequence tags, which are partial protein sequences extracted from mass spectra, to scan and filter protein sequences [15–17]. A sequence tag extracted from a query spectrum is correct if it is a substring of the protein sequence that generated the spectrum. Long correct sequence tags are extremely useful in protein sequence filtration because the chance that a long correct tag is matched to a nontarget protein sequence is low, but it’s difficult to extract long correct tags from mass spectra due to missing peaks.
To solve this problem, Jeong et al. introduced gapped tags that can be extracted from spectra with missing peaks [19]. Many MS/MS spectra do not contain correct long sequence tags but contain correct long gapped tags. The introduction of gapped tags gave rise to the blocked pattern matching problem, in which a gapped tag is searched against a text string to find matched substrings [20, 21]. It is challenging to distinguish signal peaks from noise ones due to the complexity of MS/MS spectra. Even though the gapped tag approach solves the problem of missing peaks in tag extraction, it is still highly possible that many incorrect tags are reported because of noise peaks.
This paper is an extension of the work originally reported in [22]. In this paper, we propose the spectrum graph matching (SGM) problem and a novel filtering algorithm based on SGM, which constructs spectrum graphs from subspectra of query spectra, and uses the spectrum graphs to filter protein sequences. To our best knowledge, we are the first to study the SGM problem. The SGM filtering method simplifies protein sequence filtration by skipping tag extraction and selection steps. It directly searches spectrum graphs generated from subspectra with noise peaks against protein sequences. A suffix tree based algorithm was proposed in [21] for searching gapped tags against a text string. Our main contribution in method development is to extend the algorithm in [21] for searching spectrum graphs against a protein database. Experimental results on real topdown mass spectrometry data demonstrated that the SGM filtering method achieved high efficiency in protein sequence filtration.
Methods
The topdown MS/MS spectrum of a proteoform consists of a precursor mass and a list of fragment ion peaks. The precursor mass corresponds to the molecular mass of the proteoform and the fragment ion peaks correspond to fragments of the proteoform. Because of the existence of highly charged fragment ions and isotopic peaks, topdown MS/MS spectra are usually very complex. In data preprocessing, spectral deconvolution tools [23, 24] are used to converted fragment ion peaks into monoisotopic fragment masses. The intensity of a monoisotopic mass is computed as the sum of the intensities of its corresponding fragment ions peaks.
Noise masses need to be removed from deconvoluted topdown MS/MS spectra to improve the accuracy of protein sequence filtering. Generally speaking, lowintensity masses are more possible to be noise ones than highintensity masses. An intensitybased method is used to remove noise masses from a deconvoluted spectrum. For each mass x in the spectrum, we rank all the masses in the interval [x−100,x+100] with a width of 200 Dalton (Da) in the decreasing order of their intensities. If x is not one of the top λ masses, x is treated as a noise mass and removed, where λ is a userspecified parameter. All parameters used in the SGM filtering algorithm are summarized in Additional file 1.
Spectrum graphs
Spectrum graphs have been used in sequence tagbased protein sequence filtration to extract sequence tags from mass spectra [25]. There are two steps to generate a spectrum graph from a deconvoluted query spectrum: (a) a node is added to the graph for each fragment mass in the spectrum, and (b) a directed edge from a node u to a node v is added to the graph if mass(v)−mass(u) matches the mass of one amino acid residue within an error tolerance, where mass(u) and mass(v) are the masses corresponding to u and v, respectively. The edge is labeled with the amino acid explaining the mass difference (Fig. 1b). Each path in a spectrum graph corresponds to a sequence tag, which is the readout of the labeled amino acids on its edges. For example, the only path from node v_{0} to v_{4} in the spectrum graph in Fig. 1b corresponds to the sequence tag NVRS. There are many existing methods for extracting sequence tags for protein sequence filtration from spectrum graphs [17, 26].
When a spectrum misses many fragment masses, the mass differences of many node pairs in the spectrum graph are explained by 2 or more amino acids, not single amino acids. In this case, the spectrum graph approach described in the previous paragraph may fail to extract long correct sequence tags, which are extremely useful in protein sequence filtration.
Blocked patterns [20] or gapped sequence tags [19] are often used to address the missing peak problem in tag generation. To extract blocked patterns, we change step (b) in spectrum graph generation as follows: a directed edge is added into the graph from a node u to a node v if the mass difference mass(v)−mass(u) is explained by a combination of several amino acids and is no larger than a predefined bound α (α is chosen between 200 and 400 Da in practice). The edge is labeled with the mass difference mass(v)−mass(u) (Fig. 1c). Each path in a spectrum graph with labeled masses corresponds to a mass sequence, called a blocked pattern or a gapped sequence tag [20]. In block patternbased filtration, multiple blocked patterns (gapped sequence tags) extracted from a spectrum graph are searched against protein sequences to find candidate ones. The number of blocked patterns in a spectrum graph may be an exponential function of the length of the longest blocked pattern, making it inefficient to explicitly extract all blocked patterns in the spectrum graph. As a result, it is common to report only a limited number of paths and their corresponding blocked patterns in a spectrum graph. Because of the existence of noise peaks, it is a challenging problem to determine which paths in a spectrum graph correspond to blocked patterns that match the target protein sequence. We propose to circumvent the blocked pattern selection step and directly search spectrum graphs against protein sequences for filtration.
The spectrum graph matching problem
For a gene or a transcript, most protein databases contain only one unmodified reference sequence. To simplify the description of the SGM filtering method, we assume that protein databases used in spectral identification contain only unmodified sequences and that amino acid residue masses are integers. All amino acid residue masses are discretized by first multiplying the masses by a scale factor (100 was used in the experiments) and then rounding the results to integers.
Notations introduced by Deng et al. [21] are used in the description of the SGM problem. An amino acid string is represented as a list of discretized residue masses: the mass representation of an amino acid string a_{1},a_{2},…,a_{n} is a residue mass string S=s_{1},s_{2},…,s_{n}, in which s_{i} is the discretized residue mass of a_{i} for 1≤i≤n. Residue mass strings are called text strings in the following analysis. The sum of all the masses in a substring s_{i},s_{i+1},…,s_{j} of S, i.e., \(\sum _{k=i}^{j} s_{k}\), is called the mass of the substring. Two substrings of S are called consecutive substrings if the first residue mass of the second string directly follows the last residue mass of the first string. For example, s_{i},s_{i+1},…,s_{j} and s_{j+1},s_{j+2},…,s_{k} are consecutive substrings of S. A sequence of consecutive substrings A_{1},A_{2},…,A_{k} that include all residue masses in S is called a partition of S. The masses of the consecutive substrings in a partition is called the mass string of the partition. For example, let S=114,156,99,87, A_{1}=114,A_{2}=156,99, and A_{3}=87, then A_{1},A_{2},A_{3} is a partition of S and the mass string of the partition is 114,255,87, where 255 is the mass of A_{2}. A blocked pattern obtained from a path in a spectrum graph is represented by a sequence of masses, which are labels of the edges in the path. For example, the blocked pattern corresponding to the bold path in Fig. 1c is 114.04, 225.17, 87.03. A blocked pattern can be further discretized to an integer sequence using the same method for residue mass discretization. In the following analysis, we assume that all blocked patterns are integer ones. Unlike text strings, a blocked pattern contains not only single amino acid residue masses, but also those of combinations of several amino acids. A blocked pattern P matches a text string S if P is the mass string of a partition of S. For example, the blocked pattern 114,255,87 matches the text string 114,156,99,87 because the blocked pattern is the mass string of a partition of the text string.
In protein sequence filtration, we search blocked patterns extracted from a spectrum graph against a protein database to find matched amino acid sequences (sequence tags). All protein sequences in the database are concatenated and represented as a text string over an alphabet Σ containing the discretized residue masses of the 20 common amino acids. Because the masses of leucine and isoleucine are the same, the size of Σ is 19 instead of 20. Given a text string S over an alphabet Σ and a blocked pattern P, the blocked pattern matching problem is to find all substrings of S that match P.
Definition 1
Given a text string S over an alphabet Σ and a pattern P, the blocked pattern matching (BPM) problem is to find all substrings of S that match P.
The SGM problem is an extension of BPM problem that considering the noises in the spectrum. It is more complex than the blocked pattern matching problem.
Definition 2
Given a text string S over an alphabet Σ and a spectrum graph G, the spectrum graph matching (SGM) problem is to find all substrings of S that match a blocked pattern in G.
We first study a restricted version of the SGM problem, in which a start node and an end node in G are specified and only paths from the start node to the end node are used to find matched substrings of the text string. The SGM problem is solved by enumerating all node pairs in G as the start and end nodes in the restricted spectrum graph matching (RSGM) problem.
Definition 3
Given a text string S, a spectrum graph G over an alphabet Σ, a start node s, and an end node t, the restricted spectrum graph matching (RSGM) problem is to find all substrings of S that match a blocked pattern corresponding to a path from s to t in G.
A suffix tree based algorithm for the RSGM problem
To solve the RSGM problem, we present a suffix tree based algorithm that extends the algorithm proposed by Deng et al. for the blocked pattern matching problem [21]. A suffix tree is used to represent the string S. We assume that each edge in the suffix tree is labeled with only one letter (integer residue mass) to simplify the algorithm description.
We first review the algorithm for the blocked pattern matching problem, which was proposed by Deng et al. [21]. A blocked pattern is represented as a spectrum graph in which all edges are in one path. Let G={V,E} be the graph representation of a blocked pattern P=p_{1},p_{2},…,p_{m}, where V={v_{0},v_{1}…,v_{m}} and v_{0},v_{1},…,v_{m} is the only path from v_{0} to v_{m} in G. Each prefix p_{1},p_{2},…,p_{k} of the blocked pattern P corresponds to a path v_{0},v_{1},…,v_{k}. A text string over Σ that matches the prefix p_{1},p_{2},…,p_{k} is called a prefix text string of v_{k}. A prefix text string is identifiable if it is a substring of S. For example, when P=114,255,87, both 114,156,99 and 114,99,156 are prefix text string of P. When S=114,156,99,87, the string 114,156,99 is an identifiable prefix text string of P, but 114,99,156 is not. If a prefix text string is not identifiable, then all text strings with the prefix are not identifiable, making it not necessary to search these text strings in S. Using identifiable prefix text strings significantly improves in the speed of searching a blocked pattern against S represented as a suffix tree.
Let B_{i} be the set of nodes in the suffix tree corresponding to all identifiable prefix text strings of v_{i} for 0≤i≤m, where m is the length of the blocked pattern. Specifically, B_{0} contains only the root node of the suffix tree. The blocking pattern matching algorithm progressively finds the node sets B_{1},…,B_{m} in the suffix tree. The node set B_{m} contains the solution to the blocked pattern matching problem.
Unlike the blocked pattern matching problem with only one blocked pattern, the spectrum graph G in the RSGM problem contains many paths from the start node to the end node, each path corresponding to a blocked pattern. The number of all paths in a spectrum graph may be an exponential function of the number of nodes. So it is an inefficient approach to directly extract all paths in a spectrum graph and search each path against a suffix tree separately.
Next, we describe our new algorithm for the RSGM problem, which extends the blocked pattern matching algorithm. Let V={v_{0},v_{1},…v_{m}} be the set of nodes in the increasing order of their corresponding masses, in which the start node s is v_{0} and the end node t is v_{m}. A text string is a prefix text string of node v_{i} if it matches a blocked pattern corresponding to a path from v_{0} to v_{i}. Let B_{i} be the set of nodes in the suffix tree corresponding to all identifiable prefix text strings of v_{i}, and C(e) be the set of all text strings whose masses match the labeled mass of e (Table 1). In practice, a precomputed table is used to quickly obtain C(e). Because the fragment masses in query spectra have measured errors, an error tolerance ε is allowed in generating the text strings in the table. Denote E_{i}⊆E the set of all edges whose tail nodes are v_{i}. For each edge e=(v_{j},v_{i}) in E_{i}, we search from each suffix tree node in B_{j} to find all the paths corresponding to a text string in C(e) and add the end nodes of these paths to B_{i} (Algorithm 1).
After the last set B_{m} is found, the RSGM problem is solved by reporting all the text strings corresponding to a suffix tree node in B_{m} and their positions in S, which are stored in the suffix tree. Because the string S is represented by a suffix tree, the space complexity and time complexity of the algorithm are both O(n), where n is the length of the string S.
Time complexity
We use the idea proposed by Deng et al. to prove the time complexity of the RSGM algorithm [21]. The preprocessing step, in which the text S is represented as a suffix tree, is implemented using the Ukkonen’s algorithm [27], and its time complexity is O(n). Below we study the time complexity of the pattern query steps in the RSGM algorithm (Steps 211 of the algorithm in Algorithm 1).
We divide the set C(e) of text strings for an edge e in the spectrum graph into subsets C(e,1),C(e,2), …,C(e,l), where l is the length of the longest text string in C(e). The subset C(e,j) contains all text strings in C(e) with length j. The expansion factor of the set C(e,j) is defined as \(r(e,j) = C(e,j)^{\frac {1} {j}}\), where C(e,j) is the size of C(e,j). The largest expansion factor of all edges in G is denoted as r= maxe,jr(e,j). Let N be the maximum size of C(e) for all edges in G, that is, N= maxe∈EC(e).
The running time of the graph query (Steps 2  11) in the RSGM algorithm is related to \(\sum _{i=1}^{m}B_{i}\), the sum of the sizes of the sets B_{i}. Next we will describe how to compute \(\sum _{i=1}^{m} B_{i}\). Let U be the set of all prefix text strings of v_{1},…,v_{m}, and L be the length of the longest prefix text string in U. We define X_{l} as the set of all length l prefix text strings in U, and Y_{l} as the set of all length l identifiable prefix strings in U, for 1≤l≤L. Each node in \(\cup _{i=1}^{m}B_{i}\) corresponds to an identifiable prefix text string in \(\cup _{l=1}^{L}Y_{l}\), that is, \(\sum _{i=1}^{m}B_{i} = \sum _{l=1}^{L}Y_{l}\). We compute the expectation of Y_{l} by multiplying the size of X_{l} and the probability that a length l text string can be found in S.
A path in G starting from node s is called a prefix path. Let Q be a prefix path of G and X_{l}(Q) the set all prefix text strings with length l that match a prefix subpath of Q. Deng et al. proved the following Lemma about the size of X_{l}(Q) [21].
Lemma 1
The size of X_{l}(Q) is bounded by (2r)^{l}, where r is the maximum expansion factor.
Based on Lemma 1, we give an upper bound of the size of X_{l}.
Lemma 2
The size of X_{l} is bounded by (2rd)^{l}, where d is the maximum outdegree of the nodes in G.
Proof
Each length l prefix text string in X_{l} matches at least one prefix path. The total number of prefix paths in G with length l is bounded by d^{l}. Because of Lemma 1, the number of prefix text strings in X_{l} matching one prefix path in G is bounded by (2r)^{l}. As a result, the size of X_{l} is bounded by (2r)^{l}d^{l}=(2rd)^{l}. □
Let g=Σ be the size of the alphabet Σ. Using the same proof method in Lemmas 1 and 2 in the supplementary material in [21], we can prove the following two lemmas.
Lemma 3
When 2rd<g, the expectation of Y_{l} is bounded by \(\phantom {\dot {i}\!}(2rd)^{{\text {log}_{g}}n}=n^{{\text {log}_{g}}(2rd)}\), where n is the length of the text string S.
Lemma 4
When 1<2rd<g, the expectation of \({\sum \nolimits }_{i=1}^{m}B_{i}\) in the RSGM algorithm is bounded by \(\phantom {\dot {i}\!}c\,n^{{\text {log}_{g}}(2rd)}\), where \(\phantom {\dot {i}\!}c=\frac {2rd}{2rd1}+\frac {g}{g2rd}\).
Theorem 1
When Σ is a finite set and 1<2rd<g, the averagecase time complexity of the graph query steps in the RSGM algorithm is O(dNn^{k}+M), where d is the maximum outdegree of the nodes in G, N= maxe∈EC(e), k= logg(2rd)<1, and M is the number of matched substrings in S.
Proof
For each suffix tree node u in \(\cup _{i=0}^{m} B_{i}\), we find its corresponding node v in the spectrum graph. In Step 6 of the RSGM algorithm, the length of the text string x is a constant. In addition, the set B_{i} is stored as a hash table, and the number of operations to check if u^{′} exists in B_{i} is a constant. As a result, the time complexity of one search in Step 6 is O(1). The total number of searches starting from u is bounded by the outdegree of v multiplied by the largest size of C(e) for all edges e whose head node is v. That is, the number of searches of u is bounded by dN. Because \(\left \cup _{i=0}^{m} B_{i}\right \) is O(n^{k}), the total number of searches in the algorithm is O(dNn^{k}). As a result, the time complexity of the algorithm is O(dNn^{k}). □
Theorem 1 given the time complexity of the graph query steps in the RSGM algorithm when 2rd is no larger than the size of the alphabet Σ. In practice, the default value of the parameter α (the maximum difference between the masses corresponding to two nodes connected by an edge) is 350 Da and the scale factor is 100. In this case, the value of r is about 2.91. In addition, the outdegree of a node v in G is restricted to be at most 3 by ranking all the edges with the head node v in the increasing order with respect to the labeled masses and keeping only the top 3 ones. Because the maximum outdegree of the nodes in G is 3, the condition 2rd<g (2rd<18<g=19) is satisfied. In the experiments, the maximum outdegree of nodes in spectrum graphs was set to 3.
When 2rd≥g, the number of the suffix tree nodes searched by the algorithm is O(n), that is, the number of searched nodes is bounded by the size of the suffix tree. Each suffix tree node u corresponds to a spectrum graph node v. The total number of searches starting from u is bounded by the outdegree of v multiplied by the largest size of C(e) for all edges e whose head node is v. Therefore, the number of searches starting from u is bounded by dN, and the time complexity of the algorithm is O(dNn).
The RSGM algorithm gives all substrings in S that match a path from v_{0} to v_{i} by reporting the suffix tree nodes in B_{i}. That is, the algorithm reports all substrings in S that match a path starting from v_{0}. We execute the RSGM algorithm m−1 rounds to solve the SGM problem. In the ith round, the start node is set to v_{i} and the end node to v_{m}. All matched substrings in the m−1 rounds are combined as the solution to the SGM problem.
In practice, we are interested in finding only text strings that match a long path in G. Let V_{s} be the set of nodes v in G such that the sum of the labeled masses on the shortest path from v_{0} to v is no larger than β, where β is a userspecified parameter. Let V_{t} be the set of nodes v in G such that the sum of the labeled masses on the shortest path from v to v_{m} is no larger than β. We only report text strings that match a path from a node in V_{s} to a node in V_{t}.
Protein sequence filtration
The spectrum graph of a topdown MS/MS spectrum usually consists of several disconnected components because the spectrum often has low fragment coverage. Based on this observation, we propose to extract γ mass intervals (subspectra) that are abundant with fragment masses from a query spectrum and construct a spectrum graph for each extracted mass interval, where γ is a userspecified parameter. In practice, the value of γ is chosen between 1 and 20. The spectrum graphs are searched against the protein database separately and the search results are combined to find the best sequence candidates. The filtering algorithm is called the SGM filtering algorithm.
In mass interval selection, mass intervals with the same width δ are reported and the width δ is a userspecified parameter. In practice, the value of δ is chosen between 500 and 1400 Da so that each reported mass interval corresponds to a sequence tag with 5−14 amino acids. (See “Parameter settings” section) The score of a mass interval is defined as the number of masses in it. The overlapping ratio of two intervals [a,a+δ] and [b,b+δ] with a<b<a+δ is defined as the ratio between the width of the overlapping region [b,a+δ] and the interval width δ, that is, \(\frac {a+\delta b}{\delta }\). If a<a+δ<b, the overlapping ratio is 0. A greedy approach is employed to find multiple topscoring mass intervals. First, the topscoring mass interval of the spectrum is reported. Second, we remove all mass intervals that have an overlapping ratio ≥ρ with the reported one, where ρ is a userspecified parameter, and then report the best scoring one from the remaining mass intervals. The second step is performed iteratively until a total of γ mass intervals are reported or no candidate mass intervals can be reported. In addition, only mass intervals with at least 6 fragment masses are reported.
We use reversed mass intervals to find text strings that match suffix fragment masses in the query spectrum. Let b_{1},b_{2},…,b_{k} be the masses in a mass interval [m_{l},m_{r}] extracted from a collisioninduced dissociation (CID) MS/MS spectrum with a precursor mass M. We generate a reversed mass interval as follows: (a) the mass interval [m_{l},m_{r}] is converted into [M−m_{r},M−m_{l}], and (b) for each mass b_{i}, 1≤i≤k, a reversed mass M−b_{i} is added to the reversed mass list. For example, the reversed mass interval of a mass interval [100,400] with masses 114,213 and a precursor mass 644 is [244,544] with masses 431,530. Each extracted mass interval is further reversed to obtain a reversed mass interval in which suffix fragment masses are converted into prefix fragment masses. We search the spectrum graphs generated from the extracted and reversed mass intervals against the protein database represented by a suffix tree.
We describe three functions for assigning scores to fragment masses. For a fragment mass (x,h) with a mass value x and its intensity h in a query spectrum, the first function assigns the score 1 to the mass, that is, score(x,h)=1. The second function uses the logarithm function to normalize mass intensities to compute scores of masses. We find the lowest mass intensity b in the query spectrum, the score for the mass (x,h) is computed as \(\text {score}(x,h) = \log _{2} \left (\frac {2h}{b}\right)\). The constant number 2 in the function is to guarantee that all scores are positive. The third function is based on the rankings of mass intensities. The fragment masses in the query spectrum are sorted in the increasing order of their corresponding intensities. Let i be the rank of the mass (x,h). The score of the mass (x,h) is defined as score(x,h)=1+i/k where k is the total number of fragment masses in the spectrum. The score of the lowest intensity mass is about 1 and the score of the highest intensity mass is 2. The three scoring functions are called the mass count score, log intensity score, and rank score, respectively. The score of a node in a spectrum graph is assigned as the score of its corresponding fragment mass.
Here we introduce a similarity score between a query spectrum and the protein sequence. A protein sequence matches a blocked pattern P if its text string representation contains a substring that matches P. The score of the pattern P is the number of the nodes in the corresponding path in the spectrum graph. The similarity score between a protein sequence and a spectrum graph is the score of the best scoring pattern extracted from the graph that matches the protein sequence. Let G_{1},G_{2},…,G_{k} be the set of spectrum graphs extracted from a query spectrum, the similarity score between a protein sequence and the query spectrum is the best similarity score between the protein sequence and G_{1},G_{2},…,G_{k}.
After finding the best scoring pattern from G_{1}, G_{2},…,G_{k} that matches a substring of the protein sequence, we compute the mass shift between the experimental fragment masses in the spectrum and the theoretical fragment masses of the protein sequence. By shifting all theoretical masses by the mass shift, we recompute the similarity score between the experimental masses and shifted theoretical ones. The new score is called the extended similarity score between the protein sequence and the query spectrum, which is used to rank and filter protein sequences.
Results
Java was used to implement the SGM filtering algorithm. A 64bit Linux desktop PC with an Intel 3.4 GHz CPU and 16 GB memory was the platform for testing all algorithms.
Data sets
The SGM filtering algorithm was evaluated on two data sets. The first is a topdown MS data set with 2027 CID and 2027 electrontransfer dissociation (ETD) MS/MS spectra from Escherichia coli (EC) K12 MG1655. The EC sample was analyzed by a liquid chromatography system coupled with an LTQ Orbitrap Velos mass spectrometer. MS and MS/MS spectra were collected at a 60000 resolution and the top 4 ions in each MS spectrum were selected for MS/MS analysis.
The second topdown MS data set was generated from MCF7 cells. MCF7 cells were obtained from American Tissue Culture Collection (ATCC) and maintained in minimum essential media with 5% charcoaldextran treated fetal calf serum. In the MS experiment, soluble MCF7 intact proteins were separated using the beadbeating based cell lysis approach followed by a filterbased desalting step. A liquid chromatograph system with a homemade long column was directly coupled with an LTQ Orbitrap Velos Pro mass spectrometer with a customized ion source. A total of 5309 CID MS/MS spectra were collected at a 60000 resolution.
Protein spectrummatches for evaluation
The EC raw data were centroided using msconvert in ProteoWizard [28] and deconvoluted using MSDeconv [24]. The Escherichia coli K12 MG1655 proteome database (September 12, 2016 version, 4306 entries) was downloaded from the UniProt database [14] and was concatenated with a shuffled decoy database of the same size. TopPIC [5] was employed to search the deconvoluted spectra against the targetdecoy database. In TopPIC, unexpected modifications were not allowed in identified proteoforms, and the error tolerances for precursor and fragment masses were set to 15 part per million (ppm). With a 1% spectrumlevel false discovery rate (FDR), a total of 1866 proteoform spectrummatches were identified. Because of the stringent FDR cutoff, we assume that all the matches are correct in the following analysis. The 1866 proteoform spectrummatches are referred to as the EC evaluation data set.
The SGM filtering algorithm was employed to search the spectrum in each of the 1866 matches against the EC proteome database and report 20 top scoring proteins. If the top 20 proteins contain the protein in the proteoform spectrummatch reported by TopPIC, we say the filtering is efficient. The filtration efficiency rate of the filtering algorithm is defined as the ratio between the number of efficiently filtered spectra and the number of all spectra in the experiment.
Parameter settings
The EC evaluation data set was used to test the performance of the SGM filtering algorithm with various settings of the parameters (Additional file 1). In the experiments, the mass count score for fragment masses was used, only one spectrum graph was extracted from each query spectrum (γ=1), and reversed mass intervals were not included.
We first evaluated the filtration efficiency with various settings of the three parameters α, β and δ. In the filtering algorithm, no masses are removed in the spectral preprocessing (λ=∞), the error tolerance ε was set to 0.02 Da. To test the 3 parameters, we fixed the settings of 2 parameters and compared the performances with various settings of the third parameter. First, the parameters α and β were set to α=300 Da and β=200 Da, and the parameter δ was set to various values between 500 to 1400 Da. The algorithm obtained the best filtration efficient rate (29.21%) when δ=900 Da (Fig. 2). Although the running time decreases as the value of δ increases, there is no significant difference in the running time when δ≥900 Da.
Similarly, we evaluated the performance of the SGM filtering algorithm with various settings of β between 0 and 400 Da and fixed settings δ=900 Da and α=300 Da (Fig. 3) and that with various settings of α between 200 and 500 Da and fixed settings δ=900 Da and β=250 Da (Fig. 4). The algorithm achieved the best filtration efficiency when β=250 Da in the first experiment and when α=350 Da in the second experiment. The best filtration efficiency rate 33.07% was obtained when α=350 Da, β=250 Da and δ=900 Da.
In practice, the error tolerance ε in the SGM filtering algorithm is determined by the precision of the mass spectrometer. By using the following parameter settings: α=350 Da, β=250 Da, δ=900 Da, λ=∞, we compared the performances of the filtering algorithm on the EC evaluation data set with various settings of the error tolerance ε in [0,0.05] Da. The best filtration efficient rate was achieved when ε=0.02 Da (Table 2).
We also tested how the parameter λ in spectral preprocessing affects the performance of the SGM filtering algorithm. We set α=350 Da, β=250 Da, δ=900 Da, ε=0.02 Da, and compared the performances of the filtering algorithm on the EC evaluation data set with various settings of the parameter λ between 2 and 12. By setting λ=8, we achieved the best filtration efficiency (Table 3). Based on the experimental results, the default settings of the parameters α, β, δ, ε, and λ are given in the Additional file 1.
Multiple spectrum graphs
The methods described in “Protein sequence filtration” section was used to extract multiple spectrum graphs with two parameters: the number γ of extracted spectrum graphs and the maximum overlapping ratio ρ between two mass intervals. We compared the performances of the SGM filtering algorithm with various settings of the two parameters: γ=1,3,5,10,20, and ρ=0%,20%,50%,80%. Other parameters were set to the default values. By using the parameter settings γ=20 and ρ=50%, the best filtration efficiency was obtained (Table 4), but the running time with the setting was about 7 times longer than that with the setting γ=20 ρ=20% (Table 5). Because the numbers of fragment masses in many query spectra are limited, the total number of spectrum graphs reported from a query spectrum often depends on the maximum allowed overlapping ratio. When the maximum overlapping ratio is 0%, that is, only a few no overlapped spectrum graphs are generated in most cases. When the maximum overlapping ratio is 80%, more spectrum graphs are generated, significantly increasing the running time. This is the reason why the parameter setting γ=20 and ρ=20% achieved a good balance between the filtration efficiency and speed.
Reversed mass intervals
To evaluate the performance of the SGM algorithm with reverse mass intervals. The spectrum graphs generated from reversed mass intervals were used to improve the filtration efficiency. When γ=20 and ρ=20%, experiments showed the filtration efficiency rate (71.01%) of the SGM filtering algorithm with reversed mass intervals was much higher than that (54.44%) without reversed mass intervals.
Scoring functions
Using the parameter settings in Additional file 1, we compared the performances of the SGM filtering algorithm with three scoring functions: the mass count score, the log intensity score, and the rank score, on the EC evaluation data set. The mass count score and the rank score obtained the same filtration efficiency rate 71.01%, which was higher than that of the log intensity score (65.38%). When the query spectrum contains several very high intensity fragment masses, many random text strings that match these masses may have high scores and the target protein may be missed in the reported top proteins. This might be the reason that using the log intensity score does not achieve good filtration efficiency. In addition, the extended similarity score (see “Protein sequence filtration” section) based on mass count scores further improved the filtration efficiency rate to 76.63%. Based on the experimental results, we use the extended mass count score as the default scoring function in the SGM filtering algorithm.
Comparison with other filtering algorithms
Two tagbased algorithms for protein sequence filtration were compared with the SGM filtering algorithm on the MCF7 data set. The first tagbased algorithm is implemented in MSAlign+Tag (http://bioinf.spbau.ru/proteomics/msalignplustag) and referred to as TAG1; the second tagbased algorithm is implemented in MSPathFinder [29] and referred to as TAG2. The two tagbased algorithms were implemented in C++. The MCF7 raw data was centroided using msconvert in ProteoWizard [28] and deconvoluted using MSDeconv [24]. The human proteome database (July 9, 2016 version, 20191 entries) was downloaded from the UniProt database and concatenated with a shuffled decoy database with the same size.
The SGM filtering algorithm was coupled with the spectral alignment algorithm in TopPIC for spectral identification. The coupled method consists of four steps: (1) the SGM filtering algorithm reports 20 top scoring protein sequences for each query spectrum, (2) the 20 protein sequences are aligned with the query spectrum to report the best scoring proteoform spectrummatch, (3) an Evalue of the best scoring match is computed, and (4) the targetdecoy approach [30] is used to estimate FDRs and filter identified proteoform spectrummatches with a 1% spectrumlevel FDR. Similarly, the two tagbased algorithms were coupled with the spectral alignment for spectral identification. The three filtering algorithms were compared on the number of proteoform spectrummatches identified from the MCF7 data set.
Both TAG1 and TAG2 use spectrum graphs without gaps to obtain sequence tags. TAG1 first finds the longest path in a spectrum graph and then generates all substrings with a fixed length l of the longest path as sequence tags. TAG2 generates all possible sequence tags with a length in [l_{min},l_{max}] from a spectrum graph, where l_{min} is a minimum length and l_{max} is a maximum length. To find the best parameter settings, we compared the performances of TAG1 with various settings l=3,4,5 and those of TAG2 with various settings of the parameters l_{min}=3,4,5,6 and l_{max}=7,8,9. The error tolerance for matching a mass difference to an amino acid residue in spectrum graph generation was set as 15 ppm. Experimental results showed that the TAG1 algorithm reported the largest number of identifications when l=4 (Fig. 5) and that the TAG2 algorithm achieved the best performance when l_{min}=4 and l_{max}=9 (Table 6). These parameter settings in the tagbased algorithms were used in the following comparison. The parameter settings of the SGM filtering algorithm are given in Additional file 1. The parameter setting of TopPIC were the same as those in Additional file 2 except that at most 1 unexpected mass shift was allowed in an identified proteoform.
The average running time (540 ms) of the SGM algorithm for a query spectrum was about 2 times of the tagbased algorithms (TAG1: 250 ms; TAG2: 166 ms). Because the SGM algorithm has a larger search space compared with the tagbased algorithms, it is reasonable that its running time is longer than TAG1 and TAG2. With a 1% spectrumlevel FDR, the SGM, TAG1, TAG2 filtering algorithms identified 897, 439, and 597 proteoform spectrummatches, respectively. A total of 346 spectra were identified by all the three methods. TAG1 missed 506 spectra and TAG2 missed 390 spectra which were identified by the SGM method (Fig. 6), showing that the filtration efficiency of the SGM algorithm is much better than the two tagbased algorithms.
Discussion
Many software tools for topdown mass spectral identification use protein sequence filtering methods based on sequence tags or gapped tags. But these methods often fail to achieve high sensitivity because of missing peaks and noise peaks in spectra. The proposed SGM filtering method provides to a solution to the problem of noise peaks by skipping the step of determining which peaks are signal ones. Compared with the methods based on gapped tags, the SGM filtering method includes all possible gapped tags in tagsequence matching, increasing the sensitivity of protein sequence filtration. Although the SGM filtering algorithm is more complex than the blocked pattern match algorithm, its speed is still acceptable. The main reason is that high accuracy fragment masses efficiently filter out most candidate substrings in the intermediate steps in the search.
The SGM filtering method can be combined with other methods to further improve the performance of protein sequence filtration. For example, a tagbased method is used as the first filtering method for spectral identification, and only spectra that are not identified in the first found are searched by the SGM filtering method. Because the tagbased method is fast and the SGM filtering method is sensitive, the combined approach is capable of achieving both high speed and high sensitivity.
The SGM filtering method still has some limitations. First, it often reports many candidate proteins for spectra with many noise peaks. As a result, the target protein may be missed when only a limited number of proteins are reported as filtering results. Second, the three scoring functions used in the SGM filtering method are not powerful in distinguishing correct matches between spectra and protein sequences from incorrect ones. Advanced machine learning methods are needed to design a good scoring function.
Conclusions
In this paper, we proposed an efficient spectrum graphbased filtering algorithm for topdown mass spectral identification and tested the algorithm on two real topdown MS data sets. Compared with tagbased methods, the SGM filtering algorithm circumvents the steps of tag generation and directly searches spectrum graph against the protein database, simplifying data processing and increasing filtration efficiency. The experimental results on the real data demonstrate that the SGM filtering algorithm outperforms the two tagbased algorithms in filtration efficiency.
Abbreviations
 ATCC:

American tissue culture collection
 CID:

Collisioninduced dissociation
 Da:

Dalton
 EC:

Escherichia coli
 ETD:

Electrontransfer dissociation
 FDR:

False discovery rate
 LTQ:

Linear trap quadrupole
 MCF7:

Michigan cancer foundation7
 MS:

Mass spectrometry
 MS/MS:

Tandem mass spectra
 ppm:

Part per million
 PTM:

Posttranslational modification
 RSGM:

Restricted spectrum graph matching
 SGM:

Spectrum graph matching
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Funding
The research and publication costs were funded by the National Natural Science Foundation of China(NSFC) through Grants 61472222 and 61732009.
Availability of data and materials
The data sets used in this paper are available at Massive (http://massive.ucsd.edu) with identification numbers MSV000082061 and MSV000082064.
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This article has been published as part of BMC Genomics Volume 19 Supplement 7, 2018: Selected articles from the IEEE BIBM International Conference on Bioinformatics & Biomedicine (BIBM) 2017: genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume19supplement7.
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DZ and RY designed the methods and conceived the experiments. RY implemented the methods in Java, All authors have read and approved the final manuscript.
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Additional files
Additional file 1
A summary of parameters and their default values of the SGM filtering algorithm. (PDF 28 kb)
Additional file 2
The parameter settings of TopPIC used in the experiments. (PDF 13 kb)
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Yang, R., Zhu, D. A graphbased filtering method for topdown mass spectral identification. BMC Genomics 19 (Suppl 7), 666 (2018). https://doi.org/10.1186/s128640185026x
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DOI: https://doi.org/10.1186/s128640185026x