Evolutionary Algorithms for Improving De Novo Peptide Sequencing

<p>De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results.</p>

ON PREPROCESSING AND ANTISYMMETRY IN DE NOVO PEPTIDE SEQUENCING: IMPROVING EFFICIENCY AND ACCURACY

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720008003503 ◽

2008 ◽

Vol 06 (03) ◽

pp. 467-492 ◽

Cited By ~ 7

Author(s):

KANG NING ◽

NAN YE ◽

HON WAI LEONG

Keyword(s):

Mass Spectrometry ◽

Tandem Mass Spectrometry ◽

Computational Models ◽

De Novo ◽

Noise Removal ◽

Peptide Sequencing ◽

De Novo Sequencing ◽

Tandem Mass ◽

Peptide sequencing plays a fundamental role in proteomics. Tandem mass spectrometry, being sensitive and efficient, is one of the most commonly used techniques in peptide sequencing. Many computational models and algorithms have been developed for peptide sequencing using tandem mass spectrometry. In this paper, we investigate general issues in de novo sequencing, and present results that can be used to improve current de novo sequencing algorithms. We propose a general preprocessing scheme that performs binning, pseudo-peak introduction, and noise removal, and present theoretical and experimental analyses on each of the components. Then, we study the antisymmetry problem and current assumptions related to it, and propose a more realistic way to handle the antisymmetry problem based on analysis of some datasets. We integrate our findings on preprocessing and the antisymmetry problem with some current models for peptide sequencing. Experimental results show that our findings help to improve accuracies for de novo sequencing.

De novo peptide sequencing using ion peak intensity and amino acid cleavage intensity ratio

Bioinformatics ◽

10.1093/bioinformatics/btm062 ◽

2007 ◽

Vol 23 (9) ◽

pp. 1068-1072 ◽

Cited By ~ 3

Author(s):

M. Kanazawa ◽

H. Anyoji ◽

A. Ogiwara ◽

U. Nagashima

Keyword(s):

Amino Acid ◽

Intensity Ratio ◽

De Novo ◽

Peptide Sequencing ◽

Peak Intensity ◽

Acid Cleavage ◽

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework

Bioinformatics ◽

10.1093/bioinformatics/btz366 ◽

2019 ◽

Vol 35 (14) ◽

pp. i183-i190 ◽

Cited By ~ 10

Author(s):

Hao Yang ◽

Hao Chi ◽

Wen-Feng Zeng ◽

Wen-Jing Zhou ◽

Si-Min He

Keyword(s):

Deep Learning ◽

De Novo ◽

Learning To Rank ◽

Peptide Sequencing ◽

De Novo Sequencing ◽

Supplementary Information ◽

Theoretical Spectrum ◽

Tandem Mass ◽

AbstractMotivationDe novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.ResultsIn order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29–102% more correct spectra, and the precision was 11–89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21–50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.Availability and implementationpNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.

Uncovering thousands of new HLA antigens and phosphopeptides with deep learning-based sequence-mask-search de novo peptide sequencing framework

10.1101/667527 ◽

2019 ◽

Author(s):

Korrawe Karunratanakul ◽

Hsin-Yao Tang ◽

David W. Speicher ◽

Ekapol Chuangsuwanich ◽

Sira Sriswasdi

Keyword(s):

Deep Learning ◽

Amino Acid ◽

De Novo ◽

Hla Antigens ◽

Peptide Identification ◽

Peptide Sequencing ◽

Amino Acid Sequences ◽

Mass Spectrometry Data ◽

Model Organisms ◽

Invaluable Tool

ABSTRACTTypical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based hybrid de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptideomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies – especially cancer neoantigen discovery and proteome characterization of non-model organisms.

De novo peptide sequencing by deep learning

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1705691114 ◽

2017 ◽

Vol 114 (31) ◽

pp. 8247-8252 ◽

Cited By ~ 95

Author(s):

Ngoc Hieu Tran ◽

Xianglilan Zhang ◽

Lei Xin ◽

Baozhen Shan ◽

Ming Li

Keyword(s):

Neural Networks ◽

Dynamic Programming ◽

Deep Learning ◽

De Novo ◽

Amino Acid Level ◽

Peptide Sequencing ◽

De Novo Sequencing ◽

Local Dynamic ◽

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7–22.9% higher accuracy at the amino acid level and 38.1–64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5–100% coverage and 97.2–99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.

Deep Novo A+: Improving the Deep Learning Model for De Novo Peptide Sequencing with Additional Ion Types and Validation Set

Current Bioinformatics ◽

10.2174/1574893615666200204112347 ◽

2021 ◽

Vol 15 (8) ◽

pp. 949-954

Author(s):

Lei Di ◽

Yongxing He ◽

Yonggang Lu

Keyword(s):

Deep Learning ◽

De Novo ◽

Peptide Sequencing ◽

De Novo Sequencing ◽

Superior Performance ◽

Validation Set ◽

De Novo Peptide ◽

Deep Learning Model

Background: De novo peptide sequencing is one of the key technologies in proteomics, which can extract peptide sequences directly from tandem mass spectrometry (MS/MS) spectra without any protein databases. Since the accuracy and efficiency of de novo peptide sequencing can be affected by the quality of the MS/MS data, the DeepNovo method using deep learning for de novo peptide sequencing is introduced, which outperforms the other state-of-the-art de novo sequencing methods. Objective: For superior performance and better generalization ability, additional ion types of spectra should be considered and the model of DeepNovo should be adaptive. Methods: Two improvements are introduced in the DeepNovo A+ method: a_ions are added in the spectral analysis, and the validation set is used to automatically determine the number of training epochs. Results: Experiments show that compared to the DeepNovo method, the DeepNovo A+ method can consistently improve the accuracy of de novo sequencing under different conditions. Conclusion: By adding a_ions and using the validation set, the performance of de novo sequencing can be improved effectively.

SPIDER: SOFTWARE FOR PROTEIN IDENTIFICATION FROM SEQUENCE TAGS WITH DE NOVO SEQUENCING ERROR

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001247 ◽

2005 ◽

Vol 03 (03) ◽

pp. 697-716 ◽

Cited By ~ 138

Author(s):

YONGHUA HAN ◽

BIN MA ◽

KAIZHONG ZHANG

Keyword(s):

Amino Acid ◽

Protein Identification ◽

De Novo ◽

Peptide Identification ◽

De Novo Sequencing ◽

Amino Acid Sequences ◽

Sequencing Error ◽

Protein Database ◽

Common Error ◽

Amino Acid Mutations

For the identification of novel proteins using MS/MS, de novo sequencing software computes one or several possible amino acid sequences (called sequence tags) for each MS/MS spectrum. Those tags are then used to match, accounting amino acid mutations, the sequences in a protein database. If the de novo sequencing gives correct tags, the homologs of the proteins can be identified by this approach and software such as MS-BLAST is available for the matching. However, de novo sequencing very often gives only partially correct tags. The most common error is that a segment of amino acids is replaced by another segment with approximately the same masses. We developed a new efficient algorithm to match sequence tags with errors to database sequences for the purpose of protein and peptide identification. A software package, SPIDER, was developed and made available on Internet for free public use. This paper describes the algorithms and features of the SPIDER software.