- Open Access
Identifying the missing proteins in human proteome by biological language model
© The Author(s). 2016
- Published: 23 December 2016
With the rapid development of high-throughput sequencing technology, the proteomics research becomes a trendy field in the post genomics era. It is necessary to identify all the native-encoding protein sequences for further function and pathway analysis. Toward that end, the Human Proteome Organization lunched the Human Protein Project in 2011. However many proteins are hard to be detected by experiment methods, which becomes one of the bottleneck in Human Proteome Project. In consideration of the complicatedness of detecting these missing proteins by using wet-experiment approach, here we use bioinformatics method to pre-filter the missing proteins.
Since there are analogy between the biological sequences and natural language, the n-gram models from Natural Language Processing field has been used to filter the missing proteins. The dataset used in this study contains 616 missing proteins from the “uncertain” category of the neXtProt database. There are 102 proteins deduced by the n-gram model, which have high probability to be native human proteins. We perform a detail analysis on the predicted structure and function of these missing proteins and also compare the high probability proteins with other mass spectrum datasets. The evaluation shows that the results reported here are in good agreement with those obtained by other well-established databases.
The analysis shows that 102 proteins may be native gene-coding proteins and some of the missing proteins are membrane or natively disordered proteins which are hard to be detected by experiment methods.
- Human proteome
- Missing protein
- Biological language model
Proteins play important roles in biology. The Human Genome Sequence Project  provides a comprehensive compendium about all the human protein encoding genes. However, due to the diversity of proteins and the under-development of current proteomics technology, there are many proteins which have not been identified and annotated.
The Human Proteome Project (HPP)  was launched by the Human Proteome Organization (HUPO) in 2011, which contains the Chromosome-centric HPP (C-HPP)  and Biology/Disease-Driven HPP (B/DHPP) . This project tries to identify as more proteins as possible with the goal of covering all human protein-encoding genes. This great goal is cooperated by an international associations contains 25 members . The baseline metrics for the HPP contains five annually updated data resources : the Ensembl database  provides the possible genes coding proteins; Peptide Atlas  and GPMdb  separately screen high confident proteins from mass spectrometry data; the Human Protein Atlas  is in charge of extracting proteins by antibody-based research; and finally neXtProt  collects all human proteins and assigns confidence level (PE 1-5) by protein expression evidence. Proteins at the PE1 level are identified at protein expression level by mass spectrometry, immunohistochemistry, 3D structure, and/or amino acid sequencing. The proteins at PE2 level is detected by transcript expression but not by protein expression. At PE3 level, there are no protein or transcript evidence, but have homologies represented in related species. Proteins at PE4 level are speculated from gene models. Finally, the protein sequences at PE5 level are generated from “dubious” or “uncertain” genes which seemed to have some protein-level evidence in the past but such identifications are doubtful by curation.
Much progress has been achieved since 2011 by the proteomics community and the HPP. Based on the curation of neXtProt  database, currently 82% of the protein-coding genes in human have protein expressions with high-confidence. However, there are 3, 564 genes at levels PE2-5 which have no or insufficient evidence of identification by any experimental methods and are thus named as “missing proteins” . Many of these missing proteins are hard to be detected because of low abundance, poor solubility, or indistinguishable peptide sequences within protein families. The missing of such a significant amount of proteins marks a significant problem about our current understanding of the human proteome, with particularly important questions including, e.g., whether these proteins are essential to the cell functions and if yes what biological roles they play in cell and why they are not detectable by the current instruments of both transcription and translation levels. Thus identifying the missing proteins will be a challenging task.
Previous study has shown that there are analogies between biological sequences and natural language. In linguistics, some words and phrases can form a meaningful sentence; in biology, the tactic nucleotides denote gene, and the fixed protein sequences can determine its structure and function. Tsonis  discussed that whether DNA is a language or not. Many linguistic approaches have been used in computational biology [13–15]. Ganapathiraju et al.  analyzed the language feature of whole-genome protein sequence. Many techniques of Natural Language Pprocess have been used in bioinformatics, such as protein domain recognition based on language modeling , dictionary-driven protein annotation , protein remote homology detection by latent semantic analysis [19–22], identification of DNA-binding protein [23, 24], and so on.
In this study, the missing proteins in human proteome are identified by using biological language model. The amino acid n-gram models for human and non-human protein sequences are constructed. These models are subsequently used to discriminate whether the missing proteins are natively gene-coding proteins in human or not. The identified proteins are then analyzed by their predicted structures and functions, annotation from neXtProt database , HGNC database  and other mass spectrometry dataset .
The native gene-coding proteins are downloaded from Swiss-Prot database . To construct the reliable models, only the protein sequences with reviewed items are selected. Totally, there are 14565 human proteins and 70854 non-human proteins. The redundant sequences are then filtered by using CD-HIT program  with the sequence identity threshold of 90%. Finally, we get 14189 human proteins and 59060 non-human proteins, which are used to build the n-gram model for human and non-human respectively.
The “dubious” or “uncertain” missing proteins with confidence code “PE5” are extracted from the neXtProt database  that was released at Sep. 19, 2014. There are in total 616 proteins in this category with length ranging from 21 to 2, 252 residues. The structures of these proteins are predicted by using I-TASSER software . The functions including the EC number, the GO terms and the binding sites are predicted by using COFACTOR software . Both I-TASSER and COFACTOR are run in non-homology mode where all the homologous structures identified with sequence identities greater than or equal to 30% are removed. The subcellular localization is predicted by using Hum-mPLoc .
Biological language models to discriminate the native gene-coding human and non-human proteins
The same procedure can be applied to construct the n-gram model of non-human proteins. The missing proteins can be identified by using n-gram models to get whether it’s native human proteins or not.
Based on the n-gram models, there are 102 proteins in the neXtProt “PE5” category which have high probabilities to be native human proteins. In the following sections, these proteins are analyzed by the predicted structure and function and annotations from other databases.
The structure and function analysis of the high-probability proteins
Structural topology analyses of the I-TASSER models
Evaluation of the function base on gene ontology
Comparision of subcellular localization
HGNC mapping analysis
The gene loci types and the number of proteins for the high-probability missing proteins after hgnc mapping
Gene loci type
No. missing proteins
Gene with protein product
RNA, long non-coding
Consistence analysis with other mass spectrometry dataset
Mass spectrometry is currently one of the efficient method to identify protein peptides. There are many mass spectrometry data deposited in public database, such as PeptideAtlas  and GPMDB . The sketch of human proteome is drawing by mass spectrometry data [26, 43]. Recently Kim et al  reported that about two-third (2535/3844) of the “missing proteins”  have been identified. Actually the “missing proteins” used by Kim et al. are constituted by the neXtProt proteins with evidence codes of “PE2”, “PE3” or “PE4”. This paper aims to identify the “PE5” missing proteins. By RefSeq  mapping, we found that there are 41 “PE5” proteins which are also in Kim’s dataset. Among these 41 missing proteins, there are 6 proteins are foldable based on the structure prediction results in non-homology mode. These results indicate that our finding are in good consistent with Kim’s results.
In this study, the human gene-coding proteins currently undetected are identified by using biological language models. The amino acid n-gram models of human and non-human proteins are constructed. These models are then used to identify the “uncertain” missing proteins with evidence code “PE5” from neXtProt database. The results show that 102 high probability proteins may be gene-coding proteins. The structure, function and subcellular localization of these proteins are then inferred by using the advanced programs. The identified missing proteins are then analyzed with the annotation from other database. Without using homology templates, 7 proteins have correct structure topology with I-TASSER C-score larger than -1.5. The predicted functions are mainly within GO items ‘binding’ (GO:0005488) and ‘catalytic activity’ (GO:0003824). 9 missing proteins are confirmed by HGNC with gene loci type “gene with protein product”. 6 missing proteins are also detected by mass spectrometry experiment. The analysis also shows that many of the unknown proteins are membrane or natively disordered proteins which are difficult to be detected. The identified missing proteins need to be further validated by experimental approach. The results in this study provides valuable complementary resource for the human proteome.
This article has been published as part of BMC Systems Biology Volume 10, Supplement 4, 2016: Proceedings of the 27th International Conference on Genome Informatics: systems biology. The full contents of the supplement are available online at http://bmcsystbiol.biomedcentral.com/articles/supplements/volume-10-supplement-4.
Financial support funding for publication costs was provided by the National Key Research and Development Program of China (Grant No. 2016YFB1000905) and National Natural Science Foundation of China (Grant No. 61672234, U1401256 and 61402177).
Availability of data and materials
The datasets of the current study are freely available by requesting on the corresponding authors.
DQW performed the experiment and wrote the paper. WK proposed the idea and polished the paper. LX designed the architecture and analyzed the results. All authors read and approved the paper.
The authors declare that they have no competing interest.
Consent for publication
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