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Pan H, Wu Z, Liu W, Zhang G. AlphaFun: Structural-Alignment-Based Proteome Annotation Reveals why the Functionally Unknown Proteins (uPE1) Are So Understudied. J Proteome Res 2024; 23:1593-1602. [PMID: 38626392 PMCID: PMC11078154 DOI: 10.1021/acs.jproteome.3c00678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 03/27/2024] [Accepted: 04/03/2024] [Indexed: 04/18/2024]
Abstract
With the rapid expansion of sequencing of genomes, the functional annotation of proteins becomes a bottleneck in understanding proteomes. The Chromosome-centric Human Proteome Project (C-HPP) aims to identify all proteins encoded by the human genome and find functional annotations for them. However, until now there are still 1137 identified human proteins without functional annotation, called uPE1 proteins. Sequence alignment was insufficient to predict their functions, and the crystal structures of most proteins were unavailable. In this study, we demonstrated a new functional annotation strategy, AlphaFun, based on structural alignment using deep-learning-predicted protein structures. Using this strategy, we functionally annotated 99% of the human proteome, including the uPE1 proteins and missing proteins, which have not been identified yet. The accuracy of the functional annotations was validated using the known-function proteins. The uPE1 proteins shared similar functions to the known-function PE1 proteins and tend to express only in very limited tissues. They are evolutionally young genes and thus should conduct functions only in specific tissues and conditions, limiting their occurrence in commonly studied biological models. Such functional annotations provide hints for functional investigations on the uPE1 proteins. This proteome-wide-scale functional annotation strategy is also applicable to any other species.
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Affiliation(s)
- Hengxin Pan
- MOE Key Laboratory of Tumor
Molecular Biology and Key Laboratory of Functional Protein Research
of Guangdong Higher Education Institutes, Institute of Life and Health
Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Zhenqi Wu
- MOE Key Laboratory of Tumor
Molecular Biology and Key Laboratory of Functional Protein Research
of Guangdong Higher Education Institutes, Institute of Life and Health
Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Wanting Liu
- MOE Key Laboratory of Tumor
Molecular Biology and Key Laboratory of Functional Protein Research
of Guangdong Higher Education Institutes, Institute of Life and Health
Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Gong Zhang
- MOE Key Laboratory of Tumor
Molecular Biology and Key Laboratory of Functional Protein Research
of Guangdong Higher Education Institutes, Institute of Life and Health
Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
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2
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Wu W, Huang Z, Kong W, Peng H, Goh WWB. Optimizing the PROTREC network-based missing protein prediction algorithm. Proteomics 2024; 24:e2200332. [PMID: 37876146 DOI: 10.1002/pmic.202200332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 09/30/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.
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Affiliation(s)
- Wenshan Wu
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Zelu Huang
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
| | - Weijia Kong
- Department of Computer Science, National University of Singapore, Singapore, Singapore
- School of Biological Science, Nanyang Technological University, Singapore, Singapore
| | - Hui Peng
- School of Biological Science, Nanyang Technological University, Singapore, Singapore
| | - Wilson Wen Bin Goh
- School of Biological Science, Nanyang Technological University, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
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3
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Goh WWB, Hui HWH, Wong L. How missing value imputation is confounded with batch effects and what you can do about it. Drug Discov Today 2023; 28:103661. [PMID: 37301250 DOI: 10.1016/j.drudis.2023.103661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.
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Affiliation(s)
- Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Center for Biomedical Informatics, Nanyang Technological University, Singapore.
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore.
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The importance of batch sensitization in missing value imputation. Sci Rep 2023; 13:3003. [PMID: 36810890 PMCID: PMC9944322 DOI: 10.1038/s41598-023-30084-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 02/15/2023] [Indexed: 02/23/2023] Open
Abstract
Data analysis is complex due to a myriad of technical problems. Amongst these, missing values and batch effects are endemic. Although many methods have been developed for missing value imputation (MVI) and batch correction respectively, no study has directly considered the confounding impact of MVI on downstream batch correction. This is surprising as missing values are imputed during early pre-processing while batch effects are mitigated during late pre-processing, prior to functional analysis. Unless actively managed, MVI approaches generally ignore the batch covariate, with unknown consequences. We examine this problem by modelling three simple imputation strategies: global (M1), self-batch (M2) and cross-batch (M3) first via simulations, and then corroborated on real proteomics and genomics data. We report that explicit consideration of batch covariates (M2) is important for good outcomes, resulting in enhanced batch correction and lower statistical errors. However, M1 and M3 are error-generating: global and cross-batch averaging may result in batch-effect dilution, with concomitant and irreversible increase in intra-sample noise. This noise is unremovable via batch correction algorithms and produces false positives and negatives. Hence, careless imputation in the presence of non-negligible covariates such as batch effects should be avoided.
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Kong W, Hui HWH, Peng H, Goh WWB. Dealing with missing values in proteomics data. Proteomics 2022; 22:e2200092. [PMID: 36349819 DOI: 10.1002/pmic.202200092] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/15/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022]
Abstract
Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.
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Affiliation(s)
- Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Hui Peng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.,Centre for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
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What can scatterplots teach us about doing data science better? INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2022. [DOI: 10.1007/s41060-022-00362-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Protocol for Increasing the Sensitivity of MS-Based Protein Detection in Human Chorionic Villi. Curr Issues Mol Biol 2022; 44:2069-2088. [PMID: 35678669 PMCID: PMC9164042 DOI: 10.3390/cimb44050140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 11/17/2022] Open
Abstract
An important step in the proteomic analysis of missing proteins is the use of a wide range of tissues, optimal extraction, and the processing of protein material in order to ensure the highest sensitivity in downstream protein detection. This work describes a purification protocol for identifying low-abundance proteins in human chorionic villi using the proposed “1DE-gel concentration” method. This involves the removal of SDS in a short electrophoresis run in a stacking gel without protein separation. Following the in-gel digestion of the obtained holistic single protein band, we used the peptide mixture for further LC–MS/MS analysis. Statistically significant results were derived from six datasets, containing three treatments, each from two tissue sources (elective or missed abortions). The 1DE-gel concentration increased the coverage of the chorionic villus proteome. Our approach allowed the identification of 15 low-abundance proteins, of which some had not been previously detected via the mass spectrometry of trophoblasts. In the post hoc data analysis, we found a dubious or uncertain protein (PSG7) encoded on human chromosome 19 according to neXtProt. A proteomic sample preparation workflow with the 1DE-gel concentration can be used as a prospective tool for uncovering the low-abundance part of the human proteome.
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Are batch effects still relevant in the age of big data? Trends Biotechnol 2022; 40:1029-1040. [DOI: 10.1016/j.tibtech.2022.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/13/2022] [Accepted: 02/18/2022] [Indexed: 12/30/2022]
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9
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Kong W, Wong BJH, Gao H, Guo T, Liu X, Du X, Wong L, Goh WWB. PROTREC: A probability-based approach for recovering missing proteins based on biological networks. J Proteomics 2022; 250:104392. [PMID: 34626823 DOI: 10.1016/j.jprot.2021.104392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/30/2021] [Accepted: 09/02/2021] [Indexed: 12/18/2022]
Abstract
A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face.
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Affiliation(s)
- Weijia Kong
- School of Biological Sciences, Nanyang Technological University, Singapore; Department of Computer Science, National University of Singapore, Singapore
| | | | - Huanhuan Gao
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Zhejiang Province, China
| | - Tiannan Guo
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Zhejiang, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Zhejiang Province, China
| | - Xianming Liu
- Bruker (Beijing) Scientific Technology Co., Ltd, Shanghai, China
| | - Xiaoxian Du
- Bruker (Beijing) Scientific Technology Co., Ltd, Shanghai, China
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore.
| | - Wilson Wen Bin Goh
- School of Biological Sciences, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
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10
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Radko SP, Poverennaya EV, Kurbatov LK, Ponomarenko EA, Lisitsa AV, Archakov AI. The "Missing" Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes. J Proteome Res 2019; 18:4273-4276. [PMID: 31621326 DOI: 10.1021/acs.jproteome.9b00383] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Chromosome-centric Human Proteome Project aims at characterizing the expression of proteins encoded in each chromosome at the tissue, cell, and subcellular levels. The proteomic profiling of a particular tissue or cell line commonly results in a substantial portion of proteins that are not observed (the "missing" proteome). The concurrent transcriptome profiling of the analyzed tissue/cells samples may help define the set of untranscribed genes in a given type of tissue or cell, thus narrowing the size of the "missing" proteome and allowing us to focus on defining the reasons behind undetected proteins, namely, whether they are technical (insufficient sensitivity of protein detection) or biological (correspond to not-translated transcripts). We believe that the quantitative polymerase chain reaction (qPCR) can provide an efficient approach to studying low-abundant transcripts related to undetected proteins due to its high sensitivity and the possibility of ensuring the specificity of detection via the simple Sanger sequencing of PCR products. Here we illustrated the feasibility of such an approach on a set of low-abundant transcripts. Although inapplicable to the analysis of whole transcriptome, qPCR can successfully be utilized to profile a limited cohort of transcripts encoded on a particular chromosome, as we previously demonstrated for human chromosome 18.
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Affiliation(s)
- Sergey P Radko
- Institute of Biomedical Chemistry , 119121 Moscow , Russia
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Wen Bin Goh W, Thalappilly S, Thibault G. Moving beyond the current limits of data analysis in longevity and healthy lifespan studies. Drug Discov Today 2019; 24:2273-2285. [PMID: 31499187 DOI: 10.1016/j.drudis.2019.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 08/03/2019] [Accepted: 08/28/2019] [Indexed: 11/19/2022]
Abstract
Living longer with sustainable quality of life is becoming increasingly important in aging populations. Understanding associative biological mechanisms have proven daunting, because of multigenicity and population heterogeneity. Although Big Data and Artificial Intelligence (AI) could help, naïve adoption is ill advised. We hold the view that model organisms are better suited for big-data analytics but might lack relevance because they do not immediately reflect the human condition. Resolving this hurdle and bridging the human-model organism gap will require some finesse. This includes improving signal:noise ratios by appropriate contextualization of high-throughput data, establishing consistency across multiple high-throughput platforms, and adopting supporting technologies that provide useful in silico and in vivo validation strategies.
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Affiliation(s)
- Wilson Wen Bin Goh
- Bio-Data Science and Education Research Group, School of Biological Sciences, Nanyang Technological University, 637551, Singapore.
| | - Subhash Thalappilly
- Lipid Regulation and Cell Stress Research Group, School of Biological Sciences, Nanyang Technological University, 637551, Singapore
| | - Guillaume Thibault
- Lipid Regulation and Cell Stress Research Group, School of Biological Sciences, Nanyang Technological University, 637551, Singapore; Institute of Molecular and Cell Biology, A*STAR, 138673, Singapore.
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Zhao Y, Sue ACH, Goh WWB. Deeper investigation into the utility of functional class scoring in missing protein prediction from proteomics data. J Bioinform Comput Biol 2019; 17:1950013. [DOI: 10.1142/s0219720019500136] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Functional Class Scoring (FCS) is a network-based approach previously demonstrated to be powerful in missing protein prediction (MPP). We update its performance evaluation using data derived from new proteomics technology (SWATH) and also checked for reproducibility using two independent datasets profiling kidney tissue proteome. We also evaluated the objectivity of the FCS p-value, and followed up on the value of MPP from predicted complexes. Our results suggest that (1) FCS [Formula: see text]-values are non-objective, and are confounded strongly by complex size, (2) best recovery performance do not necessarily lie at standard [Formula: see text]-value cutoffs, (3) while predicted complexes may be used for augmenting MPP, they are inferior to real complexes, and are further confounded by issues relating to network coverage and quality and (4) moderate sized complexes of size 5 to 10 still exhibit considerable instability, we find that FCS works best with big complexes. While FCS is a powerful approach, blind reliance on its non-objective [Formula: see text]-value is ill-advised.
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Affiliation(s)
- Yaxing Zhao
- School of Pharmaceutical Science and Technology, Tianjin University, No. 92, Weijin Road, 30072 Tianjin, P. R. China
| | - Andrew Chi-Hau Sue
- School of Pharmaceutical Science and Technology, Tianjin University, No. 92, Weijin Road, 30072 Tianjin, P. R. China
| | - Wilson Wen Bin Goh
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, 637551, Singapore
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