scholarly journals Machine learning empowers phosphoproteome prediction in cancers

2019 ◽  
Vol 36 (3) ◽  
pp. 859-864 ◽  
Author(s):  
Hongyang Li ◽  
Yuanfang Guan
Keyword(s):  
Signaling Pathways ◽  
Cancer Patients ◽  
Protein Interactions ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Cellular Processes ◽  
Testing Dataset ◽  
Protein Functions ◽  
Cancer Tissues

Abstract Motivation Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic and genomic data. Results Here, we present a winning algorithm in the 2017 NCI-CPTAC DREAM Proteogenomics Challenge for predicting phosphorylation levels of the proteome across cancer patients. We integrate four components into our algorithm, including (i) baseline correlations between protein and phosphoprotein abundances, (ii) universal protein–protein interactions, (iii) shareable regulatory information across cancer tissues and (iv) associations among multi-phosphorylation sites of the same protein. When tested on a large held-out testing dataset of 108 breast and 62 ovarian cancer samples, our method ranked first in both cancer tissues, demonstrating its robustness and generalization ability. Availability and implementation Our code and reproducible results are freely available on GitHub: https://github.com/GuanLab/phosphoproteome_prediction. Supplementary information Supplementary data are available at Bioinformatics online.

Applications of Mass Spectrometry in Proteomics

2013 ◽  
Vol 66 (7) ◽  
pp. 721 ◽  
Author(s):  
Izabela Sokolowska ◽  
Armand G. Ngounou Wetie ◽  
Alisa G. Woods ◽  
Costel C. Darie
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Identification ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Cellular Processes ◽  
Physiological Processes ◽  
Protein Functions ◽  
Instruments And Techniques ◽  
Accurate Mass Spectrometry

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

scholarly journals In Vivo Methods to Study Protein–Protein Interactions as Key Players in Mycobacterium Tuberculosis Virulence

Pathogens ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 173 ◽  
Author(s):  
Veyron-Churlet ◽  
Locht
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Unknown Protein ◽  
Protein Functions ◽  
Transient Interactions ◽  
Protein Complementation

Studies on protein–protein interactions (PPI) can be helpful for the annotation of unknown protein functions and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher tuberculosis (TB) pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

scholarly journals Regulation of Protein Post-Translational Modifications on Metabolism of Actinomycetes

Biomolecules ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1122
Author(s):  
Chen-Fan Sun ◽  
Yong-Quan Li ◽  
Xu-Ming Mao
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Post Translational Modifications ◽  
Cellular Processes ◽  
Structure Activity ◽  
Variable Environments ◽  
Metabolic States ◽  
Metabolite Synthesis ◽  
External Stimuli

Protein post-translational modification (PTM) is a reversible process, which can dynamically regulate the metabolic state of cells through regulation of protein structure, activity, localization or protein–protein interactions. Actinomycetes are present in the soil, air and water, and their life cycle is strongly determined by environmental conditions. The complexity of variable environments urges Actinomycetes to respond quickly to external stimuli. In recent years, advances in identification and quantification of PTMs have led researchers to deepen their understanding of the functions of PTMs in physiology and metabolism, including vegetative growth, sporulation, metabolite synthesis and infectivity. On the other hand, most donor groups for PTMs come from various metabolites, suggesting a complex association network between metabolic states, PTMs and signaling pathways. Here, we review the mechanisms and functions of PTMs identified in Actinomycetes, focusing on phosphorylation, acylation and protein degradation in an attempt to summarize the recent progress of research on PTMs and their important role in bacterial cellular processes.

scholarly journals Poly(ADP-ribosyl)ation reactions in the regulation of nuclear functions

1999 ◽  
Vol 342 (2) ◽  
pp. 249-268 ◽  
Author(s):  
Damien d'AMOURS ◽  
Serge DESNOYERS ◽  
Icy d'SILVA ◽  
Guy G. POIRIER
Keyword(s):  
Dna Repair ◽  
Protein Interactions ◽  
Physiological Role ◽  
Polymer Chains ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Strand Breaks ◽  
Cellular Processes ◽  
Dna Strand

Poly(ADP-ribosyl)ation is a post-translational modification of proteins. During this process, molecules of ADP-ribose are added successively on to acceptor proteins to form branched polymers. This modification is transient but very extensive in vivo, as polymer chains can reach more than 200 units on protein acceptors. The existence of the poly(ADP-ribose) polymer was first reported nearly 40 years ago. Since then, the importance of poly(ADP-ribose) synthesis has been established in many cellular processes. However, a clear and unified picture of the physiological role of poly(ADP-ribosyl)ation still remains to be established. The total dependence of poly(ADP-ribose) synthesis on DNA strand breaks strongly suggests that this post-translational modification is involved in the metabolism of nucleic acids. This view is also supported by the identification of direct protein-protein interactions involving poly(ADP-ribose) polymerase (113 kDa PARP), an enzyme catalysing the formation of poly(ADP-ribose), and key effectors of DNA repair, replication and transcription reactions. The presence of PARP in these multiprotein complexes, in addition to the actual poly(ADP-ribosyl)ation of some components of these complexes, clearly supports an important role for poly(ADP-ribosyl)ation reactions in DNA transactions. Accordingly, inhibition of poly(ADP-ribose) synthesis by any of several approaches and the analysis of PARP-deficient cells has revealed that the absence of poly(ADP-ribosyl)ation strongly affects DNA metabolism, most notably DNA repair. The recent identification of new poly(ADP-ribosyl)ating enzymes with distinct (non-standard) structures in eukaryotes and archaea has revealed a novel level of complexity in the regulation of poly(ADP-ribose) metabolism.

PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i735-i744
Author(s):  
Fuhao Zhang ◽  
Wenbo Shi ◽  
Jian Zhang ◽  
Min Zeng ◽  
Min Li ◽  
...  
Keyword(s):  
Protein Binding ◽  
Protein Interactions ◽  
Predictive Performance ◽  
Protein Docking ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Cross Prediction ◽  
Predictive Quality ◽  
Protein Functions ◽  
Binding Residues

Abstract Motivation Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods. Results We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein. Availability and implementation PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Androgen signaling uses a writer and a reader of ADP-ribosylation to regulate protein complex assembly

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chun-Song Yang ◽  
Kasey Jividen ◽  
Teddy Kamata ◽  
Natalia Dworak ◽  
Luke Oostdyk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Prostate Cancer Cells ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Complex Assembly ◽  
Adp Ribosylation ◽  
Signaling Axis ◽  
Regulate Protein ◽  
Protein Complex Assembly

AbstractAndrogen signaling through the androgen receptor (AR) directs gene expression in both normal and prostate cancer cells. Androgen regulates multiple aspects of the AR life cycle, including its localization and post-translational modification, but understanding how modifications are read and integrated with AR activity has been difficult. Here, we show that ADP-ribosylation regulates AR through a nuclear pathway mediated by Parp7. We show that Parp7 mono-ADP-ribosylates agonist-bound AR, and that ADP-ribosyl-cysteines within the N-terminal domain mediate recruitment of the E3 ligase Dtx3L/Parp9. Molecular recognition of ADP-ribosyl-cysteine is provided by tandem macrodomains in Parp9, and Dtx3L/Parp9 modulates expression of a subset of AR-regulated genes. Parp7, ADP-ribosylation of AR, and AR-Dtx3L/Parp9 complex assembly are inhibited by Olaparib, a compound used clinically to inhibit poly-ADP-ribosyltransferases Parp1/2. Our study reveals the components of an androgen signaling axis that uses a writer and reader of ADP-ribosylation to regulate protein-protein interactions and AR activity.

scholarly journals The Protein Interactome of Glycolysis in Escherichia coli

Proteomes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 16
Author(s):  
Shomeek Chowdhury ◽  
Stephen Hepper ◽  
Mudassir K. Lodi ◽  
Milton H. Saier ◽  
Peter Uetz
Keyword(s):  
Escherichia Coli ◽  
Protein Interactions ◽  
Allosteric Regulation ◽  
Glycolytic Enzymes ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Interacting Protein ◽  
E Coli ◽  
Protein Interactome ◽  
The Impact

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

scholarly journals Targeted disruption of PKC from AKAP Signaling Complexes

2021 ◽  
Author(s):  
Ameya J. Limaye ◽  
George N. Bendzunas ◽  
Eileen Kennedy
Keyword(s):  
Protein Kinase C ◽  
Protein Kinase ◽  
Signaling Pathways ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Targeted Disruption ◽  
Physiological Processes ◽  
Kinase C ◽  
P Protein

Protein Kinase C (PKC) is a member of the AGC subfamily of kinases and regulates a wide array of signaling pathways and physiological processes. Protein-protein interactions involving PKC and its...

Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

2018 ◽  
Vol 25 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Ylenia Cau ◽  
Daniela Valensin ◽  
Mattia Mori ◽  
Sara Draghi ◽  
Maurizio Botta
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Physiological Role ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Partners ◽  
High Sequence Homology ◽  
Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document