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Zhang R, Jia W. Supramolecular self-assembly strategies of natural-based β-lactoglobulin modulating bitter perception of goat milk-derived bioactive peptides. J Dairy Sci 2024; 107:4174-4188. [PMID: 38310962 DOI: 10.3168/jds.2023-24386] [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: 11/02/2023] [Accepted: 01/01/2024] [Indexed: 02/06/2024]
Abstract
Complete self-assembly and reassembly behavior of bitter peptide-protein necessitates multilevel theories that encompass phenomena ranging from the self-assembly of recombinant complex to atomic trajectories. An extension to the level of mechanism method was put forth, involves limited enzymatic digestion and bottom-up proteomics to dissect inherent heterogeneity within β-LG and β-LG-PPGLPDKY complex and uncover conformational and dynamic alterations occurring in specific local regions of the model protein. Bitter peptide PPGLPDKY spontaneously bound to IIAEKTK, IDALNENK, and YLLFCMENSAEPEQSLACQCLVR regions of β-LG in a 1:1 stoichiometric ratio to mask bitterness perception. Molecular dynamic simulation and free energy calculation provided time-varying atomic trajectories of the recombinant complex and found that a peptide was stabilized in the upper region of the hydrophobic cavity with the binding free energy of -30.56 kJ mol-1 through 4 hydrogen bonds (Glu74, Glu55, Lys69, and Ser116) and hydrophobic interactions (Asn88, Asn90, and Glu112). Current research aims to provide valuable physical insights into the macroscopic self-assembly behavior between proteins and bitter peptides, and the meticulous design of highly acceptable taste characteristics in goat milk products.
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Affiliation(s)
- Rong Zhang
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
| | - Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China.
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Ergin EK, Myung JJ, Lange PF. Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems. J Proteome Res 2024; 23:2169-2185. [PMID: 38804581 PMCID: PMC11166143 DOI: 10.1021/acs.jproteome.4c00131] [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: 02/23/2024] [Revised: 05/04/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.
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Affiliation(s)
- Enes K. Ergin
- Department
of Pathology, University of British Columbia, Vancouver, British Columbia V6T 1Z7, Canada
- Michael
Cuccione Childhood Cancer Research Program, BC Children’s Hospital Research Institute, Vancouver, British Columbia V5Z 2H4, Canada
| | - Junia J.K. Myung
- Department
of Pathology, University of British Columbia, Vancouver, British Columbia V6T 1Z7, Canada
- Michael
Cuccione Childhood Cancer Research Program, BC Children’s Hospital Research Institute, Vancouver, British Columbia V5Z 2H4, Canada
| | - Philipp F. Lange
- Department
of Pathology, University of British Columbia, Vancouver, British Columbia V6T 1Z7, Canada
- Michael
Cuccione Childhood Cancer Research Program, BC Children’s Hospital Research Institute, Vancouver, British Columbia V5Z 2H4, Canada
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Jia W, Peng J, Zhang Y, Zhu J, Qiang X, Zhang R, Shi L. Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction? Food Res Int 2023; 174:113640. [PMID: 37986483 DOI: 10.1016/j.foodres.2023.113640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/22/2023]
Abstract
Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.
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Affiliation(s)
- Wei Jia
- School of Food and Bioengineering, Shaanxi University of Science and Technology, Xi'an 710021, China; Inspection and Testing Center of Fuping County (Shaanxi goat milk product quality supervision and Inspection Center), Weinan 711700, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China.
| | - Jian Peng
- School of Food and Bioengineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Yan Zhang
- Inspection and Testing Center of Fuping County (Shaanxi goat milk product quality supervision and Inspection Center), Weinan 711700, China
| | - Jiying Zhu
- School of Food and Bioengineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Xin Qiang
- Inspection and Testing Center of Fuping County (Shaanxi goat milk product quality supervision and Inspection Center), Weinan 711700, China
| | - Rong Zhang
- School of Food and Bioengineering, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Lin Shi
- School of Food and Bioengineering, Shaanxi University of Science and Technology, Xi'an 710021, China
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Orsburn BC. Metabolomic, Proteomic, and Single-Cell Proteomic Analysis of Cancer Cells Treated with the KRAS G12D Inhibitor MRTX1133. J Proteome Res 2023; 22:3703-3713. [PMID: 37983312 PMCID: PMC10696623 DOI: 10.1021/acs.jproteome.3c00212] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Indexed: 11/22/2023]
Abstract
Mutations in KRAS are common drivers of human cancers and are often those with the poorest overall prognosis for patients. A recently developed compound, MRTX1133, has shown promise in inhibiting the activity of KRASG12D mutant proteins, which is one of the main drivers of pancreatic cancer. To better understand the mechanism of action of this compound, I performed both proteomics and metabolomics on four KRASG12D mutant pancreatic cancer cell lines. To obtain increased granularity in the proteomic observations, single-cell proteomics was successfully performed on two of these lines. Following quality filtering, a total of 1498 single cells were analyzed. From these cells, 3140 total proteins were identified with approximately 953 proteins quantified per cell. At 48 h of treatment, two distinct populations of cells can be observed based on the level of effectiveness of the drug in decreasing the total abundance of the KRAS protein in each respective cell, with results that are effectively masked in the bulk cell analysis. All mass spectrometry data and processed results are publicly available at www.massive.ucsd.edu at accessions PXD039597, PXD039601, and PXD039600.
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Affiliation(s)
- Benjamin C. Orsburn
- The Department of Pharmacology and
Molecular Sciences, The Johns Hopkins University
School of Medicine, Baltimore, Maryland 21205, United States
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Orsburn BC. An integrated method for single cell proteomics with simultaneous measurements of intracellular drug concentration implicates new mechanisms for adaptation to KRAS G12D inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.18.567669. [PMID: 38014353 PMCID: PMC10680798 DOI: 10.1101/2023.11.18.567669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
It is well established that a population of single human cells will often respond to the same drug treatment in a heterogeneous manner. In the context of chemotherapeutics, these diverse responses may lead to individual adaptation mechanisms and ultimately multiple distinct methods of resistance. The obvious question from a pharmacology perspective is how intracellular concentrations of active drug varies between individual cells, and what role does that variation play in drug response heterogeneity? To date, no integrated methods for rapidly measuring intracellular drug levels while simultaneously measuring drug responses have been described. This study describes a method for single cell preparation that allows proteins to be extracted and digested from single cells while maintaining conditions for small molecules to be simultaneously measured. The method as described allows up to 40 cells to be analyzed per instrument per day. When applied to a KRASG12D small molecule inhibitor I observe a wide degree of intracellular levels of the drug, and that proteomic responses largely stratify based on the concentration of drug within each single cell. Further work is in progress to develop and standardize this method and - more importantly - to normalize drug measurements against direct measurements of cell volume. However, these preliminary results appear promising for the identification of single cells with unique drug response mechanisms. All data described in this study has been made publicly available through the ProteomeXchange consortium under accession PXD046002.
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Affiliation(s)
- Benjamin C. Orsburn
- The Department of Pharmacology and Molecular Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205
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Warshanna A, Orsburn BC. SCP Viz - A universal graphical user interface for single protein analysis in single cell proteomics datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.29.555397. [PMID: 37693496 PMCID: PMC10491148 DOI: 10.1101/2023.08.29.555397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Single cell proteomics (SCP) requires the analysis of dozens to thousands of single human cells to draw biological conclusions. However, assessing of the abundance of single proteins in output data presents a considerable challenge, and no simple universal solutions currently exist. To address this, we developed SCP Viz, a statistical package with a graphical user interface that can handle small and large scale SCP output from any instrument or data processing software. In this software, the abundance of individual proteins can be plotted in a variety of ways, using either unadjusted or normalized outputs. These outputs can also be transformed or imputed within the software. SCP Viz offers a variety of plotting options which can help identify significantly altered proteins between groups, both before and after quantitative transformations. Upon the discovery of subpopulations of single cells, users can easily regroup the cells of interest using straightforward text-based filters. When used in this way, SCP Viz allows users to visualize proteomic heterogeneity at the level of individual proteins, cells, or identified subcellular populations. SCP Viz is compatible with output files from MaxQuant, FragPipe, SpectroNaut, and Proteome Discoverer, and should work equally well with other formats. SCP Viz is publicly available at https://github.com/orsburn/SCPViz. For demonstrations, users can download our test data from GitHub and use an online version that accepts user input for analysis at https://orsburnlab.shinyapps.io/SCPViz/.
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Affiliation(s)
- Ahmed Warshanna
- The Department of Pharmacology and Molecular Sciences; The Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205
| | - Benjamin C. Orsburn
- The Department of Pharmacology and Molecular Sciences; The Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21205
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