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Special Issue "Feature Papers in Protein and Proteomics"

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A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Proteins and Proteomics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 9520

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Prof. Dr. Yudong Cai

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Guest Editor

College of Life Science, Shanghai University, Shanghai 200244, China
Interests: systems biology; bioinformatics; protein sequence; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Tao Huang

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Guest Editor

Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
Interests: bioinformatics; genetics; genomics; machine learning; ceRNA network; predictive modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the application of deep learning in protein and proteomics studies, the 3D structure of protein can be accurately predicted based only on sequence information, allowing biomarkers for various diseases to be discovered via proteomics. Proteomics is focused on proteins, which are directly responsible for the biological or pathological changes, in contrast to genomic, epigenetic or transcriptomic data. Changes in proteins are closest to determining phenotypes. To investigate the molecular mechanisms of phenotypes, we must incorporate protein and proteomics perspectives.

To accelerate the applications of state-of-art technologies, such as artificial intelligence (AI), in protein and proteomics studies, we encourage submissions from researchers in biology, medicine, computer science and mathematics. Potential topics of interest include but are not limited to:

Protein Structure Prediction
Protein–Protein Interaction Prediction
Protein Network Analysis
Protein–Phenotype/Disease Association
Protein Function Prediction
Mass Spectrometry Data Analysis Software Development
Proteomics Biomarker Discovery
Post-Translational Modification Study
Multi-omics Integrative Analysis
Proteogenomics Study
Single Cell Proteomics
Spatial Proteomics

Prof. Dr. Yudong Cai
Dr. Tao Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

protein structure
protein function
protein–protein interaction
mass spectrometry
proteomics
biomarker
post-translational modification
proteogenomics
single cell proteomics
spatial proteomics

Published Papers (7 papers)

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Research

13 pages, 2287 KiB

Open AccessArticle

Identification of Phase-Separation-Protein-Related Function Based on Gene Ontology by Using Machine Learning Methods

and

Life 2023, 13(6), 1306; https://doi.org/10.3390/life13061306 - 31 May 2023

Viewed by 1091

Abstract

Phase-separation proteins (PSPs) are a class of proteins that play a role in the process of liquid–liquid phase separation, which is a mechanism that mediates the formation of membranelle compartments in cells. Identifying phase separation proteins and their associated function could provide insights into cellular biology and the development of diseases, such as neurodegenerative diseases and cancer. Here, PSPs and non-PSPs that have been experimentally validated in earlier studies were gathered as positive and negative samples. Each protein’s corresponding Gene Ontology (GO) terms were extracted and used to create a 24,907-dimensional binary vector. The purpose was to extract essential GO terms that can describe essential functions of PSPs and build efficient classifiers to identify PSPs with these GO terms at the same time. To this end, the incremental feature selection computational framework and an integrated feature analysis scheme, containing categorical boosting, least absolute shrinkage and selection operator, light gradient-boosting machine, extreme gradient boosting, and permutation feature importance, were used to build efficient classifiers and identify GO terms with classification-related importance. A set of random forest (RF) classifiers with F1 scores over 0.960 were established to distinguish PSPs from non-PSPs. A number of GO terms that are crucial for distinguishing between PSPs and non-PSPs were found, including GO:0003723, which is related to a biological process involving RNA binding; GO:0016020, which is related to membrane formation; and GO:0045202, which is related to the function of synapses. This study offered recommendations for future research aimed at determining the functional roles of PSPs in cellular processes by developing efficient RF classifiers and identifying the representative GO terms related to PSPs. Full article

(This article belongs to the Special Issue Feature Papers in Protein and Proteomics)

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26 pages, 4086 KiB

Open AccessArticle

Regulation of Cell Proliferation and Nrf2-Mediated Antioxidant Defense: Conservation of Keap1 Cysteines and Nrf2 Binding Site in the Context of the Evolution of KLHL Family

Gregory A. Shilovsky

and

Daria V. Dibrova

Life 2023, 13(4), 1045; https://doi.org/10.3390/life13041045 - 19 Apr 2023

Cited by 3 | Viewed by 1247

Abstract

Keap1 (Kelch-like ECH-associated protein 1) is one of the major negative regulators of the transcription factor Nrf2 (nuclear factor erythroid-2-related factor 2), which induces the expression of numerous proteins defending the cell against different stress conditions. Keap1 is generally negatively regulated by post-translational modification (mostly via its cysteine residues) and interaction with other proteins that compete with Nrf2 for binding. Cysteine residues in Keap1 have different effects on protein regulation, as basic residues (Lys, Arg, and His) in close proximity to them increase cysteine modification potential. In this paper, we present an evolutionary analysis of residues involved in both mechanisms of Keap1 regulation in the broader context of the KLHL protein family in vertebrates. We identified the typical domain structure of the KLHL protein family in several proteins outside of this family (namely in KBTBD proteins 2, 3, 4, 6, 7, 8, 12 and 14). We found several cysteines that are flanked by basic residues (namely, C14, C38, C151, C226, C241, C273, C288, C297, C319, and C613) and, therefore, may be considered more susceptible to regulatory modification. The Nrf2 binding site is completely conserved in Keap1 in vertebrates but is absent or located in nonaligned DA and BC loops of the Kelch domain within the KLHL family. The development of specific substrate binding regions could be an evolutionary factor of diversification in the KLHL protein family. Full article

(This article belongs to the Special Issue Feature Papers in Protein and Proteomics)

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22 pages, 1591 KiB

Open AccessArticle

Characterization of Cystatin B Interactome in Saliva from Healthy Elderly and Alzheimer’s Disease Patients

Giuseppina Maccarrone

and

Tiziana Cabras

Life 2023, 13(3), 748; https://doi.org/10.3390/life13030748 - 10 Mar 2023

Cited by 1 | Viewed by 1343

Abstract

Cystatin B is a small, multifunctional protein involved in the regulation of inflammation, innate immune response, and neuronal protection and found highly abundant in the brains of patients with Alzheimer’s disease (AD). Recently, our study demonstrated a significant association between the level of salivary cystatin B and AD. Since the protein is able to establish protein-protein interaction (PPI) in different contexts and aggregation-prone proteins and the PPI networks are relevant for AD pathogenesis, and due to the relevance of finding new AD markers in peripheral biofluids, we thought it was interesting to study the possible involvement of cystatin B in PPIs in saliva and to evaluate differences and similarities between AD and age-matched elderly healthy controls (HC). For this purpose, we applied a co-immunoprecipitation procedure and a bottom-up proteomics analysis to purify, identify, and quantify cystatin B interactors. Results demonstrated for the first time the existence of a salivary cystatin B-linked multi-protein complex composed by 82 interactors and largely expressed in the body. Interactors are involved in neutrophil activation, antimicrobial activity, modulation of the cytoskeleton and extra-cellular matrix (ECM), and glucose metabolism. Preliminary quantitative data showed significantly lower levels of triosophosphate isomerase 1 and higher levels of mucin 7, BPI, and matrix Gla protein in AD with respect to HC, suggesting implications associated with AD of altered glucose metabolism, antibacterial activities, and calcification-associated processes. Data are available via ProteomeXchange with identifiers PXD039286 and PXD030679. Full article

(This article belongs to the Special Issue Feature Papers in Protein and Proteomics)

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10 pages, 2595 KiB

Open AccessArticle

Development of a Qualitative Test to Detect the Presence of Organophosphate Pesticides on Fruits and Vegetables

Valentina De Luca

Luigi Mandrich

and

Giuseppe Manco

Life 2023, 13(2), 490; https://doi.org/10.3390/life13020490 - 10 Feb 2023

Cited by 2 | Viewed by 1012

Abstract

Background: In recent decades, the use of pesticides in agriculture has increased at a fast pace, highlighting safety problems for the environment and human health, which in turn has made it necessary to develop new detection and decontamination systems for pesticides. Methods: A new qualitative test capable of detecting the presence of pesticides on fruits and vegetables by using thermostable enzymes was discovered, and the test was carried out on apples and aubergines. The contaminating pesticides were extracted from fruits with acetonitrile and analyzed with a biosensor system based on the thermostable esterase EST2 immobilized on a nitrocellulose filter. This enzyme is irreversibly inhibited mainly in the presence of organophosphates pesticides. Therefore, by observing esterase activity inhibition, we revealed the presence of residual pesticides on the fruits and vegetables. Results: By analyzing the rate of esterase activity inhibition, we predicted that residual pesticides are present on the surface of the fruits. When we cleaned the fruits by washing them in the presence of the phosphotriesterase SsoPox before the detection of the esterase activity on filters, we observed a full recovery of the activity for apples and 30% for aubergines, indicating that the enzymatic decontamination of organophosphates pesticides took place. Conclusions: The reported method permitted us to assess the pesticides present on the vegetables and their decontamination. Full article

(This article belongs to the Special Issue Feature Papers in Protein and Proteomics)

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9 pages, 1322 KiB

Open AccessArticle

New PAM Improves the Single-Base Specificity of crRNA-Guided LbCas12a Nuclease

Mariia A. Misiurina

Angelina V. Chirinskaite

and

Life 2022, 12(11), 1927; https://doi.org/10.3390/life12111927 - 18 Nov 2022

Viewed by 1759

Abstract

The RNA-guided Cas12a nuclease forms a complex with a CRISPR RNA (crRNA) to cleave the double-stranded DNA target. Among others, Cas12a protein from Lachnospiraceae bacterium (LbCas12a) is widely used for biomedical research. For target recognition, LbCas12a requires a specific nucleotide sequence, named a protospacer adjacent motif (PAM). Besides the canonical TTTV PAM, LbCas12a can recognize other suboptimal PAMs. We examined a novel TTAA PAM for the LbCas12a nuclease and found that the specificity of cleavage was increased. We found that single nucleotide substitutions at all positions of the guide RNA except the 20th position blocked the cleavage of the target DNA. The type of nucleotide substitutions (U-A, U-C or U-G) did not affect the efficiency of cleavage in the 20th position. When we used the canonical PAM under the same conditions, we observed the cleavage of target DNA by LbCas12a in many positions, showing less specificity in given conditions. The efficiency and specificity of the LbCas12a nuclease were evaluated both by gel-electrophoresis and using FAM-labeled single-stranded probes. We were able to assess the change in fluorescence intensity only for several variants of guide RNAs. High specificity allows us to type single nucleotide substitutions and small deletions/insertions (1–2 nucleotides) and look for target mutations when knocking out. Full article

(This article belongs to the Special Issue Feature Papers in Protein and Proteomics)

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15 pages, 357 KiB

Open AccessArticle

Heritability Estimation of Multiple Sclerosis Related Plasma Protein Levels in Sardinian Families with Immunochip Genotyping Data

Andrea Nova

Giulia Nicole Baldrighi

and

Life 2022, 12(7), 1101; https://doi.org/10.3390/life12071101 - 21 Jul 2022

Viewed by 1299

Abstract

This work aimed at estimating narrow-sense heritability, defined as the proportion of the phenotypic variance explained by the sum of additive genetic effects, via Haseman–Elston regression for a subset of 56 plasma protein levels related to Multiple Sclerosis (MS). These were measured in 212 related individuals (with 69 MS cases and 143 healthy controls) obtained from 20 Sardinian families with MS history. Using pedigree information, we found seven statistically significant heritable plasma protein levels (after multiple testing correction), i.e., Gc (

h^{2}

= 0.77; 95%CI: 0.36, 1.00), Plat (

h^{2}

= 0.70; 95%CI: 0.27, 0.95), Anxa1 (

h^{2}

= 0.68; 95%CI: 0.27, 1.00), Sod1 (

h^{2}

= 0.58; 95%CI: 0.18, 0.96), Irf8 (

h^{2}

= 0.56; 95%CI: 0.19, 0.99), Ptger4 (

h^{2}

= 0.45; 95%CI: 0.10, 0.96), and Fadd (

h^{2}

= 0.41; 95%CI: 0.06, 0.84). A subsequent analysis was performed on these statistically significant heritable plasma protein levels employing Immunochip genotyping data obtained in 155 healthy controls (92 related and 63 unrelated); we found a meaningful proportion of heritable plasma protein levels’ variability explained by a small set of SNPs. Overall, the results obtained, for these seven MS-related proteins, emphasized a high additive genetic variance component explaining plasma levels’ variability. Full article

(This article belongs to the Special Issue Feature Papers in Protein and Proteomics)

11 pages, 1065 KiB

Open AccessArticle

Survey of the Intermolecular Disulfide Bonds Observed in Protein Crystal Structures Deposited in the Protein Data Bank

Oliviero Carugo

Life 2022, 12(7), 986; https://doi.org/10.3390/life12070986 - 30 Jun 2022

Cited by 1 | Viewed by 1050

Abstract

About 5% of the disulfide bonds (DBs) observed in the Protein Data Bank bridge two protein chains. Several of their features were comprehensively analyzed, resulting in a structural atlas of the intermolecular DBs. The analysis was performed on a very large set of data extracted from the Protein Data Bank, according to the RaSPDB procedure. It was observed that the two chains tend to have different sequences and belong to the same structural class. Intermolecular DBs tend to be more solvent accessible and less distorted from the most stable conformation than intermolecular DBs while showing similar B-factors. They tend to occur in beta strands and in mainly-beta structures. These and other data should prove useful in protein modelling and design. Full article

(This article belongs to the Special Issue Feature Papers in Protein and Proteomics)

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