1
|
Ahmmed R, Hossen MB, Ajadee A, Mahmud S, Ali MA, Mollah MMH, Reza MS, Islam MA, Mollah MNH. Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications. Sci Rep 2024; 14:19133. [PMID: 39160196 PMCID: PMC11333728 DOI: 10.1038/s41598-024-69302-w] [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: 06/12/2024] [Accepted: 08/02/2024] [Indexed: 08/21/2024] Open
Abstract
Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.
Collapse
Affiliation(s)
- Reaz Ahmmed
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Department of Biochemistry & Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Bayazid Hossen
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Department of Agricultural and Applied Statistics, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh
| | - Alvira Ajadee
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Sabkat Mahmud
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Ahad Ali
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Department of Chemistry, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Manir Hossain Mollah
- Department of Physical Sciences, Independent University, Bangladesh (IUB), Dhaka, Bangladesh
| | - Md Selim Reza
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
- Division of Biomedical Informatics and Genomics, School of Medicine, Tulane University, 1440 Canal St., RM 1621C, New Orleans, LA, 70112, USA
| | - Mohammad Amirul Islam
- Department of Biochemistry & Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
| |
Collapse
|
2
|
Zhou Y, Huang Z, Gou Y, Liu S, Yang W, Zhang H, Dzisoo AM, Huang J. AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains. Antib Ther 2023; 6:147-156. [PMID: 37492587 PMCID: PMC10365155 DOI: 10.1093/abt/tbad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 07/27/2023] Open
Abstract
Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.
Collapse
Affiliation(s)
- Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Yushu Gou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Siqi Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Wei Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Hongyu Zhang
- Research and Development, Zhanyuan Therapeutics Ltd., Hangzhou, Zhejiang 310000, China
| | - Anthony Mackitz Dzisoo
- Bioinformatics, Data and Medical Reporting, Arcencsus GmbH, Rostock, Mecklenburg-Vorpommern 18055, Germany
| | - Jian Huang
- To whom correspondence should be addressed. Jian Huang, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 610054, China.
| |
Collapse
|
3
|
Shomali A, Vafaei Sadi MS, Bakhtiarizadeh MR, Aliniaeifard S, Trewavas A, Calvo P. Identification of intelligence-related proteins through a robust two-layer predictor. Commun Integr Biol 2022; 15:253-264. [DOI: 10.1080/19420889.2022.2143101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Aida Shomali
- Department of Horticulture, College of Aburaihan, University of Tehran, Tehran, Iran
| | | | | | - Sasan Aliniaeifard
- Department of Horticulture, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Anthony Trewavas
- School of Biological Sciences, Institute of Molecular Plant Science, University of Edinburgh, UK
| | - Paco Calvo
- Minimal Intelligence Lab, University of Murcia, Spain
| |
Collapse
|
4
|
Aslam L, Kaur R, Hussain S, Kapoor N, Mahajan R. LC-MS/MS identification and structural characterization of isolated cyclotides from precursor sequences of Viola odorata L. petiole tissue using computational approach. J Biosci 2022. [DOI: 10.1007/s12038-022-00283-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
5
|
Application of kNN and SVM to predict the prognosis of advanced schistosomiasis. Parasitol Res 2022; 121:2457-2460. [PMID: 35767047 DOI: 10.1007/s00436-022-07583-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 06/17/2022] [Indexed: 10/17/2022]
Abstract
Predictive models for prognosis of small sample advanced schistosomiasis patients have not been well studied. We aimed to construct prognostic predictive models of small sample advanced schistosomiasis patients using two machine learning algorithms, k nearest neighbour (kNN) and support vector machine (SVM) utilising routinely available data under the government medical assistance programme. The predictive models were derived from 229 patients from Xiantao and externally validated by 77 patients of Jiayu, two county-level cities in Hubei province, China. Candidate predictors were selected according to expert opinions and literature reports, including clinical features, sociodemographic characteristics, and medical examinations results. An area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models' predictive performances. The AUC values were 0.879 for the kNN model and 0.890 for the SVM model in the training set, 0.852 for the kNN model, and 0.785 for the SVM model in the external validation set. The kNN and SVM models can be used to improve the health services provided by healthcare planners, clinicians, and policymakers.
Collapse
|
6
|
Experimental Study of Potential CD8+ Trivalent Synthetic Peptides for Liver Cancer Vaccine Development Using Sprague Dawley Rat Models. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4792374. [PMID: 35686237 PMCID: PMC9173915 DOI: 10.1155/2022/4792374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 11/17/2022]
Abstract
Background. Liver cancer (LC) is the most devastating disease affecting a large set of populations in the world. The mortality due to LC is escalating, indicating the lack of effective therapeutic options. Immunotherapeutic agents may play an important role against cancer cells. As immune cells, especially T lymphocytes, which are part of cancer immunology, the design of vaccine candidates for cytotoxic T lymphocytes may be an effective strategy for curing liver cancer. Results. In our study, based on an immunoinformatics approach, we predicted potential T cell epitopes of MHC class I molecules using integrated steps of data retrieval, screening of antigenic proteins, functional analysis, peptide synthesis, and experimental in vivo investigations. We predicted the binding affinity of epitopes LLECADDRADLAKY, VSEHRIQDKDGLFY, and EYILSLEELVNGMY of LC membrane-bounded extracellular proteins including butyrophilin-like protein-2 (BTNL2), glypican-3 (GPC3), and serum albumin (ALB), respectively, with MHC class I molecules (allele: HLA-A
01:01). These T cell epitopes rely on the level of their binding energy and antigenic properties. We designed and constructed a trivalent immunogenic model by conjugating these epitopes with linkers to activate cytotoxic T cells. For validation, the nonspecific hematological assays showed a significant rise in the count of white blood cells (
), lymphocytes (
), and granulocytes (
) compared to the control after administration of trivalent peptides. Specific immunoassays including granzyme B and IgG ELISA exhibited the significant concentration of these effector molecules in blood serum, indicating the activity of cytotoxic T cells. Granzyme concentration increased to 1050 pg/ml at the second booster dose compared to the control (95 pg/ml), while the concentration of IgG raised to 6 g/l compared to the control (2 g/l). Conclusion. We concluded that a potential therapeutic trivalent vaccine can activate and modulate the immune system to cure liver cancer on the basis of significant outcomes of specific and nonspecific assays.
Collapse
|
7
|
Hwang W, Lei W, Katritsis NM, MacMahon M, Chapman K, Han N. Current and prospective computational approaches and challenges for developing COVID-19 vaccines. Adv Drug Deliv Rev 2021; 172:249-274. [PMID: 33561453 PMCID: PMC7871111 DOI: 10.1016/j.addr.2021.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.
Collapse
Affiliation(s)
- Woochang Hwang
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Winnie Lei
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Nicholas M Katritsis
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Méabh MacMahon
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Centre for Therapeutics Discovery, LifeArc, Stevenage, UK
| | - Kathryn Chapman
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Namshik Han
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
| |
Collapse
|
8
|
Moyer TB, Allen JL, Shaw LN, Hicks LM. Multiple Classes of Antimicrobial Peptides in Amaranthus tricolor Revealed by Prediction, Proteomics, and Mass Spectrometric Characterization. JOURNAL OF NATURAL PRODUCTS 2021; 84:444-452. [PMID: 33576231 PMCID: PMC8601116 DOI: 10.1021/acs.jnatprod.0c01203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Traditional medicinal plants are rich reservoirs of antimicrobial agents, including antimicrobial peptides (AMPs). Advances in genomic sequencing, in silico AMP predictions, and mass spectrometry-based peptidomics facilitate increasingly high-throughput bioactive peptide discovery. Herein, Amaranthus tricolor aerial tissue was profiled via MS-based proteomics/peptidomics, identifying AMPs predicted in silico. Bottom-up proteomics identified seven novel peptides spanning three AMP classes including lipid transfer proteins, snakins, and a defensin. Characterization via top-down peptidomic analysis of Atr-SN1, Atr-DEF1, and Atr-LTP1 revealed unexpected proteolytic processing and enumerated disulfide bonds. Bioactivity screening of isolated Atr-LTP1 showed activity against the high-risk ESKAPE bacterial pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Enterobacter cloacae). These results highlight the potential for integrating AMP prediction algorithms with complementary -omics approaches to accelerate characterization of biologically relevant AMP peptidoforms.
Collapse
Affiliation(s)
- Tessa B Moyer
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jessie L Allen
- Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, Tampa, Florida 33620, United States
| | - Lindsey N Shaw
- Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, Tampa, Florida 33620, United States
| | - Leslie M Hicks
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| |
Collapse
|
9
|
Dos Santos-Silva CA, Zupin L, Oliveira-Lima M, Vilela LMB, Bezerra-Neto JP, Ferreira-Neto JR, Ferreira JDC, de Oliveira-Silva RL, Pires CDJ, Aburjaile FF, de Oliveira MF, Kido EA, Crovella S, Benko-Iseppon AM. Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era. Bioinform Biol Insights 2020; 14:1177932220952739. [PMID: 32952397 PMCID: PMC7476358 DOI: 10.1177/1177932220952739] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 07/30/2020] [Indexed: 12/14/2022] Open
Abstract
Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.
Collapse
Affiliation(s)
| | - Luisa Zupin
- Genetic Immunology laboratory, Institute for Maternal and Child Health-IRCCS, Burlo Garofolo, Trieste, Italy
| | - Marx Oliveira-Lima
- Departamento de Genética, Universidade Federal de Pernambuco, Recife, Brazil
| | | | | | | | - José Diogo Cavalcanti Ferreira
- Departamento de Genética, Universidade Federal de Pernambuco, Recife, Brazil.,Departamento de Genética, Instituto Federal de Pernambuco, Pesqueira, Brazil
| | | | | | | | | | - Ederson Akio Kido
- Departamento de Genética, Universidade Federal de Pernambuco, Recife, Brazil
| | - Sergio Crovella
- Genetic Immunology laboratory, Institute for Maternal and Child Health-IRCCS, Burlo Garofolo, Trieste, Italy.,Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | | |
Collapse
|
10
|
Muhammad SA, Ashfaq H, Zafar S, Munir F, Jamshed MB, Chen J, Zhang Q. Polyvalent therapeutic vaccine for type 2 diabetes mellitus: Immunoinformatics approach to study co-stimulation of cytokines and GLUT1 receptors. BMC Mol Cell Biol 2020; 21:56. [PMID: 32703184 PMCID: PMC7376330 DOI: 10.1186/s12860-020-00279-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 04/28/2020] [Indexed: 12/14/2022] Open
Abstract
Background Type 2 diabetes mellitus (T2DM) is a worldwide disease that have an impact on individuals of all ages causing micro and macro vascular impairments due to hyperglycemic internal environment. For ultimate treatment to cure T2DM, association of diabetes with immune components provides a strong basis for immunotherapies and vaccines developments that could stimulate the immune cells to minimize the insulin resistance and initiate gluconeogenesis through an insulin independent route. Methodology Immunoinformatics based approach was used to design a polyvalent vaccine for T2DM that involved data accession, antigenicity analysis, T-cell epitopes prediction, conservation and proteasomal evaluation, functional annotation, interactomic and in silico binding affinity analysis. Results We found the binding affinity of antigenic peptides with major histocompatibility complex (MHC) Class-I molecules for immune activation to control T2DM. We found 13-epitopes of 9 amino acid residues for multiple alleles of MHC class-I bears significant binding affinity. The downstream signaling resulted by T-cell activation is directly regulated by the molecular weight, amino acid properties and affinity of these epitopes. Each epitope has important percentile rank with significant ANN IC50 values. These high score potential epitopes were linked using AAY, EAAAK linkers and HBHA adjuvant to generate T-cell polyvalent vaccine with a molecular weight of 35.6 kDa containing 322 amino acids residues. In silico analysis of polyvalent construct showed the significant binding affinity (− 15.34 Kcal/mol) with MHC Class-I. This interaction would help to understand our hypothesis, potential activation of T-cells and stimulatory factor of cytokines and GLUT1 receptors. Conclusion Our system-level immunoinformatics approach is suitable for designing potential polyvalent therapeutic vaccine candidates for T2DM by reducing hyperglycemia and enhancing metabolic activities through the immune system.
Collapse
Affiliation(s)
- Syed Aun Muhammad
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Multan, Pakistan.
| | - Hiba Ashfaq
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Multan, Pakistan
| | - Sidra Zafar
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Multan, Pakistan
| | - Fahad Munir
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Muhammad Babar Jamshed
- School of Pharmaceutical Sciences of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
| | - Jake Chen
- Informatics Institute, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Qiyu Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China.
| |
Collapse
|
11
|
Ghidey M, Islam SMA, Pruett G, Kearney CM. Making plants into cost-effective bioreactors for highly active antimicrobial peptides. N Biotechnol 2020; 56:63-70. [PMID: 31812667 DOI: 10.1016/j.nbt.2019.12.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 11/28/2019] [Accepted: 12/04/2019] [Indexed: 12/11/2022]
Abstract
As antibiotic-resistant bacterial pathogens become an ever-increasing concern, antimicrobial peptides (AMPs) have grown increasingly attractive as alternatives. Potentially, plants could be used as cost-effective AMP bioreactors; however, reported heterologous AMP expression is much lower in plants than in E. coli expression systems and often results in plant cytotoxicity, even for AMPs fused to carrier proteins. This suggests that there may be a physical characteristic of the previously described heterologous AMPs which impedes efficient expression in plants. Using a meta-analysis of protein databases, this study has determined that native plant AMPs were significantly less cationic than AMPs native to other taxa. To apply this finding to plant expression, the transient expression of 10 different heterologous AMPs, ranging in charge from +7 to -5, was tested in the tobacco, Nicotiana benthamiana. Elastin-like polypeptide (ELP) was used as the carrier protein for AMP expression. ELP fusion allowed for a simple, cost-effective temperature shift purification. Using this system, all five anionic AMPs expressed well, with two at unusually high levels (375 and 563 μg/gfw). Furthermore, antimicrobial activity against Staphylococcus epidermidis was an order of magnitude greater (average minimum inhibitory concentration MIC of 0.26μM) than that typically seen for AMPs expressed in E. coli systems and was associated with the uncleaved fusion peptide. In summary, this study describes a means of expressing AMP fusions in plants in high yield, purified by a simple temperature-shift protocol, resulting in a fusion peptide with high antimicrobial activity and without the need for a peptide cleavage step.
Collapse
Affiliation(s)
- Meron Ghidey
- Biomedical Studies Program, Baylor University, Waco, TX, 76798, USA
| | | | - Grace Pruett
- Department of Biology, Baylor University, One Bear Place #7388, Waco, TX, 76798, USA
| | - Christopher Michel Kearney
- Biomedical Studies Program, Baylor University, Waco, TX, 76798, USA; Department of Biology, Baylor University, One Bear Place #7388, Waco, TX, 76798, USA.
| |
Collapse
|
12
|
Choudhury A, Islam SMA, Ghidey MR, Kearney CM. Repurposing a drug targeting peptide for targeting antimicrobial peptides against Staphylococcus. Biotechnol Lett 2019; 42:287-294. [DOI: 10.1007/s10529-019-02779-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 11/26/2019] [Indexed: 11/28/2022]
|
13
|
Postic G, Gracy J, Périn C, Chiche L, Gelly JC. KNOTTIN: the database of inhibitor cystine knot scaffold after 10 years, toward a systematic structure modeling. Nucleic Acids Res 2019; 46:D454-D458. [PMID: 29136213 PMCID: PMC5753296 DOI: 10.1093/nar/gkx1084] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/20/2017] [Indexed: 12/25/2022] Open
Abstract
Knottins, or inhibitor cystine knots (ICKs), are ultra-stable miniproteins with multiple applications in drug design and medical imaging. These widespread and functionally diverse proteins are characterized by the presence of three interwoven disulfide bridges in their structure, which form a unique pseudoknot. Since 2004, the KNOTTIN database (www.dsimb.inserm.fr/KNOTTIN/) has been gathering standardized information about knottin sequences, structures, functions and evolution. The website also provides access to bibliographic data and to computational tools that have been specifically developed for ICKs. Here, we present a major upgrade of our database, both in terms of data content and user interface. In addition to the new features, this article describes how KNOTTIN has seen its size multiplied over the past ten years (since its last publication), notably with the recent inclusion of predicted ICKs structures. Finally, we report how our web resource has proved usefulness for the researchers working on ICKs, and how the new version of the KNOTTIN website will continue to serve this active community.
Collapse
Affiliation(s)
- Guillaume Postic
- INSERM, U 1134, DSIMB, 6, rue Alexandre Cabanel, 75739, Paris Cedex 15, France
- Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, 75739 Paris, France
- Institut National de la Transfusion Sanguine, 75739 Paris, France
- Laboratory of Excellence GR-Ex, 75739 Paris, France
| | - Jérôme Gracy
- CNRS UMR 5048, INSERM U1054, Centre de Biochimie Structurale, Université Montpellier, 34090 Montpellier, France
| | - Charlotte Périn
- INSERM, U 1134, DSIMB, 6, rue Alexandre Cabanel, 75739, Paris Cedex 15, France
- Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, 75739 Paris, France
- Institut National de la Transfusion Sanguine, 75739 Paris, France
- Laboratory of Excellence GR-Ex, 75739 Paris, France
| | - Laurent Chiche
- CNRS UMR 5048, INSERM U1054, Centre de Biochimie Structurale, Université Montpellier, 34090 Montpellier, France
| | - Jean-Christophe Gelly
- INSERM, U 1134, DSIMB, 6, rue Alexandre Cabanel, 75739, Paris Cedex 15, France
- Université Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, 75739 Paris, France
- Institut National de la Transfusion Sanguine, 75739 Paris, France
- Laboratory of Excellence GR-Ex, 75739 Paris, France
- To whom correspondence should be addressed.
| |
Collapse
|
14
|
Islam SMA, Heil BJ, Kearney CM, Baker EJ. Protein classification using modified n-grams and skip-grams. Bioinformatics 2019; 34:1481-1487. [PMID: 29309523 DOI: 10.1093/bioinformatics/btx823] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 12/21/2017] [Indexed: 12/24/2022] Open
Abstract
Motivation Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). Results A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. Availability and implementation m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. Contact erich_baker@baylor.edu. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | | | | | - Erich J Baker
- Institute of Biomedical Studies.,Department of Computer Science
| |
Collapse
|
15
|
Bioinformatic prediction of plant–pathogenicity effector proteins of fungi. Curr Opin Microbiol 2018; 46:43-49. [DOI: 10.1016/j.mib.2018.01.017] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 01/16/2018] [Accepted: 01/31/2018] [Indexed: 12/12/2022]
|
16
|
Classes, Databases, and Prediction Methods of Pharmaceutically and Commercially Important Cystine-Stabilized Peptides. Toxins (Basel) 2018; 10:toxins10060251. [PMID: 29921767 PMCID: PMC6024828 DOI: 10.3390/toxins10060251] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 06/12/2018] [Accepted: 06/14/2018] [Indexed: 12/13/2022] Open
Abstract
Cystine-stabilized peptides represent a large family of peptides characterized by high structural stability and bactericidal, fungicidal, or insecticidal properties. Found throughout a wide range of taxa, this broad and functionally important family can be subclassified into distinct groups dependent upon their number and type of cystine bonding patters, tertiary structures, and/or their species of origin. Furthermore, the annotation of proteins related to the cystine-stabilized family are under-represented in the literature due to their difficulty of isolation and identification. As a result, there are several recent attempts to collate them into data resources and build analytic tools for their dynamic prediction. Ultimately, the identification and delivery of new members of this family will lead to their growing inclusion into the repertoire of commercial viable alternatives to antibiotics and environmentally safe insecticides. This review of the literature and current state of cystine-stabilized peptide biology is aimed to better describe peptide subfamilies, identify databases and analytics resources associated with specific cystine-stabilized peptides, and highlight their current commercial success.
Collapse
|
17
|
Islam SMA, Kearney CM, Baker EJ. Assigning biological function using hidden signatures in cystine-stabilized peptide sequences. Sci Rep 2018; 8:9049. [PMID: 29899538 PMCID: PMC5998126 DOI: 10.1038/s41598-018-27177-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 05/25/2018] [Indexed: 12/19/2022] Open
Abstract
Cystine-stabilized peptides have great utility as they naturally block ion channels, inhibit acetylcholine receptors, or inactivate microbes. However, only a tiny fraction of these peptides has been characterized. Exploration for novel peptides most efficiently starts with the identification of candidates from genome sequence data. Unfortunately, though cystine-stabilized peptides have shared structures, they have low DNA sequence similarity, restricting the utility of BLAST and even more powerful sequence alignment-based annotation algorithms, such as PSI-BLAST and HMMER. In contrast, a supervised machine learning approach may improve discovery and function assignment of these peptides. To this end, we employed our previously described m-NGSG algorithm, which utilizes hidden signatures embedded in peptide primary sequences that define and categorize structural or functional classes of peptides. From the generalized m-NGSG framework, we derived five specific models that categorize cystine-stabilized peptide sequences into specific functional classes. When compared with PSI-BLAST, HMMER and existing function-specific models, our novel approach (named CSPred) consistently demonstrates superior performance in discovery and function-assignment. We also report an interactive version of CSPred, available through download ( https://bitbucket.org/sm_islam/cystine-stabilized-proteins/src ) or web interface (watson.ecs.baylor.edu/cspred), for the discovery of cystine-stabilized peptides of specific function from genomic datasets and for genome annotation. We fully describe, in the Availability section following the Discussion, the quick and simple usage of the CsPred website to automatically deliver function assignments for batch submissions of peptide sequences.
Collapse
Affiliation(s)
- S M Ashiqul Islam
- Institute of Biomedical Studies, Baylor University, Waco, 76798, USA
| | - Christopher Michel Kearney
- Institute of Biomedical Studies, Baylor University, Waco, 76798, USA.,Department of Biology, Baylor University, Waco, 76798, USA
| | - Erich J Baker
- Institute of Biomedical Studies, Baylor University, Waco, 76798, USA. .,Department of Computer Science, Baylor University, Waco, 76798, USA.
| |
Collapse
|
18
|
Franceschi N, Paraskevopoulos K, Waigmann E, Ramon M. Predictive Protein Toxicity and Its Use in Risk Assessment. Trends Biotechnol 2017; 35:483-486. [DOI: 10.1016/j.tibtech.2017.03.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 03/20/2017] [Accepted: 03/21/2017] [Indexed: 11/29/2022]
|