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Pharmaceuticals
Special Issues
Structural and Other Proteomics Approaches in Drug Discovery
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Structural and Other Proteomics Approaches in Drug Discovery

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Biopharmaceuticals".

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 4956

Special Issue Editors

Dr. Marino F. A. Santos

E-Mail Website
Guest Editor
1. Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal
2. UCIBIO – Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
Interests: X-ray crystallography; biophysical and structural protein characterization; drug design
Dr. Luís Passarinha

E-Mail Website
Guest Editor
1. Associate Laboratory i4HB—Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal
2. UCIBIO—Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
3. CICS-UBI—Health Sciences Research Centre, University of Beira Interior, 6201-506 Covilhã, Portugal
Interests: method development and validation; electrochemical detection; proteomics; protein biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Proteins are the primary biological targets for numerous drugs. As such, the relevance of high-throughput structural proteomics and other proteomics-related approaches in the drug discovery is easily comprehensible at different levels including target identification, target validation, potential drug toxicity and protein-protein or protein-ligand interactions. In addition to the classic experimental proteomics techniques (e.g., mass spectrometry), the emergence of new in silico methods (e.g., bioinformatics and computational modelling) as well as artificial intelligence (AI) tools can provide valuable insights to improve the current knowledge and contribute to the design of new useful drugs.

In this sense, the present Special Issue entitled “Structural and other Proteomics Approaches in Drug Discovery” aims to collect reviews that consider the current state of the art and future prospects in this field, as well as original research articles reflecting.

We are looking forward to receiving your valuable contributions.

Dr. Marino F. A. Santos
Dr. Luís Passarinha
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. Pharmaceuticals 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 2900 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

  • artificial intelligence
  • bioinformatics
  • chemoproteomics
  • computational modelling
  • drug discovery
  • mass spectrometry
  • protein structure
  • proteomics structural
  • proteomics

Published Papers (3 papers)

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Research

21 pages, 8695 KiB  
Article
Neurogenesis-Associated Protein, a Potential Prognostic Biomarker in Anti-PD-1 Based Kidney Renal Clear Cell Carcinoma Patient Therapeutics
by Rui Gao, Zixue Liu, Mei Meng, Xuefei Song and Jian He
Pharmaceuticals 2024, 17(4), 451; https://doi.org/10.3390/ph17040451 - 30 Mar 2024
Viewed by 684
Abstract
The transketolase 1 gene (TKTL1) is an essential factor that contributes to brain development. Some studies have shown the influence of TKTL1 in cancers, but it has been rarely reported in kidney cancer. Furthermore, the role of TKTL1 in the prognosis and tumor [...] Read more.
The transketolase 1 gene (TKTL1) is an essential factor that contributes to brain development. Some studies have shown the influence of TKTL1 in cancers, but it has been rarely reported in kidney cancer. Furthermore, the role of TKTL1 in the prognosis and tumor infiltration of immune cells in various cancers, particularly kidney cancer, remains unknown. In this study, TKTL1 expression and its clinical characteristics were investigated using a variety of databases. TIMER was used to investigate the relationship between TKTL1 and immune infiltrates in various types of cancer. We also studied the relationship between TKTL1 expression and response to PD-1 blocker immunotherapy in renal cancer. We conducted TKTL1 agonists virtual screening from 13,633 natural compounds (L6020), implemented secondary library construction according to the types of top results, and then conducted secondary virtual screening for 367 alkaloids. Finally, in vitro assays of cell viability assays and colony formation assays were performed to demonstrate the pharmacological potency of the screening of TKTL1 agonists. Using these methods, we determined that TKTL1 significantly affects the prognostic potential in different types of kidney cancer patients. The underlying mechanism might be that the TKTL1 expression level was positively associated with devious immunocytes in kidney renal clear cell carcinoma (KIRC) rather than in kidney renal papillary cell carcinoma (KIRP) and kidney chromophobe (KICH). This recruitment may result from the up-regulation of the mTOR signaling pathway affecting T cell metabolism. We also found that TKTL1 may act as an immunomodulator in KIRC patients’ response to anti-PD-1 therapy. Moreover, we also found that piperine and glibenclamide are potent agonists of TKTL1. We have demonstrated, in vitro, that piperine and glibenclamide can inhibit the proliferation and clone formation of Caki-2 cell lines by agonizing the expression of TKTL1. In summary, our discovery implies that TKTL1 may be a promising prognostic biomarker for KIRC patients who respond to anti-PD-1 therapy. Piperine and glibenclamide may be effective therapeutic TKTL1 agonists, providing a theoretical basis for the clinical treatment of kidney cancer. Full article
(This article belongs to the Special Issue Structural and Other Proteomics Approaches in Drug Discovery)
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52 pages, 9964 KiB  
Article
Construction of an Exudative Age-Related Macular Degeneration Diagnostic and Therapeutic Molecular Network Using Multi-Layer Network Analysis, a Fuzzy Logic Model, and Deep Learning Techniques: Are Retinal and Brain Neurodegenerative Disorders Related?
by Hamid Latifi-Navid, Amir Barzegar Behrooz, Saleh Jamehdor, Maliheh Davari, Masoud Latifinavid, Narges Zolfaghari, Somayeh Piroozmand, Sepideh Taghizadeh, Mahsa Bourbour, Golnaz Shemshaki, Saeid Latifi-Navid, Seyed Shahriar Arab, Zahra-Soheila Soheili, Hamid Ahmadieh and Nader Sheibani
Pharmaceuticals 2023, 16(11), 1555; https://doi.org/10.3390/ph16111555 - 02 Nov 2023
Cited by 1 | Viewed by 1920
Abstract
Neovascular age-related macular degeneration (nAMD) is a leading cause of irreversible visual impairment in the elderly. The current management of nAMD is limited and involves regular intravitreal administration of anti-vascular endothelial growth factor (anti-VEGF). However, the effectiveness of these treatments is limited by [...] Read more.
Neovascular age-related macular degeneration (nAMD) is a leading cause of irreversible visual impairment in the elderly. The current management of nAMD is limited and involves regular intravitreal administration of anti-vascular endothelial growth factor (anti-VEGF). However, the effectiveness of these treatments is limited by overlapping and compensatory pathways leading to unresponsiveness to anti-VEGF treatments in a significant portion of nAMD patients. Therefore, a system view of pathways involved in pathophysiology of nAMD will have significant clinical value. The aim of this study was to identify proteins, miRNAs, long non-coding RNAs (lncRNAs), various metabolites, and single-nucleotide polymorphisms (SNPs) with a significant role in the pathogenesis of nAMD. To accomplish this goal, we conducted a multi-layer network analysis, which identified 30 key genes, six miRNAs, and four lncRNAs. We also found three key metabolites that are common with AMD, Alzheimer’s disease (AD) and schizophrenia. Moreover, we identified nine key SNPs and their related genes that are common among AMD, AD, schizophrenia, multiple sclerosis (MS), and Parkinson’s disease (PD). Thus, our findings suggest that there exists a connection between nAMD and the aforementioned neurodegenerative disorders. In addition, our study also demonstrates the effectiveness of using artificial intelligence, specifically the LSTM network, a fuzzy logic model, and genetic algorithms, to identify important metabolites in complex metabolic pathways to open new avenues for the design and/or repurposing of drugs for nAMD treatment. Full article
(This article belongs to the Special Issue Structural and Other Proteomics Approaches in Drug Discovery)
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16 pages, 8991 KiB  
Article
Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study
by Mayra Alejandra Jaimes Campos, Iván Andújar, Felix Keller, Gert Mayer, Peter Rossing, Jan A. Staessen, Christian Delles, Joachim Beige, Griet Glorieux, Andrew L. Clark, William Mullen, Joost P. Schanstra, Antonia Vlahou, Kasper Rossing, Karlheinz Peter, Alberto Ortiz, Archie Campbell, Frederik Persson, Agnieszka Latosinska, Harald Mischak, Justyna Siwy and Joachim Jankowskiadd Show full author list remove Hide full author list
Pharmaceuticals 2023, 16(9), 1298; https://doi.org/10.3390/ph16091298 - 14 Sep 2023
Cited by 2 | Viewed by 1600
Abstract
(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases [...] Read more.
(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient’s urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted. Full article
(This article belongs to the Special Issue Structural and Other Proteomics Approaches in Drug Discovery)
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