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Molecular Advances in Protein-Ligand Interactions
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Molecular Advances in Protein-Ligand Interactions

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 2671

Special Issue Editor

Dr. Ewa A. Bienkiewicz

E-Mail Website
Guest Editor
Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL 32306, USA
Interests: translational research; stroke; Alzheimer's disease; neurodegeneration; neuroinflammation; intrinsically disordered proteins; prion, protein structure/function

Special Issue Information

Dear Colleagues,

Protein–protein and protein–ligand interactions are at the center stage of the cellular processes and regulatory mechanisms of living systems. Spanning a spectrum from well-folded to intrinsically disordered proteins, these interactions serve as a basis for molecular recognition and facilitation of transient and stable interactions within the cellular communication pathways. Examination of these interactions identifies the key players and elucidates mechanisms that are essential for maintaining homeostasis under normal physiological conditions. Ultimately, this knowledge can be translated into probing biomolecular interactions in the context of pathological states, many of which involve proteins and their ligands. This approach expands our fundamental understanding and illuminates approaches for the development of novel treatments for various medical conditions. Technical advances and new methodologies accelerate these efforts, broaden the scope of the investigations, and provide transformational perspectives. For this Special Issue of IJMS, we aim to collect contributions in the form of either original research articles or reviews to add new insight into the role of protein–protein and protein–ligand interactions in biological processes. We seek to highlight the state of the art, challenges, and open issues in this field of investigation. We are also looking for contributions underlining the impact of the investigated processes in biomedicine and molecular pathology, as well as applications of protein–protein and protein–ligand interactions in food industry and biotechnology.

Dr. Ewa A. Bienkiewicz
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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-protein interactions
  • protein-ligand interactions
  • multiomics
  • artificial intelligence (AI)
  • biophysics
  • molecular biology
  • disease

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Published Papers (2 papers)

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Research

16 pages, 2880 KiB  
Article
Identification of Putative Serum Autoantibodies Associated with Post-Acute Sequelae of COVID-19 via Comprehensive Protein Array Analysis
by Yasuyoshi Hatayama, Kei Miyakawa, Yayoi Kimura, Kazuo Horikawa, Kouichi Hirahata, Hirokazu Kimura, Hideaki Kato, Atsushi Goto and Akihide Ryo
Int. J. Mol. Sci. 2025, 26(4), 1751; https://doi.org/10.3390/ijms26041751 - 19 Feb 2025
Viewed by 1116
Abstract
Post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as “Long COVID”, represents a significant clinical challenge characterized by persistent symptoms following acute COVID-19 infection. We conducted a comprehensive retrospective cohort study to identify serum autoantibody biomarkers associated with PASC. Initial screening using a [...] Read more.
Post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as “Long COVID”, represents a significant clinical challenge characterized by persistent symptoms following acute COVID-19 infection. We conducted a comprehensive retrospective cohort study to identify serum autoantibody biomarkers associated with PASC. Initial screening using a protein bead array comprising approximately 20,000 human proteins identified several candidate PASC-associated autoantibodies. Subsequent validation by enzyme-linked immunosorbent assay (ELISA) in an expanded cohort—consisting of PASC patients, non-PASC COVID-19 convalescents, and pre-pandemic healthy controls—revealed two promising biomarkers: autoantibodies targeting PITX2 and FBXO2. PITX2 autoantibodies demonstrated high accuracy in distinguishing PASC patients from both non-PASC convalescents (area under the curve [AUC] = 0.891) and healthy controls (AUC = 0.866), while FBXO2 autoantibodies showed moderate accuracy (AUC = 0.762 and 0.786, respectively). Notably, the levels of these autoantibodies were associated with several PASC symptoms, including fever, dyspnea, palpitations, loss of appetite, and brain fog. The identification of PITX2 and FBXO2 autoantibodies as biomarkers not only enhances our understanding of PASC pathophysiology but also provides promising candidates for further investigation. Full article
(This article belongs to the Special Issue Molecular Advances in Protein-Ligand Interactions)
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24 pages, 2753 KiB  
Article
Development of Drug-Induced Gene Expression Ranking Analysis (DIGERA) and Its Application to Virtual Screening for Poly (ADP-Ribose) Polymerase 1 Inhibitor
by Hyein Cho, Kyoung Tai No and Hocheol Lim
Int. J. Mol. Sci. 2025, 26(1), 224; https://doi.org/10.3390/ijms26010224 - 30 Dec 2024
Viewed by 937
Abstract
Understanding drug-target interactions is crucial for identifying novel lead compounds, enhancing efficacy, and reducing toxicity. Phenotype-based approaches, like analyzing drug-induced gene expression changes, have shown effectiveness in drug discovery and precision medicine. However, experimentally determining gene expression for all relevant chemicals is impractical, [...] Read more.
Understanding drug-target interactions is crucial for identifying novel lead compounds, enhancing efficacy, and reducing toxicity. Phenotype-based approaches, like analyzing drug-induced gene expression changes, have shown effectiveness in drug discovery and precision medicine. However, experimentally determining gene expression for all relevant chemicals is impractical, limiting large-scale gene expression-based screening. In this study, we developed DIGERA (Drug-Induced Gene Expression Ranking Analysis), a Lasso-based ensemble framework utilizing LINCS L1000 data to predict drug-induced gene expression rankings. We created novel numerical features for chemicals, cell lines, and experimental conditions, allowing the prediction of gene expression rankings across eight key cell lines. DIGERA outperformed baseline models in the F1@K metric, demonstrating improved precision in gene expression ranking. We also combined DIGERA with an iterative fine-tuning process for de novo design, suggesting 10 PARP1 inhibitors with favorable predicted properties like binding affinity, synthetic accessibility, solubility, membrane permeability, drug-likeness, and similar gene expression ranking to olaparib. Notably, nine compounds were novel, and six analogs of these compounds had references linked to PARP1 inhibition. These results underscore DIGERA’s potential to boost model performance and robustness through novel features and ensemble learning, aiding virtual screening for new PARP1 inhibitors. Full article
(This article belongs to the Special Issue Molecular Advances in Protein-Ligand Interactions)
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