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Recent Advances in Biomolecular Recognition: 3rd Edition

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

Deadline for manuscript submissions: 20 December 2025 | Viewed by 967

Special Issue Editor


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Guest Editor
Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
Interests: computational analysis of biomolecular sequence, structure, dynamics, and function
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue, titled "Recent Advances in Biomolecular Recognition II".

Living cells are extremely complicated systems and comprise hundreds of thousands of different biomolecules that interact with each other to maintain fundamental cellular functions. The disruption of these interactions is frequently linked to various human diseases. These interactions, i.e., biomolecular recognitions, are very specific and are mediated by both polar and nonpolar interactions. They are also mediated strongly by water.

The goal of this Special Issue is to bring together wet- and dry-lab researchers with common interests in biomolecular recognition and to share their research efforts and to seek collaborations addressing common interesting questions. A Special Issue featuring works from researchers utilizing and developing different experimental and computational methods would certainly help to reveal the strengths and weaknesses of their approaches and enable one to make further improvements. Topics of interest include protein–protein binding; protein–membrane interactions; protein–nucleic acid interactions; interactions with small molecules and drug design; electrostatics, polarization, and solvation in molecular recognition; and mutations in molecular recognition.

Prof. Dr. Ray Luo
Guest Editor

Manuscript Submission Information

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Keywords

  • proteins
  • nucleic acids
  • membrane
  • ligand
  • binding

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Published Papers (1 paper)

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Research

18 pages, 2230 KB  
Article
Cat-PIPpred: Pro-Inflammatory Peptide Predictor Integrating CatBoost and Cross-Modal Feature Fusion
by Jia Zheng, Xianmian Qin and Yue Yao
Int. J. Mol. Sci. 2025, 26(21), 10484; https://doi.org/10.3390/ijms262110484 - 28 Oct 2025
Viewed by 334
Abstract
Pro-inflammatory peptides (PIPs) play a pivotal role in the initiation, progression, and sustenance of inflammation. A more in-depth analysis of PIPs requires precise identification, for which computational methodologies have proven to be remarkably cost-effective and accurate. In this study, we introduce Cat-PIPpred, a [...] Read more.
Pro-inflammatory peptides (PIPs) play a pivotal role in the initiation, progression, and sustenance of inflammation. A more in-depth analysis of PIPs requires precise identification, for which computational methodologies have proven to be remarkably cost-effective and accurate. In this study, we introduce Cat-PIPpred, a sophisticated predictor for PIPs that combines CatBoost with cross-modal feature integration. Through a comprehensive evaluation involving cross-validation and independent testing, an optimized model is developed by employing various feature extraction techniques, refinement protocols, and classifier architectures. The integration of ESM-2 structural embeddings with Dipeptide Deviation from Expected Mean (DDE) evolutionary features allows for an extensive representation of sequences. Feature refinement effectively decreases memory consumption while enhancing operational efficiency. The final Cat-PIPpred surpasses existing predictors targeting PIPs, as well as general peptide classifiers. These findings affirm the efficacy of integrating multiple feature sets with advanced ensemble learning algorithms. The proposed framework not only ensures reliable PIP predictions but also offers valuable insights into the functional predictions of specialized peptides. Full article
(This article belongs to the Special Issue Recent Advances in Biomolecular Recognition: 3rd Edition)
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