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Research on Computational Biology and Bioinformatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 August 2025 | Viewed by 526

Special Issue Editors

Dr. Kai Hu

E-Mail Website
Guest Editor
Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
Interests: applied bioinformatics; analytical pipeline development; single-cell data analysis; applied machine learning
Dr. Haibo Liu

E-Mail Website
Guest Editor
Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, 364 Plantation Street, Worcester, MA 01605, USA
Interests: bioinformatics in animals

Special Issue Information

Dear Colleagues,

We invite you to contribute a paper to the Special Issue “Research on Computational Biology and Bioinformatics” of Applied Sciences (https://www.mdpi.com/journal/applsci, Impact Factor: 2.5, CiteScore: 5.3).

In recent decades, we have witnessed an unprecedented surge in the volume, diversity, and complexity of biological data, driven by significant advancements in high-throughput technologies such as next-generation sequencing, long-read sequencing, and single-cell techniques. This rapid expansion of data presents both challenges and opportunities to the broad research community; thus, easy-to-use, efficient, and novel computational biological and bioinformatics tools are in great need to manage, analyze, and interpret this wealth of data.

This Special Issue will highlight cutting-edge research across all aspects of Computational Biology and Bioinformatics. We welcome original contributions on topics including, but not limited to, the following:

(1) Development of novel algorithms and tools for analyzing existing or emerging biological data;

(2) Application of innovative computational biology methods or bioinformatics strategies to address specific biological questions;

(3) Benchmarking studies or comprehensive reviews of contemporary computational biology tools and algorithms.

We look forward to receiving your submissions.

Dr. Kai Hu
Dr. Haibo Liu
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • computational biology
  • bioinformatics
  • biological data analysis
  • high-throughput technologies

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Published Papers (1 paper)

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Research

31 pages, 16368 KiB  
Article
Bioinformatics-Based Management of Vitellogenin-like Protein’s Role in Pathogen Defense in Nicotiana tabacum L.
by Hanan Maoz, Amir Elalouf and Amit Yaniv Rosenfeld
Appl. Sci. 2025, 15(8), 4463; https://doi.org/10.3390/app15084463 - 18 Apr 2025
Viewed by 314
Abstract
The primary objective of this study was to identify and characterize pathogen defense proteins in the Nicotiana tabacum L. proteome, focusing on their structural, functional, and evolutionary properties, as well as their interactions with pathogen-derived molecules. Specifically, we aimed to comprehensively analyze the [...] Read more.
The primary objective of this study was to identify and characterize pathogen defense proteins in the Nicotiana tabacum L. proteome, focusing on their structural, functional, and evolutionary properties, as well as their interactions with pathogen-derived molecules. Specifically, we aimed to comprehensively analyze the proteome to pinpoint potential uncharacterized defense-related protein that has emerging roles in immune responses and antioxidant activity across plants and animals. Through integrated computational approaches, we determined evolutionary relationships, and structural modeling of the selected protein was performed using different modeling software, followed by validation through multiple metrics, including stereochemical checks (Ramachandran plot), MolProbity analysis, and Z-scores. We further investigated the functional binding regions or interaction sites. We performed molecular docking to investigate the molecular interactions between selected proteins and pathogen-associated molecular patterns (PAMPs), specifically β-glucan and peptidoglycan (PGN), to elucidate their defensive mechanisms. Last, normal mode analysis (NMA), molecular dynamics simulation (MDS), and post-simulation analyses were employed to evaluate the stability and mobility of the protein–ligand complexes. Uncharacterized vitellogenin-like protein (VLP: ID A0A1S4CXB2) with the potential defense domain chosen because of its predicted immune-related features, stress response patterns, and unknown pathogen role at new immunity functions. Phylogenetic analysis revealed significant sequence homology with VLPs from other members of the Solanaceae family. Structural modeling showed a high-quality model, with docking studies indicating a stronger affinity for PGN (−10.16 kcal/mol) and β-glucan (−7.19 kcal/mol), highlighting its potential involvement in pathogen defense. NMA, MDS, and post-simulation analyses revealed that PGN exhibits more substantial binding stability and more extensive interactions with VLP than β-glucan. Our findings confirmed that VLPs in N. tabacum may function as pattern recognition receptors (PRRs), capable of recognizing and responding to pathogens by activating immune signaling pathways. Future experimental validation of these interactions could further elucidate the role of VLPs in plant defense and their potential application in biotechnological approaches for sustainable agriculture. Full article
(This article belongs to the Special Issue Research on Computational Biology and Bioinformatics)
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