Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
4.8
Journals
Biomolecules
Special Issues
Computational Analysis and Conformational Modeling for Protein Structure and Interaction
share announcement

Computational Analysis and Conformational Modeling for Protein Structure and Interaction

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Biomacromolecules: Proteins, Nucleic Acids and Carbohydrates".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 6500

Special Issue Editors

Dr. Clement Agoni

E-Mail Website
Guest Editor
1. UCD Conway Institute of Biomolecular and Biomedical Research, Belfield, D04 V1W8 Dublin, Ireland
2. School of Medicine, University College of Dublin, Belfield, D04 V1W8 Dublin, Ireland
3. Discipline of Pharmaceutical Sciences, School of Health Sciences, Westville Campus, University of KwaZulu-Natal, Durban 4001, South Africa
Interests: peptides; bioactive peptides; computer-aided drug design, structural bioinformatics and molecular modelling
Dr. Indrani Bera

E-Mail Website
Guest Editor
School of Medicine, University College Dublin and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: structural bioinformatics; computational proteomics; molecular dynamics simulations; proteins and bioactive peptides

Special Issue Information

Dear Colleagues,

Protein structure and interaction are fundamental aspects of molecular biology and biochemistry, playing crucial roles in various biological processes, notably, signal transduction, molecular recognition, and enzymatic reactions. Understanding protein structure and interaction is essential for elucidating the molecular mechanisms underlying biological processes, designing new therapeutic agents, and engineering proteins with desired functions. Experimental techniques such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryo-electron microscopy (cryo-EM), and most recently, deep learning-based methods such as AlphaFold2, ESMFold, and RosettaFold, are commonly used to study protein structure and characterization. However, these techniques are notably limited by their inability to elucidate the atomistic-level conformational dynamics associated with protein interaction. To address this limitation, computational analysis and conformational modeling present powerful tools for analyzing protein structures, predicting their conformational dynamics, and elucidating their interactions. Augmenting experimental methods, computational analysis, and conformational modeling techniques unravel crucial structural insights that further our understanding of protein structure and interactions.

This Special Issue highlights and disseminates cutting-edge research in computational analysis and conformational modeling of protein structure and interaction. Thematic areas of interest include, but are not limited to, the following:

  1. Strengths and limitations of current computational analysis and conformational modeling techniques for investigating protein structure and interaction.
  2. Molecular dynamics simulations and computational modeling to explore protein dynamics and conformational changes.
  3. Molecular docking and molecular modeling techniques for studying protein–protein, protein–peptide, and protein–small molecule interactions.
  4. Applications of computational methods in drug discovery and design targeting protein–protein interactions.
  5. Integration of experimental and computational approaches to validate protein structures and interactions and to also provide a comprehensive understanding of protein dynamics and function.
  6. Artificial intelligence and machine learning-based approaches for predicting protein structure, functions, interactions, and conformational analysis.

We cordially invite investigators to contribute high-quality original research and review articles that cover any relevant topics that advance our understanding of protein structure and interaction through computational analysis and modeling. By bringing together diverse perspectives and methodologies, this Special Issue aims to drive innovation in the field and facilitate the development of new therapeutic strategies targeting protein function and interaction.

Dr. Clement Agoni
Dr. Indrani Bera
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. Biomolecules 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 2700 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

  • protein structure prediction
  • drug discovery targeting proteins
  • structural bioinformatics
  • molecular dynamics simulations
  • protein–protein interactions
  • protein–ligand binding
  • conformational dynamics
  • molecular docking studies

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

19 pages, 3095 KiB  
Article
CellGAT: A GAT-Based Method for Constructing a Cell Communication Network Integrating Multiomics Information
by Tianjiao Zhang, Zhenao Wu, Liangyu Li, Jixiang Ren, Ziheng Zhang, Jingyu Zhang and Guohua Wang
Biomolecules 2025, 15(3), 342; https://doi.org/10.3390/biom15030342 - 27 Feb 2025
Viewed by 532
Abstract
The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of protein–protein interactions, including ligand–receptor; receptor–receptor, and extracellular matrix–receptor interactions. Currently, computational [...] Read more.
The growth, development, and differentiation of multicellular organisms are primarily driven by intercellular communication, which coordinates the activities of diverse cell types. This cell-to-cell signaling is typically mediated by various types of protein–protein interactions, including ligand–receptor; receptor–receptor, and extracellular matrix–receptor interactions. Currently, computational methods for inferring ligand–receptor communication primarily depend on gene expression data of ligand–receptor pairs and spatial information of cells. Some approaches integrate protein complexes; transcription factors; or pathway information to construct cell communication networks. However, few methods consider the critical role of protein–protein interactions (PPIs) in intercellular communication networks, especially when predicting communication between different cell types in the absence of cell type information. These methods often rely on ligand–receptor pairs that lack PPI evidence, potentially compromising the accuracy of their predictions. To address this issue, we propose CellGAT, a framework that infers intercellular communication by integrating gene expression data of ligand–receptor pairs, PPI information, protein complex data, and experimentally validated pathway information. CellGAT not only builds a priori models but also uses node embedding algorithms and graph attention networks to build cell communication networks based on scRNA-seq (single-cell RNA sequencing) datasets and includes a built-in cell clustering algorithm. Through comparisons with various methods, CellGAT accurately predicts cell–cell communication (CCC) and analyzes its impact on downstream pathways; neighboring cells; and drug interventions. Full article
(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)
►▼ Show Figures

Figure 1

20 pages, 4624 KiB  
Article
Computational Elucidation of a Monobody Targeting the Phosphatase Domain of SHP2
by Yang Wang, Xin Qiao, Ruidi Zhu, Linxuan Zhou, Quan Zhang, Shaoyong Lu and Zongtao Chai
Biomolecules 2025, 15(2), 217; https://doi.org/10.3390/biom15020217 - 2 Feb 2025
Cited by 1 | Viewed by 850
Abstract
Src homology 2 (SH2) domain-containing phosphatase 2 (SHP2) is a key regulator in cellular signaling pathways because its dysregulation has been implicated in various pathological conditions, including cancers and developmental disorders. Despite its importance, the molecular basis of SHP2’s regulatory mechanism remains poorly [...] Read more.
Src homology 2 (SH2) domain-containing phosphatase 2 (SHP2) is a key regulator in cellular signaling pathways because its dysregulation has been implicated in various pathological conditions, including cancers and developmental disorders. Despite its importance, the molecular basis of SHP2’s regulatory mechanism remains poorly understood, hindering the development of effective targeted therapies. In this study, we utilized the high-specificity monobody Mb11 to investigate its interaction with the SHP2 phosphatase domain (PTP) using multiple replica molecular dynamics simulations. Our analyses elucidate the precise mechanisms through which Mb11 achieves selective recognition and stabilization of the SHP2-PTP domain, identifying key residues and interaction networks essential for its high binding specificity and regulatory dynamics. Furthermore, the study highlights the pivotal role of residue C459 in preserving the structural integrity and functional coherence of the complex, acting as a central node within the interaction network and underpinning its stability and efficiency. These findings have significantly advanced the understanding of the mechanisms underlying SHP2’s involvement in disease-related signaling and pathology while simultaneously paving the way for the rational design of targeted inhibitors, offering significant implications for therapeutic strategies in SHP2-associated diseases and contributing to the broader scope of precision medicine. Full article
(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)
►▼ Show Figures

Figure 1

18 pages, 3854 KiB  
Article
IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network
by Ruifen Cao, Qiangsheng Li, Pijing Wei, Yun Ding, Yannan Bin and Chunhou Zheng
Biomolecules 2025, 15(1), 99; https://doi.org/10.3390/biom15010099 - 10 Jan 2025
Viewed by 860
Abstract
Interleukin-6 (IL-6) is a potent glycoprotein that plays a crucial role in regulating innate and adaptive immunity, as well as metabolism. The expression and release of IL-6 are closely correlated with the severity of various diseases. IL-6-inducing peptides are critical for the development [...] Read more.
Interleukin-6 (IL-6) is a potent glycoprotein that plays a crucial role in regulating innate and adaptive immunity, as well as metabolism. The expression and release of IL-6 are closely correlated with the severity of various diseases. IL-6-inducing peptides are critical for the development of immunotherapy and diagnostic biomarkers for some diseases. Most existing methods for predicting IL-6-induced peptides use traditional machine learning methods, whose feature selection is based on prior knowledge. In addition, none of these methods take into account the three-dimensional (3D) structure of peptides, which is essential for their functional properties. In this study, we propose a novel IL-6-inducing peptide prediction method called DGIL-6, which integrates 3D structural information with graph neural networks. DGIL-6 represents a peptide sequence as a graph, where each amino acid is treated as a node, and the adjacency matrix, representing the relationships between nodes, is derived from the predicted residue contact graph of the peptide sequence. In addition to commonly used amino acid representations, such as one-hot encoding and position encoding, the pre-trained model ESM-1b is employed to extract amino acid features as node features. In order to simultaneously consider node weights and information updates, a dual-channel method combining Graph Attention Network (GAT) and Graph Convolutional Network (GCN) is adopted. Finally, the extracted features from both channels are merged for the classification of IL-6-inducing peptides. A series of experiments including cross-validation, independent testing, ablation studies, and visualizations demonstrate the effectiveness of the DGIL-6 method. Full article
(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)
►▼ Show Figures

Figure 1

16 pages, 2606 KiB  
Article
Affinity Tag-Free Purification of SARS-CoV-2 N Protein and Its Crystal Structure in Complex with ssDNA
by Atanu Maiti and Hiroshi Matsuo
Biomolecules 2024, 14(12), 1538; https://doi.org/10.3390/biom14121538 - 30 Nov 2024
Viewed by 1009
Abstract
The nucleocapsid (N) protein is one of the four structural proteins in SARS-CoV-2, playing key roles in viral assembly, immune evasion, and stability. One of its primary functions is to protect viral RNA by forming the nucleocapsid. However, the precise mechanisms by which [...] Read more.
The nucleocapsid (N) protein is one of the four structural proteins in SARS-CoV-2, playing key roles in viral assembly, immune evasion, and stability. One of its primary functions is to protect viral RNA by forming the nucleocapsid. However, the precise mechanisms by which the N protein interacts with viral RNA and assembles into a nucleocapsid remain unclear. Compared to other SARS-CoV-2 components, targeting the N protein has several advantages: it exhibits higher sequence conservation, lower mutation rates, and stronger immunogenicity, making it an attractive target for antiviral drug development and diagnostics. Therefore, a detailed understanding of the N protein’s structure is essential for deciphering its role in viral assembly and developing effective therapeutics. In this study, we report the expression and purification of a soluble recombinant N protein, along with a 1.55 Å resolution crystal structure of its nucleic acid-binding domain (N-NTD) in complex with ssDNA. Our structure revealed new insights into the conformation and interaction of the flexible N-arm, which could aid in understanding nucleocapsid assembly. Additionally, we identified residues that are critical for ssDNA interaction. Full article
(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)
►▼ Show Figures

Figure 1

17 pages, 8494 KiB  
Article
Computational Modeling Study of the Molecular Basis of dNTP Selectivity in Human Terminal Deoxynucleotidyltransferase
by Egor O. Ukladov, Timofey E. Tyugashev and Nikita A. Kuznetsov
Biomolecules 2024, 14(8), 961; https://doi.org/10.3390/biom14080961 - 7 Aug 2024
Cited by 1 | Viewed by 1151
Abstract
Human terminal deoxynucleotidyl transferase (TdT) can catalyze template-independent DNA synthesis during the V(D)J recombination and DNA repair through nonhomologous end joining. The capacity for template-independent random addition of nucleotides to single-stranded DNA makes this polymerase useful in various molecular biological applications involving sequential [...] Read more.
Human terminal deoxynucleotidyl transferase (TdT) can catalyze template-independent DNA synthesis during the V(D)J recombination and DNA repair through nonhomologous end joining. The capacity for template-independent random addition of nucleotides to single-stranded DNA makes this polymerase useful in various molecular biological applications involving sequential stepwise synthesis of oligonucleotides using modified dNTP. Nonetheless, a serious limitation to the applications of this enzyme is strong selectivity of human TdT toward dNTPs in the order dGTP > dTTP ≈ dATP > dCTP. This study involved molecular dynamics to simulate a potential impact of amino acid substitutions on the enzyme’s selectivity toward dNTPs. It was found that the formation of stable hydrogen bonds between a nitrogenous base and amino acid residues at positions 395 and 456 is crucial for the preferences for dNTPs. A set of single-substitution and double-substitution mutants at these positions was analyzed by molecular dynamics simulations. The data revealed two TdT mutants—containing either substitution D395N or substitutions D395N+E456N—that possess substantially equalized selectivity toward various dNTPs as compared to the wild-type enzyme. These results will enable rational design of TdT-like enzymes with equalized dNTP selectivity for biotechnological applications. Full article
(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)
►▼ Show Figures

Figure 1

Review

Jump to: Research

45 pages, 2793 KiB  
Review
Molecular Modelling in Bioactive Peptide Discovery and Characterisation
by Clement Agoni, Raúl Fernández-Díaz, Patrick Brendan Timmons, Alessandro Adelfio, Hansel Gómez and Denis C. Shields
Biomolecules 2025, 15(4), 524; https://doi.org/10.3390/biom15040524 - 3 Apr 2025
Viewed by 1095
Abstract
Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino [...] Read more.
Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide–protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations. Full article
(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop