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Computational Analysis and Conformational Modeling for Protein Structure and Interaction

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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 9163

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Special Issue Editors

Dr. Clement Agoni

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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

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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:

Strengths and limitations of current computational analysis and conformational modeling techniques for investigating protein structure and interaction.
Molecular dynamics simulations and computational modeling to explore protein dynamics and conformational changes.
Molecular docking and molecular modeling techniques for studying protein–protein, protein–peptide, and protein–small molecule interactions.
Applications of computational methods in drug discovery and design targeting protein–protein interactions.
Integration of experimental and computational approaches to validate protein structures and interactions and to also provide a comprehensive understanding of protein dynamics and function.
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

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Keywords

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

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Published Papers (8 papers)

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Research

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32 pages, 2557 KiB

Open AccessArticle

Ensemble-Based Binding Free Energy Profiling and Network Analysis of the KRAS Interactions with DARPin Proteins Targeting Distinct Binding Sites: Revealing Molecular Determinants and Universal Architecture of Regulatory Hotspots and Allosteric Binding

by Mohammed Alshahrani, Vedant Parikh, Brandon Foley and Gennady Verkhivker

Biomolecules 2025, 15(6), 819; https://doi.org/10.3390/biom15060819 - 5 Jun 2025

Viewed by 406

Abstract

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS allostery remain poorly understood. In this study, we employ microsecond molecular dynamics simulations, mutational scanning, and binding free energy calculations together with dynamic network modeling to dissect how engineered DARPin proteins K27, K55, K13, and K19 engage KRAS through diverse molecular mechanisms ranging from effector mimicry to conformational restriction and allosteric modulation. Mutational scanning across all four DARPin systems identifies a core set of evolutionarily constrained residues that function as universal hotspots in KRAS recognition. KRAS residues I36, Y40, M67, and H95 consistently emerge as critical contributors to binding stability. Binding free energy computations show that, despite similar binding modes, K27 relies heavily on electrostatic contributions from major binding hotspots while K55 exploits a dense hydrophobic cluster enhancing its effector-mimetic signature. The allosteric binders K13 and K19, by contrast, stabilize a KRAS-specific pocket in the α3–loop–α4 motif, introducing new hinges and bottlenecks that rewire the communication architecture of KRAS without full immobilization. Network-based analysis reveals a strikingly consistent theme: despite their distinct mechanisms of recognition, all systems engage a unifying allosteric architecture that spans multiple functional motifs. This architecture is not only preserved across complexes but also mirrors the intrinsic communication framework of KRAS itself, where specific residues function as central hubs transmitting conformational changes across the protein. By integrating dynamic profiling, energetic mapping, and network modeling, our study provides a multi-scale mechanistic roadmap for targeting KRAS, revealing how engineered proteins can exploit both conserved motifs and isoform-specific features to enable precision modulation of KRAS signaling in oncogenic contexts. Full article

(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)

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15 pages, 5633 KiB

Open AccessArticle

Mechanistic Insights into the Mechanism of Allosteric Inhibition of Ubiquitin-Specific Protease 7 (USP7)

by Xuebin Wang, Ning Liu, Nuan Li, Shaoyong Lu and Zongtao Chai

Biomolecules 2025, 15(6), 749; https://doi.org/10.3390/biom15060749 - 22 May 2025

Viewed by 436

Abstract

Ubiquitin-specific protease 7 (USP7), a deubiquitinase enzyme responsible for removing ubiquitin (Ub) from target proteins, plays a crucial role in oncogenic pathways and has been implicated in various human diseases. X-ray crystallography has revealed distinct conformations of USP7, including apo (ligand-free), allosteric inhibitor-, and Ub-bound states. However, the dynamic mechanisms underlying the allosteric inhibition of USP7 remain unclear. This study investigates the effect of allosteric inhibitor binding on the dynamics of USP7 through multiple replica molecular dynamics simulations. Our results demonstrate that Ub binding stabilizes the USP7 conformation, while allosteric inhibitor binding increases flexibility and variability in the fingers and palm domains of USP7. Furthermore, our analysis of USP7 local regions reveals that allosteric inhibitor binding not only restrains the dynamics of the C-terminal Ub binding site, thereby impeding the accessibility of Ub to USP7, but also disrupts the proper alignment of the catalytic triad (Cys223-His464-Asp481) in USP7. Additionally, community network analysis indicates that intra-domain communications within the fingers domain in USP7 are significantly enhanced upon allosteric inhibitor binding. This study reveals that the binding of an allosteric inhibitor induces a dynamic shift in enzyme’s conformational equilibrium, effectively disrupting its catalytic activity through allosteric modulation. Full article

(This article belongs to the Special Issue Computational Analysis and Conformational Modeling for Protein Structure and Interaction)

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19 pages, 3095 KiB

Open AccessArticle

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 726

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 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)

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20 pages, 4624 KiB

Open AccessArticle

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 3 | Viewed by 1055

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 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)

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18 pages, 3854 KiB

Open AccessArticle

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 1042

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 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)

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16 pages, 2606 KiB

Open AccessArticle

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 1122

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 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)

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17 pages, 8494 KiB

Open AccessArticle

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 1229

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 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)

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Review

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45 pages, 2793 KiB

Open AccessReview

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 1989

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 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)

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