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New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery
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New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery

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: 20 July 2025 | Viewed by 7499

Special Issue Editor

Prof. Dr. Fei Guo

E-Mail Website
Guest Editor
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: machine learning; deep learning; data mining; bioinformatics

Special Issue Information

Dear Colleagues,

With the accumulation of large-scale data in bioinformatics, researchers deal with several types of biomolecule data, such as sequences, structures, and functions. The question of how to effectively use these data in the fields of biology is an important one, as many researchers employ computational methods to analyze enzymes, identify biomolecules, biological networks, and structural proteomics, for gene expression analysis, molecular docking, post-translational modifications, etc. Many new computational methodologies have been developed for biomolecule sequence, structure, and function discovery. The novel methods presented in these studies propose new tools for tacking different problems in bioinformatics, and the new findings therein promise to provide new insights for biologists and medical scientists.

This Special Issue is focused on studies relating to novel computational methodologies and golden benchmark datasets for biomolecule sequence, structure, and function discovery. Thus, we welcome original research articles, review articles, and communications covering one or more of the following topics:

  • Bioinformatics;
  • Machine learning;
  • Deep learning;
  • Biomolecule identification;
  • Biological networks;
  • Structural proteomics;
  • Gene expression analysis;
  • Molecular docking;
  • Post-translational modifications.

Prof. Dr. Fei Guo
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

  • bioinformatics
  • machine learning
  • deep learning
  • biomolecule identification
  • biological networks
  • structural proteomics
  • gene expression analysis
  • molecular docking
  • post-translational modifications

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Published Papers (6 papers)

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Research

19 pages, 2799 KiB  
Article
Unraveling the COVID-19 Severity Hubs and Interplays in Inflammatory-Related RNA–Protein Networks
by Heewon Park, Qingbo S. Wang, Takanori Hasegawa, Ho Namkoong, Hiroko Tanaka, Ryuji Koike, Yuko Kitagawa, Akinori Kimura, Seiya Imoto, Takanori Kanai, Koichi Fukunaga, Seishi Ogawa, Yukinori Okada and Satoru Miyano
Int. J. Mol. Sci. 2025, 26(9), 4412; https://doi.org/10.3390/ijms26094412 - 6 May 2025
Viewed by 155
Abstract
The rapid worldwide transmission of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to severe cases of hypoxia, acute respiratory distress syndrome, multi-organ failure, and ultimately death. Small-scale molecular interactions have been analyzed by focusing on [...] Read more.
The rapid worldwide transmission of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to severe cases of hypoxia, acute respiratory distress syndrome, multi-organ failure, and ultimately death. Small-scale molecular interactions have been analyzed by focusing on several genes/single genes, providing important insights; however, genome-wide multi-omics comprehensive molecular interactions have not yet been well investigated with the exception of GWAS and eQTLm, both of which show genetic risks. From April of 2020 until now, we have created a Japan-wide system, initially named the Japan COVID-19 Task Force. This system has collected more than 6500 COVID-19 patients’ peripheral blood and as much associated clinical information as possible from a network of more than 120 hospitals. DNA, RNA, serum, and plasma were extracted and stored in this bank. This study unravels the interplay of inflammatory gene networks that induce different COVID-19 severity levels (mild, moderate, severe, and critical) by using multi-omics data from the Japan COVID-19 Task Force. We analyze RNA and protein expressions to estimate severity-specific inflammation networks that uncover the interplay between RNA and protein networks via ligand–receptor pairs. Our large-scale RNA/protein expression data analysis reveals that the atypical chemokine receptor 2 (ACKR2) acts as a key broker linking RNA and protein inflammation networks to induce COVID-19 critical severity. ACKR2 emerges in RNA and protein inflammation networks, showing active interplay in high-severity cases and weak interactions in mild cases. The results also show severity-specific molecular interactions between interleukin (IL), cytokine receptor activity, cell adhesion, and interactions involving the CC chemokine ligand (CCL) gene family and ACKR2. Full article
(This article belongs to the Special Issue New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery)
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27 pages, 2590 KiB  
Article
Exploring the Promoter Generation and Prediction of Halomonas spp. Based on GAN and Multi-Model Fusion Methods
by Cuihuan Zhao, Yuying Guan, Shuan Yan and Jiahang Li
Int. J. Mol. Sci. 2024, 25(23), 13137; https://doi.org/10.3390/ijms252313137 - 6 Dec 2024
Viewed by 1001
Abstract
Promoters, as core elements in the regulation of gene expression, play a pivotal role in genetic engineering and synthetic biology. The accurate prediction and optimization of promoter strength are essential for advancing these fields. Here, we present the first promoter strength database tailored [...] Read more.
Promoters, as core elements in the regulation of gene expression, play a pivotal role in genetic engineering and synthetic biology. The accurate prediction and optimization of promoter strength are essential for advancing these fields. Here, we present the first promoter strength database tailored to Halomonas, an extremophilic microorganism, and propose a novel promoter design and prediction method based on generative adversarial networks (GANs) and multi-model fusion. The GAN model effectively learns the key features of Halomonas promoter sequences, such as the GC content and Moran’s coefficients, to generate biologically plausible promoter sequences. To enhance prediction accuracy, we developed a multi-model fusion framework integrating deep learning and machine learning approaches. Deep learning models, incorporating BiLSTM and CNN architectures, capture k-mer and PSSM features, whereas machine learning models utilize engineered string and non-string features to construct comprehensive feature matrices for the multidimensional analysis and prediction of promoter strength. Using the proposed framework, newly generated promoters via mutation were predicted, and their functional validity was experimentally confirmed. The integration of multiple models significantly reduced the experimental validation space through an intersection-based strategy, achieving a notable improvement in top quantile prediction accuracy, particularly within the top five quantiles. The robustness and applicability of this model were further validated on diverse datasets, including test sets and out-of-sample promoters. This study not only introduces an innovative approach for promoter design and prediction in Halomonas but also lays a foundation for advancing industrial biotechnology. Additionally, the proposed strategy of GAN-based generation coupled with multi-model prediction demonstrates versatility, offering a valuable reference for promoter design and strength prediction in other extremophiles. Our findings highlight the promising synergy between artificial intelligence and synthetic biology, underscoring their profound academic and practical implications. Full article
(This article belongs to the Special Issue New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery)
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18 pages, 3409 KiB  
Article
Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands
by Diya Zhang, Qiaozhen Meng and Fei Guo
Int. J. Mol. Sci. 2024, 25(23), 12676; https://doi.org/10.3390/ijms252312676 - 26 Nov 2024
Cited by 1 | Viewed by 1285
Abstract
In the binding process between proteins and ligand molecules, water molecules play a pivotal role by forming hydrogen bonds that enable proteins and ligand molecules to bind more strongly. However, current methodologies for predicting binding affinity overlook the importance of water molecules. Therefore, [...] Read more.
In the binding process between proteins and ligand molecules, water molecules play a pivotal role by forming hydrogen bonds that enable proteins and ligand molecules to bind more strongly. However, current methodologies for predicting binding affinity overlook the importance of water molecules. Therefore, we developed a model called GraphWater-Net, specifically designed for predicting protein–ligand binding affinity, by incorporating water molecules. GraphWater-Net employs topological structures to represent protein atoms, ligand atoms and water molecules, and their interactions. Leveraging the Graphormer network, the model extracts interaction features between nodes within the topology, alongside the interaction features of edges and nodes. Subsequently, it generates embeddings with attention weights, inputs them into a Softmax function for regression prediction, and ultimately outputs the predicted binding affinity value. Experimental results on the Comparative Assessment of Scoring Functions (CASF) 2016 test set show that the introduction of water molecules into the complex significantly improves the prediction performance of the proposed model for protein and ligand binding affinity. Specifically, the Pearson correlation coefficient (Rp) exceeds that of current state-of-the-art methods by a margin of 0.022 to 0.129. By integrating water molecules, GraphWater-Net has the potential to facilitate the rational design of protein–ligand interactions and aid in drug discovery. Full article
(This article belongs to the Special Issue New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery)
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20 pages, 6077 KiB  
Article
DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition
by Tao Song, Haonan Song, Zhiyi Pan, Yuan Gao, Huanhuan Dai and Xun Wang
Int. J. Mol. Sci. 2024, 25(21), 11744; https://doi.org/10.3390/ijms252111744 - 1 Nov 2024
Cited by 1 | Viewed by 1605
Abstract
Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convolutional neural network, [...] Read more.
Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a DNABert-based method using a multi-scale convolutional neural network, BiLSTM, for enhancer identification. We first designed the DeepDualEnhancer method based only on the DNA sequence input. It mainly consists of a multi-scale Convolutional Neural Network, and BiLSTM to extract features by DNABert and embedding, respectively. Meanwhile, we collected new datasets from the enhancer–promoter interaction field and designed the method DeepDualEnhancer-genomic for inputting DNA sequences and genomic signals, which consists of the transformer sequence attention. Extensive comparisons of our method with 20 other excellent methods through 5-fold cross validation, ablation experiments, and an independent test demonstrated that DeepDualEnhancer achieves the best performance. It is also found that the inclusion of genomic signals helps the enhancer recognition task to be performed better. Full article
(This article belongs to the Special Issue New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery)
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13 pages, 1732 KiB  
Article
Preimplantation Genetic Testing of Spinocerebellar Ataxia Type 3/Machado–Joseph Disease—Robust Tools for Direct and Indirect Detection of the ATXN3 (CAG)n Repeat Expansion
by Mulias Lian, Vivienne J. Tan, Riho Taguchi, Mingjue Zhao, Gui-Ping Phang, Arnold S. Tan, Shuling Liu, Caroline G. Lee and Samuel S. Chong
Int. J. Mol. Sci. 2024, 25(15), 8073; https://doi.org/10.3390/ijms25158073 - 24 Jul 2024
Viewed by 1326
Abstract
Spinocerebellar ataxia type 3/Machado–Joseph disease (SCA3/MJD) is a neurodegenerative disorder caused by the ATXN3 CAG repeat expansion. Preimplantation genetic testing for monogenic disorders (PGT-M) of SCA3/MJD should include reliable repeat expansion detection coupled with high-risk allele determination using informative linked markers. One couple [...] Read more.
Spinocerebellar ataxia type 3/Machado–Joseph disease (SCA3/MJD) is a neurodegenerative disorder caused by the ATXN3 CAG repeat expansion. Preimplantation genetic testing for monogenic disorders (PGT-M) of SCA3/MJD should include reliable repeat expansion detection coupled with high-risk allele determination using informative linked markers. One couple underwent SCA3/MJD PGT-M combining ATXN3 (CAG)n triplet-primed PCR (TP-PCR) with customized linkage-based risk allele genotyping on whole-genome-amplified trophectoderm cells. Microsatellites closely linked to ATXN3 were identified and 16 markers were genotyped on 187 anonymous DNAs to verify their polymorphic information content. In the SCA3/MJD PGT-M case, the ATXN3 (CAG)n TP-PCR and linked marker analysis results concurred completely. Among the three unaffected embryos, a single embryo was transferred and successfully resulted in an unaffected live birth. A total of 139 microsatellites within 1 Mb upstream and downstream of the ATXN3 CAG repeat were identified and 8 polymorphic markers from each side were successfully co-amplified in a single-tube reaction. A PGT-M assay involving ATXN3 (CAG)n TP-PCR and linkage-based risk allele identification has been developed for SCA3/MJD. A hexadecaplex panel of highly polymorphic microsatellites tightly linked to ATXN3 has been developed for the rapid identification of informative markers in at-risk couples for use in the PGT-M of SCA3/MJD. Full article
(This article belongs to the Special Issue New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery)
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31 pages, 9364 KiB  
Article
Variations of VEGFR2 Chemical Space: Stimulator and Inhibitory Peptides
by Claudiu N. Lungu, Ionel I. Mangalagiu, Gabriela Gurau and Mihaela Cezarina Mehedinti
Int. J. Mol. Sci. 2024, 25(14), 7787; https://doi.org/10.3390/ijms25147787 - 16 Jul 2024
Viewed by 1408
Abstract
The kinase pathway plays a crucial role in blood vessel function. Particular attention is paid to VEGFR type 2 angiogenesis and vascular morphogenesis as the tyrosine kinase pathway is preferentially activated. In silico studies were performed on several peptides that affect VEGFR2 in [...] Read more.
The kinase pathway plays a crucial role in blood vessel function. Particular attention is paid to VEGFR type 2 angiogenesis and vascular morphogenesis as the tyrosine kinase pathway is preferentially activated. In silico studies were performed on several peptides that affect VEGFR2 in both stimulating and inhibitory ways. This investigation aims to examine the molecular properties of VEGFR2, a molecule primarily involved in the processes of vasculogenesis and angiogenesis. These relationships were defined by the interactions between Vascular Endothelial Growth Factor receptor 2 (VEGFR2) and the structural features of the systems. The chemical space of the inhibitory peptides and stimulators was described using topological and energetic properties. Furthermore, chimeric models of stimulating and inhibitory proteins (for VEGFR2) were computed using the protein system structures. The interaction between the chimeric proteins and VEGFR was computed. The chemical space was further characterized using complex manifolds and high-dimensional data visualization. The results show that a slightly similar chemical area is shared by VEGFR2 and stimulating and inhibitory proteins. On the other hand, the stimulator peptides and the inhibitors have distinct chemical spaces. Full article
(This article belongs to the Special Issue New Computational Methodologies for Biomolecule Sequence, Structure and Function Discovery)
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