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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: closed (30 June 2023) | Viewed by 12351

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Special Issue Editor

Prof. Dr. Quan Zou

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Collection Editor

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: bioinformatics; parallel computing; deep learning; protein classification; genome assembly
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Collection aims to publish contributions on all aspects of the physical principles governing biomolecular and biomimetic systems in China. We welcome submissions that provide novel and mechanistic insights and papers that report significant advances in the fields. Topics include, but are not limited to:

bioinformatics;
machine learning;
DNA/RNA binding proteins;
molecular structure

The only limitation is that the main part of the study has to have been carried out in China or by Chinese researchers.

The reliability of the results provided by novel tools for virtual screening and/or the discovery of new actives by virtual screening must be either validated in silico or in vitro before first submission of a manuscript to IJMS. Theoretical studies should offer new insights into understanding experimental results and/or suggest new experimentally testable hypotheses.

Prof. Dr. Quan Zou
Collection 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
computational biophysics
molecular structure
DNA/RNA binding proteins
MHC binding peptide

Published Papers (5 papers)

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Research

17 pages, 4043 KiB

Open AccessArticle

A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder

by Zixuan Wang, Yongqing Zhang, Yun Yu, Junming Zhang, Yuhang Liu and Quan Zou

Int. J. Mol. Sci. 2023, 24(5), 4784; https://doi.org/10.3390/ijms24054784 - 01 Mar 2023

Cited by 1 | Viewed by 2578

Abstract

Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular states and dynamics. However, few research efforts have been dedicated to modeling the relationship between regulatory grammars and single-cell chromatin accessibility and incorporating different analysis scenarios of scATAC-seq data into the general framework. To this end, we propose a unified deep learning framework based on the ProdDep Transformer Encoder, dubbed PROTRAIT, for scATAC-seq data analysis. Specifically motivated by the deep language model, PROTRAIT leverages the ProdDep Transformer Encoder to capture the syntax of transcription factor (TF)-DNA binding motifs from scATAC-seq peaks for predicting single-cell chromatin accessibility and learning single-cell embedding. Based on cell embedding, PROTRAIT annotates cell types using the Louvain algorithm. Furthermore, according to the identified likely noises of raw scATAC-seq data, PROTRAIT denoises these values based on predated chromatin accessibility. In addition, PROTRAIT employs differential accessibility analysis to infer TF activity at single-cell and single-nucleotide resolution. Extensive experiments based on the Buenrostro2018 dataset validate the effeteness of PROTRAIT for chromatin accessibility prediction, cell type annotation, and scATAC-seq data denoising, therein outperforming current approaches in terms of different evaluation metrics. Besides, we confirm the consistency between the inferred TF activity and the literature review. We also demonstrate the scalability of PROTRAIT to analyze datasets containing over one million cells. Full article

(This article belongs to the Special Issue State-of-the-Art Molecular Biophysics in China)

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12 pages, 2481 KiB

Open AccessArticle

MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning

by Yongqing Zhang, Maocheng Wang, Zixuan Wang, Yuhang Liu, Shuwen Xiong and Quan Zou

Int. J. Mol. Sci. 2023, 24(3), 2595; https://doi.org/10.3390/ijms24032595 - 30 Jan 2023

Cited by 5 | Viewed by 2905

Abstract

Regulators in gene regulatory networks (GRNs) are crucial for identifying cell states. However, GRN inference based on scRNA-seq data has several problems, including high dimensionality and sparsity, and requires more label data. Therefore, we propose a meta-learning GRN inference framework to identify regulatory factors. Specifically, meta-learning solves the parameter optimization problem caused by high-dimensional sparse data features. In addition, a few-shot solution was used to solve the problem of lack of label data. A structural equation model (SEM) was embedded in the model to identify important regulators. We integrated the parameter optimization strategy into the bi-level optimization to extract the feature consistent with GRN reasoning. This unique design makes our model robust to small-scale data. By studying the GRN inference task, we confirmed that the selected regulators were closely related to gene expression specificity. We further analyzed the GRN inferred to find the important regulators in cell type identification. Extensive experimental results showed that our model effectively captured the regulator in single-cell GRN inference. Finally, the visualization results verified the importance of the selected regulators for cell type recognition. Full article

(This article belongs to the Special Issue State-of-the-Art Molecular Biophysics in China)

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Figure 1

19 pages, 2269 KiB

Open AccessArticle

HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA–Disease Association Prediction

by Liang Yu, Bingyi Ju and Shujie Ren

Int. J. Mol. Sci. 2022, 23(21), 13155; https://doi.org/10.3390/ijms232113155 - 29 Oct 2022

Cited by 4 | Viewed by 1646

Abstract

Identifying disease-related miRNAs can improve the understanding of complex diseases. However, experimentally finding the association between miRNAs and diseases is expensive in terms of time and resources. The computational screening of reliable miRNA–disease associations has thus become a necessary tool to guide biological experiments. “Similar miRNAs will be associated with the same disease” is the assumption on which most current miRNA–disease association prediction methods rely; however, biased prior knowledge, and incomplete and inaccurate miRNA similarity data and disease similarity data limit the performance of the model. Here, we propose heuristic learning based on graph neural networks to predict microRNA–disease associations (HLGNN-MDA). We learn the local graph topology features of the predicted miRNA–disease node pairs using graph neural networks. In particular, our improvements to the graph convolution layer of the graph neural network enable it to learn information among homogeneous nodes and among heterogeneous nodes. We illustrate the performance of HLGNN-MDA by performing tenfold cross-validation against excellent baseline models. The results show that we have promising performance in multiple metrics. We also focus on the role of the improvements to the graph convolution layer in the model. The case studies are supported by evidence on breast cancer, hepatocellular carcinoma and renal cell carcinoma. Given the above, the experiments demonstrate that HLGNN-MDA can serve as a reliable method to identify novel miRNA–disease associations. Full article

(This article belongs to the Special Issue State-of-the-Art Molecular Biophysics in China)

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13 pages, 2501 KiB

Open AccessArticle

Melatonin Inhibits hIAPP Oligomerization by Preventing β-Sheet and Hydrogen Bond Formation of the Amyloidogenic Region Revealed by Replica-Exchange Molecular Dynamics Simulation

by Gang Wang, Xinyi Zhu, Xiaona Song, Qingwen Zhang and Zhenyu Qian

Int. J. Mol. Sci. 2022, 23(18), 10264; https://doi.org/10.3390/ijms231810264 - 06 Sep 2022

Cited by 8 | Viewed by 1681

Abstract

The pathogenesis of type 2 diabetes (T2D) is highly related to the abnormal self-assembly of the human islet amyloid polypeptide (hIAPP) into amyloid aggregates. To inhibit hIAPP aggregation is considered a promising therapeutic strategy for T2D treatment. Melatonin (Mel) was reported to effectively impede the accumulation of hIAPP aggregates and dissolve preformed fibrils. However, the underlying mechanism at the atomic level remains elusive. Here, we performed replica-exchange molecular dynamics (REMD) simulations to investigate the inhibitory effect of Mel on hIAPP oligomerization by using hIAPP_20–29 octamer as templates. The conformational ensemble shows that Mel molecules can significantly prevent the β-sheet and backbone hydrogen bond formation of hIAPP_20–29 octamer and remodel hIAPP oligomers and transform them into less compact conformations with more disordered contents. The interaction analysis shows that the binding behavior of Mel is dominated by hydrogen bonding with a peptide backbone and strengthened by aromatic stacking and CH–π interactions with peptide sidechains. The strong hIAPP–Mel interaction disrupts the hIAPP_20–29 association, which is supposed to inhibit amyloid aggregation and cytotoxicity. We also performed conventional MD simulations to investigate the influence and binding affinity of Mel on the preformed hIAPP_1–37 fibrillar octamer. Mel was found to preferentially bind to the amyloidogenic region hIAPP_20–29, whereas it has a slight influence on the structural stability of the preformed fibrils. Our findings illustrate a possible pathway by which Mel alleviates diabetes symptoms from the perspective of Mel inhibiting amyloid deposits. This work reveals the inhibitory mechanism of Mel against hIAPP_20–29 oligomerization, which provides useful clues for the development of efficient anti-amyloid agents. Full article

(This article belongs to the Special Issue State-of-the-Art Molecular Biophysics in China)

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15 pages, 2715 KiB

Open AccessArticle

Identify Bitter Peptides by Using Deep Representation Learning Features

by Jici Jiang, Xinxu Lin, Yueqi Jiang, Liangzhen Jiang and Zhibin Lv

Int. J. Mol. Sci. 2022, 23(14), 7877; https://doi.org/10.3390/ijms23147877 - 17 Jul 2022

Cited by 13 | Viewed by 2230

Abstract

A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too. Full article

(This article belongs to the Special Issue State-of-the-Art Molecular Biophysics in China)

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