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Structural and Functional Prediction of RNA and Proteins

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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: closed (20 January 2025) | Viewed by 7605

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Dr. Lei Deng

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School of Computer Science and Engineering, Central South University, Changsha 410075, China
Interests: bioinformatics; systems biology; biomedical data mining; protein structure and function; ncRNA interactions and functions; drug discovery
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Special Issue Information

Dear Colleagues,

In the ever-evolving landscape of molecular biology, the accurate prediction of the structural and functional attributes of RNA and proteins stands as a pivotal pursuit with profound implications across numerous scientific domains. Recent breakthroughs in computational biology, machine learning, and structural biology have ushered in a new era of predictive capabilities. Researchers are now able to decode the complex three-dimensional architectures of RNA and proteins with unprecedented precision, often leading to novel insights into their functions and interactions. These predictive approaches hold the potential to revolutionize drug discovery, disease diagnostics, and our understanding of biological systems at the molecular level.

This Special Issue aims to showcase the latest advancements, methodologies, and insights that contribute to unraveling the intricate interplay between sequence, structure, and function in these biomolecules. We invite contributions from researchers and experts working at the intersection of bioinformatics, structural biology, computational biology, and related fields. Manuscripts encompassing a wide range of topics are welcome, including, but not limited to, the following:

Machine learning and deep learning approaches for the prediction of RNA and protein structures and functions.
Integrated omics analyses that leverage multi-dimensional data to enhance prediction accuracy.
Novel computational tools and algorithms for structure-based function prediction.
Case studies showcasing successful applications of predictive models in drug design, functional annotation, and disease understanding.
Exploration of non-coding RNA structures and functions, shedding light on their roles in cellular regulation and disease.

Thank you for your participation, and we eagerly await the remarkable contributions that will define this Special Issue.

Prof. Dr. Lei Deng
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

RNA and protein structures and functions
machine learning
deep learning
omics analyses
drug design
non-coding RNAs

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Published Papers (4 papers)

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Research

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15 pages, 1127 KiB

Open AccessArticle

TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning

by Yihan Dong, Hanming Quan, Chenxi Ma, Linchao Shan and Lei Deng

Int. J. Mol. Sci. 2024, 25(13), 7228; https://doi.org/10.3390/ijms25137228 - 30 Jun 2024

Cited by 1 | Viewed by 1784

Abstract

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model’s ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs. Full article

(This article belongs to the Special Issue Structural and Functional Prediction of RNA and Proteins)

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12 pages, 2768 KiB

Open AccessArticle

Differential Synonymous Codon Selection in the B56 Gene Family of PP2A Regulatory Subunits

by Gabriel Corzo, Claire E. Seeling-Branscomb and Joni M. Seeling

Int. J. Mol. Sci. 2024, 25(1), 392; https://doi.org/10.3390/ijms25010392 - 27 Dec 2023

Viewed by 1334

Abstract

Protein phosphatase 2A (PP2A) functions as a tumor suppressor and consists of a scaffolding, catalytic, and regulatory subunit. The B56 gene family of regulatory subunits impart distinct functions onto PP2A. Codon usage bias (CUB) involves the selection of synonymous codons, which can affect gene expression by modulating processes such as transcription and translation. CUB can vary along the length of a gene, and differential use of synonymous codons can be important in the divergence of gene families. The N-termini of the gene product encoded by B56α possessed high CUB, high GC content at the third codon position (GC3), and high rare codon content. In addition, differential CUB was found in the sequence encoding two B56γ N-terminal splice forms. The sequence encoding the N-termini of B56γ/γ, relative to B56δ/γ, displayed CUB, utilized more frequent codons, and had higher GC3 content. B56α mRNA had stronger than predicted secondary structure at their 5′ end, and the B56δ/γ splice variants had long regions of weaker than predicted secondary structure at their 5′ end. The data suggest that B56α is expressed at relatively low levels as compared to the other B56 isoforms and that the B56δ/γ splice variant is expressed more highly than B56γ/γ. Full article

(This article belongs to the Special Issue Structural and Functional Prediction of RNA and Proteins)

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14 pages, 3365 KiB

Open AccessArticle

Evaluation of the Potential Impact of In Silico Humanization on V_HH Dynamics

by Carla Martins, Julien Diharce, Aravindan Arun Nadaradjane and Alexandre G. de Brevern

Int. J. Mol. Sci. 2023, 24(19), 14586; https://doi.org/10.3390/ijms241914586 - 26 Sep 2023

Cited by 2 | Viewed by 2151

Abstract

Camelids have the peculiarity of having classical antibodies composed of heavy and light chains as well as single-chain antibodies. They have lost their light chains and one heavy-chain domain. This evolutionary feature means that their terminal heavy-chain domain, VH, called V_HH here, has no partner and forms an independent domain. The V_HH is small and easy to express alone; it retains thermodynamic and interaction properties. Consequently, V_HHs have garnered significant interest from both biotechnological and pharmaceutical perspectives. However, due to their origin in camelids, they cannot be used directly on humans. A humanization step is needed before a possible use. However, changes, even in the constant parts of the antibodies, can lead to a loss of quality. A dedicated tool, Llamanade, has recently been made available to the scientific community. In a previous paper, we already showed the different types of V_HH dynamics. Here, we have selected a representative V_HH and tested two humanization hypotheses to accurately assess the potential impact of these changes. This example shows that despite the non-negligible change (1/10th of residues) brought about by humanization, the effect is not drastic, and the humanized V_HH retains conformational properties quite similar to those of the camelid V_HH. Full article

(This article belongs to the Special Issue Structural and Functional Prediction of RNA and Proteins)

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Review

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21 pages, 23426 KiB

Open AccessReview

Inconspicuous Yet Indispensable: The Coronavirus Spike Transmembrane Domain

by Elena T. Aliper and Roman G. Efremov

Int. J. Mol. Sci. 2023, 24(22), 16421; https://doi.org/10.3390/ijms242216421 - 16 Nov 2023

Viewed by 1812

Abstract

Membrane-spanning portions of proteins’ polypeptide chains are commonly known as their transmembrane domains (TMDs). The structural organisation and dynamic behaviour of TMDs from proteins of various families, be that receptors, ion channels, enzymes etc., have been under scrutiny on the part of the scientific community for the last few decades. The reason for such attention is that, apart from their obvious role as an “anchor” in ensuring the correct orientation of the protein’s extra-membrane domains (in most cases functionally important), TMDs often actively and directly contribute to the operation of “the protein machine”. They are capable of transmitting signals across the membrane, interacting with adjacent TMDs and membrane-proximal domains, as well as with various ligands, etc. Structural data on TMD arrangement are still fragmentary at best due to their complex molecular organisation as, most commonly, dynamic oligomers, as well as due to the challenges related to experimental studies thereof. Inter alia, this is especially true for viral fusion proteins, which have been the focus of numerous studies for quite some time, but have provoked unprecedented interest in view of the SARS-CoV-2 pandemic. However, despite numerous structure-centred studies of the spike (S) protein effectuating target cell entry in coronaviruses, structural data on the TMD as part of the entire spike protein are still incomplete, whereas this segment is known to be crucial to the spike’s fusogenic activity. Therefore, in attempting to bring together currently available data on the structure and dynamics of spike proteins’ TMDs, the present review aims to tackle a highly pertinent task and contribute to a better understanding of the molecular mechanisms underlying virus-mediated fusion, also offering a rationale for the design of novel efficacious methods for the treatment of infectious diseases caused by SARS-CoV-2 and related viruses. Full article

(This article belongs to the Special Issue Structural and Functional Prediction of RNA and Proteins)

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