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New Advances in Protein Structure, Function and Design
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New Advances in Protein Structure, Function and Design

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 June 2025 | Viewed by 5745

Special Issue Editor

Dr. Angelo Facchiano

E-Mail Website
Guest Editor
National Research Council, 00185 Rome, Italy
Interests: protein structure; molecular function; bioinformatics; computational biology; molecular simulations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics and Computational Biology have revolutionized research in several biomedical fields, in molecular sciences in particular through studies on the structure and function of biomolecules. Thanks to the opportunities provided by increasingly advanced tools in protein structure prediction and molecular simulations, studies and advances in knowledge have been possible that were previously unimaginable with the potential of the instrumentation available to “wet” laboratories. In this Special Issue, we aim to present new tools developed in the field of bioinformatics and computational biology for the study of the structure or function of proteins, as well as new studies on structure and function that take advantage of the latest bioinformatics technologies. Possible development fields include, just for example, AI applications, computational techniques for conformational sampling, protein ligand screening, and graph modeling. Possible applications include, for example, studies on protein–protein/nucleic acid/small ligand interactions, molecular modeling of multimers, sampling of protein conformational space, network analysis, and temperature adaption.

This Special Issue also welcomes submissions from contributions to BBCC2023, the 18th edition of Bioinformatics and Computational Biology Conference, Naples, Italy, December 2023 (https://www.bbcc-meetings.it/).

Dr. Angelo Facchiano
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

  • AI applications in molecular structure
  • molecular simulations
  • new bioinformatics tools
  • protein structure
  • protein function
  • protein–protein interaction
  • online resources

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Published Papers (3 papers)

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Research

18 pages, 646 KiB  
Article
GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks
by Zeyu Wang, Xiaoli Yang, Songye Gao, Yanchun Liang and Xiaohu Shi
Int. J. Mol. Sci. 2025, 26(3), 941; https://doi.org/10.3390/ijms26030941 - 23 Jan 2025
Viewed by 999
Abstract
Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site-prediction model based on graph neural networks named GraphPhos, which combines sequence features with structure features. [...] Read more.
Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site-prediction model based on graph neural networks named GraphPhos, which combines sequence features with structure features. Sequence features are derived from manual extraction and the calculation of protein pre-trained language models, and the structure feature is the secondary structure contact map calculated from protein tertiary structure. These features are then innovatively applied to graph neural networks. By inputting the features of the entire protein sequence and its contact graph, GraphPhos achieves the goal of predicting phosphorylation sites along the entire protein. Experimental results indicate that GraphPhos improves the accuracy of serine, threonine, and tyrosine site prediction by at least 8%, 15%, and 12%, respectively, exhibiting an average 7% improvement in accuracy compared to individual amino acid category prediction models. Full article
(This article belongs to the Special Issue New Advances in Protein Structure, Function and Design)
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13 pages, 745 KiB  
Article
ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention
by Yan Wang, Huiting Sun, Nan Sheng, Kai He, Wenjv Hou, Ziqi Zhao, Qixing Yang and Lan Huang
Int. J. Mol. Sci. 2024, 25(12), 6371; https://doi.org/10.3390/ijms25126371 - 9 Jun 2024
Viewed by 1952
Abstract
The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application [...] Read more.
The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins. Full article
(This article belongs to the Special Issue New Advances in Protein Structure, Function and Design)
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10 pages, 1999 KiB  
Article
GNN Codon Adjacency Tunes Protein Translation
by Joyce Sun, Pete Hwang, Eric D. Sakkas, Yancheng Zhou, Luis Perez, Ishani Dave, Jack B. Kwon, Audrey E. McMahon, Mia Wichman, Mitsu Raval, Kristen Scopino, Daniel Krizanc, Kelly M. Thayer and Michael P. Weir
Int. J. Mol. Sci. 2024, 25(11), 5914; https://doi.org/10.3390/ijms25115914 - 29 May 2024
Viewed by 1831
Abstract
The central dogma treats the ribosome as a molecular machine that reads one mRNA codon at a time as it adds each amino acid to its growing peptide chain. However, this and previous studies suggest that ribosomes actually perceive pairs of adjacent codons [...] Read more.
The central dogma treats the ribosome as a molecular machine that reads one mRNA codon at a time as it adds each amino acid to its growing peptide chain. However, this and previous studies suggest that ribosomes actually perceive pairs of adjacent codons as they take three-nucleotide steps along the mRNA. We examined GNN codons, which we find are surprisingly overrepresented in eukaryote protein-coding open reading frames (ORFs), especially immediately after NNU codons. Ribosome profiling experiments in yeast revealed that ribosomes with NNU at their aminoacyl (A) site have particularly elevated densities when NNU is immediately followed (3′) by a GNN codon, indicating slower mRNA threading of the NNU codon from the ribosome’s A to peptidyl (P) sites. Moreover, if the assessment was limited to ribosomes that have only recently arrived at the next codon, by examining 21-nucleotide ribosome footprints (21-nt RFPs), elevated densities were observed for multiple codon classes when followed by GNN. This striking translation slowdown at adjacent 5′-NNN GNN codon pairs is likely mediated, in part, by the ribosome’s CAR surface, which acts as an extension of the A-site tRNA anticodon during ribosome translocation and interacts through hydrogen bonding and pi stacking with the GNN codon. The functional consequences of 5′-NNN GNN codon adjacency are expected to influence the evolution of protein coding sequences. Full article
(This article belongs to the Special Issue New Advances in Protein Structure, Function and Design)
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