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Structural and Functional Analysis of Amino Acids and Proteins
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Structural and Functional Analysis of Amino Acids and Proteins

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 8883

Special Issue Editors

Dr. Yang Yang

E-Mail Website
Guest Editor
School of Computer Science & Technology, Soochow University, Suzhou 215000, China
Interests: bioinformatics; machine learning; deep learning; text mining
Dr. Wenying Yan

E-Mail Website
Guest Editor
1. Department of Bioinformatics, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China
2. Center for Systems Biology, Soochow University, Suzhou 215000, China
Interests: bioinformatics; systems biology; biomedical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to announce a new Special Issue, entitled "Structural and Functional Analysis of Amino Acids and Proteins". As fundamental knowledge in the field of life sciences, understanding protein structure and function is important for both biology and medicine. Amino acids are the building blocks of proteins, and understanding their structure and function is essential for unraveling various biochemical processes within organisms.

In recent decades, several methods have been developed for predicting proteins’ structure and function, employing sequence-to-function, a sequence-to-structure-to-function or network paradigms. The aim of this Special Issue is to highlight the recent advances in various analysis and prediction methods and their applications in biomedical field.

We are soliciting contributions (comprehensive reviews on general areas, mini reviews on specialized subjects, research work, short communications, technical notes) covering a broad range of topics on the structural and functional analysis of amino acids and proteins and their application, including (though not limited to) the following:

  • Protein structure and functions prediction;
  • Protein–protein interaction analysis or prediction;
  • Amino acids network construction and analysis;
  • Drug target discovery;
  • Biomarker discovery.

We hope that this Special Issue will provide researchers with a comprehensive understanding of the structural and functional analysis of amino acids and proteins. Whether you are a biological researcher, medical professional, or simply someone interested in life sciences, we believe this Special Issue will offer new knowledge and inspiration.

We look forward to receiving your contributions. 

Dr. Yang Yang
Dr. Wenying Yan
Guest Editors

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

  • protein structure
  • protein function
  • amino acids network
  • deep learning
  • bioinformatics
  • protein–protein interaction
  • drug discovery
  • biomarker

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Published Papers (7 papers)

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Research

22 pages, 4278 KiB  
Article
Conservation of OFD1 Protein Motifs: Implications for Discovery of Novel Interactors and the OFD1 Function
by Przemysław Jagodzik, Ewa Zietkiewicz and Zuzanna Bukowy-Bieryllo
Int. J. Mol. Sci. 2025, 26(3), 1167; https://doi.org/10.3390/ijms26031167 - 29 Jan 2025
Viewed by 940
Abstract
OFD1 is a protein involved in many cellular processes, including cilia biogenesis, mitotic spindle assembly, translation, autophagy and the repair of double-strand DNA breaks. Despite many potential interactors identified in high-throughput studies, only a few have been directly confirmed with their binding sites [...] Read more.
OFD1 is a protein involved in many cellular processes, including cilia biogenesis, mitotic spindle assembly, translation, autophagy and the repair of double-strand DNA breaks. Despite many potential interactors identified in high-throughput studies, only a few have been directly confirmed with their binding sites identified. We performed an analysis of the evolutionary conservation of the OFD1 sequence in three clades: 80 Tetrapoda, 144 Vertebrata or 26 Animalia species, and identified 59 protein-binding motifs localized in the OFD1 regions conserved in various clades. Our results indicate that OFD1 contains 14 potential post-translational modification (PTM) sites targeted by at least eight protein kinases, seven motifs bound by proteins recognizing phosphorylated aa residues and a binding site for phosphatase 2A. Moreover, OFD1 harbors both a motif that enables its phosphorylation by mitogen-activated protein kinases (MAPKs) and a specific docking site for these proteins. Generally, our results suggest that OFD1 forms a scaffold for interaction with many proteins and is tightly regulated by PTMs and ligands. Future research on OFD1 should focus on the regulation of OFD1 function and localization. Full article
(This article belongs to the Special Issue Structural and Functional Analysis of Amino Acids and Proteins)
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13 pages, 885 KiB  
Communication
Cholesterol Attenuates the Pore-Forming Capacity of CARC-Containing Amphipathic Peptides
by Ilya P. Oleynikov, Alexander M. Firsov, Natalia V. Azarkina and Tatiana V. Vygodina
Int. J. Mol. Sci. 2025, 26(2), 533; https://doi.org/10.3390/ijms26020533 - 10 Jan 2025
Viewed by 641
Abstract
Artificial peptides P4, A1 and A4 are homologous to amphipathic α-helical fragments of the influenza virus M1 protein. P4 and A4 contain the cholesterol recognition sequence CARC, which is absent in A1. As shown previously, P4 and A4 but not A1 have cytotoxic [...] Read more.
Artificial peptides P4, A1 and A4 are homologous to amphipathic α-helical fragments of the influenza virus M1 protein. P4 and A4 contain the cholesterol recognition sequence CARC, which is absent in A1. As shown previously, P4 and A4 but not A1 have cytotoxic effects on some eukaryotic and bacterial cells. This might be caused by the dysfunction of cholesterol-dependent cellular structures, inhibition of the respiratory chain, or disruption of the membrane. Here, we analyzed the latter hypothesis by studying the uncoupling effect of the peptides on asolectin membranes. The influence of A4 on Δψ pre-formed either by the valinomycin-dependent K+ diffusion or by the activity of membrane-built cytochrome c oxidase (CcO) was studied on (proteo)liposomes. Also, we investigated the effect of P4, A1 and A4 on liposomes loaded with calcein. It is found that A4 in a submicromolar range causes an immediate and complete dissipation of diffusion Δψ across the liposomal membrane. Uncoupling of the CcO-containing proteoliposomes requires an order of magnitude of higher peptide concentration, which may indicate the sorption of A4 on the enzyme. The presence of cholesterol in the membrane significantly weakens the uncoupling. Submicromolar A4 and P4 cause the release of calcein from liposomes, indicating the formation of membrane pores. The process develops in minutes and is significantly decelerated by cholesterol. Micromolar A1 induces pore formation in a cholesterol-independent manner. We conclude that the peptides P4, A4 and, in higher concentrations, A1 form pores in the asolectin membrane. The CARC-mediated interaction of A4 and P4 with cholesterol impedes the peptide oligomerization necessary for pore formation. The rapid uncoupling effect of A4 is apparently caused by an increase in the proton conductivity of the membrane without pore formation. Full article
(This article belongs to the Special Issue Structural and Functional Analysis of Amino Acids and Proteins)
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18 pages, 11651 KiB  
Article
Tertiary Structures of Haseki Tick Virus Nonstructural Proteins Are Similar to Those of Orthoflaviviruses
by Anastasia Gladysheva, Irina Osinkina, Nikita Radchenko, Daria Alkhireenko and Alexander Agafonov
Int. J. Mol. Sci. 2024, 25(24), 13654; https://doi.org/10.3390/ijms252413654 - 20 Dec 2024
Viewed by 816
Abstract
Currently, a large number of novel tick-borne viruses potentially pathogenic to humans are discovered. Studying many of them by classical methods of virology is difficult due to the absence of live viral particles or a sufficient amount of their genetic material. In this [...] Read more.
Currently, a large number of novel tick-borne viruses potentially pathogenic to humans are discovered. Studying many of them by classical methods of virology is difficult due to the absence of live viral particles or a sufficient amount of their genetic material. In this case, the use of modern methods of bioinformatics and synthetic and structural biology can help. Haseki tick virus (HSTV) is a recently discovered tick-borne unclassified ssRNA(+) virus. HSTV-positive patients experienced fever and an elevated temperature. However, at the moment, there is no information on the tertiary structure and functions of its proteins. In this work, we used AlphaFold 3 and other bioinformatic tools for the annotation of HSTV nonstructural proteins, based on the principle that the tertiary structure of a protein is inextricably linked with its molecular function. We were the first to obtain models of tertiary structures and describe the putative functions of HSTV nonstructural proteins (NS3 helicase, NS3 protease, NS5 RNA-dependent RNA-polymerase, and NS5 methyltransferase), which play a key role in viral genome replication. Our results may help in further taxonomic identification of HSTV and the development of direct-acting antiviral drugs, POC tests, and vaccines. Full article
(This article belongs to the Special Issue Structural and Functional Analysis of Amino Acids and Proteins)
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28 pages, 51329 KiB  
Article
MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction
by Runqiu Feng, Xun Wang, Zhijun Xia, Tongyu Han, Hanyu Wang and Wenqian Yu
Int. J. Mol. Sci. 2024, 25(24), 13444; https://doi.org/10.3390/ijms252413444 - 15 Dec 2024
Viewed by 939
Abstract
Accurate protein secondary structure prediction (PSSP) plays a crucial role in biopharmaceutics and disease diagnosis. Current prediction methods are mainly based on multiple sequence alignment (MSA) encoding and collaborative operations of diverse networks. However, existing encoding approaches lead to poor feature space utilization, [...] Read more.
Accurate protein secondary structure prediction (PSSP) plays a crucial role in biopharmaceutics and disease diagnosis. Current prediction methods are mainly based on multiple sequence alignment (MSA) encoding and collaborative operations of diverse networks. However, existing encoding approaches lead to poor feature space utilization, and encoding quality decreases with fewer homologous proteins. Moreover, the performance of simple stacked networks is greatly limited by feature extraction capabilities and learning strategies. To this end, we propose MHTAPred-SS, a novel PSSP framework based on the fusion of six features, including the embedding feature derived from a pre-trained protein language model. First, we propose a highly targeted autoencoder (HTA) as the driver to encode sequences in a homologous protein-independent manner. Second, under the guidance of biological knowledge, we design a protein secondary structure prediction model based on the multi-task learning strategy (PSSP-MTL). Experimental results on six independent test sets show that MHTAPred-SS achieves state-of-the-art performance, with values of 88.14%, 84.89%, 78.74% and 77.15% for Q3, SOV3, Q8 and SOV8 metrics on the TEST2016 dataset, respectively. Additionally, we demonstrate that MHTAPred-SS has significant advantages in single-category and boundary secondary structure prediction, and can finely capture the distribution of secondary structure segments, thereby contributing to subsequent tasks. Full article
(This article belongs to the Special Issue Structural and Functional Analysis of Amino Acids and Proteins)
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14 pages, 3683 KiB  
Article
Analysis of Correlation Effects of Double Mutations in Enzymes: A Revised Residual-Contact Network Clique Model
by Xianbo Zhang, Junpeng Xu and Dengming Ming
Int. J. Mol. Sci. 2024, 25(16), 9114; https://doi.org/10.3390/ijms25169114 - 22 Aug 2024
Viewed by 1703
Abstract
The relationship between amino acid mutations and enzyme bioactivity is a significant challenge in modern bio-industrial applications. Despite many successful designs relying on complex correlations among mutations at different enzyme sites, the underlying mechanisms of these correlations still need to be explored. In [...] Read more.
The relationship between amino acid mutations and enzyme bioactivity is a significant challenge in modern bio-industrial applications. Despite many successful designs relying on complex correlations among mutations at different enzyme sites, the underlying mechanisms of these correlations still need to be explored. In this study, we introduced a revised version of the residual-contact network clique model to investigate the additive effect of double mutations based on the mutation occurrence topology, secondary structures, and physicochemical properties. The model was applied to a set of 182 double mutations reported in three extensively studied enzymes, and it successfully identified over 90% of additive double mutations and a majority of non-additive double mutations. The calculations revealed that the mutation additivity depends intensely on the studied mutation sites’ topology and physicochemical properties. For example, double mutations on irregular secondary structure regions tend to be non-additive. Our method provides valuable tools for facilitating enzyme design and optimization. The code and relevant data are available at Github. Full article
(This article belongs to the Special Issue Structural and Functional Analysis of Amino Acids and Proteins)
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14 pages, 1758 KiB  
Article
PON-Tm: A Sequence-Based Method for Prediction of Missense Mutation Effects on Protein Thermal Stability Changes
by Jiahao Kuang, Zhihong Zhao, Yang Yang and Wenying Yan
Int. J. Mol. Sci. 2024, 25(15), 8379; https://doi.org/10.3390/ijms25158379 - 31 Jul 2024
Cited by 1 | Viewed by 1302
Abstract
Proteins, as crucial macromolecules performing diverse biological roles, are central to numerous biological processes. The ability to predict changes in protein thermal stability due to mutations is vital for both biomedical research and industrial applications. However, existing experimental methods are often costly and [...] Read more.
Proteins, as crucial macromolecules performing diverse biological roles, are central to numerous biological processes. The ability to predict changes in protein thermal stability due to mutations is vital for both biomedical research and industrial applications. However, existing experimental methods are often costly and labor-intensive, while structure-based prediction methods demand significant computational resources. In this study, we introduce PON-Tm, a novel sequence-based method for predicting mutation-induced thermal stability variations in proteins. PON-Tm not only incorporates features predicted by a protein language model from protein sequences but also considers environmental factors such as pH and the thermostability of the wild-type protein. To evaluate the effectiveness of PON-Tm, we compared its performance to four well-established methods, and PON-Tm exhibited superior predictive capabilities. Furthermore, to facilitate easy access and utilization, we have developed a web server. Full article
(This article belongs to the Special Issue Structural and Functional Analysis of Amino Acids and Proteins)
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12 pages, 5199 KiB  
Article
EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks
by Andrew Jordan Siciliano, Chenguang Zhao, Tong Liu and Zheng Wang
Int. J. Mol. Sci. 2024, 25(11), 6250; https://doi.org/10.3390/ijms25116250 - 6 Jun 2024
Viewed by 1404
Abstract
Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell biology, systems biology, disease formation, and disease treatment. Accurately determining [...] Read more.
Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell biology, systems biology, disease formation, and disease treatment. Accurately determining the quality of multimeric protein models is still computationally challenging, as the space of possible conformations is significantly larger when proteins form in complex with one another. Here, we present EGG (energy and graph-based architectures) to assess the accuracy of predicted multimeric protein models. We implemented message-passing and transformer layers to infer the overall fold and interface accuracy scores of predicted multimeric protein models. When evaluated with CASP15 targets, our methods achieved promising results against single model predictors: fourth and third place for determining the highest-quality model when estimating overall fold accuracy and overall interface accuracy, respectively, and first place for determining the top three highest quality models when estimating both overall fold accuracy and overall interface accuracy. Full article
(This article belongs to the Special Issue Structural and Functional Analysis of Amino Acids and Proteins)
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