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Predicting Drug Targets Using Bioinformatics Methods

A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 7696

Special Issue Editor

Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
Interests: bioinformatics; data mining; machine learning; kernel method; fuzzy systems; sparse representation; neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The function of protein molecules is influenced by their structure and participation in various aspects of life activities. The emergence of a series of bioinformatics methods such as AlphaFold has improved the efficiency of protein structure analysis and functional annotation. Bioinformatics methods and tools are accelerating the development of proteomics and pharmacomics. In addition, the function of protein molecules can also affect their networks, including protein–protein interaction networks, drug–protein interactions, DNA–protein interactions, and non-coding RNA–protein interactions. A protein’s function and its network complement each other in analyzing, predicting, and guiding experiments. This Special Issue focuses on analyzing and predicting the function and network of protein molecules using bioinformatics methods. The relevant content includes, but is not limited to:

  • Protein–protein interaction recognition and analysis;
  • Identification and analysis of drug–protein interactions;
  • RNA/DNA–protein interaction analysis;
  • Identification and analysis of protein structure and function.

Dr. Yijie Ding
Guest Editor

Manuscript Submission Information

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Keywords

  • bioinformatics methods
  • drug–protein interactions
  • protein–protein interaction networks
  • drug–protein interactions
  • DNA–protein interactions
  • non-coding RNA–protein interactions

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Published Papers (4 papers)

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Research

22 pages, 2087 KiB  
Article
Bio-Chemoinformatics-Driven Analysis of nsp7 and nsp8 Mutations and Their Effects on Viral Replication Protein Complex Stability
by Bryan John J. Subong and Takeaki Ozawa
Curr. Issues Mol. Biol. 2024, 46(3), 2598-2619; https://doi.org/10.3390/cimb46030165 - 18 Mar 2024
Viewed by 1501
Abstract
The nonstructural proteins 7 and 8 (nsp7 and nsp8) of SARS-CoV-2 are highly important proteins involved in the RNA-dependent polymerase (RdRp) protein replication complex. In this study, we analyzed the global mutation of nsp7 and nsp8 in 2022 and 2023 and analyzed the [...] Read more.
The nonstructural proteins 7 and 8 (nsp7 and nsp8) of SARS-CoV-2 are highly important proteins involved in the RNA-dependent polymerase (RdRp) protein replication complex. In this study, we analyzed the global mutation of nsp7 and nsp8 in 2022 and 2023 and analyzed the effects of mutation on the viral replication protein complex using bio-chemoinformatics. Frequently occurring variants are found to be single amino acid mutations for both nsp7 and nsp8. The most frequently occurring mutations for nsp7 which include L56F, L71F, S25L, M3I, D77N, V33I and T83I are predicted to cause destabilizing effects, whereas those in nsp8 are predicted to cause stabilizing effects, with the threonine to isoleucine mutation (T89I, T145I, T123I, T148I, T187I) being a frequent mutation. A conserved domain database analysis generated critical interaction residues for nsp7 (Lys-7, His-36 and Asn-37) and nsp8 (Lys-58, Pro-183 and Arg-190), which, according to thermodynamic calculations, are prone to destabilization. Trp-29, Phe-49 of nsp7 and Trp-154, Tyr-135 and Phe-15 of nsp8 cause greater destabilizing effects to the protein complex based on a computational alanine scan suggesting them as possible new target sites. This study provides an intensive analysis of the mutations of nsp7 and nsp8 and their possible implications for viral complex stability. Full article
(This article belongs to the Special Issue Predicting Drug Targets Using Bioinformatics Methods)
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14 pages, 991 KiB  
Article
Role of Optimization in RNA–Protein-Binding Prediction
by Shrooq Alsenan, Isra Al-Turaiki, Mashael Aldayel and Mohamed Tounsi
Curr. Issues Mol. Biol. 2024, 46(2), 1360-1373; https://doi.org/10.3390/cimb46020087 - 4 Feb 2024
Viewed by 1523
Abstract
RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning algorithms [...] Read more.
RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning algorithms including deep convolutional neural network models. One of the integral parts of modeling deep learning is achieving optimal hyperparameter tuning and minimizing a loss function using optimization algorithms. In this paper, we investigate the role of optimization in the RBP classification problem using the CLIP-Seq 21 dataset. Three optimization methods are employed on the RNA–protein binding CNN prediction model; namely, grid search, random search, and Bayesian optimizer. The empirical results show an AUC of 94.42%, 93.78%, 93.23% and 92.68% on the ELAVL1C, ELAVL1B, ELAVL1A, and HNRNPC datasets, respectively, and a mean AUC of 85.30 on 24 datasets. This paper’s findings provide evidence on the role of optimizers in improving the performance of RNA–protein binding prediction. Full article
(This article belongs to the Special Issue Predicting Drug Targets Using Bioinformatics Methods)
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24 pages, 14551 KiB  
Article
In Silico Analysis of SARS-CoV-2 Non-Structural Proteins Reveals an Interaction with the Host’s Heat Shock Proteins That May Contribute to Viral Replications and Development
by Mthembu Yamkela, Zingisa Sitobo and Xolani H. Makhoba
Curr. Issues Mol. Biol. 2023, 45(12), 10225-10247; https://doi.org/10.3390/cimb45120638 - 18 Dec 2023
Cited by 1 | Viewed by 1596
Abstract
The non-structural protein 2 (NSP2) is an RNA-binding protein involved in coronavirus genome replication, and it often decreases human immune response to promote viral invasion and development. It is believed that the NSP2 associates itself with polyamines and heat shock proteins inside the [...] Read more.
The non-structural protein 2 (NSP2) is an RNA-binding protein involved in coronavirus genome replication, and it often decreases human immune response to promote viral invasion and development. It is believed that the NSP2 associates itself with polyamines and heat shock proteins inside the host cell to proceed with viral development. This study aimed to investigate how the SARS-CoV-2 virus’ key non-structural proteins (NSP2) utilize polyamines and heat shock proteins using a molecular docking approach and molecular dynamics (MD). ClusPro and HADDOCK servers were used for the docking and Discovery Studio, chimera, and PyMOL were used for analysis. Docking of the heat shock proteins 40 (HSP40), 70 (HSP70), and 90 (HSP90) with SARS-CoV-2 NSP2 resulted in 32, 28, and 19 interactions, respectively. Molecular dynamics revealed Arg458, Asn508, Met297, Arg301, and Trp417 as active residues, and pharmacophore modeling indicated ZINC395648, ZINC01150525, and ZINC85324008 from the zinc database as possible inhibitors for this NSP2. Full article
(This article belongs to the Special Issue Predicting Drug Targets Using Bioinformatics Methods)
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17 pages, 7311 KiB  
Article
Protein Lactylation Modification and Proteomics Features in Cirrhosis Patients after UC-MSC Treatment
by Ye Xie, Ying Li, Jia Yao, Xiaojing Song, Haiping Wang, Jianjun Zhang and Xun Li
Curr. Issues Mol. Biol. 2023, 45(10), 8444-8460; https://doi.org/10.3390/cimb45100532 - 18 Oct 2023
Viewed by 2394
Abstract
Umbilical cord mesenchymal stem cell (UC-MSC) therapy improves liver function in liver cirrhosis patients. This study aimed to elucidate the therapeutic mechanism underlying cell therapy by analyzing changes in the modification and expression of proteins 1 month post-treatment with UC-MSCs. This prospective study [...] Read more.
Umbilical cord mesenchymal stem cell (UC-MSC) therapy improves liver function in liver cirrhosis patients. This study aimed to elucidate the therapeutic mechanism underlying cell therapy by analyzing changes in the modification and expression of proteins 1 month post-treatment with UC-MSCs. This prospective study included 11 cirrhosis patients who received MSC injection. The laboratory indexes before and after treatment were collected to evaluate the clinical treatment effect of UC-MSCs, and the protein expression and lactylation modification in the liver were comprehensively revealed. Meanwhile, weighted gene co-expression network analysis was used to analyze the co-expression protein modules and their relationship with clinical features. The patients with liver cirrhosis showed an improvement trend after receiving UC-MSC treatment; specifically, the liver protein synthesis function was significantly improved and the coagulation function was also significantly improved. Proteomics combined with lactic acid proteomics revealed 160 lysine lactylation (Kla) sites of 119 proteins. Functional analysis showed that the lactylation-modified proteins were enriched in the pathway of glucose and other substances’ metabolism, and many key enzymes of glycolysis and gluconeogenesis were lactated. UC-MSC therapy has a certain clinical effect in the treatment of liver cirrhosis and may act by regulating material metabolism, because the lactylation protein points to energy metabolism. Full article
(This article belongs to the Special Issue Predicting Drug Targets Using Bioinformatics Methods)
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