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Bioengineering
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Bio-Macromolecular Modeling and Computational Design
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Bio-Macromolecular Modeling and Computational Design

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biochemical Engineering".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 9174

Special Issue Editor

Dr. Chin-Hung Lai

E-Mail Website
Guest Editor
Department of Medical Applied Chemistry, Chung Shan Medical University, Taichung, Taiwan
Interests: biomacromolecular; acid-catalyzed; computational chemistry; theoretical chemistry; medical applied chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue "Bio-Macromolecular Modeling and Computational Design" focuses on the application of computational methods for the modeling and design of macromolecules in the field of biochemistry. For this Issue, we have collected original research articles, reviews, and perspectives that address different aspects of macromolecular modeling and design, including molecular dynamics simulations, machine learning algorithms, structure prediction, ligand–protein docking, and drug discovery.

The articles cover a broad range of biological macromolecules, including proteins, nucleic acids, and carbohydrates. They address a variety of topics, such as protein–ligand binding, protein stability, catalytic mechanisms, drug resistance, and enzyme engineering. Additionally, the Issue includes some articles that are relevant to biotechnological applications, such as in the development of novel therapeutics and biocatalysts.

The contributions in this Special Issue are authored by experts in the field, representing different research groups from around the world. Together, the articles provide a comprehensive overview of the current state-of-the-art computational methods for the modeling and design of bio-macromolecules, highlighting the potential of these methods to address challenges in the field of biochemistry and biotechnology.

Dr. Chin-Hung Lai
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. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). 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 prediction
  • molecular dynamics simulations
  • computational protein design
  • RNA structure modeling
  • drug discovery
  • systems biology

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Published Papers (4 papers)

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Research

15 pages, 2419 KiB  
Article
Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell
by Mehwish Faiz, Saad Jawaid Khan, Fahad Azim, Nazia Ejaz and Fahad Shamim
Bioengineering 2024, 11(11), 1150; https://doi.org/10.3390/bioengineering11111150 - 15 Nov 2024
Viewed by 921
Abstract
Membrane proteins constitute essential biomolecules attached to or integrated into cellular and organelle membranes, playing diverse roles in cellular processes. Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes [...] Read more.
Membrane proteins constitute essential biomolecules attached to or integrated into cellular and organelle membranes, playing diverse roles in cellular processes. Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes for membrane proteins, explicitly via deep learning models. To address this challenge, the proposed study segregates membrane proteins into three distinct locations, including the plasma membrane, internal membrane, and membrane of the organelle, using deep learning algorithms including recurrent neural networks (RNN) and Long Short-Term Memory (LSTM). A redundancy-curtailed dataset of 3000 proteins from the MemLoci approach is selected for the investigation, along with incorporating pseudo amino acid composition (PseAAC). PseAAC is an exemplary technique for extracting protein information hidden in the amino acid sequences. After extensive testing, the results show that the accuracy for LSTM and RNN is 83.4% and 80.5%, respectively. The results show that the LSTM model outperforms the RNN and is most commonly employed in proteomics. Full article
(This article belongs to the Special Issue Bio-Macromolecular Modeling and Computational Design)
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17 pages, 2189 KiB  
Article
A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets
by Zhen-Xuan Fan and Sheng D. Chao
Bioengineering 2024, 11(1), 51; https://doi.org/10.3390/bioengineering11010051 - 3 Jan 2024
Cited by 2 | Viewed by 2086
Abstract
Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and [...] Read more.
Accurate energy data from noncovalent interactions are essential for constructing force fields for molecular dynamics simulations of bio-macromolecular systems. There are two important practical issues in the construction of a reliable force field with the hope of balancing the desired chemical accuracy and working efficiency. One is to determine a suitable quantum chemistry level of theory for calculating interaction energies. The other is to use a suitable continuous energy function to model the quantum chemical energy data. For the first issue, we have recently calculated the intermolecular interaction energies using the SAPT0 level of theory, and we have systematically organized these energies into the ab initio SOFG-31 (homodimer) and SOFG-31-heterodimer datasets. In this work, we re-calculate these interaction energies by using the more advanced SAPT2 level of theory with a wider series of basis sets. Our purpose is to determine the SAPT level of theory proper for interaction energies with respect to the CCSD(T)/CBS benchmark chemical accuracy. Next, to utilize these energy datasets, we employ one of the well-developed machine learning techniques, called the CLIFF scheme, to construct a general-purpose force field for biomolecular dynamics simulations. Here we use the SOFG-31 dataset and the SOFG-31-heterodimer dataset as the training and test sets, respectively. Our results demonstrate that using the CLIFF scheme can reproduce a diverse range of dimeric interaction energy patterns with only a small training set. The overall errors for each SAPT energy component, as well as the SAPT total energy, are all well below the desired chemical accuracy of ~1 kcal/mol. Full article
(This article belongs to the Special Issue Bio-Macromolecular Modeling and Computational Design)
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22 pages, 11885 KiB  
Article
Synthesis, Characterization, DFT, and In Silico Investigation of Two Newly Synthesized β-Diketone Derivatives as Potent COX-2 Inhibitors
by Malahat Musrat Kurbanova, Abel Mammadali Maharramov, Arzu Zabit Sadigova, Fidan Zaur Gurbanova, Suraj Narayan Mali, Rashad Al-Salahi, Youness El Bakri and Chin-Hung Lai
Bioengineering 2023, 10(12), 1361; https://doi.org/10.3390/bioengineering10121361 - 27 Nov 2023
Cited by 2 | Viewed by 1822
Abstract
Despite extensive genetic and biochemical characterization, the molecular genetic basis underlying the biosynthesis of β-diketones remains largely unexplored. β-Diketones and their complexes find broad applications as biologically active compounds. In this study, in silico molecular docking results revealed that two β-diketone derivatives, namely [...] Read more.
Despite extensive genetic and biochemical characterization, the molecular genetic basis underlying the biosynthesis of β-diketones remains largely unexplored. β-Diketones and their complexes find broad applications as biologically active compounds. In this study, in silico molecular docking results revealed that two β-diketone derivatives, namely 2-(2-(4-fluorophenyl)hydrazono)-5,5-dimethylcyclohexane-1,3-dione and 5,5-dimethyl-2-(2-(2-(trifluoromethyl)phenyl)hydrazono)cyclohexane-1,3-dione, exhibit anti-COX-2 activities. However, recent docking results indicated that the relative anti-COX-2 activity of these two studied β-diketones was influenced by the employed docking programs. For improved design of COX-2 inhibitors from β-diketones, we conducted molecular dynamics simulations, density functional theory (DFT) calculations, Hirshfeld surface analysis, energy framework, and ADMET studies. The goal was to understand the interaction mechanisms and evaluate the inhibitory characteristics. The results indicate that 5,5-dimethyl-2-(2-(2-(trifluoromethyl)phenyl)hydrazono)cyclohexane-1,3-dione shows greater anti-COX-2 activity compared to 2-(2-(4-fluorophenyl)hydrazono)-5,5-dimethylcyclohexane-1,3-dione. Full article
(This article belongs to the Special Issue Bio-Macromolecular Modeling and Computational Design)
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20 pages, 9280 KiB  
Article
Comparison, Analysis, and Molecular Dynamics Simulations of Structures of a Viral Protein Modeled Using Various Computational Tools
by Hemalatha Mani, Chun-Chun Chang, Hao-Jen Hsu, Chin-Hao Yang, Jui-Hung Yen and Je-Wen Liou
Bioengineering 2023, 10(9), 1004; https://doi.org/10.3390/bioengineering10091004 - 24 Aug 2023
Cited by 7 | Viewed by 3345
Abstract
The structural analysis of proteins is a major domain of biomedical research. Such analysis requires resolved three-dimensional structures of proteins. Advancements in computer technology have led to progress in biomedical research. In silico prediction and modeling approaches have facilitated the construction of protein [...] Read more.
The structural analysis of proteins is a major domain of biomedical research. Such analysis requires resolved three-dimensional structures of proteins. Advancements in computer technology have led to progress in biomedical research. In silico prediction and modeling approaches have facilitated the construction of protein structures, with or without structural templates. In this study, we used three neural network-based de novo modeling approaches—AlphaFold2 (AF2), Robetta-RoseTTAFold (Robetta), and transform-restrained Rosetta (trRosetta)—and two template-based tools—the Molecular Operating Environment (MOE) and iterative threading assembly refinement (I-TASSER)—to construct the structure of a viral capsid protein, hepatitis C virus core protein (HCVcp), whose structure have not been fully resolved by laboratory techniques. Templates with sufficient sequence identity for the homology modeling of complete HCVcp are currently unavailable. Therefore, we performed domain-based homology modeling for MOE simulations. The templates for each domain were obtained through sequence-based searches on NCBI and the Protein Data Bank. Then, the modeled domains were assembled to construct the complete structure of HCVcp. The full-length structure and two truncated forms modeled using various computational tools were compared. Molecular dynamics (MD) simulations were performed to refine the structures. The root mean square deviation of backbone atoms, root mean square fluctuation of Cα atoms, and radius of gyration were calculated to monitor structural changes and convergence in the simulations. The model quality was evaluated through ERRAT and phi–psi plot analysis. In terms of the initial prediction for protein modeling, Robetta and trRosetta outperformed AF2. Regarding template-based tools, MOE outperformed I-TASSER. MD simulations resulted in compactly folded protein structures, which were of good quality and theoretically accurate. Thus, the predicted structures of certain proteins must be refined to obtain reliable structural models. MD simulation is a promising tool for this purpose. Full article
(This article belongs to the Special Issue Bio-Macromolecular Modeling and Computational Design)
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