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Advances in Molecular Modeling, Docking and Simulations of Protein Structure, 2nd Edition

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Macromolecules".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 3342

Special Issue Editor

Dr. Lorant Janosi

E-Mail Website
Guest Editor
Molecular and Biomolecular Physics Department, National Institute for Research and Development of Isotopic and Molecular Technologies, 65-103 Donath Street, 400293 Cluj-Napoca, Romania
Interests: molecular modeling; peptide design; protein structure; transmembrane channel and protein; oncogenic proteins; potential of mean force
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Molecular modeling has been used for decades as a support for or in combination with experimental results. Most in silico experiments in this area have covered nano-, micro-, and, recently, meso-scale events/phenomena/mechanisms of interest.

This Special Issue of IJMS aims to compile original research articles or novel communications that address the use of molecular modeling, molecular docking, and computer simulations in the context of their predictive power/essential contribution in: (i) identifying novel aspects of molecular mechanisms/structure–activity relationships within protein complexes (e.g., looking into protein associations); (ii) analyses/predictions of protein/peptide structures (e.g., peptides’ structural transitions/pathways); (iii) understanding interactions in protein complexes (e.g., determining ligand/protein–protein binding sites/affinities); (iv) designing novel protein-binding ligands/peptides (e.g., design of novel anti-microbial/anti-cancer peptides), etc.

Works should utilize computational tools such as molecular dynamics simulations (in all their varieties), molecular docking, Monte Carlo methods, molecular modeling methods, QM/MM, etc. Researchers are encouraged to develop symbiotic relationships with experimental results.

Systems simulated/analyzed should include amino acid-based proteins or peptides. The study of complexes with DNA/RNA/lipids/ligands, etc., is also encouraged.

Dr. Lorant Janosi
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

  • molecular modeling
  • protein structure
  • molecular mechanisms
  • protein interactomics
  • ligand design
  • peptides
  • molecular dynamics
  • molecular docking

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Related Special Issue

Published Papers (3 papers)

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Research

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25 pages, 6753 KiB  
Article
Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins—The Case Study of Coiled-Coil NOD-like Receptors
by Teodor Asvadur Șulea, Eliza Cristina Martin, Cosmin Alexandru Bugeac, Floriana Sibel Bectaș, Anca-L Iacob, Laurențiu Spiridon and Andrei-Jose Petrescu
Int. J. Mol. Sci. 2025, 26(2), 500; https://doi.org/10.3390/ijms26020500 - 9 Jan 2025
Viewed by 429
Abstract
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain-like (NOD-like) receptors from A. thaliana and a [...] Read more.
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain-like (NOD-like) receptors from A. thaliana and a few extra examples for reference. Results reveal a truly remarkable ability of these platforms to correctly predict the 3D structure of modules that fold in well-established topologies. A lower performance is noticed in modeling morphing regions of these proteins, such as the coiled coils. Predictors also display a good sensitivity to local sequence drifts upon the modeling solution of the overall modular configuration. In multivalued 1D to 3D mappings, the platforms display a marked tendency to model proteins in the most compact configuration and must be retrained by information filtering to drive modeling toward the sparser ones. Bias toward order and compactness is seen at the secondary structure level as well. All in all, using AI predictors for modeling multidomain multistate proteins when global templates are at hand is fruitful, but the above challenges have to be taken into account. In the absence of global templates, a piecewise modeling approach with experimentally constrained reconstruction of the global architecture might give more realistic results. Full article
(This article belongs to the Special Issue Advances in Molecular Modeling, Docking and Simulations of Protein Structure, 2nd Edition)
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27 pages, 3670 KiB  
Article
Helichrysum populifolium Compounds Inhibit MtrCDE Efflux Pump Transport Protein for the Potential Management of Gonorrhoea Infection
by Vhangani E. Mulaudzi, Idowu J. Adeosun, Adeniyi T. Adewumi, Mahmoud E. S. Soliman and Sekelwa Cosa
Int. J. Mol. Sci. 2024, 25(24), 13310; https://doi.org/10.3390/ijms252413310 - 11 Dec 2024
Viewed by 799
Abstract
The progressive development of resistance in Neisseria gonorrhoeae to almost all available antibiotics has made it crucial to develop novel approaches to tackling multi-drug resistance (MDR). One of the primary causes of antibiotic resistance is the over-expression of the MtrCDE efflux pump protein, [...] Read more.
The progressive development of resistance in Neisseria gonorrhoeae to almost all available antibiotics has made it crucial to develop novel approaches to tackling multi-drug resistance (MDR). One of the primary causes of antibiotic resistance is the over-expression of the MtrCDE efflux pump protein, making this protein a vital target for fighting against antimicrobial resistance (AMR) in N. gonorrhoeae. This study was aimed at evaluating the potential MtrCDE efflux pump inhibitors (EPIs) and their stability in treating gonorrhoea infection. This is significant because finding novel EPIs would allow for the longer maintenance of antibiotics at therapeutic levels, thereby prolonging the susceptibility of currently available antibiotics. A virtual screening of the selected Helichrysum populifolium compounds (4,5-dicaffeoylquinic acid, apigeninin-7-glucoside, and carvacrol) was conducted to evaluate their potential EPI activity. An integrated computational framework consisting of molecular docking (MD), molecular mechanics generalized born, and surface area solvation (MMGBSA) analysis, molecular dynamics simulations (MDS), and absorption, distribution, metabolism, and excretion (ADME) properties calculations were conducted. Of the tested compounds, 4,5-dicaffeoylquinic acid revealed the highest molecular docking binding energies (−8.8 kcal/mol), equivalent MMGBSA binding free energy (−54.82 kcal/mol), indicative of consistent binding affinity with the MtrD protein, reduced deviations and flexibility (root mean square deviation (RMSD) of 5.65 Å) and, given by root mean square fluctuation (RMSF) of 1.877 Å. Carvacrol revealed a docking score of −6.0 kcal/mol and a MMGBSA computed BFE of −16.69 kcal/mol, demonstrating the lowest binding affinity to the MtrD efflux pump compared to the remaining test compounds. However, the average RMSD (4.45 Å) and RMSF (1.638 Å) of carvacrol-bound MtrD protein showed no significant difference from the unbound MtrD protein, except for the reference compounds, implying consistent MtrD conformation throughout simulations and indicates a desirable feature during drug design. Additionally, carvacrol obeyed the Lipinski rule of five which confirmed the compound’s drug-likeness properties making it the most promising EPI candidate based on its combined attributes of a reasonable binding affinity, sustained stability during MDS, its obedience to the Lipinski rule of five and compliance with drug-likeness criteria. An in vitro validation of the potential EPI activities of H. populifolium compounds confirmed that 4,5-dicaffeoylquinic acid reduced the expulsion of the bis-benzimide dye by MtrCDE pump, while carvacrol showed low accumulation compared to other compounds. While 4,5-dicaffeoylquinic acid demonstrated the highest binding affinity in computational analysis and an EPI activity in vitro, it showed lower stability compared to the other compounds, as indicated in MDS. This leaves carvacrol, as a better EPI candidate for the management of gonorrhoea infection. Full article
(This article belongs to the Special Issue Advances in Molecular Modeling, Docking and Simulations of Protein Structure, 2nd Edition)
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Review

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26 pages, 2338 KiB  
Review
Peptides Evaluated In Silico, In Vitro, and In Vivo as Therapeutic Tools for Obesity: A Systematic Review
by Ana Júlia Felipe Camelo Aguiar, Wendjilla Fortunato de Medeiros, Juliana Kelly da Silva-Maia, Ingrid Wilza Leal Bezerra, Grasiela Piuvezam and Ana Heloneida de Araújo Morais
Int. J. Mol. Sci. 2024, 25(17), 9646; https://doi.org/10.3390/ijms25179646 - 6 Sep 2024
Viewed by 1594
Abstract
Bioinformatics has emerged as a valuable tool for screening drugs and understanding their effects. This systematic review aimed to evaluate whether in silico studies using anti-obesity peptides targeting therapeutic pathways for obesity, when subsequently evaluated in vitro and in vivo, demonstrated effects consistent [...] Read more.
Bioinformatics has emerged as a valuable tool for screening drugs and understanding their effects. This systematic review aimed to evaluate whether in silico studies using anti-obesity peptides targeting therapeutic pathways for obesity, when subsequently evaluated in vitro and in vivo, demonstrated effects consistent with those predicted in the computational analysis. The review was framed by the question: “What peptides or proteins have been used to treat obesity in in silico studies?” and structured according to the acronym PECo. The systematic review protocol was developed and registered in PROSPERO (CRD42022355540) in accordance with the PRISMA-P, and all stages of the review adhered to these guidelines. Studies were sourced from the following databases: PubMed, ScienceDirect, Scopus, Web of Science, Virtual Heath Library, and EMBASE. The search strategies resulted in 1015 articles, of which, based on the exclusion and inclusion criteria, 7 were included in this systematic review. The anti-obesity peptides identified originated from various sources including bovine alpha-lactalbumin from cocoa seed (Theobroma cacao L.), chia seed (Salvia hispanica L.), rice bran (Oryza sativa), sesame (Sesamum indicum L.), sea buckthorn seed flour (Hippophae rhamnoides), and adzuki beans (Vigna angularis). All articles underwent in vitro and in vivo reassessment and used molecular docking methodology in their in silico studies. Among the studies included in the review, 46.15% were classified as having an “uncertain risk of bias” in six of the thirteen criteria evaluated. The primary target investigated was pancreatic lipase (n = 5), with all peptides targeting this enzyme demonstrating inhibition, a finding supported both in vitro and in vivo. Additionally, other peptides were identified as PPARγ and PPARα agonists (n = 2). Notably, all peptides exhibited different mechanisms of action in lipid metabolism and adipogenesis. The findings of this systematic review underscore the effectiveness of computational simulation as a screening tool, providing crucial insights and guiding in vitro and in vivo investigations for the discovery of novel anti-obesity peptides. Full article
(This article belongs to the Special Issue Advances in Molecular Modeling, Docking and Simulations of Protein Structure, 2nd Edition)
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