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Computational Approaches to Bioactive Peptide Prediction and Discovery
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Computational Approaches to Bioactive Peptide Prediction and Discovery

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 7944

Special Issue Editor

Dr. Gerard Pujadas

E-Mail Website
Guest Editor
Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
Interests: drug discovery; drug design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Peptides derived from the hydrolysis of naturally occurring proteins are known to contain a large number of interesting bioactivities (antidiabetic, antihypertensive, antimicrobial, etc.). In addition, they can be obtained from a wide variety of sources (even by-products) and, depending on hydrolytic conditions (e.g., hydrolases, pH, temperature), the same source can provide a different set of peptides. There is a large number of peptides readily available today and computational tools can be very useful in: (i) predicting their bioactivity; (ii) help to find easy ways to obtain them from the available raw materials.

Therefore, a first goal of the current Special Issue is to describe the state of the art of the computational tools that can be used for this bioactivity prediction. This includes, but is not limited to, protein–peptide docking tools, protein–peptide complex free-energy prediction, and deep/machine-learning approaches. In all cases, only manuscripts that contain a computational or in vitro assessment of the reliability of their predictions will be considered for peer review.

The second goal of this Special Issue is to emphasize web servers and databases that help to discover easy ways: (1) to obtain a specific bioactive peptide from available sources; (2) to obtain different bioactive peptides from a specific protein source.

Dr. Gerard Pujadas
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.

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Keywords

  • naturally occurring peptides
  • protein–peptide docking
  • bioactivity prediction of peptides
  • peptide database
  • peptide webserver

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Published Papers (3 papers)

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Research

20 pages, 2258 KiB  
Article
Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning
by Josep-Ramon Codina, Marcello Mascini, Emre Dikici, Sapna K. Deo and Sylvia Daunert
Int. J. Mol. Sci. 2023, 24(15), 12144; https://doi.org/10.3390/ijms241512144 - 29 Jul 2023
Cited by 3 | Viewed by 2294
Abstract
This research introduces a novel pipeline that couples machine learning (ML), and molecular docking for accelerating the process of small peptide ligand screening through the prediction of peptide-protein docking. Eight ML algorithms were analyzed for their potential. Notably, Light Gradient Boosting Machine (LightGBM), [...] Read more.
This research introduces a novel pipeline that couples machine learning (ML), and molecular docking for accelerating the process of small peptide ligand screening through the prediction of peptide-protein docking. Eight ML algorithms were analyzed for their potential. Notably, Light Gradient Boosting Machine (LightGBM), despite having comparable F1-score and accuracy to its counterparts, showcased superior computational efficiency. LightGBM was used to classify peptide-protein docking performance of the entire tetrapeptide library of 160,000 peptide ligands against four viral envelope proteins. The library was classified into two groups, ‘better performers’ and ‘worse performers’. By training the LightGBM algorithm on just 1% of the tetrapeptide library, we successfully classified the remaining 99%with an accuracy range of 0.81–0.85 and an F1-score between 0.58–0.67. Three different molecular docking software were used to prove that the process is not software dependent. With an adjustable probability threshold (from 0.5 to 0.95), the process could be accelerated by a factor of at least 10-fold and still get 90–95% concurrence with the method without ML. This study validates the efficiency of machine learning coupled to molecular docking in rapidly identifying top peptides without relying on high-performance computing power, making it an effective tool for screening potential bioactive compounds. Full article
(This article belongs to the Special Issue Computational Approaches to Bioactive Peptide Prediction and Discovery)
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25 pages, 5118 KiB  
Article
Uncovering a Hub Signaling Pathway of Antimicrobial-Antifungal-Anticancer Peptides’ Axis on Short Cationic Peptides via Network Pharmacology Study
by Ki-Kwang Oh, Md. Adnan and Dong-Ha Cho
Int. J. Mol. Sci. 2022, 23(4), 2055; https://doi.org/10.3390/ijms23042055 - 12 Feb 2022
Cited by 2 | Viewed by 2818
Abstract
Short cationic peptides (SCPs) with therapeutic efficacy of antimicrobial peptides (AMPs), antifungal peptides (AFPs), and anticancer peptides (ACPs) are known as an enhancement of the host defense system. Here, we investigated the uppermost peptide(s), hub signaling pathway(s), and their associated target(s) through network [...] Read more.
Short cationic peptides (SCPs) with therapeutic efficacy of antimicrobial peptides (AMPs), antifungal peptides (AFPs), and anticancer peptides (ACPs) are known as an enhancement of the host defense system. Here, we investigated the uppermost peptide(s), hub signaling pathway(s), and their associated target(s) through network pharmacology. Firstly, we selected SCPs with positive amino acid residues on N- and C- terminals under 500 Dalton via RStudio. Secondly, the overlapping targets between the bacteria-responsive targets (TTD and OMIM) and AMPs’ targets were visualized by VENNY 2.1. Thirdly, the overlapping targets between AFPs’ targets and fungal-responsive targets were exhibited by VENNY 2.1. Fourthly, the overlapping targets between cancer-related targets (TTD and OMIM) and fungal-responsive targets were displayed by VENNY 2.1. Finally, a molecular docking study (MDS) was carried out to discover the most potent peptides on a hub signaling pathway. A total of 1833 SCPs were identified, and AMPs’, AFPs’, and ACPs’ filtration suggested that 197 peptides (30 targets), 81 peptides (6 targets), and 59 peptides (4 targets) were connected, respectively. The AMPs―AFPs―ACPs’ axis indicated that 27 peptides (2 targets) were associated. Each hub signaling pathway for the enhancement of the host defense system was “Inactivation of Rap1 signaling pathway on AMPs”, “Activation of Notch signaling pathway on AMPs―AFPs’ axis”, and “Inactivation of HIF-1 signaling pathway on AMPs―AFPs―ACPs’ axis”. The most potent peptides were assessed via MDS; finally, HPIK on STAT3 and HVTK on NOS2 and on HIF-1 signaling pathway were the most stable complexes. Furthermore, the two peptides had better affinity scores than standard inhibitors (Stattic, 1400 W). Overall, the most potent SCPs for the human defense system were HPIK on STAT3 and HVTK on NOS2, which might inactivate the HIF-1 signaling pathway. Full article
(This article belongs to the Special Issue Computational Approaches to Bioactive Peptide Prediction and Discovery)
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26 pages, 3265 KiB  
Article
Regulation of Antimicrobial Peptide Activity via Tuning Deformation Fields by Membrane-Deforming Inclusions
by Oleg V. Kondrashov and Sergey A. Akimov
Int. J. Mol. Sci. 2022, 23(1), 326; https://doi.org/10.3390/ijms23010326 - 28 Dec 2021
Cited by 10 | Viewed by 1615
Abstract
Antimicrobial peptides (AMPs) are considered prospective antibiotics. Some AMPs fight bacteria via cooperative formation of pores in their plasma membranes. Most AMPs at their working concentrations can induce lysis of eukaryotic cells as well. Gramicidin A (gA) is a peptide, the transmembrane dimers [...] Read more.
Antimicrobial peptides (AMPs) are considered prospective antibiotics. Some AMPs fight bacteria via cooperative formation of pores in their plasma membranes. Most AMPs at their working concentrations can induce lysis of eukaryotic cells as well. Gramicidin A (gA) is a peptide, the transmembrane dimers of which form cation-selective channels in membranes. It is highly toxic for mammalians as being majorly hydrophobic gA incorporates and induces leakage of both bacterial and eukaryotic cell membranes. Both pore-forming AMPs and gA deform the membrane. Here we suggest a possible way to reduce the working concentrations of AMPs at the expense of application of highly-selective amplifiers of AMP activity in target membranes. The amplifiers should alter the deformation fields in the membrane in a way favoring the membrane-permeabilizing states. We developed the statistical model that allows describing the effect of membrane-deforming inclusions on the equilibrium between AMP monomers and cooperative membrane-permeabilizing structures. On the example of gA monomer-dimer equilibrium, the model predicts that amphipathic peptides and short transmembrane peptides playing the role of the membrane-deforming inclusions, even in low concentration can substantially increase the lifetime and average number of gA channels. Full article
(This article belongs to the Special Issue Computational Approaches to Bioactive Peptide Prediction and Discovery)
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