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Biomolecules
Special Issues
Protein Structure Prediction in Drug Discovery II
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Protein Structure Prediction in Drug Discovery II

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (30 May 2024) | Viewed by 4332

Special Issue Editor

Dr. Alessandro Paiardini

E-Mail Website
Guest Editor
Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
Interests: drug design; molecular docking and virtual screening; protein structure prediction and homology modeling; protein structure and evolution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following a very successful first run, we are pleased to announce the launch of a second edition of a Special Issue on “Protein Structure Prediction in Drug Discovery”.

When the results of DeepMind's AlphaFold2 at CASP were announced in 2020, the scientific world was amazed by how effectively it performed; "it will change everything" became the motto for this revolution. As a result, it should come as no surprise that "Protein Structure Prediction" was named Nature's Method of the Year 2021. Structure-based drug discovery (SBDD) is the one area of biology and medicine that is expected to bring the most benefits and make a huge leap as a result of the developments of AlphaFold2 and comparable tools, such as RoseTTAFold. However, since the accuracy of the residues’ conformations at the active sites remains a key limitation in SBDD, as does the inability to guess which conformational state of a protein these tools will predict, it is still necessary to associate and integrate previous physically based models and expert-driven knowledge with new machine learning approaches, as well as experimentally derived structural data.

We encourage articles centered around the promising fields of protein structure prediction and drug development to be published for this timely Special Issue of Biomolecules. New machine learning approaches and tools, as well as developments and applications in previously existing techniques, such as threading and homology modeling, for the protein structure prediction of therapeutic intervention targets, are all areas of interest. Furthermore, scientists working in the broad field of drug discovery are encouraged to submit original research and review articles describing new tools or solutions; the characterization and/or refinement of novel structures; and the design of small molecules, peptides, or peptidomimetics that were discovered using protein structure prediction methods.

Dr. Alessandro Paiardini
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. Biomolecules 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
  • drug design
  • docking
  • virtual screening
  • drug discovery
  • machine learning
  • alphafold

Related Special Issue

Published Papers (3 papers)

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Research

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13 pages, 1030 KiB  
Article
Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction
by Luxuan Wang, Xibing He, Beihong Ji, Fengyang Han, Taoyu Niu, Lianjin Cai, Jingchen Zhai, Dongxiao Hao and Junmei Wang
Biomolecules 2024, 14(6), 648; https://doi.org/10.3390/biom14060648 - 31 May 2024
Abstract
Structure-based virtual screening utilizes molecular docking to explore and analyze ligand–macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In [...] Read more.
Structure-based virtual screening utilizes molecular docking to explore and analyze ligand–macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule–macromolecule and 12 peptide–macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson’s and Spearman’s correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery II)
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15 pages, 2014 KiB  
Article
BSA Binding and Aggregate Formation of a Synthetic Amino Acid with Potential for Promoting Fibroblast Proliferation: An In Silico, CD Spectroscopic, DLS, and Cellular Study
by Hayarpi Simonyan, Rosanna Palumbo, Satenik Petrosyan, Anna Mkrtchyan, Armen Galstyan, Ashot Saghyan, Pasqualina Liana Scognamiglio, Caterina Vicidomini, Marta Fik-Jaskólka and Giovanni N. Roviello
Biomolecules 2024, 14(5), 579; https://doi.org/10.3390/biom14050579 - 14 May 2024
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Abstract
This study presents the chemical synthesis, purification, and characterization of a novel non-natural synthetic amino acid. The compound was synthesized in solution, purified, and characterized using NMR spectroscopy, polarimetry, and melting point determination. Dynamic Light Scattering (DLS) analysis demonstrated its ability to form [...] Read more.
This study presents the chemical synthesis, purification, and characterization of a novel non-natural synthetic amino acid. The compound was synthesized in solution, purified, and characterized using NMR spectroscopy, polarimetry, and melting point determination. Dynamic Light Scattering (DLS) analysis demonstrated its ability to form aggregates with an average size of 391 nm, extending to the low micrometric size range. Furthermore, cellular biological assays revealed its ability to enhance fibroblast cell growth, highlighting its potential for tissue regenerative applications. Circular dichroism (CD) spectroscopy showed the ability of the synthetic amino acid to bind serum albumins (using bovine serum albumin (BSA) as a model), and CD deconvolution provided insights into the changes in the secondary structures of BSA upon interaction with the amino acid ligand. Additionally, molecular docking using HDOCK software elucidated the most likely binding mode of the ligand inside the BSA structure. We also performed in silico oligomerization of the synthetic compound in order to obtain a model of aggregate to investigate computationally. In more detail, the dimer formation achieved by molecular self-docking showed two distinct poses, corresponding to the lowest and comparable energies, with one pose exhibiting a quasi-coplanar arrangement characterized by a close alignment of two aromatic rings from the synthetic amino acids within the dimer, suggesting the presence of π-π stacking interactions. In contrast, the second pose displayed a non-coplanar configuration, with the aromatic rings oriented in a staggered arrangement, indicating distinct modes of interaction. Both poses were further utilized in the self-docking procedure. Notably, iterative molecular docking of amino acid structures resulted in the formation of higher-order aggregates, with a model of a 512-mer aggregate obtained through self-docking procedures. This model of aggregate presented a cavity capable of hosting therapeutic cargoes and biomolecules, rendering it a potential scaffold for cell adhesion and growth in tissue regenerative applications. Overall, our findings highlight the potential of this synthetic amino acid for tissue regenerative therapeutics and provide valuable insights into its molecular interactions and aggregation behavior. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery II)
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Review

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16 pages, 2285 KiB  
Review
Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development
by Xinru Qiu, Han Li, Greg Ver Steeg and Adam Godzik
Biomolecules 2024, 14(3), 339; https://doi.org/10.3390/biom14030339 - 12 Mar 2024
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Abstract
Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function [...] Read more.
Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery II)
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