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Complexity and Networking in Molecular Systems
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Complexity and Networking in Molecular Systems

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 10633

Special Issue Editors

Dr. Csaba Hetényi

E-Mail Website1 Website2
Guest Editor
Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
Interests: proteins; molecular structure; thermodynamics; drug design; chemoinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Uko Maran

E-Mail Website
Guest Editor
Institute of Chemistry, University of Tartu, Ravila 14A, 50411 Tartu, Estonia
Interests: theoretical and computer chemistry; malaria drug development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding complex systems, and their intra- or intermolecular networking is paramount in drug research, material sciences, and the various fields of applied and basic sciences. The Special Issue covers structural and functional networks, their interrelationships, and the complexity of molecular systems. For example, systems formed by hard (structural) links of inter-atomic bonds in molecules or intermolecular interactions. The soft (functional) links between biological macromolecules are also in the scope of the Special Issue. Methods and applications that focus on the role and use of complexity and networking in such systems are also of interest. Submission of both experimental and theoretical original research and review papers are welcome to the Special Issue.

Dr. Csaba Hetényi
Dr. Uko Maran
Guest Editors

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

  • connectivity
  • node
  • QSAR
  • signal transduction
  • cancer
  • epigenetics
  • pathway
  • docking
  • bond
  • graph
  • neuron
  • quantum mechanics

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Published Papers (4 papers)

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Research

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16 pages, 5112 KiB  
Article
Oscillation of Autophagy Induction under Cellular Stress and What Lies behind It, a Systems Biology Study
by Bence Hajdú, Luca Csabai, Margita Márton, Marianna Holczer, Tamás Korcsmáros and Orsolya Kapuy
Int. J. Mol. Sci. 2023, 24(8), 7671; https://doi.org/10.3390/ijms24087671 - 21 Apr 2023
Cited by 4 | Viewed by 2183
Abstract
One of the main inducers of autophagy-dependent self-cannibalism, called ULK1, is tightly regulated by the two sensor molecules of nutrient conditions and energy status, known as mTOR and AMPK kinases, respectively. Recently, we developed a freely available mathematical model to explore the oscillatory [...] Read more.
One of the main inducers of autophagy-dependent self-cannibalism, called ULK1, is tightly regulated by the two sensor molecules of nutrient conditions and energy status, known as mTOR and AMPK kinases, respectively. Recently, we developed a freely available mathematical model to explore the oscillatory characteristic of the AMPK-mTOR-ULK1 regulatory triangle. Here, we introduce a systems biology analysis to explain in detail the dynamical features of the essential negative and double-negative feedback loops and also the periodic repeat of autophagy induction upon cellular stress. We propose an additional regulatory molecule in the autophagy control network that delays some of AMPK’s effect on the system, making the model output more consistent with experimental results. Furthermore, a network analysis on AutophagyNet was carried out to identify which proteins could be the proposed regulatory components in the system. These regulatory proteins should satisfy the following rules: (1) they are induced by AMPK; (2) they promote ULK1; (3) they down-regulate mTOR upon cellular stress. We have found 16 such regulatory components that have been experimentally proven to satisfy at least two of the given rules. Identifying such critical regulators of autophagy induction could support anti-cancer- and ageing-related therapeutic efforts. Full article
(This article belongs to the Special Issue Complexity and Networking in Molecular Systems)
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15 pages, 2385 KiB  
Article
Binding Networks Identify Targetable Protein Pockets for Mechanism-Based Drug Design
by Mónika Bálint, Balázs Zoltán Zsidó, David van der Spoel and Csaba Hetényi
Int. J. Mol. Sci. 2022, 23(13), 7313; https://doi.org/10.3390/ijms23137313 - 30 Jun 2022
Cited by 1 | Viewed by 1997
Abstract
The human genome codes only a few thousand druggable proteins, mainly receptors and enzymes. While this pool of available drug targets is limited, there is an untapped potential for discovering new drug-binding mechanisms and modes. For example, enzymes with long binding cavities offer [...] Read more.
The human genome codes only a few thousand druggable proteins, mainly receptors and enzymes. While this pool of available drug targets is limited, there is an untapped potential for discovering new drug-binding mechanisms and modes. For example, enzymes with long binding cavities offer numerous prerequisite binding sites that may be visited by an inhibitor during migration from a bulk solution to the destination site. Drug design can use these prerequisite sites as new structural targets. However, identifying these ephemeral sites is challenging. Here, we introduce a new method called NetBinder for the systematic identification and classification of prerequisite binding sites at atomic resolution. NetBinder is based on atomistic simulations of the full inhibitor binding process and provides a networking framework on which to select the most important binding modes and uncover the entire binding mechanism, including previously undiscovered events. NetBinder was validated by a study of the binding mechanism of blebbistatin (a potent inhibitor) to myosin 2 (a promising target for cancer chemotherapy). Myosin 2 is a good test enzyme because, like other potential targets, it has a long internal binding cavity that provides blebbistatin with numerous potential prerequisite binding sites. The mechanism proposed by NetBinder of myosin 2 structural changes during blebbistatin binding shows excellent agreement with experimentally determined binding sites and structural changes. While NetBinder was tested on myosin 2, it may easily be adopted to other proteins with long internal cavities, such as G-protein-coupled receptors or ion channels, the most popular current drug targets. NetBinder provides a new paradigm for drug design by a network-based elucidation of binding mechanisms at an atomic resolution. Full article
(This article belongs to the Special Issue Complexity and Networking in Molecular Systems)
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Review

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19 pages, 2205 KiB  
Review
The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering
by Balázs Zoltán Zsidó, Bayartsetseg Bayarsaikhan, Rita Börzsei, Viktor Szél, Violetta Mohos and Csaba Hetényi
Int. J. Mol. Sci. 2023, 24(14), 11784; https://doi.org/10.3390/ijms241411784 - 22 Jul 2023
Cited by 5 | Viewed by 1948
Abstract
Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target–ligand interactions in drug design, with a focus on biomolecules, [...] Read more.
Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target–ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering. Full article
(This article belongs to the Special Issue Complexity and Networking in Molecular Systems)
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13 pages, 15170 KiB  
Review
Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry
by Jaroslaw Polanski
Int. J. Mol. Sci. 2022, 23(5), 2797; https://doi.org/10.3390/ijms23052797 - 3 Mar 2022
Cited by 9 | Viewed by 3629
Abstract
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has [...] Read more.
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited. Full article
(This article belongs to the Special Issue Complexity and Networking in Molecular Systems)
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