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Molecular Advances in Enzyme Kinetics

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Dr. Marko Goliĉnik

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Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: enzyme kinetics; enzyme characterization; enzyme data documentation; numerical data analysis; enzyme high-throughput screening
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Dear Colleagues,

It is an honour and great pleasure to invite you to contribute original research or review articles to this Special Issue of IJMS entitled “Molecular Advances in Enzyme Kinetics”. Enzymes are catalysts that allow biochemistry to run efficiently during biological processes. Hence, understanding the kinetics and molecular mechanisms of enzyme-catalyzed reactions has played an important role in biochemistry since the early discoveries at the beginning of the past century. At present, enzyme kinetics significantly complement big omics data and other molecular sciences.

Therefore, the main goal of this Special Issue is to share modern findings and results from this field among scientists. We welcome manuscripts that cover the following topics:

Modern aspects of measuring enzyme kinetics data;
Steady-state and pre-steady-state enzyme kinetics;
Numerical enzyme kinetics data analysis;
Enzyme kinetic characterization for indicating some diseases;
Enzyme kinetic characterization for biological pathways modelling;
High-throughput technologies for enzyme and inhibitors screening;
Comprehensive documentation of enzymatic data.

Dr. Marko Goliĉnik
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

biocatalysis
enzymes
enzyme and inhibitors screening
enzyme computational modelling
kinetic data analysis
documentation of enzymatic data

Published Papers (2 papers)

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Research

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21 pages, 2620 KiB

Open AccessArticle

Self-Organization of Enzyme-Catalyzed Reactions Studied by the Maximum Entropy Production Principle

by Andrej Dobovišek, Marko Vitas, Tina Blaževič, Rene Markovič, Marko Marhl and Aleš Fajmut

Int. J. Mol. Sci. 2023, 24(10), 8734; https://doi.org/10.3390/ijms24108734 - 13 May 2023

Viewed by 1467

Abstract

The self-organization of open reaction systems is closely related to specific mechanisms that allow the export of internally generated entropy from systems to their environment. According to the second law of thermodynamics, systems with effective entropy export to the environment are better internally organized. Therefore, they are in thermodynamic states with low entropy. In this context, we study how self-organization in enzymatic reactions depends on their kinetic reaction mechanisms. Enzymatic reactions in an open system are considered to operate in a non-equilibrium steady state, which is achieved by satisfying the principle of maximum entropy production (MEPP). The latter is a general theoretical framework for our theoretical analysis. Detailed theoretical studies and comparisons of the linear irreversible kinetic schemes of an enzyme reaction in two and three states are performed. In both cases, in the optimal and statistically most probable thermodynamic steady state, a diffusion-limited flux is predicted by MEPP. Several thermodynamic quantities and enzymatic kinetic parameters, such as the entropy production rate, the Shannon information entropy, reaction stability, sensitivity, and specificity constants, are predicted. Our results show that the optimal enzyme performance may strongly depend on the number of reaction steps when linear reaction mechanisms are considered. Simple reaction mechanisms with a smaller number of intermediate reaction steps could be better organized internally and could allow fast and stable catalysis. These could be features of the evolutionary mechanisms of highly specialized enzymes. Full article

(This article belongs to the Special Issue Molecular Advances in Enzyme Kinetics)

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Review

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18 pages, 809 KiB

Open AccessReview

Enzyme Databases in the Era of Omics and Artificial Intelligence

by Uroš Prešern and Marko Goličnik

Int. J. Mol. Sci. 2023, 24(23), 16918; https://doi.org/10.3390/ijms242316918 - 29 Nov 2023

Cited by 1 | Viewed by 1158

Abstract

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use. Full article

(This article belongs to the Special Issue Molecular Advances in Enzyme Kinetics)

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