Advancements in small molecule drug design: A structural perspective

Wu, Ke; Karapetyan, Eduard; Schloss, John; Vadgama, Jaydutt; Wu, Yong

doi:10.1016/j.drudis.2023.103730

Cited by 16 publications

(6 citation statements)

References 97 publications

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“… 30 Therefore, a comprehensive understanding of the mechanisms of enzyme action has become essential to maximize their potential in a wide range of applications, from biotechnology 31 − 34 to medicine. 35 − 42 This understanding has also contributed significantly to our broader understanding of chemical processes in biology and life.…”

Section: Introductionmentioning

confidence: 94%

“…However, enzymes are not ordinary catalysts; they exhibit exceptional selectivity in recognizing and binding specific substrates, ensuring efficient catalysis while preventing wasteful or undesirable side reactions. − What further distinguishes enzymes is that they are regulated by multiple and customized mechanisms so that each chemical process occurs with exquisite timing within the relevant cellular context. − Throughout evolution, these unique features of enzymes have exerted evolutionary pressure, resulting in the optimization of their functions and even the development of new capabilities. − In addition, the design of robust enzymes has received considerable attention in recent years . Therefore, a comprehensive understanding of the mechanisms of enzyme action has become essential to maximize their potential in a wide range of applications, from biotechnology − to medicine. − This understanding has also contributed significantly to our broader understanding of chemical processes in biology and life.…”

Section: Introductionmentioning

confidence: 99%

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Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Nam,

Shao,

Major

et al. 2024

ACS Omega

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Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Nam,

Shao,

Major

et al. 2024

ACS Omega

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“…Protein structure prediction is crucial for identifying STSs with no reported crystal structure. For STSs that possess highly homologous crystal structures, homology modeling is a powerful tool for predicting target protein structures , In addition, for that single static “snapshot” structure may not fulfill the requirements for analyzing dynamic enzyme-catalyzed processes, there is a growing trend in employing QM/MM and MD techniques for dynamic conformational combinatorial sampling to improve prediction accuracy.…”

Section: Summary and Perspectivesmentioning

confidence: 99%

Catalytic Mechanism and Heterologous Biosynthesis Application of Sesquiterpene Synthases

Nie,

Wang,

Chen

et al. 2024

J. Agric. Food Chem.

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Sesquiterpenes comprise a diverse group of natural products with a wide range of applications in cosmetics, food, medicine, agriculture, and biofuels. Heterologous biosynthesis is increasingly employed for sesquiterpene production, aiming to overcome the limitations associated with chemical synthesis and natural extraction. Sesquiterpene synthases (STSs) play a crucial role in the heterologous biosynthesis of sesquiterpene. Under the catalysis of STSs, over 300 skeletons are produced through various cyclization processes (C1-C10 closure, C1-C11 closure, C1-C6 closure, and C1-C7 closure), which are responsible for the diversity of sesquiterpenes. According to the cyclization types, we gave an overview of advances in understanding the mechanism of STSs cyclization from the aspects of protein crystal structures and site-directed mutagenesis. We also summarized the applications of engineering STSs in the heterologous biosynthesis of sesquiterpene. Finally, the bottlenecks and potential research directions related to the STSs cyclization mechanism and application of modified STSs were presented.

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“…The partial charges calculated by high-level quantum mechanics-based methods are relatively accurate but computationally expensive and time-consuming, compared to conventional semiempirical algorithms which are fast but with greater error . As the design of drug molecules continues to develop, there is a great demand of highly efficient methods to search and filter among millions of candidate molecules for proper properties like atomic charge distribution . Machine learning (ML) models that can scale to these large systems while maintaining sufficient accuracy are thus required.…”

Section: Introductionmentioning

confidence: 99%

“… 4 As the design of drug molecules continues to develop, there is a great demand of highly efficient methods to search and filter among millions of candidate molecules for proper properties like atomic charge distribution. 5 Machine learning (ML) models that can scale to these large systems while maintaining sufficient accuracy are thus required. We propose a possible model here.…”

Section: Introductionmentioning

confidence: 99%

Coordinate-Free and Low-Order Scaling Machine Learning Model for Atomic Partial Charge Prediction for Any Size of Molecules

Xie,

Horsfield

2024

J. Chem. Inf. Model.

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The atomic partial charge is of great importance in many fields, such as chemistry and drug-target recognition. However, conventional quantum-based computing of atomic charges is relatively slow, limiting further applications of atomic charge analysis. With the help of machine learning methods, various kinds of models appear to speed up atomic charge calculations. However, there are still some concerning problems. Some models based on geometric coordinates require highaccuracy geometry optimization as a preprocess, while other models have a limitation on the size of input molecules that narrow the applications of the model. Here, we propose a machine learning atomic charge model based on a message-passing featurizer. This preprocessing featurizer can quickly extract atomic environment information from a molecule according to the connectivity inside the molecule. The resulting descriptor can be used with a neural network to quickly predict the atomic partial charge. The model is able to automatically adapt to any size of molecule while remaining efficient and achieves a root-mean-square error in the Hirshfeld charge prediction of 0.018e, with an overall time complexity of O(n 2 ). Thus, this model could enlarge the range of applications of atomic partial charge to more fields and cases.

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Advancements in small molecule drug design: A structural perspective

Cited by 16 publications

References 97 publications

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Catalytic Mechanism and Heterologous Biosynthesis Application of Sesquiterpene Synthases

Coordinate-Free and Low-Order Scaling Machine Learning Model for Atomic Partial Charge Prediction for Any Size of Molecules

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