scholarly journals Glycosynthesis in a waterworld: new insight into the molecular basis of transglycosylation in retaining glycoside hydrolases

2015 ◽  
Vol 467 (1) ◽  
pp. 17-35 ◽  
Author(s):  
Bastien Bissaro ◽  
Pierre Monsan ◽  
Régis Fauré ◽  
Michael J. O’Donohue
Keyword(s):  
Molecular Basis ◽  
State Of The Art ◽  
Careful Analysis ◽  
Glycoside Hydrolases ◽  
Rational Basis ◽  
Substrate Specificities ◽  
Glycosidic Bonds ◽  
Hydrolytic Reaction ◽  
Wide Range ◽  
Insight Into

Carbohydrates are ubiquitous in Nature and play vital roles in many biological systems. Therefore the synthesis of carbohydrate-based compounds is of considerable interest for both research and commercial purposes. However, carbohydrates are challenging, due to the large number of sugar subunits and the multiple ways in which these can be linked together. Therefore, to tackle the challenge of glycosynthesis, chemists are increasingly turning their attention towards enzymes, which are exquisitely adapted to the intricacy of these biomolecules. In Nature, glycosidic linkages are mainly synthesized by Leloir glycosyltransferases, but can result from the action of non-Leloir transglycosylases or phosphorylases. Advantageously for chemists, non-Leloir transglycosylases are glycoside hydrolases, enzymes that are readily available and exhibit a wide range of substrate specificities. Nevertheless, non-Leloir transglycosylases are unusual glycoside hydrolases in as much that they efficiently catalyse the formation of glycosidic bonds, whereas most glycoside hydrolases favour the mechanistically related hydrolysis reaction. Unfortunately, because non-Leloir transglycosylases are almost indistinguishable from their hydrolytic counterparts, it is unclear how these enzymes overcome the ubiquity of water, thus avoiding the hydrolytic reaction. Without this knowledge, it is impossible to rationally design non-Leloir transglycosylases using the vast diversity of glycoside hydrolases as protein templates. In this critical review, a careful analysis of literature data describing non-Leloir transglycosylases and their relationship to glycoside hydrolase counterparts is used to clarify the state of the art knowledge and to establish a new rational basis for the engineering of glycoside hydrolases.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1640 ◽  
Author(s):  
Amirhosein Mosavi ◽  
Yaser Faghan ◽  
Pedram Ghamisi ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
High Dimensional ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Yaser Faghan ◽  
Pedram Ghamisi ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
High Dimensional ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Shahab Shamshirband
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Business Environments ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals Sequence and Structural Analysis of AA9 and AA10 LPMOs: An Insight into the Basis of Substrate Specificity and Regioselectivity

2019 ◽  
Vol 20 (18) ◽  
pp. 4594 ◽  
Author(s):  
Xiaoli Zhou ◽  
Xiaohua Qi ◽  
Hongxia Huang ◽  
Honghui Zhu
Keyword(s):  
Structural Analysis ◽  
Substrate Specificity ◽  
Molecular Basis ◽  
Substrate Binding ◽  
Binding Modes ◽  
Substrate Specificities ◽  
Lytic Polysaccharide Monooxygenases ◽  
Polysaccharide Monooxygenases ◽  
Natural Carbon ◽  
Insight Into

Lytic polysaccharide monooxygenases (LPMOs) are key enzymes in both the natural carbon cycle and the biorefinery industry. Understanding the molecular basis of LPMOs acting on polysaccharide substrates is helpful for improving industrial cellulase cocktails. Here we analyzed the sequences, structures, and substrate binding modes of LPMOs to uncover the factors that influence substrate specificity and regioselectivity. Our results showed that the different compositions of a motif located on L2 affect the electrostatic potentials of substrate binding surfaces, which in turn affect substrate specificities of AA10 LPMOs. A conserved Asn at a distance of 7 Å from the active center Cu might, together with the conserved Ser immediately before the second catalytic His, determine the localization of LPMOs on substrate, and thus contribute to C4-oxidizing regioselectivity. The findings in this work provide an insight into the molecular basis of substrate specificity and regioselectivity of LPMOs.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

scholarly journals State of FAIRness in ESFRI Projects

2020 ◽  
Vol 2 (1-2) ◽  
pp. 230-237 ◽  
Author(s):  
Peter Wittenburg ◽  
Franciska de Jong ◽  
Dieter van Uytvanck ◽  
Massimo Cocco ◽  
Keith Jeffery ◽  
...  
Keyword(s):  
State Of The Art ◽  
Careful Analysis ◽  
Wide Acceptance ◽  
Wide Range ◽  
Research Infrastructures ◽  
Fair Principles ◽  
Art Research ◽  
Definition Of ◽  
Research Facilities ◽  
Distributed Infrastructure

Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines. An important result of the strategic discussions was that distributed infrastructure scenarios were now seen as “complex research facilities” in addition to, for example traditional centralised infrastructures such as CERN. In this paper we look at five typical examples of such distributed infrastructures where many researchers working in different centres are contributing data, tools/services and knowledge and where the major task of the research infrastructure initiative is to create a virtually integrated suite of resources allowing researchers to carry out state-of-the-art research. Careful analysis shows that most of these research infrastructures worked on the Findability, Accessibility, Interoperability and Reusability dimensions before the term “FAIR” was actually coined. The definition of the FAIR principles and their wide acceptance can be seen as a confirmation of what these initiatives were doing and it gives new impulse to close still existing gaps. These initiatives also seem to be ready to take up the next steps which will emerge from the definition of FAIR maturity indicators. Experts from these infrastructures should bring in their 10-years' experience in this definition process.

scholarly journals Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

2020 ◽  
Author(s):  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Yaser Faghan ◽  
Puhong Duan ◽  
Sina Faizollahzadeh Ardabili ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Economic Data ◽  
Economic Problems ◽  
Wide Range ◽  
Survey Results ◽  
Risk Parameters ◽  
Accuracy Performance ◽  
Insight Into

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated economics dynamic systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

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