Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning
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Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning
allosteric regulation is a common mechanism used by the complex biomolecular systems for regulatory activities and adaptability in a mobile environment, serving as an effective molecular tools for mobile communication.
As an intrinsic property but elusive, allostery is a ubiquitous phenomenon in which the binding or distracting from the distal site within the functional protein can control the activity and is considered a “second secret of life.” Fundamental biological importance and complexity of this process requires a multi-faceted platform integrated synergistic approach for the prediction and characterization of a functional state allosteric, atomistic reconstruction allosteric regulatory mechanisms and the discovery of allosteric modulators.
The unifying theme and objectives of allosteric regulation studies in recent years has been the integration between experimental and computational approaches and emerging technologies to advance the quantitative characterization of the mechanism of allosteric proteins. Despite significant advances, quantitative characterization and reliable prediction of the functional state allosteric interactions and mechanisms continue to present a very challenging problem in the field. In this review, we discuss the multiscale simulation-based approach, Markov model of experimental-information, and network modeling allostery and information-theoretic approach to explain thermodynamics and state hierarchy and the basic molecule allosteric allosteric mechanism.
A wealth of structural and functional information along with the diversity and complexity of the allosteric mechanism in important therapeutic protein families have been provided a suitable platform for the development of data-driven research strategy. data-centric integration of chemistry, biology and computer science use artificial intelligence technology has gained significant momentum and at the forefront of efforts across disciplines.
We discuss the new developments in the field of machine learning and the emergence of deep learning and reinforcement learning in-depth applications in molecular modeling and protein allosteric mechanism. An integrated approach-guided trial is powered by the latest advances in multiscale modeling, knowledge networks, and machine learning can lead to more reliable predictions of allosteric regulation mechanism and the discovery of allosteric modulators for important therapeutic protein targets.
Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning
Roadmap on emerging hardware and technology for machine learning
The latest advances in artificial intelligence is largely due to the rapid development of machine learning, especially in algorithms and neural network models. However, the performance of the hardware, especially the energy efficiency of a computing system that sets a fundamental limit on the ability of machine learning.
data-centric computing requires a revolution in the system hardware, as the traditional digital computers are based on transistors and the von Neumann architecture was not intentionally designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is hope for the future of computing to dramatically increase throughput and energy efficiency.
Description: CRK, also known as p38, is a protein that in humans is encoded by the CRK gene. This gene is a member of an adapter protein family that binds to several tyrosine-phosphorylated proteins. It is mapped to 17p13.3. The protein participates in the Reelin signaling cascade downstream of DAB1. The product of this gene has several SH2 and SH3 domains (src-homology domains) and is involved in several signaling pathways, recruiting cytoplasmic proteins in the vicinity of tyrosine kinase through SH2-phosphotyrosine interaction. The N-terminal SH2 domain of Crk functions as a positive regulator of transformation whereas the C-terminal SH3 domain functions as a negative regulator of transformation. Two alternative transcripts encoding different isoforms with distinct biological activity have been described.
Build such a system, however, faces a number of challenges, ranging from material selection, optimization devices, circuit fabrication, and system integration, to name a few. The objective of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially useful for machine learning, provide the reader with a perspective Nanotechnology challenges and opportunities in this growing field.
