Thermodynamics of Protein Folding
원문 초록
While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter, "Introduction to Protein Folding", we introduced the concept of free energy and the protein folding landscape. Here, we provide a deeper, more formal underpinning of free energy in terms of the entropy and enthalpy; to this end, we will first need to better define the meaning of equilibrium, entropy and enthalpy. When we understand these concepts, we will come back for a more quantitative explanation of protein folding and dynamics. We will discuss the influence of temperature on the free energy landscape, and the difference between microstates and macrostates.
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MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction Models
PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOL
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation
Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
Protein Language Models and Structure Prediction: Connection and Progression
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
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