Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built fr...Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex couformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer＇s disease.展开更多
Proteins are essential parts of living organisms and participate in virtually every process within cells. As the genomlc sequences for increasing number of organisms are completed, research into how proteins can perfo...Proteins are essential parts of living organisms and participate in virtually every process within cells. As the genomlc sequences for increasing number of organisms are completed, research into how proteins can perform such a variety of functions has become much more intensive because the value of the genomic sequences relies on the accuracy of understanding the encoded gene products. Although the static three-dimensional structures of many proteins are known, the functions of proteins are ulti- mately governed by their dynamic characteristics, including the folding process, conformational fluctuations, molecular mo- tions, and protein-ligand interactions. In this review, the physicochemical principles underlying these dynamic processes are discussed in depth based on the free energy landscape （FEL） theory. Questions of why and how proteins fold into their native conformational states, why proteins are inherently dynamic, and how their dynamic personalities govern protein functions are answered. This paper will contribute to the understanding of structure-function relationship of proteins in the post-genome era of life science research.展开更多
To understand how the stabilities of key nuclei fragments affect protein folding dynamics, we simulate by molecular dynamics （MD） simulation in aqueous solution four fragments cut out of a protein G, including one a...To understand how the stabilities of key nuclei fragments affect protein folding dynamics, we simulate by molecular dynamics （MD） simulation in aqueous solution four fragments cut out of a protein G, including one a-helix （seqB： KVFKQYAN）, two -turns （seqA： LNGKTLKG and seqC： YDDATKTF）, and one -strand （seqD： DGEWTYDD）. The Markov State Model clustering method combined with the coarse-grained conformation letters method are employed to analyze the data sampled from 2-#s equilibrium MD simulation trajectories. We find that seqA and seqB have more stable structures than their native structures which become metastable when cut out of the protein structure. As expected, seqD alone is flexible and does not have a stable structure. Throughout our simulations, the native structure of seqC is stable but cannot be reached if starting from a structure other than the native one, implying a funnel-shape free energy landscape of seqC in aqueous solution. All the above results suggest that different nuclei have different formation dynamics during protein folding, which may have a major contribution to the hierarchy of protein folding dynamics.展开更多
Protein folding is regarded as a quantum transition between the torsion states of a polypeptide chain.According to the quantum theory of conformational dynamics,we propose the dynamical contact order(DCO) defined as a...Protein folding is regarded as a quantum transition between the torsion states of a polypeptide chain.According to the quantum theory of conformational dynamics,we propose the dynamical contact order(DCO) defined as a characteristic of the contact described by the moment of inertia and the torsion potential energy of the polypeptide chain between contact residues.Conse-quently,the protein folding rate can be quantitatively studied from the point of view of dynamics.By comparing theoretical calculations and experimental data on the folding rate of 80 proteins,we successfully validate the view that protein folding is a quantum conformational transition.We conclude that(i) a correlation between the protein folding rate and the contact inertial moment exists;(ii) multi-state protein folding can be regarded as a quantum conformational transition similar to that of two-state proteins but with an intermediate delay.We have estimated the order of magnitude of the time delay;(iii) folding can be classified into two types,exergonic and endergonic.Most of the two-state proteins with higher folding rate are exergonic and most of the multi-state proteins with low folding rate are endergonic.The folding speed limit is determined by exergonic folding.展开更多
文摘Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex couformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer＇s disease.
基金supported by the National Natural Science Foundation of China(31370715,31160181,31360277,30860011)the National Basic Research Program of China(2013CB127500)+1 种基金the Program of Innovation Group of Yunnan Province(2011CI123)Foundation for Key Teacher in Yunnan University(XT412003)
文摘Proteins are essential parts of living organisms and participate in virtually every process within cells. As the genomlc sequences for increasing number of organisms are completed, research into how proteins can perform such a variety of functions has become much more intensive because the value of the genomic sequences relies on the accuracy of understanding the encoded gene products. Although the static three-dimensional structures of many proteins are known, the functions of proteins are ulti- mately governed by their dynamic characteristics, including the folding process, conformational fluctuations, molecular mo- tions, and protein-ligand interactions. In this review, the physicochemical principles underlying these dynamic processes are discussed in depth based on the free energy landscape （FEL） theory. Questions of why and how proteins fold into their native conformational states, why proteins are inherently dynamic, and how their dynamic personalities govern protein functions are answered. This paper will contribute to the understanding of structure-function relationship of proteins in the post-genome era of life science research.
基金Supported by the National Basic Research Program of China under Grant No.2013CB932804the National Natural Science Foundation of China under Grant No.11421063the CAS Biophysics Interdisciplinary Innovation Team Project
文摘To understand how the stabilities of key nuclei fragments affect protein folding dynamics, we simulate by molecular dynamics （MD） simulation in aqueous solution four fragments cut out of a protein G, including one a-helix （seqB： KVFKQYAN）, two -turns （seqA： LNGKTLKG and seqC： YDDATKTF）, and one -strand （seqD： DGEWTYDD）. The Markov State Model clustering method combined with the coarse-grained conformation letters method are employed to analyze the data sampled from 2-#s equilibrium MD simulation trajectories. We find that seqA and seqB have more stable structures than their native structures which become metastable when cut out of the protein structure. As expected, seqD alone is flexible and does not have a stable structure. Throughout our simulations, the native structure of seqC is stable but cannot be reached if starting from a structure other than the native one, implying a funnel-shape free energy landscape of seqC in aqueous solution. All the above results suggest that different nuclei have different formation dynamics during protein folding, which may have a major contribution to the hierarchy of protein folding dynamics.
基金supported by the Distinguished Scientist Award of Inner Mongolia Autonomous Region(2008)a Major Project Fund of Inner Mongolia University of Technology(Grant No.ZD200917)a Project Fund of Inner Mongolia Natural Science(Grant No.2010BS0104)
文摘Protein folding is regarded as a quantum transition between the torsion states of a polypeptide chain.According to the quantum theory of conformational dynamics,we propose the dynamical contact order(DCO) defined as a characteristic of the contact described by the moment of inertia and the torsion potential energy of the polypeptide chain between contact residues.Conse-quently,the protein folding rate can be quantitatively studied from the point of view of dynamics.By comparing theoretical calculations and experimental data on the folding rate of 80 proteins,we successfully validate the view that protein folding is a quantum conformational transition.We conclude that(i) a correlation between the protein folding rate and the contact inertial moment exists;(ii) multi-state protein folding can be regarded as a quantum conformational transition similar to that of two-state proteins but with an intermediate delay.We have estimated the order of magnitude of the time delay;(iii) folding can be classified into two types,exergonic and endergonic.Most of the two-state proteins with higher folding rate are exergonic and most of the multi-state proteins with low folding rate are endergonic.The folding speed limit is determined by exergonic folding.
