An inspection of the φ-ψ angle distribution strongly suggests that protein folding is highly constrained. A number of researchers have even suggested that a relatively small set of discrete φ-ψ regions might be su...An inspection of the φ-ψ angle distribution strongly suggests that protein folding is highly constrained. A number of researchers have even suggested that a relatively small set of discrete φ-ψ regions might be sufficient to describe most protein conformation. The total of 541 tight turns from 101 non-identical proteins were extracted form Brookhaven DataBank. The dihedral values of tight turns were scattered into the seven regions on the Ramachandran plot. These seven regions were called A1, A2, B1, B2, B22, T1 and T2. A1 and A2 are the traditional a-helix regions, B1, B2 and B22 the β-strand regions, Tl and T2 the β-turn regions. The A2 and T2 regions were not defined as 'discrete' or single points but rather as one dimensional extended states. Based on the geometry of the two central residues of the tight turns, the new classification of β-turn was defined. This classification of the majority of β-turns fell into only six of the possible forty nine region combinations and were identifiable with the traditional nomenclature of Venkat-achalam(1), but much simpler.The function of β-turn in the conformation of proteins was studied. The hydrophobicity for different type turns was discussed. It shows that β-turns have very strong hydrophilic property, so they are usually situated at the folding protein surface. The features of β-turn and its amino acid distribution in this 541 β-turn group and different type β-turn were given.展开更多
This paper offers a new combined approach to predict and characterize β-turns in proteins.The approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first st...This paper offers a new combined approach to predict and characterize β-turns in proteins.The approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first step is to use factor analysis scales of generalized amino acid information(FASGAI),involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in proteins.The second step is to construct a support vector machine(SVM) predictor of β-turns based on 426 training proteins by a sevenfold cross validation test.The SVM predictor thus predicted β-turns on 547 and 823 proteins by an external validation test,separately.Our results are compared with the previously best known β-turn prediction methods and are shown to give comparative performance.Most significantly,the SVM model provides some information related to β-turn residues in proteins.The results demonstrate that the present combination approach may be used in the prediction of protein structures.展开更多
针对目标物的结构特征,设计C-端和N-端二肽模块,通过液相分段合成法,分段合成Boc-Val-D-Pro-OH、H-Gly-Leu-Obn二肽模块,再经两段缩合形成具有构象限制性模块D-Pro-Gly内核的β-转角四肽化合物:H-Val-D-Pro-Gly-Leu-OH,总产率达到45.7%...针对目标物的结构特征,设计C-端和N-端二肽模块,通过液相分段合成法,分段合成Boc-Val-D-Pro-OH、H-Gly-Leu-Obn二肽模块,再经两段缩合形成具有构象限制性模块D-Pro-Gly内核的β-转角四肽化合物:H-Val-D-Pro-Gly-Leu-OH,总产率达到45.7%。关键中间体与最终产物的化学结构经红外、1H NMR、13 C NMR、MS-ESI和HRMS等表征及分析予以确认。展开更多
基金This work was supported by the National Natural Sciences Foundation.
文摘An inspection of the φ-ψ angle distribution strongly suggests that protein folding is highly constrained. A number of researchers have even suggested that a relatively small set of discrete φ-ψ regions might be sufficient to describe most protein conformation. The total of 541 tight turns from 101 non-identical proteins were extracted form Brookhaven DataBank. The dihedral values of tight turns were scattered into the seven regions on the Ramachandran plot. These seven regions were called A1, A2, B1, B2, B22, T1 and T2. A1 and A2 are the traditional a-helix regions, B1, B2 and B22 the β-strand regions, Tl and T2 the β-turn regions. The A2 and T2 regions were not defined as 'discrete' or single points but rather as one dimensional extended states. Based on the geometry of the two central residues of the tight turns, the new classification of β-turn was defined. This classification of the majority of β-turns fell into only six of the possible forty nine region combinations and were identifiable with the traditional nomenclature of Venkat-achalam(1), but much simpler.The function of β-turn in the conformation of proteins was studied. The hydrophobicity for different type turns was discussed. It shows that β-turns have very strong hydrophilic property, so they are usually situated at the folding protein surface. The features of β-turn and its amino acid distribution in this 541 β-turn group and different type β-turn were given.
基金supported by the National Natural Science Foundation of China(10901169)Innovation Ability Training Foundation of Chongqing University(CDCX008)
文摘This paper offers a new combined approach to predict and characterize β-turns in proteins.The approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first step is to use factor analysis scales of generalized amino acid information(FASGAI),involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in proteins.The second step is to construct a support vector machine(SVM) predictor of β-turns based on 426 training proteins by a sevenfold cross validation test.The SVM predictor thus predicted β-turns on 547 and 823 proteins by an external validation test,separately.Our results are compared with the previously best known β-turn prediction methods and are shown to give comparative performance.Most significantly,the SVM model provides some information related to β-turn residues in proteins.The results demonstrate that the present combination approach may be used in the prediction of protein structures.
基金supported by grants from The Science Foundation for Distinguished Young Scholars of Hunan Province,China(10JJ1005)The Research Fund for The Doctoral Program of Higher Education of China(200805370002)~~
文摘针对目标物的结构特征,设计C-端和N-端二肽模块,通过液相分段合成法,分段合成Boc-Val-D-Pro-OH、H-Gly-Leu-Obn二肽模块,再经两段缩合形成具有构象限制性模块D-Pro-Gly内核的β-转角四肽化合物:H-Val-D-Pro-Gly-Leu-OH,总产率达到45.7%。关键中间体与最终产物的化学结构经红外、1H NMR、13 C NMR、MS-ESI和HRMS等表征及分析予以确认。
