Prediction of β-turns in proteins by 1-4 and 2-3 correlation model

Zhang, Chun‐Ting; Chou, Kou-Chen

doi:10.1002/(sici)1097-0282(199705)41:6<673::aid-bip7>3.0.co;2-n

Cited by 43 publications

(45 citation statements)

References 15 publications

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“…BC021477) was cloned into the pET28a expression vector that contains an N-terminal 6ϫ His-tag. After extensive unsuccessful crystallization trials, surface-site mutations (K141A and K142A) were made within the same construct to enhance crystallization (10)(11)(12)(13). Mutations were introduced with the QuikChange Site-Directed Mutagenesis kit (Stratagene).…”

Section: Methodsmentioning

confidence: 99%

Structure and activity of the axon guidance protein MICAL

Nadella

Bianchet

Gabelli

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

132

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During development, neurons are guided to their targets by shortand long-range attractive and repulsive cues. MICAL, a large multidomain protein, is required for the combined action of semaphorins and plexins in axon guidance. Here, we present the structure of the N-terminal region of MICAL (MICAL fd) determined by x-ray diffraction to 2.0 Å resolution. The structure shows that MICAL fd is an FAD-containing module structurally similar to aromatic hydroxylases and amine oxidases. In addition, we present biochemical data that show that MICAL fd is a flavoenzyme that in the presence of NADPH reduces molecular oxygen to H 2O2 (Km,NAPDH ‫؍‬ 222 M; kcat ‫؍‬ 77 sec ؊1 ), a molecule with known signaling properties. We propose that the H 2O2 produced by this reaction may be one of the signaling molecules involved in axon guidance by MICAL.hydrogen peroxide ͉ hydroxylase ͉ monooxygenase ͉ x-ray diffraction

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Section: Methodsmentioning

confidence: 99%

Structure and activity of the axon guidance protein MICAL

Nadella

Bianchet

Gabelli

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

132

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“…Table 1 shows that factor score of proline (3.847) is larger than that of other amino acids; consequently, it can be concluded that the preference of proline in 7-residue fragments will difficultly lead to the formation of -turns. In the -turn prediction model developed by Zhang and Chou [18], the coupling effects between the 1st and 4th residue and the 2nd and 3rd residue were given a special consideration. When a tetrapeptide folds into a -turn (Figure 2), the interactions between the 1st and 4th residue and the 2nd and 3rd residue play an important role.…”

Section: Characteristic Analysis Of -Turnsmentioning

confidence: 99%

“…Currently, a number of outstanding -turn structure prediction methods [4,[18][19][20][21][22][23][24][25][26][27][28][29] have been developed. In the Chou-Fasman method [4], a set of probabilities was assigned to each residue and the conformational parameters and positional frequencies were determined by calculating the relative frequency of a given type of secondary structure.…”

Section: Introductionmentioning

confidence: 99%

“…In the Chou-Fasman method [4], a set of probabilities was assigned to each residue and the conformational parameters and positional frequencies were determined by calculating the relative frequency of a given type of secondary structure. The 1-4 and 2-3 correlation model [18] took the coupling effects between the 1st and 4th residues and the 2nd and 3rd residues into consideration. The sequence coupled model proposed by Chou [19] within the first-order Markov chain framework took the sequence correlation effect for an entire oligopeptide into account.…”

Section: Introductionmentioning

confidence: 99%

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Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of β-turns in proteins based on a support vector machine model

Liang

Zhao

2010

Sci. China Chem.

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This paper offers a new combined approach to predict and characterize -turns in proteins. The approach includes two key steps, i.e., how to represent the features of -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 -turns in proteins. The second step is to construct a support vector machine (SVM) predictor of -turns based on 426 training proteins by a sevenfold cross validation test. The SVM predictor thus predicted -turns on 547 and 823 proteins by an external validation test, separately. Our results are compared with the previously best known -turn prediction methods and are shown to give comparative performance. Most significantly, the SVM model provides some information related to -turn residues in proteins. The results demonstrate that the present combination approach may be used in the prediction of protein structures.-turns, factor analysis scales of generalized amino acid information, support vector machine

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“…In this case, any predicted beta-turn type is considered as a generic beta-turn and the proposed method is compared against several related methods [13,22,23,27,43,44,[52][53][54] based on 7-fold cross validation on dataset 426, see Table 3. We note that several other beta-turn prediction methods, which are not included in our comparison, were also developed [55,56].…”

Section: Quality Of Beta-turn Vs Non-beta-turn Predictionsmentioning

confidence: 99%

Sequence-Only Based Prediction of β -Turn Location and Type Using Collocation of Amino Acid Pairs

Campbell¹,

Kurgan²

2008

TOBIOIJ

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Development of accurate -turn (beta-turn) type prediction methods would contribute towards the prediction of the tertiary protein structure and would provide useful insights/inputs for the fold recognition and drug design. Only one existing sequence-only method is available for the prediction of beta-turn types (for type I and II) for the entire protein chains, while the proposed method allows for prediction of type I, II, IV, VII, and non-specific (NS) beta-turns, filling in the gap. The proposed predictor, which is based solely on protein sequence, is shown to provide similar performance to other sequence-only methods for prediction of beta-turns and beta-turn types. The main advantage of the proposed method is simplicity and interpretability of the underlying model. We developed novel sequence-based features that allow identifying beta-turns types and differentiating them from non-beta-turns. The features, which are based on tetrapeptides (entire beta-turns) rather than a window centered over the predicted residues as in the case of recent competing methods, provide a more biologically sound model. They include 12 features based on collocation of amino acid pairs, focusing on amino acids (Gly, Asp, and Asn) that are known to be predisposed to form beta-turns. At the same time, our model also includes features that are geared towards exclusion of non-beta-turns, which are based on amino acids known to be strongly detrimental to formation of beta-turns (Met, Ile, Leu, and Val).

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Prediction of β-turns in proteins by 1-4 and 2-3 correlation model

Cited by 43 publications

References 15 publications

Structure and activity of the axon guidance protein MICAL

Structure and activity of the axon guidance protein MICAL

Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of β-turns in proteins based on a support vector machine model

Sequence-Only Based Prediction of β -Turn Location and Type Using Collocation of Amino Acid Pairs

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