Factor analysis scales of generalized amino acid information as applied in predicting interactions between the human amphiphysin-1 SH3 domains and their peptide ligands.
ABSTRACT Factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha-helix and beta-turn propensities, bulky properties, compositional characteristics, local flexibility, and electronic properties, was proposed to represent the structures of the decapeptides binding the human amphiphysin-1 SH3 domains. Parameters being responsible for the binding affinities were selected by genetic algorithm, and a quantitative structure-affinity relationship (QSAR) model by partial least square was established to predict the peptide-SH3 domain interactions. Diversified properties of the residues between P(2) and P(-3) (including P(2) and P(-3)) of the decapeptide (P(4)P(3)P(2)P(1)P(0)P(-1)P(-2)P(-3)P(-4)P(-5)) may contribute remarkable effect to the interactions between the SH3 domain and the decapeptide. Particularly, electronic properties of P(2) may provide relatively large positive contributions to the interactions, and reversely, hydrophobicity of P(2) may be largely negative to the interactions. These results showed that FASGAI vectors can well represent the structural characteristics of the decapeptides. Furthermore, the model obtained, which showed low computational complexity, correlated FASGAI descriptors with the binding affinities as well as that FASGAI vectors may also be applied in QSAR studies of peptides.
- [show abstract] [hide abstract]
ABSTRACT: The relationship among 222 published indices representing various physicochemical and biochemical properties of amino acid residues has been investigated by hierarchical cluster analysis. The clustering result is illustrated by the minimum spanning tree, which is conveniently divided into four regions: alpha and turn propensities, beta propensity, hydrophobicity and other physicochemical properties including, among others, bulkiness of amino acid residues. In addition, several subclasses of hydrophobicity scales have been identified: preference of inside and outside, accessible surface area, surrounding hydrophobicity and other mostly experimental scales including transfer free energy, partition coefficients, HPLC parameters and polarity. Representative amino acid indices are identified in each of these groups. The collection of amino acid indices is a useful resource for empirical analyses correlating sequence information with structural and functional properties of proteins. As an example, the indices that best reproduce the amino acid mutation data matrix are searched against this collection.Protein engineering 08/1988; 2(2):93-100.
- [show abstract] [hide abstract]
ABSTRACT: The Src homology 3 (SH3) region is a small protein domain present in a very large group of proteins, including cytoskeletal elements and signaling proteins. It is believed that SH3 domains serve as modules that mediate protein-protein associations and, along with Src homology 2 (SH2) domains, regulate cytoplasmic signaling. The SH3 binding sites of two SH3 binding proteins were localized to a nine- or ten-amino acid stretch very rich in proline residues. Similar SH3 binding motifs exist in the formins, proteins that function in pattern formation in embryonic limbs of the mouse, and one subtype of the muscarinic acetylcholine receptor. Identification of the SH3 binding site provides a basis for understanding the interaction between the SH3 domains and their targets.Science 03/1993; 259(5098):1157-61. · 31.03 Impact Factor
Article: Beware of q2![show abstract] [hide abstract]
ABSTRACT: Validation is a crucial aspect of any quantitative structure-activity relationship (QSAR) modeling. This paper examines one of the most popular validation criteria, leave-one-out cross-validated R2 (LOO q2). Often, a high value of this statistical characteristic (q2 > 0.5) is considered as a proof of the high predictive ability of the model. In this paper, we show that this assumption is generally incorrect. In the case of 3D QSAR, the lack of the correlation between the high LOO q2 and the high predictive ability of a QSAR model has been established earlier [Pharm. Acta Helv. 70 (1995) 149; J. Chemomet. 10(1996)95; J. Med. Chem. 41 (1998) 2553]. In this paper, we use two-dimensional (2D) molecular descriptors and k nearest neighbors (kNN) QSAR method for the analysis of several datasets. No correlation between the values of q2 for the training set and predictive ability for the test set was found for any of the datasets. Thus, the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power. We argue that this is the general property of QSAR models developed using LOO cross-validation. We emphasize that the external validation is the only way to establish a reliable QSAR model. We formulate a set of criteria for evaluation of predictive ability of QSAR models.Journal of Molecular Graphics and Modelling 01/2002; 20(4):269-76. · 2.33 Impact Factor