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Khan Le Tuan

Publications and source records attributed to Khan Le Tuan.

2 recordsLinked to original sources

A new protein folding algorithm based on hydrophobic compactness: Rigid Unconnected Secondary Structure Iterative Assembly (RUSSIA). I: Methodology.

We present an algorithm that is able to propose compact models of protein 3D structures, only starting from the prediction of the nature and length of regular secondary structures. Helices are modeled by cylinders and sheets by helicoid surfaces, all strands of a sheet being considered as a single block. It means that relative topology of the strands inside one sheet is a prerequisite. Loops are only considered as constraints, given by the maximal distance between their Calpha extremities according to their sequence length. Unconnected regular secondary structures are reduced to a single point, the center of their hydrophobic faces. These centers are then repeatedly moved in order to obtain a compact hydrophobic core. To prevent secondary structures from interpenetrating, a repulsive term is introduced in the function whose minimization leads to the compact structure. This RUSSIA (Rigid Unconnected Secondary Structure Assembly) algorithm has the advantage of relying on a small number of variables and therefore many initial conformations can be tested. Flexibility is produced in the following way: helices or sheets are allowed to rotate around the direction leading to the center of the model; residues in a sheet can slide along the main direction of the strand where they are embedded. RUSSIA is fast and simple and it produces on a test set several neighbor good models with an r.m.s. to the native structures in the range 1.4-3.7 A. These models can be further treated by statistical potentials used in threading approaches in order to detect the best candidate. The limits of the present method are the following: small proteins with few secondary structures are excluded; multi domain proteins must be split into several compact globular domains from their sequences; sheets of more than five strands and completely buried helices are not treated. In this first paper the algorithm is developed and in Part II, which follows, some applications are presented and the program is evaluated.

Algorithms↗

A new protein folding algorithm based on hydrophobic compactness: Rigid Unconnected Secondary Structure Iterative Assembly (RUSSIA). II: Applications.

The RUSSIA procedure (Rigid Unconnected Secondary Structure Iterative Assembly) produces structural models of cores of small- and medium-sized proteins. Loops are omitted from this treatment and regular secondary structures are reduced to points, the centers of their hydrophobic faces. This methodology relies on the maximum compactness of the hydrophobic residues, as described in detail in Part I. Starting data are the sequence and the predicted limits and natures of regular secondary structures (alpha or beta). Helices are treated as rigid cylinders, whereas beta-strands are collectively taken into account within beta-sheets modeled by helicoid surfaces. Strands are allowed to shift along their mean axis to allow some flexibility and the alpha-helices can be placed on either side of beta-sheets. Numerous initial conformations are produced by discrete rotations of the helices and sheets around the direction going from the center of their hydrophobic face to the global center of the protein. Selection of proposed models is based upon a criterion lying on the minimization of distances separating hydrophobic residues belonging to different regular secondary structures. The procedure is rapid and appears to be robust relative to the quality of starting data (nature and length of regular secondary structures). This dependence of the quality of the model on secondary structure prediction and in particular the beta-sheet topology, is one of the limits of the present algorithm. We present here some results for a set of 12 proteins (alpha, beta and alpha/beta classes) of lengths 40-166 amino acids. The r.m.s. deviations for core models with respect to the native proteins are in the range 1.4-3.7 A.

Algorithms↗