Biomedical subjects

Itay Lotan

Publications and source records attributed to Itay Lotan.

3 recordsLinked to original sources

PubMed2004

Real-space protein-model completion: an inverse-kinematics approach.

Rapid protein-structure determination relies greatly on software that can automatically build a protein model into an experimental electron-density map. In favorable circumstances, various software systems are capable of building over 90% of the final model. However, completeness falls off rapidly with the resolution of the diffraction data. Manual completion of these partial models is usually feasible, but is time-consuming and prone to subjective interpretation. Except for the N- and C-termini of the chain, the end points of each missing fragment are known from the initial model. Hence, fitting fragments reduces to an inverse-kinematics problem. A method has been developed that combines fast inverse-kinematics algorithms with a real-space torsion-angle refinement procedure in a two-stage approach to fit missing main-chain fragments into the electron density between two anchor points. The first stage samples a large number of closing conformations, guided by the electron density. These candidates are ranked according to density fit. In a subsequent refinement stage, optimization steps are projected onto a carefully chosen subspace of conformation space to preserve rigid geometry and closure. Experimental results show that fitted fragments are in excellent agreement with the final refined structure for lengths of up to 12-15 residues in areas of weak or ambiguous electron density, even at medium to low resolution.

Algorithms↗

PubMed2004

Approximation of protein structure for fast similarity measures.

The structural comparison of two proteins comes up in many applications in structural biology where it is often necessary to find similarities in very large conformation sets. This work describes techniques to achieve significant speedup in the computation of structural similarity between two given conformations, at the expense of introducing a small error in the similarity measure. Furthermore, the proposed computational scheme allows for a tradeoff between speedup and error. This scheme exploits the fact that the Calpha representation of a protein conformation contains redundant information, due to the chain topology and limited compactness of proteins. This redundancy can be reduced by approximating subchains of a protein by their centers of mass, resulting in a smaller number of points to describe a conformation. A Haar wavelet analysis of random chains and proteins is used to justify this approximated representation. Similarity measures computed with this representation are highly correlated to the measures computed with the original Calpha representation. Therefore, they can be used in applications where small similarity errors can be tolerated or as fast filters in applications that require exact measures. Computational tests have been conducted on two applications, nearest neighbor search and automatic structural classification.

Computational Biology↗

PubMed2004

Algorithm and data structures for efficient energy maintenance during Monte Carlo simulation of proteins.

Monte Carlo simulation (MCS) is a common methodology to compute pathways and thermodynamic properties of proteins. A simulation run is a series of random steps in conformation space, each perturbing some degrees of freedom of the molecule. A step is accepted with a probability that depends on the change in value of an energy function. Typical energy functions sum many terms. The most costly ones to compute are contributed by atom pairs closer than some cutoff distance. This paper introduces a new method that speeds up MCS by exploiting the facts that proteins are long kinematic chains and that few degrees of freedom are changed at each step. A novel data structure, called the ChainTree, captures both the kinematics and the shape of a protein at successive levels of detail. It is used to efficiently detect self-collision (steric clash between atoms) and/or find all atom pairs contributing to the energy. It also makes it possible to identify partial energy sums left unchanged by a perturbation, thus allowing the energy value to be incrementally updated. Computational tests on four proteins of sizes ranging from 68 to 755 amino acids show that MCS with the ChainTree method is significantly faster (as much as 10 times faster for the largest protein) than with the widely used grid method. They also indicate that speed-up increases with larger proteins.

Algorithms↗