Biomedical subjects

Thomas Hamelryck

Publications and source records attributed to Thomas Hamelryck.

4 recordsLinked to original sources

PubMed2005

Full cyclic coordinate descent: solving the protein loop closure problem in Calpha space.

BACKGROUND: Various forms of the so-called loop closure problem are crucial to protein structure prediction methods. Given an N- and a C-terminal end, the problem consists of finding a suitable segment of a certain length that bridges the ends seamlessly. In homology modelling, the problem arises in predicting loop regions. In de novo protein structure prediction, the problem is encountered when implementing local moves for Markov Chain Monte Carlo simulations. Most loop closure algorithms keep the bond angles fixed or semi-fixed, and only vary the dihedral angles. This is appropriate for a full-atom protein backbone, since the bond angles can be considered as fixed, while the (phi, psi) dihedral angles are variable. However, many de novo structure prediction methods use protein models that only consist of Calpha atoms, or otherwise do not make use of all backbone atoms. These methods require a method that alters both bond and dihedral angles, since the pseudo bond angle between three consecutive Calpha atoms also varies considerably. RESULTS: Here we present a method that solves the loop closure problem for Calpha only protein models. We developed a variant of Cyclic Coordinate Descent (CCD), an inverse kinematics method from the field of robotics, which was recently applied to the loop closure problem. Since the method alters both bond and dihedral angles, which is equivalent to applying a full rotation matrix, we call our method Full CCD (FCDD). FCCD replaces CCD's vector-based optimization of a rotation around an axis with a singular value decomposition-based optimization of a general rotation matrix. The method is easy to implement and numerically stable. CONCLUSION: We tested the method's performance on sets of random protein Calpha segments between 5 and 30 amino acids long, and a number of loops of length 4, 8 and 12. FCCD is fast, has a high success rate and readily generates conformations close to those of real loops. The presence of constraints on the angles only has a small effect on the performance. A reference implementation of FCCD in Python is available as supplementary information.

Algorithms↗

PubMed2005

An amino acid has two sides: a new 2D measure provides a different view of solvent exposure.

The concept of amino acid solvent exposure is crucial for understanding and predicting various aspects of protein structure and function. The traditional measures of solvent exposure however suffer from various shortcomings, like for example the inability to distinguish exposed, partly exposed, buried, and deeply buried residues. This article introduces a new measure of solvent exposure called Half-Sphere Exposure that addresses many of the shortcomings of other methods. The new measure outperforms other measures with respect to correlation with protein stability, conservation among fold homologs, amino acid-type dependency and interpretation. The measure consists of the number of Calpha atoms in two half spheres around a residue's Calpha atom. Conceptually, one of the half spheres corresponds to the side chain's neighborhood, the other half sphere being in the opposite direction. We show here that the two half spheres correspond to two regions around an amino acid that are surprisingly distinct in terms of geometry and energy. This aspect of protein structure introduced here forms the basis of the Half-Sphere Exposure measure. The results strongly suggest that in many respects, a 2D measure is inherently much better suited to describe solvent exposure than the traditional 1D measures. Importantly, Half-Sphere Exposure can be calculated from the Calpha atom coordinates only, which abolishes the need for a full-atom model to calculate solvent exposure. Hence, the measure can be used in protein structure prediction methods that are based on various simplified models. Half-Sphere Exposure has great potential for use in protein structure prediction and analysis.

Amino Acids↗

PubMed2003

PDB file parser and structure class implemented in Python.

UNLABELLED: The biopython project provides a set of bioinformatics tools implemented in Python. Recently, biopython was extended with a set of modules that deal with macromolecular structure. Biopython now contains a parser for PDB files that makes the atomic information available in an easy-to-use but powerful data structure. The parser and data structure deal with features that are often left out or handled inadequately by other packages, e.g. atom and residue disorder (if point mutants are present in the crystal), anisotropic B factors, multiple models and insertion codes. In addition, the parser performs some sanity checking to detect obvious errors. AVAILABILITY: The Biopython distribution (including source code and documentation) is freely available (under the Biopython license) from http://www.biopython.org

Computer Simulation↗

PubMed2003

Efficient identification of side-chain patterns using a multidimensional index tree.

Convergent evolution often produces similar functional sites in nonhomologous proteins. The identification of these sites can make it possible to infer function from structure, to pinpoint the location of a functional site, to identify enzymes with similar enzymatic mechanisms, or to discover putative functional sites. In this article, a novel method is presented that (a) queries a database of protein structures for the occurrence of a given side chain pattern and (b) identifies interesting side-chain patterns in a given structure. For efficiency and to make a robust statistical evaluation of the significance of a similarity possible, patterns of three residues (or triads) are considered. Each triad is encoded as a high-dimensional vector and stored in an SR (Sphere/Rectangle) tree, an efficient multidimensional index tree. Identifying similar triads can then be reformulated as identifying neighboring vectors. The method deals with many features that otherwise complicate the identification of meaningful patterns: shifted backbone positions, conservative substitutions, various atom label ambiguities and mirror imaged geometries. The combined treatment of these features leads to the identification of previously unidentified patterns. In particular, the identification of mirror imaged side-chain patterns is unique to the here-described method. Interesting triads in a given structure can be identified by extracting all triads and comparing them with a database of triads involved in ligand binding. The approach was tested by an all-against-all comparison of unique representatives of all SCOP superfamilies. New findings include mirror imaged metal binding and active sites, and a putative active site in bacterial luciferase.

3-Isopropylmalate Dehydrogenase↗