Biomedical subjects

Jaroslaw Pillardy

Publications and source records attributed to Jaroslaw Pillardy.

4 recordsLinked to original sources

PubMed2004

Enriching the sequence substitution matrix by structural information.

A fundamental step in homology modeling is the comparison of two protein sequences: a probe sequence with an unknown structure and function and a template sequence for which the structure and function are known. The detection of protein similarities relies on a substitution matrix that scores the proximity of the aligned amino acids. Sequence-to-sequence alignments use symmetric substitution matrices, whereas the threading protocols use asymmetric matrices, testing the fitness of the probe sequence into the structure of the template protein. We propose a linear combination of threading and sequence-alignment scoring function, to produce a single (mixed) scoring table. By fitting a single parameter (which is the relative contribution of the BLOSUM 50 matrix and the threading energy table of THOM2) we obtain a significant increase in prediction capacity in the twilight zone of homology modeling (detecting sequences with <25% sequence identity and with very similar structures). For a difficult test of 176 homologous pairs, with no signal of sequence similarity, the mixed model makes it possible to detect between 40 and 100% more protein pairs than the number of pairs that are detected by pure threading. Surprisingly, the linear combination of the two models is performing better than threading and than sequence alignment when the percentage of sequence identity is low. We finally suggest that further enrichment of substitution matrices, combing more structural descriptors such as exposed surface area, or secondary structure is expected to enhance the signal as well.

Algorithms↗

PubMed2003

Clp protease complexes from photosynthetic and non-photosynthetic plastids and mitochondria of plants, their predicted three-dimensional structures, and functional implications.

Tetradecameric Clp protease core complexes in non-photosynthetic plastids of roots, flower petals, and in chloroplasts of leaves of Arabidopsis thaliana were purified based on native mass and isoelectric point and identified by mass spectrometry. The stoichiometry between the subunits was determined. The protease complex consisted of one to three copies of five different serine-type protease Clp proteins (ClpP1,3-6) and four non-proteolytic ClpR proteins (ClpR1-4). Three-dimensional homology modeling showed that the ClpP/R proteins fit well together in a tetradecameric complex and also indicated unique contributions for each protein. Lateral exit gates for proteolysis products are proposed. In addition, ClpS1,2, unique to land plants, tightly interacted with this core complex, with one copy of each per complex. The three-dimensional modeling show that they do fit well on the axial sites of the ClpPR cores. In contrast to plastids, plant mitochondria contained a single approximately 320-kDa homo-tetradecameric ClpP2 complex, without association of ClpR or ClpS proteins. It is surprising that the Clp core composition appears identical in all three plastid types, despite the remarkable differences in plastid proteome composition. This suggests that regulation of plastid proteolysis by the Clp machinery is not through differential regulation of ClpP/R/S gene expression, but rather through substrate recognition mechanisms and regulated interaction of chaperone-like molecules (ClpS1,2 and others) to the ClpP/R core.

Adenosine Triphosphatases↗

PubMed2002

A method for optimizing potential-energy functions by a hierarchical design of the potential-energy landscape: application to the UNRES force field.

A method for optimizing potential-energy functions of proteins is proposed. The method assumes a hierarchical structure of the energy landscape, which means that the energy decreases as the number of native-like elements in a structure increases, being lowest for structures from the native family and highest for structures with no native-like element. A level of the hierarchy is defined as a family of structures with the same number of native-like elements (or degree of native likeness). Optimization of a potential-energy function is aimed at achieving such a hierarchical structure of the energy landscape by forcing appropriate free-energy gaps between hierarchy levels to place their energies in ascending order. This procedure is different from methods developed thus far, in which the energy gap and/or the Z score between the native structure and all non-native structures are maximized, regardless of the degree of native likeness of the non-native structures. The advantage of this approach lies in reducing the number of structures with decreasing energy, which should ensure the searchability of the potential. The method was tested on two proteins, PDB ID codes and, with an off-lattice united-residue force field. For, the search of the conformational space with the use of the conformational space annealing method and the newly optimized potential-energy function found the native structure very quickly, as opposed to the potential-energy functions obtained by former optimization methods. After even incomplete optimization, the force field obtained by using located the native-like structures of two peptides, and betanova (a designed three-stranded beta-sheet peptide), as the lowest-energy conformations, whereas for the 46-residue N-terminal fragment of staphylococcal protein A, the native-like conformation was the second-lowest-energy conformation and had an energy 2 kcal/mol above that of the lowest-energy structure.

Algorithms↗

PubMed2002

Evolution of physics-based methodology for exploring the conformational energy landscape of proteins.

The evolution of our physics-based computational methods for determining protein conformation without the introduction of secondary-structure predictions, homology modeling, threading, or fragment coupling is described. Initial use of a hard-sphere potential captured much of the structural properties of polypeptide chains, and subsequent more refined force fields, together with efficient methods of global optimization provide indications that progress is being made toward an understanding of the interresidue interactions that underlie protein folding.

Computational Biology↗