Comparative genomic mapping of uncharacterized canine retinal ESTs to identify novel candidate genes for hereditary retinal disorders.

PURPOSE: To identify the genomic location of previously uncharacterized canine retina-expressed expressed sequence tags (ESTs), and thus identify potential candidate genes for heritable retinal disorders. METHODS: A set of over 500 retinal canine ESTs were mapped onto the canine genome using the RHDF(5000-2) radiation hybrid (RH) panel, and the resulting map positions were compared to their respective localization in the CanFam2 assembly of the canine genome sequence. RESULTS: Unique map positions could be assigned for 99% of the mapped clones, of which only 29% showed significant homology to known RefSeq sequences. A comparison between RH map and sequence assembly indicated some areas of discrepancy. Retinal expressed genes were not concentrated in particular areas of the canine genome, and also were located on the canine Y chromosome (CFAY). Several of the EST clones were located within areas of conserved synteny to human retinal disease loci. CONCLUSIONS: RH mapping of canine retinal ESTs provides insight into the location of potential candidate genes for hereditary retinal disorders, and, by comparison with the assembled canine genome sequence, highlights inconsistencies with the current assembly. Regions of conserved synteny between the canine and the human genomes allow this information to be extrapolated to identify potential positional candidate genes for mapped human retinal disorders. Furthermore, these ESTs can help identify novel or uncharacterized genes of significance for better understanding of retinal morphology, physiology, and pathology.

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests.

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of alpha and alpha+beta proteins [60-70 residues with C(alpha) rms deviation (rmsd) <6 A]. However, for alpha+beta proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two alpha and three alpha+beta, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue alpha+beta protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-A C(alpha) rmsd. So far this protein is the largest alpha+beta protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly alpha-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 A rmsd, except the 32 C-terminal residues, most of which form a beta-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.

The protein folding problem: global optimization of the force fields.

The evolutionary development of a theoretical approach to the protein folding problem, in our laboratory, is traced. The theoretical foundations and the development of a suitable empirical all-atom potential energy function and a global optimization search are examined. Whereas the all-atom approach has thus far succeeded for relatively small molecules and for alpha-helical proteins containing up to 46 residues, it has been necessary to develop a hierarchical approach to treat larger proteins. In the hierarchical approach to single- and multiple-chain proteins, global optimization is carried out for a simplified united residue (UNRES) description of a polypeptide chain to locate the region in which the global minimum lies. Conversion of the UNRES structures in this region to all-atom structures is followed by a local search in this region. The performance of this approach in successive CASP blind tests for predicting protein structure by an ab initio physics-based method is described. Finally, a recent attempt to compute a folding pathway is discussed.

Radiation hybrid map, physical map, and low-pass genomic sequence of the canine prcd region on CFA9 and comparative mapping with the syntenic region on human chromosome 17.

Progressive rod-cone degeneration (prcd) is a canine retinal disease that maps to the centromeric end of CFA9 in a region of synteny with the distal part of HSA17q. As such, prcd has been postulated as the only animal model of RP17, a human retinitis pigmentosa locus that maps to 17q22. In an effort to establish more detailed regions of synteny between dog CFA9 and the HSA17q-ter region, we created a robust gene-enriched CFA9-RH08(3000) map with 34 gene-based markers and 12 microsatellites, with the highest resolution and number of markers for the centromeric end of CFA9. Furthermore, we built an approximately 1.5-Mb physical map containing both GRB2 and GALK1, genes so far identified by meiotic linkage analysis as being closest to the prcd locus, and generated about 1.2 Mb low-pass (3.2x) canine sequence. Canine to human comparative sequence analysis identified 49 transcripts that had been previously mapped to the HSA17q25 region. The generated low-pass canine sequence was annotated with a working draft of human sequence from HSA17q25, and we used this scaffold to order and orient the canine sequence against human. This order and orientation are preliminary, as high-throughput genomic sequencing of HSA17q-ter has not been fully completed.

Conformation-family Monte Carlo: a new method for crystal structure prediction.

A new global optimization method, Conformation-family Monte Carlo, has been developed recently for searching the conformational space of macromolecules. In the present paper, we adapted this method for prediction of crystal structures of organic molecules without assuming any symmetry constraints except the number of molecules in the unit cell. This method maintains a database of low energy structures that are clustered into families. The structures in this database are improved iteratively by a Metropolis-type Monte Carlo procedure together with energy minimization, in which the search is biased toward the regions of the lowest energy families. The Conformation-family Monte Carlo method is applied to a set of nine rigid and flexible organic molecules by using two popular force fields, AMBER and W99. The method performed well for the rigid molecules and reasonably well for the molecules with torsional degrees of freedom.

Recent improvements in prediction of protein structure by global optimization of a potential energy function.

Recent improvements of a hierarchical ab initio or de novo approach for predicting both alpha and beta structures of proteins are described. The united-residue energy function used in this procedure includes multibody interactions from a cumulant expansion of the free energy of polypeptide chains, with their relative weights determined by Z-score optimization. The critical initial stage of the hierarchical procedure involves a search of conformational space by the conformational space annealing (CSA) method, followed by optimization of an all-atom model. The procedure was assessed in a recent blind test of protein structure prediction (CASP4). The resulting lowest-energy structures of the target proteins (ranging in size from 70 to 244 residues) agreed with the experimental structures in many respects. The entire experimental structure of a cyclic alpha-helical protein of 70 residues was predicted to within 4.3 A alpha-carbon (C(alpha)) rms deviation (rmsd) whereas, for other alpha-helical proteins, fragments of roughly 60 residues were predicted to within 6.0 A C(alpha) rmsd. Whereas beta structures can now be predicted with the new procedure, the success rate for alpha/beta- and beta-proteins is lower than that for alpha-proteins at present. For the beta portions of alpha/beta structures, the C(alpha) rmsd's are less than 6.0 A for contiguous fragments of 30-40 residues; for one target, three fragments (of length 10, 23, and 28 residues, respectively) formed a compact part of the tertiary structure with a C(alpha) rmsd less than 6.0 A. Overall, these results constitute an important step toward the ab initio prediction of protein structure solely from the amino acid sequence.

Calculation of protein conformation by global optimization of a potential energy function.

A novel hierarchical approach to protein folding has been applied to compute the unknown structures of seven target proteins provided by CASP3. The approach is based exclusively on the global optimization of a potential energy function for a united-residue model by conformational space annealing, followed by energy refinement using an all-atom potential. Comparison of the submitted models for five globular proteins with the experimental structures shows that the conformations of large fragments (approximately 60 aa) were predicted with rmsds of 4.2-6.8 A for the C alpha atoms. Our lowest-energy models for targets T0056 and T0061 were particularly successful, producing the correct fold of approximately 52% and 80% of the structures, respectively. These results support the thermodynamic hypothesis that protein structure can be computed solely by global optimization of a potential energy function for a given amino acid sequence.

Protein structure prediction by global optimization of a potential energy function.

An approach based exclusively on finding the global minimum of an appropriate potential energy function has been used to predict the unknown structures of five globular proteins with sizes ranging from 89 to 140 amino acid residues. Comparison of the computed lowest-energy structures of two of them (HDEA and MarA) with the crystal structures, released by the Protein Data Bank after the predictions were made, shows that large fragments (61 residues) of both proteins were predicted with rms deviations of 4.2 and 6.0 A for the Calpha atoms, for HDEA and MarA, respectively. This represents 80% and 53% of the observed structures of HDEA and MarA, respectively. Similar rms deviations were obtained for approximately 60-residue fragments of the other three proteins. These results constitute an important step toward the prediction of protein structure based solely on global optimization of a potential energy function for a given amino acid sequence.