Structural and Activity Profile Relationships Between Drug Scaffolds.
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ABSTRACT:
SUBMITTER: Hu Y
PROVIDER: S-EPMC4406972 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDetection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.ResultsHere, we present a new homology detection method based on sequence profile-profile comparison. The method has a number of new features including position-dependent gap penalties and a global score system. Position-dependent gap penalties provide a more biologically relevant way to represent and align protein families as sequence profiles. The global score system enables an analytical solution of the statistical parameters needed to estimate the statistical significance of profile-profile similarities. The new method, together with other state-of-the-art profile-based methods (HHsearch, COMPASS and PSI-BLAST), is benchmarked in all-against-all comparison of a challenging set of SCOP domains that share at most 20% sequence identity. For benchmarking, we use a reference ("gold standard") free model-based evaluation framework. Evaluation results show that at the level of protein domains our method compares favorably to all other tested methods. We also provide examples of the new method outperforming structure-based similarity detection and alignment. The implementation of the new method both as a standalone software package and as a web server is available at http://www.ibt.lt/bioinformatics/coma.ConclusionDue to a number of developments, the new profile-profile comparison method shows an improved ability to match distantly related protein domains. Therefore, the method should be useful for annotation and homology modeling of uncharacterized proteins.
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Project description:Advanced brain imaging techniques make it possible to measure individuals' structural connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural connectome extraction pipeline produces a set of connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural connectomes and analysis method can give superior prediction accuracies compared with alternative connectome constructions and other tensor and network regression methods.
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Project description:BackgroundScaffolds are of great importance in tissue engineering applications as they provide a mechanically supportive environment for cellular activity, which is particularly necessary for hard tissues such as bone. Notably, the mechanical properties of a scaffold vary with differing design parameters such as those related to scaffold height and internal structure. Thus, the present study aimed to explore the relationship between design parameters and mechanical properties of composite polycaprolactone (PCL) and nano-hydroxyapatite (nHAp) scaffolds fabricated by three-dimensional (3D) printing.MethodsWe designed and printed scaffolds with different internal structures (lattice and staggered) and varying heights (4, 6, 8 and 10 layers), and consistent porosity (50%) for the purpose of comparison. Then, we examined the scaffold microstructure (pore size and penetration between layers) using scanning electron microscopy (SEM) and mechanical properties (elastic modulus and yield strength) using compressive testing.ResultsOur results illustrated that the microstructural parameters were related to scaffold design. At higher heights, pore size increased while penetration between layers decreased; thus, mechanical properties were affected. Results of mechanical testing demonstrated that for lattice scaffolds, elastic modulus was similar for 6 vs 4, and 8 vs 4 layers but ~33% lower for 10 layers vs 4 layers. Similarly, yield strength was comparable for 6 vs 4, and 8 vs 4 layers but ~27% lower for 10 layers vs 4 layers. With staggered scaffolds, when compared to 4-layer results, elastic modulus was similar for 6 layers but was ~43% lower for 8 layers and ~38% lower for 10 layers. Staggered scaffolds had ~38%, ~51%, and ~76% lower yield strength when the number of layers were increased from 4 to 6, 8, and 10 layers, respectively. When comparing lattice and staggered scaffolds with the same layer number, elastic modulus was similar, apart from 8-layer scaffolds where the staggered design was ~42% lower than lattice. Yield strength was similar between 4-layer staggered and lattice scaffolds, while staggered scaffolds with 6, 8, and 10 number of layers showed ~43%, ~45%, ~68% lower strength, respectively, than those found in lattice scaffolds with the same layer numbers.ConclusionsMechanical properties of 3D printed scaffolds depended on scaffold height for both lattice and staggered internal structures. Staggered scaffolds had lower mechanical properties than the lattice scaffolds with the same height and were more sensitive to the change in scaffold height. Taken together, lattice scaffolds demonstrated the advantages of more stable mechanical properties over staggered scaffolds. Also, scaffolds with lower height were more promising in terms of mechanical properties compared to scaffolds with greater height.
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Project description:Large numbers of sequence elements have been identified to be highly conserved among vertebrate genomes. These highly conserved elements (HCEs) are often located in or around genes that are involved in transcription regulation and early development. They have been shown to be involved in cis-regulatory activities through both in vivo and additional computational studies. We have investigated the structural relationships between such elements and genes in six vertebrate genomes human, mouse, rat, chicken, zebrafish and tetraodon and detected several thousand cases of conserved HCE-gene associations, and also cases of HCEs with no common target genes. A few examples underscore the potential significance of our findings about several individual genes. We found that the conserved association between HCE/HCEs and gene/genes are not restricted to elements by their absolute distance on the genome. Notably, long-range associations were identified and the molecular functions of the associated genes do not show any particular overrepresentation of the functional categories previously reported. HCEs in close proximity are found to be linked with different set of gene/genes. The results reflect the highly complex correlation between HCEs and their putative target genes.
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