Tools for integrated sequence-structure analysis with UCSF Chimera

BACKGROUND: Comparing related structures and viewing the structures in the context of sequence alignments are important tasks in protein structure-function research. While many programs exist for individual aspects of such work, there is a need for interactive visualization tools that: (a) provide a deep integration of sequence and structure, far beyond mapping where a sequence region falls in the structure and vice versa; (b) facilitate changing data of one type based on the other (for example, using only sequence-conserved residues to match structures, or adjusting a sequence alignment based on spatial fit); (c) can be used with a researcher's own data, including arbitrary sequence alignments and annotations, closely or distantly related sets of proteins, etc.; and (d) interoperate with each other and with a full complement of molecular graphics features. We describe enhancements to UCSF Chimera to achieve these goals. RESULTS: The molecular graphics program UCSF Chimera includes a suite of tools for interactive analyses of sequences and structures. Structures automatically associate with sequences in imported alignments, allowing many kinds of crosstalk. A novel method is provided to superimpose structures in the absence of a pre-existing sequence alignment. The method uses both sequence and secondary structure, and can match even structures with very low sequence identity. Another tool constructs structure-based sequence alignments from superpositions of two or more proteins. Chimera is designed to be extensible, and mechanisms for incorporating user-specific data without Chimera code development are also provided. CONCLUSION: The tools described here apply to many problems involving comparison and analysis of protein structures and their sequences. Chimera includes complete documentation and is intended for use by a wide range of scientists, not just those in the computational disciplines. UCSF Chimera is free for non-commercial use and is available for Microsoft Windows, Apple Mac OS X, Linux, and other platforms from

Elastic Network Models in Biology: From Protein Mode Spectra to Chromatin Dynamics

Biomacromolecules perform their functions by accessing conformations energetically favored by their structure-encoded equilibrium dynamics. Elastic network model (ENM) analysis has been widely used to decompose the equilibrium dynamics of a given molecule into a spectrum of modes of motions, which separates robust, global motions from local fluctuations. The scalability and flexibility of the ENMs permit us to efficiently analyze the spectral dynamics of large systems or perform comparative analysis for large datasets of structures. I showed in this thesis how ENMs can be adapted (1) to analyze protein superfamilies that share similar tertiary structures but may differ in their sequence and functional dynamics, and (2) to analyze chromatin dynamics using contact data from Hi-C experiments, and (3) to perform a comparative analysis of genome topology across different types of cell lines. The first study showed that protein family members share conserved, highly cooperative (global) modes of motion. A low-to-intermediate frequency spectral regime was shown to have a maximal impact on the functional differentiation of families into subfamilies. The second study demonstrated the Gaussian Network Model (GNM) can accurately model chromosomal mobility and couplings between genomic loci at multiple scales: it can quantify the spatial fluctuations in the positions of gene loci, detect large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. The third study revealed close similarities between chromosomal dynamics across different cell lines on a global scale, but notable cell-specific variations in the spatial fluctuations of genomic loci. It also called attention to the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation. Together, these studies provide a comprehensive view of the versatility and utility of the ENMs in analyzing spatial dynamics of biomolecules, from individual proteins to the entire chromatin