Protein dynamics plays a key role in molecular events in the cell. Significant efforts in computational biology have been invested in understanding and modeling conformational dynamics. To this aim, our lab has introduced elastic network models (ENMs) and spectral graph theoretical analysis methods for exploring structural dynamics. These methods have found utility in many applications and have helped us gain insights into the intrinsic, structure-encoded ability of proteins to favor particular changes in conformation, and their relevance to experimentally observed functional substates. We have developed two servers: anisotropic network model (ANM, and the online Gaussian network model (oGNM) for predicting the collective dynamics of known structures, which have been visited over 40,000 times in 2010. On the experimental side, the Protein Data Bank (PDB) now hosts multiple structures for hundreds of proteins, which form heterogeneous structural datasets for the same protein in different forms (e.g., orthologs, mutants, substates visited during an allosteric cycle, or different complexes/assemblies). Our recent work showed that functional changes in structure may be inferred from the principal component analysis (PCA) of these datasets. We also developed a PCA server (PCA_NEST) for analyzing NMR models. Significantly, the information inferred from these experimental datasets, and those predicted by theory and computations, can be advantageously combined to disclose the mechanisms of activation or inhibition of target proteins, and to identify key residues that modulate collective movements. Motivated by the utility of our studies, and the broad use of our existing software and servers, we propose (i) to improve, modernize and integrate our existing software for protein dynamics into an easily modifiable and extensible application programming interface (API), ProDy, which will allow for systematic analysis of experimental data in addition to theoretical predictions, (ii) to extend the interoperability of ProDy API with existing sequence- and structure-databases to enable the assessment of protein family-specific dynamics and (iii) to advance the utility of ProDy through continued development and testing, and establishing its interoperation with molecular dynamics simulation software. The deliverables will include two graphical user interfaces (a VMD plugin and a Chimera extension), and a database and web user interface that will provide convenient access to the full functionality of the software by users without experience in programming. The primarily utility of this set of tools will be elucidating the bridge between structure and function, not only via computational methods that are widely exploited toward this aim, but also by extracting and analyzing all structural data accumulated to date.

Public Health Relevance

It is now widely established that the ability of proteins to undergo collective fluctuations and cooperative rearrangements in their structure is essential to achieving their function. Our lab has developed the anisotropic network model (ANM) and related software and servers to enable users to gain insights into the collective motions accessible to proteins while maintaining their native fold. In view of the growing recognition of the significance of protein dynamics in defining the mechanisms of biomolecular function, and the rapidly growing experimental data on protein structures in multiple forms, we propose herein to modernize and extend our protein dynamics software, ProDy, into an easily modifiable and integrative application programming interface (API). ProDy will not only evaluate protein dynamics using extensive experimental data and analytical tools, but also offer the community a database of dominant motions and accessible conformers for well-studied protein families.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM099738-03
Application #
8604163
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2012-03-26
Project End
2015-12-31
Budget Start
2014-01-01
Budget End
2014-12-31
Support Year
3
Fiscal Year
2014
Total Cost
$260,846
Indirect Cost
$85,346
Name
University of Pittsburgh
Department
Biology
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Kurkcuoglu, Zeynep; Bahar, Ivet; Doruker, Pemra (2016) ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution. J Chem Theory Comput 12:4549-62
Li, Hongchun; Chang, Yuan-Yu; Yang, Lee-Wei et al. (2016) iGNM 2.0: the Gaussian network model database for biomolecular structural dynamics. Nucleic Acids Res 44:D415-22
Jun, Ikhyun; Cheng, Mary Hongying; Sim, Eunji et al. (2016) Pore dilatation increases the bicarbonate permeability of CFTR, ANO1 and glycine receptor anion channels. J Physiol 594:2929-55
Cheng, Mary Hongying; Bahar, Ivet (2015) Molecular Mechanism of Dopamine Transport by Human Dopamine Transporter. Structure 23:2171-81
Savol, Andrej J; Chennubhotla, Chakra S (2015) Approximating frustration scores in complex networks via perturbed Laplacian spectra. Phys Rev E Stat Nonlin Soft Matter Phys 92:062806
Haliloglu, Turkan; Bahar, Ivet (2015) Adaptability of protein structures to enable functional interactions and evolutionary implications. Curr Opin Struct Biol 35:17-23
Bahar, Ivet; Cheng, Mary Hongying; Lee, Ji Young et al. (2015) Structure-Encoded Global Motions and Their Role in Mediating Protein-Substrate Interactions. Biophys J 109:1101-9
Mao, Wenzhi; Kaya, Cihan; Dutta, Anindita et al. (2015) Comparative study of the effectiveness and limitations of current methods for detecting sequence coevolution. Bioinformatics 31:1929-37
Bakan, Ahmet; Kapralov, Alexandr A; Bayir, Hulya et al. (2015) Inhibition of Peroxidase Activity of Cytochrome c: De Novo Compound Discovery and Validation. Mol Pharmacol 88:421-7
Eyal, Eran; Lum, Gengkon; Bahar, Ivet (2015) The anisotropic network model web server at 2015 (ANM 2.0). Bioinformatics 31:1487-9

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