Knowledge of three-dimensional protein structure is indispensable in biomedical research. Protein structure and function are intimately linked, and thus structure facilitates drug discovery, aids investigations of protein-protein interactions, informs mutagenesis analysis, guides protein engineering and the design of new proteins, and provides a foundation for understanding the molecular basis of disease. However, the number of protein sequences available in the genomic era far exceeds the capacity of the main experimental structure determination techniques of X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, resulting in a substantial sequence- structure gap. We address this ever-widening gap by developing and disseminating novel protein structure modeling tools. This renewal project is a new collaboration between experts in computational modeling (Cheng) and experimental structural biology (Tanner). We plan to develop innovative, integrated machine learning (e.g., deep learning), data mining and statistical modeling methods to address major challenges in both template-based structure modeling and template-free (ab initio) structure modeling. We will apply these tools to enzymes in the aldehyde dehydrogenase (ALDH) superfamily, a group of enzymes that are involved in numerous important biological processes and implicated in many diseases due to mutations. The ALDH models will be experimentally validated using X-ray crystallography and biochemical assays. Furthermore, we will combine the modeling power of our structural Input-Output hidden Markov model with experimental small- angle X-ray scattering (SAXS) to predict the tertiary structures of large multi-domain proteins. The integration of computational and experimental sciences in this project positions us uniquely in structure modeling space.

Public Health Relevance

Three-dimensional protein structure information is indispensable in modern biomedical research. However, gene sequencing technology has far exceeded the capacity of experimental protein structure determination methods, giving rise to an ever-widening sequence-structure gap. This project addresses the gap by developing new computational methods for predicting protein structure, validating these methods with experiments, and disseminating the methods freely through user-friendly tools and web services.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM093123-05A1
Application #
8961784
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2010-06-01
Project End
2019-07-31
Budget Start
2015-08-01
Budget End
2016-07-31
Support Year
5
Fiscal Year
2015
Total Cost
$326,428
Indirect Cost
$101,428
Name
University of Missouri-Columbia
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
153890272
City
Columbia
State
MO
Country
United States
Zip Code
65211
Korasick, David A; White, Tommi A; Chakravarthy, Srinivas et al. (2018) NAD+ promotes assembly of the active tetramer of aldehyde dehydrogenase 7A1. FEBS Lett 592:3229-3238
Adhikari, Badri; Hou, Jie; Cheng, Jianlin (2018) DNCON2: improved protein contact prediction using two-level deep convolutional neural networks. Bioinformatics 34:1466-1472
Hou, Jie; Adhikari, Badri; Cheng, Jianlin (2018) DeepSF: deep convolutional neural network for mapping protein sequences to folds. Bioinformatics 34:1295-1303
Liu, Li-Kai; Tanner, John J (2018) Crystal Structure of Aldehyde Dehydrogenase 16 Reveals Trans-Hierarchical Structural Similarity and a New Dimer. J Mol Biol :
Adhikari, Badri; Cheng, Jianlin (2018) CONFOLD2: improved contact-driven ab initio protein structure modeling. BMC Bioinformatics 19:22
Korasick, David A; Kon?itíková, Radka; Kope?ná, Martina et al. (2018) Structural and Biochemical Characterization of Aldehyde Dehydrogenase 12, the Last Enzyme of Proline Catabolism in Plants. J Mol Biol :
Adhikari, Badri; Hou, Jie; Cheng, Jianlin (2018) Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning. Proteins 86 Suppl 1:84-96
Keasar, Chen; McGuffin, Liam J; Wallner, Björn et al. (2018) An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Sci Rep 8:9939
Adhikari, Badri; Cheng, Jianlin (2017) Improved protein structure reconstruction using secondary structures, contacts at higher distance thresholds, and non-contacts. BMC Bioinformatics 18:380
Korasick, David A; Tanner, John J; Henzl, Michael T (2017) Impact of disease-Linked mutations targeting the oligomerization interfaces of aldehyde dehydrogenase 7A1. Chem Biol Interact 276:31-39

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