A gap exists as to how to interpret the information in the enormous number of sequenced human exomes in terms of the functional consequences of the observed variations in amino acids and their connection to human diseases. This gap also underlies the failure to develop drugs, without side effects, to treat these diseases. This failureis exacerbated by the fact that a given drug molecule binds to different proteins involved in numerous cellular processes. This proposal lays out the details as to how and why these problems occur, and in the context of protein structure, how our existing and proposed progress can help surmount them. We first elucidate the design principles underlying protein structure and function and then apply them to repurpose FDA approved drugs to treat Mendelian diseases and to identify the genetic variations underlying such diseases. We begin by examining whether the stereo chemical space of small molecule drugs and endogenous metabolites is complete and also the differences in the properties of drugs and metabolites. From these analyses, we will suggest how binding specificity might emerge from a highly promiscuous background. This might enable the design of better drugs with minimal side effects and a better understanding of how cells work. Employing these insights, we then develop better structure-based approaches to virtual ligand screening and enzyme function inference. The ability to predict enzymatic function is particularly essential as residue mutations associated wit loss of enzymatic function are the most important missense mutations associated with Mendelian disease. These approaches will use the conservation of ligand-protein microenvironments in stereochemically similar ligand binding sites or active sites in different proteins, regardless of their evolutionary relationship. We will explore the biochemical consequences of a class of enzymes that we discovered - dizymes, single domain proteins that perform two different enzymatic activities at two different active sites. For representative cases, we will experimentally test our predictions of ligand binding and enzymatic activity and their influence on cellular biochemical function. All developed tools will be combined in a comprehensive exome annotation approach. First, it will identify disease associated residue variations. Then, it will predict diseases a protein might be associated with and suggest the best protein targets. Finally, it will suggest what might be the best drugs to treat the disease.

Public Health Relevance

In principle, the information provided by the ~228,000 human exomes sequenced this year could provide tremendous insights into personalized disease diagnosis and treatment. However, this potential is often unrealized as many genetic variations in an exome are of unknown significance and small molecule therapies to redress the functional effects of the associated disease(s) are unknown. This project will develop the tools needed for a comprehensive approach to exome annotation that will suggest which variations might have significant disease association, what diseases they might cause, and what repurposed drugs might assist in the treatment of the identified disease(s).

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Unknown (R35)
Project #
5R35GM118039-03
Application #
9479167
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Lyster, Peter
Project Start
2016-05-06
Project End
2021-04-30
Budget Start
2018-05-01
Budget End
2019-04-30
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Georgia Institute of Technology
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
097394084
City
Atlanta
State
GA
Country
United States
Zip Code
30318
Srinivasan, Bharath; Tonddast-Navaei, Sam; Roy, Ambrish et al. (2018) Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs. Med Res Rev :
Eimon, Peter M; Ghannad-Rezaie, Mostafa; De Rienzo, Gianluca et al. (2018) Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects. Nat Commun 9:219
Snell, Terry W; Johnston, Rachel K; Matthews, Amelia B et al. (2018) Repurposed FDA-approved drugs targeting genes influencing aging can extend lifespan and healthspan in rotifers. Biogerontology 19:145-157
Cao, Hongnan; Gao, Mu; Zhou, Hongyi et al. (2018) The crystal structure of a tetrahydrofolate-bound dihydrofolate reductase reveals the origin of slow product release. Commun Biol 1:226
Tonddast-Navaei, Sam; Srinivasan, Bharath; Skolnick, Jeffrey (2017) On the importance of composite protein multiple ligand interactions in protein pockets. J Comput Chem 38:1252-1259
Srinivasan, Bharath; Tonddast-Navaei, Sam; Skolnick, Jeffrey (2017) Pocket detection and interaction-weighted ligand-similarity search yields novel high-affinity binders for Myocilin-OLF, a protein implicated in glaucoma. Bioorg Med Chem Lett 27:4133-4139
Srinivasan, Bharath; Rodrigues, João V; Tonddast-Navaei, Sam et al. (2017) Rational Design of Novel Allosteric Dihydrofolate Reductase Inhibitors Showing Antibacterial Effects on Drug-Resistant Escherichia coli Escape Variants. ACS Chem Biol 12:1848-1857
Skolnick, Jeffrey; Zhou, Hongyi (2017) Why Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods? J Phys Chem B 121:3546-3554
Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey (2016) ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures. PLoS One 11:e0150965
Srinivasan, Bharath; Marks, Hanna; Mitra, Sreyoshi et al. (2016) Catalytic and substrate promiscuity: distinct multiple chemistries catalysed by the phosphatase domain of receptor protein tyrosine phosphatase. Biochem J 473:2165-77

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