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John Kulp, PhD Associate Professor

John Kulp, PhD

Ph: (215) 589-6357
Email: john.kulp@bblumberg.org


(2006) Ph.D., New York University, New York, NY
(2009) Post doctorate, Naval Research Laboratory, Washington, DC

Appointment at the Baruch S. Blumberg Institute: Dr. Kulp is an Associate Professor in the Department of Medicinal Chemistry and the Director of Academic Affairs.

Other appointments: CEO, Conifer Point Pharmaceuticals; CSO Small Molecule PPI Mimetics

Research Overview

Research interest: Dr. Kulp’s vision is to create better drugs by rigorous molecular design.

Research staff: Professors Tianlun Zhou, Tim Block, and other faculty at the Blumberg Institute

Fellows and Students: Dr. Kulp and his staff mentor several high school, undergraduate and graduate students each year.


High Throughput Drug Screening

Principal Investigators: Brielle Dalvano, Research Fellow, and John Kulp, PhD, Computational Chemistry

The Kulp lab is interested in finding economic and robust methods to screen large numbers of drug candidates. Traditional drug screening is done in 96-well plates. The Kulp lab routinely runs 384-well assays and is beginning to use 1,536 well plates. Yes, that is 1,536 reactions on one small plate! The statistics and scientific design become increasingly important in these high-throughput assays. We are applying the screening technologies to the following therapeutics areas:

A. High-throughput polymerase chain reaction assay for the detection of viral DNA
Hepatitis B is a double-stranded DNA virus that is excreted from infected cells in virion particles. These virion particles encapsulate viral DNA in two membranes, including an outer envelope membrane containing hepatitis B virus (HBV) surface proteins and a nucleocapsid of HBV core proteins. The extraction of viral DNA for detection in high-throughput drug screening assays is complicated by the DNA’s outer membrane and nucleocapsid encapsulation. We are developing a method that allows for simple extraction of viral hepatitis B DNA from virion particles and subsequent detection using polymerase chain reaction (PCR). The method aims to allow for a novel assay for PCR based high-throughput drug screening of potential drug candidates to treat HBV that reduces the typically high costs associated with PCR and HBV DNA extraction.

B. Enzyme linked immunosorbent assay (ELISA) for the detection of surface antigen from the hepatitis B virus (HBsAg)
ELISA assays are one of the most common assays run at large pharmaceutical companies because of their high sensitivity and specificity. We are running ELISA assays in 384 well-plates looking for drug like small molecule inhibitors of a viral protein that leads to T cells exhaustion and dampens the immune system allowing the hepatitis B virus to be tolerated. Finding a functional cure for hepatitis B will likely require an immune system adjuvant that will either be host targeting or likely target a direct viral product like the S antigen.

Additional Information:
1. Bleicher, K. H.; Bohm, H. J.; Muller, K.; Alanine, A. I., Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2003, 2 (5), 369-78.
2. Gadkar, V.; Filion, M., New Developments in Quantitative Real-time Polymerase Chain Reaction Technology. Curr Issues Mol Biol 2014, 16, 1-6.
3. Gan, S. D.; Patel, K. R., Enzyme immunoassay and enzyme-linked immunosorbent assay. J Invest Dermatol 2013, 133 (9), e12.
4. Lorenz, T. C., Polymerase chain reaction: basic protocol plus troubleshooting and optimization strategies. J Vis Exp 2012, (63), e3998.


Artificial Intelligence (AI) and Cloud Computing

Principal Investigator: John Kulp, PhD, Computational Chemistry

The goal of this division of the Kulp lab is to create better drugs by rigorous molecular design. Kulp lab research starts with a detailed hypothesis of what protein binding interactions are required of a drug for inhibition or activation. Commercial and proprietary design tools are then used to search ligand-protein or fragment-protein simulation data for poses, ranked by predicted binding affinity, which can be assembled or optimized into custom compounds that satisfy the requirements. The designed compounds are synthesized and tested in assays. With success, this establishes a predictive model to guide subsequent lead optimization. This strategy is protein-centric, complementary to ligand-centric structure-activity relationships (SAR) approaches. The Kulp lab methodology provides access to broad chemical diversity, which is crucial for solving difficult problems in lead identification and optimization. Current ongoing projects in the Kulp lab:

A. Web-based drug design tools (www.boltzmannmaps.com)
The lab aims to provide web-based drug design tools so that researchers can access rigorous molecular modeling in user friendly format. To do this, the lab has made several key scientific breakthroughs in recent years in the field of fragment-based drug design that promise to have a transformative impact on the discovery of drugs. To make these innovations broadly available, the lab has developed a prototype Cloud-hosted web application for chemical modeling and drug discovery including fragment-based design software that is unavailable in any other commercial software package. Using Amazon Web Services (AWS) cloud servers, up to 1,000 simulations can be run in parallel.

B. Anti-aging and senolytic drugs
Living longer has now become a reality as the average life expectancy is higher than in any other period in history. New scientific discoveries over the past few years have shed insight into the molecular mechanisms which are fundamental in improved human life longevity. Therapeutic areas studied include diabetes, cardiovascular diseases, neurological disorders, and cancer, the major risk factors for human mortality. The anti-aging group is focused on six targets of interest and expects experimental screening to begin in 2019. To fund the drug screening, the group plans on a few campaigns to crowdfund the project.

C. Deep learning on how chemists design drugs
Deep learning, a type of machine learning or artificial intelligence, is artificial neural networks, algorithms inspired by the human brain, that learn from large amounts of data. For the Kulp lab, the team is applying deep learning to a vast array of fragment-based information. The lab has, or will soon, collect data on 1,000 fragments on 1,000 therapeutically relevant proteins. That is over 1,000,000 fragment maps. Users search the fragment maps for understanding drug binding, improving drug binding, or improving physiochemical properties. The lab collects that data about how chemists use fragments to design drugs. This wealth of information from the fragment maps to the user information will be inputs for the deep learning, AI, algorithms to determine the methods that generate the safest, most effective drugs.

Additional Information:
1. Cloudsdale, I. S.; Dickson, J. K., Jr.; Barta, T. E.; Grella, B. S.; Smith, E. D.; Kulp, J. L., 3rd; Guarnieri, F.; Kulp, J. L., Jr., Design, synthesis and biological evaluation of renin inhibitors guided by simulated annealing of chemical potential simulations. Bioorganic & Medicinal Chemistry 2017, 25 (15), 3947-3963.
2. Kulp, J. L., 3rd; Cloudsdale, I. S.; Kulp, J. L., Jr.; Guarnieri, F., Hot-spot identification on a broad class of proteins and RNA suggest unifying principles of molecular recognition. PLoS One 2017, 12 (8), e0183327.
3. Kulp, J. L., 3rd; Kulp, J. L., Jr.; Pompliano, D. L.; Guarnieri, F., Diverse fragment clustering and water exclusion identify protein hot spots. Journal of the American Chemical Society 2011, 133 (28), 10740-3.


In addition to his active investigation program, Dr. Kulp, as Director of Academics, oversees educational training at the institute. This includes high school programs, college internships, MS and PhD degrees, post-doctoral training, and doctor of medicine education.

Research Summary

Design and development of therapeutic strategies, to be used in combination, for treatment of hepatitis B virus (HBV).


View Dr. Kulp's recent publications on MyBibliography.


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