Project Summary:
As a large fraction of the population represented by the baby boomers in the United
States and other countries age, addressing the needs and providing the means for human
health care become a major challenge facing the society in the 21 century. As
skyrocketing medical costs become a major constraint to the development of the society
and the well-being of our future generation, we are in urgent need of developing and
implementing revolutionary ideas that can offer breakthroughs in providing cost-effective
human health care. We propose to establish the Engineering Research Center for
Design of Biotherapeutics.
The rapid progress of biotechnology (e.g., genome sequencing, global gene expression
profiling, structural genomics and proteomics) provides us with a global picture of the
molecular and systematic blueprint of living organisms, (e.g., how the genes are
expressed and how they interact to carry out the functions of a cell). The advances in
computing technology and computational sciences (e.g., algorithms, high performance
computing, data mining, data base analysis, artificial intelligence, robotics, visualization)
provide the necessary tools and platforms to integrate, interpret, and creatively model as
well as make predictions based on such information.
Our vision is to develop and apply quantitative and optimized engineering principles in
computational measurement, design, and analysis to turn biomedical knowledge into
discovery in disease pathogenesis, new diagnostics markers, novel therapeutic drugs, and
new health treatment, as biomedical researchers alone do not have the required expertise
to accomplish these goals. The specific aims are:
1. Global maps of binding proterties of all known protein structures to all
known prescription drugs.
Many drugs have strong efficacy for diseases other than what they were originally
developed. Conversely, side effects of drugs often arise from unintended binding of
drugs to different proteins targets than what are intended. The global maps we
developed will provide clear landmarks about utilizing exiting drugs for new disease
and reducing potential side effects when developing new drugs.
2. Developing design principles and computational tools.
We will develop predictive models and computational tools that can be used to
generate candidate compounds and peptides to inhibit biochemical interactions
important for pathogenesis of various diseases.
3. Discovery of novel diagnostic target proteins and therapeutic compounds.
We will experimentally verify the efficacy of binding properties of designed
compounds and peptides, and further refine their properties so they can be used for
clinical trials. This will be an iterative process.
Project Description
4a. List of Participants and Other Supporters
Title of ERC:
Engineering Research Center for Design of Biotherapeutics.
Lead Institution:
University of Illinois at Chicago,
Chicago,
IL
Core Partner Institutions:
Argonne National Lab,
Boston University
University of California at San Diego,
University of Illinois at Urbana-Champaign,
Argonne,
Boston,
San Diego,
Urbana,
IL
MA
CA
IL
Affliated Outreach Institutions
Chicago State University
Chicago
IL
Leadership Team
Director
Jie Liang
Bioengineering
University of Illinois at Chicago
Deputy Director
Prashant Banerjee
Mechanical Engineering
University of Illinois at Chicago
Educational Program Director
Richard Magin
Bioengineering
University of Illinois at Chicago
Educational Outreach Program Director
Evelyn Esquivel
Bioengineering
University of Illinois at Chicago
Industrial Collaboration and Technology Transfer Director
Dave Carley
Bioengineering
University of Illinois at Chicago
Administrative Director
Ying Zhong
Bioengineering
University of Illinois at Chicago
Thrusts
Theory and Algorithm Group.
Thrust Leader
Rong Chen
Information and Decision Science
UIC
Faculty Members in the Thrust:
Yang Dai
BioE
Bhaskar DasGupta
CS
Eric Jakobsson
NCSA
Simon Kasif
Biomedical Engineering
Jie Liang
BioE
Hui Lu
BioE
Dan Schoenfeld
ECE
Klaus Schulten
Physics
Shankar Subramaniam
BioE
UIC
UIC
UIUC
Boston University
UIC
UIC
UIC
UIUC
UCSD
Software, Simulation, and Application Group.
Thrust Leader
Bob Grossman,
Mathematics UIC
Faculty Members in the Thrust:
Prashant Banerjee
ME
Yang Dai
BioE
Tom DeFanti
CS
Eric Jakobsson
NCSA
Simon Kasif
Biomedical Engineering
Jie Liang
BioE
Bing Liu
CS
Hui Lu
BioE
Klaus Schulten
Physics
Shankar Subramaniam
BioE
Clement Yu
CS
UIC
UIC
UIC
UIUC
Boston University
UIC
UIC
UIC
UIUC
UCSD
UIC
Design and Application Group.
Thrust Leader
Michael Johnson,
Medicinal Chemistry UIC
Faculty Members in the Thrust:
Yang Dai
BioE
Alan Kozikowski
Medicinal Chemistry
Jie Liang
BioE
Hui Lu
BioE
Asra Malik
Pharmacology
Preb Prabhakar
Microbilogy
Brenda Russell
Physiology
John Solaro
Physiology
Carol Westbrook
Medicine
UIC
UIC
UIC
UIC
UIC
UIC
UIC
UIC
Boston University
Non-Faculty Investigators from National Lab:
Brian Kay
Biological Sciences
Andrzej Joachimiak Midwest Center for Structural Genomics
Argonne National Lab
Argonne National Lab
Advisory
Charles DeLisi (nominated)
Roderick Eckenhoff (nominated)
Herbert Edelsbrunner (nominated)
Zhipei Liang (nominated)
Ying Xu (nominated)
Biomedical Engineering
Medicine
CS
ECE
Biochemistry
Industrial/Agency Supporters
Abbot Laboratory (in contact)
Pfizer, Inc (in contact)
Eli Lilly (in contact)
Boston U
U Pennsylvania
Duke University
UIUC
University of Georgia
Pharmaceutical
Pharmaceutical
Pharmaceutical
4b. Vision and Rationale for the ERC.
The vision of the proposed ERC is to bring engineering design principles and
computational strategies to develop disruptive technology for drug discovery. Our focus
will be based on the wealth of information from rapidly accumulating data from genome
sequencing, proteomics mapping, and structural genomics. Our approach is to address
two pressing problems that currently do not have existing solutions. The first problem is
based on the observation that many drugs have strong efficacy for diseases other than
what they were originally developed. Well-known examples include antidepressant drug
prozac and erectile dysfunction drug viagra, each was originally developed for another
disease. Alternative use of existing prescription drug provide a rapid approach for drug
development. However, they are currently discovered through serendipidity. The second
problem is based on the fact that side effects of drugs are a serious issue facing health
care, and they often arise from unintended binding of drugs to different proteins targets
than what are intended. Currently there is no systematic strategy to identify potential side
effects of drugs in development, and side effects are a major cause for the high costs
associated with each new drug.
The proposed research will transform the process of drug discovery as currently practiced
in pharmaceutical and biotech industries. With the planned comprehensive mapping of
protein surfaces and drugs contained in the global atlas of protein targets for all existing
prescription drugs, we can systematically examine potential efficacy of every prescription
drug to every protein structure. The proposed research will provide a clear road map of
unknown efficacies for existing drugs. In addition, it will bring the computational power
into the process of drug discovery and to generate a clear early picture of possible side
effects of drugs under development.
The assembled research team is uniquely qualified for reaching the goals outlined in this
ERC preproposal [1-32]. An important recent develop is the work of the development of
comprehensive surface topographic maps of protein structures at UIC. A series of works
from the PI’s lab have identified the origin of pockets and voids of proteins [7,9,12, 18,
26] and point towards methods for identifying the small number of surfaces that are
biologically important, similarity measure that incorporating shape, sequence, and
evolutionary information for discover similar binding properties of proteins surfaces.
Dr. Yang Dai’s research focuses on the development of several forms of learning models.
The related work includes a new method that enables high quality prediction of drug
activity based on large scale set of descriptors of compounds in Quantitative StructureActivity Relationships (QSAR) analysis; a category of classification methods for the
prediction of peptide binding to molecules; and a graphical learning model that allows the
reconstruction of gene regulatory network based on modulated gene expression data. This
model is particularly useful for the identification of drug target genes and the
investigation of responding genes in drug response cascade.
The research in Lu lab focuses on structure and dynamics modeling of protein binding,
which include protein-protein, protein-DNA and protein-membrane interactions. In
protein-protein interaction, we have developed statistical potentials that can discriminate
true binding from false ones and a threading based interaction prediction protocol.
Furthermore, we have analyzed the dynamics behavior of protein binding site, which can
be used in prediction. In protein-DNA interaction, we have combined machine learning
techniques and biophysical analysis to build a systematic framework to build the
transcription factor prediction and their binding sites in DNA promoter sequences. The
results can be applied to predict the drug effect on gene regulation include both the
protein-drug binding and DNA-drug binding. In protein-membrane interaction, we have
focused on signaling protein and how their binding affinity is changed by interacting with
lipid and drug molecules.
Other major contributors include Dr. Bhaskar DasGupta at UIC, who have developed a
series theoretical framework including information compressed DNA coding for minimal
length discrimination of strains of microbes as bioterror weapons, protein substructure
comparison independent of sequence connections, optimal solution to protein-protein
network based on a formulation of coupled ODEs. Dr. Simon Kasif at Boston University
has pioneered many important computational techniques essential in gene finding and
gene functional annotation, including one of the first Hidden Markov Models used for
genome sequencing. He brings a wealth of experience and in-depth expertise with his
recent work on Gibbs random field and Bayesian network for protein-protein networks.
Dr. Shankar Subramaniam at UCSD pioneered evolutionary approach for system biology,
where various phenotypes of cells are mapped to specific pathways in the regulatory
networks of protein interactions. Dr. Klaus Schulten at UIUC is a world leading expert in
computer simulation of protein dynamics and pioneered in parallel simulation and
interactive simulation of biomolecules. He brings the insight and expertise for large scale
and realized simulation of proteins and their interactions. Dr. Eric Jakobsson at NIH, on
leave from UIUC, is world leader in protein membrane simulation with the focus on ion
channels. He brings expertise in a large class of drug target in ion channels.
In addition, the proposed ERC will be draw on the expertise in visualization and virtual
reality of the EVL lab at UIC, where Associate Director Dr. Prashant Banerjee who
worked closely with Dr. Tom DeFanti in developing the state-of-the-art visualization
technique will lead the efforts on developing immersive environment of protein-drug
discovery virtual environment for iterative refinement of design strategies with medicinal
chemists on this proposal.
In addition to developing breakthrough enabling technology, the proposed ERC will also
have major impact in education. UIC currently has the only Bioinformatics PHD/MS
degree program approved by the Illinois Board of Higher Education in the state of Illinois.
With annual incoming students of 7-8, the proposed research will directly involve student
team in BioE, CS, ECE, ME, and other disciplines in all phases of the proposed design,
computation, analysis, and experimental validation. Currently there are two MD/PHD
students are co-trained in bioinformatics group and UIC medical school, and one PhD
student is placed in the cardiovascular training grant from UIC physiology department.
We plan to strengthen and expand our co-training of students in both computational and
experimental skills.
The most important key barrier along our way in realizing the goals proposed by the ERC
is the integration of theory, computation, and experimental group. To address this issue,
we have assembled a strong research teams addressing each of these areas with cross
appointment, so experts in different area will provide timely advice and their voices will
be attentatively heard. Because the innovative nature of the proposed research, and
because the demand in human and financial resources for carrying out these projects, an
NSF ERC is the only route towards rapid realizing these goals.
4c. Strategic Research Plan and Research Program.
We describe the key aspects of proposed research plan
Global atlas of protein targets for all existing prescription drugs. Based on the
computational methods for characterizing the binding surface properties of proteins, we
are now ready to obtain comprehensive knowledge of both sequence and spatial
similarity of protein pockets and voids on a genome scale. Using newly developed shape
similarity measure oRMSD [12] and the traditional cRMSD , we can discover
functionally relevant similar protein surfaces for many proteins. An example is the
strong similarity relationship between the binding surface of HIV-1 protease and a
chaperone protein HSP-90 [12]. This can be rationalized by the fact that both binds to
peptide or peptide-like substrate in cell events. This functional relationship was
previously unknown, and points to the potential for identifying alternative target proteins
for existing drugs. We believe the proposed global atlas will provide a clear road map of
unknown efficacies for existing drugs [7,12, 13].
Structural Basis of Disease-Causing nsSNPs. An important result from analyzing
protein structure is relating genotypes with phenotypes. From a study of nonsynonymous
SNP, we found that non-disease associated nsSNPs are under different selection pressure
[14]. nsSNPs not associated with disease are occurring more frequently in positions of
genes whose wild-type residue is already different from that of the consensus residue, and
disease-associated nsSNPs are more likely to be mutations from the consensus residues.
This result suggest that consensus residues are phenotypically important. In addition, the
majority (88%) of disease-associated nsSNPs are located in voids or pockets of proteins,
but they are infrequently observed in buried interior (3.2%), or in convex/shallow surface
regions. This suggests that protein-protein interaction and membrane binding interface
are not a great source of disease-causing nsSNPs. For SNPs mapped to protein interior,
disease-associated mutations are more likely to be at conserved residues, but nsSNPs
found in pockets and voids are not strongly conserved. In contrast, nsSNPs in control
dbSNP database do not have these clear patterns [9, 14].
Global image of binding side effects of all known drugs on all known proteins.
Protein targets through systems biology. To further identify drug potency and side
effect, we plan to map all protein-protein and protein-DNA interactions into regulatory,
signaling, and metabolic pathways. Very rarely does a protein perform a single biological
function. Our visual perception involves many proteins (e.g., rhodopsin, G-protein,
phosphodiesterase) that form a complex network [23-25]. Thus the understanding of the
structure and the dynamics of the complex web of interactions is crucial in identifying
drug targets and drug effects. We will focus on protein interaction networks and
transcription factor-DNA interactions that are important for developmental biology and
for cancer development.
Origin of Pockets and Voids in Proteins. From the comparative study of protein
packing using off-lattice models and real protein, we found that the widely used radius of
gyration is a poor parameter describing protein packing [26,28, 32]. Instead, we
developed an alpha contact number that is based on weighted Delaunay triangulation and
alpha shape of the protein [26]. It characterizes protein packing well. We also show that
protein-like scaling relationship between packing density and chain length is observed in
off-lattice sel-avoiding walks. Our results suggest that protein-like packing is a generic
feature of random polymers satisfying loose constraint in compactness. The main
conclusion we draw is that proteins are not optimized by evolution to eliminate packing
voids, and the existence of a large number of pockets and voids are natural results of
basic packing requirement of proteins. This points out the importance of discriminating
those pockets and voids that are biologically relevant from those formed due to random
packing.
Membrane Proteins. Membrane proteins are an important class of proteins of direct
medical relevance, as >50% of drug targets are various G-protein coupled receptors,
which are 7-helical transmembrane proteins. In membrane protein studies, we plan to
develop a set of interhelical three-body interaction parameters for helical membrane
proteins. We plan to identified high propensity triplets, and determine whether they are
involved in interhelical H-bond interactions. We also hope to discover spatial-motifs of
triplet interactions with similar parallel or antiparallel orientations and handedness.
These spatial motifs will be examined for correlation with sequence motifs obtained from
genome analysis. We will study specifically the role of interhelical H-bonds are
important for function and stabilities of Ca$^{++}$-transporting ATPase as a pilot
system. The importance of these studies is that the results will be incorporated in
computational methods for predicting membrane protein structure and the results will be
useful for studying the details of membrane protein folding. The assembled PIs have
extensive experience in studying membrane proteins [15-17, 19, 22, 30, 31].
Comprehensive global structural atlas of protein-protein interaction maps. Protein
drugs are a new class of therapeutic agents that can interfere with many important
protein-protein interactions. They are the wave of the future and represent the fastest
growth area in pharmaceutical industry. We plan to devise a method for engineering
optimally biased peptide libraries, which will lead to the creation of novel protein drugs
with low side effects. The PI’s lab has recently started several projects on protein-protein
interactions and on peptide design [1, 2].
Experimental Design and Test. To test the whether additional efficacy are indeed
found for existing drugs for alternative target proteins, we will first test in vitro using
binding essays to assess the binding affinities of purified target proteins and existing
prescription drug. Dr. Michale Johnson, an world-expert in computational drug
discdovery, who leads the $15million NIH Center of Pharmaceutical Technology at UIC,
will lead in this effort.
Theoretical Development. The outcome of the proposed ERC research will also include
major algorithm and theoretical model development. Specifically, we will introduce
various geometric and topological description of proteins structures, including the
recently developed mixed cell theory from Dr. Edelsbrunner’s group for analyzing the
pattern of protein-ligand and protein-protein interactions. Additionally, we will make
major contributions in development of approximation algorithms for protein structures,
constrained sampling methods for high dimensional space, and forced molecular
dynamics simulations.
Organization and Structure,
Theory and Algorithm Group: formulate problems, develop theoretical models,
and design efficient algorithms for proteins structures, binding surfaces, reconstruction of
evolutionary history, dynamics, protein folding and binding. (Chen, Dai, DasGupta,
Jakobbson, Kasif, Liang, Lu, Schoenfeld, Schulten, Subramaniam)
Software, Simulation, and Database Group: develop and implement software
tools for high performance computing, for interactive modeling and visualization, and for
large scale simulation of interactions of all proteins versus all drugs (Banerjee, DeFanti,
Grossman, Kasif, Liang, Liu, Lu, Schulten, Subramaniam, Tsai, Yu).
Design and Application Group: designing small molecule compounds, peptide
modulators, and protein drugs based on algorithm development and computational
simulations. Design and formulate biological experiments for data gathering, for
prediction verification, and for formulating new design strategy (Dai, Joachimiak, Kay,
Johnson, Liang, Lu). Final tests of potency and efficacy of designed compound, peptides,
and protein drugs in test tube systems and in other assay systems. We will focus on
diseases systems that are currently the research topics of team members. These include:
inflammatory and immune responses and vaccine development of anthrax and other
microbes (Prabhakar), cardiac muscle contraction and heart failure for regulation of
protein synthesis (Solaro, Russell), inflammatory diseases, cancer metastasis (Malik and
Westbrook).
4d.
Education Program,
An independent bioinformatics degree program has been setup within the Bioengineering
department. The program awards PhD and MS degrees in bioinformatics. The graduate
curriculum includes 7 newly developed bioinformatics courses ranging from sequence
analysis to structure and dynamics, as well as related mathematics and algorithms. The
students are also required to take 2 to 3 biology courses from UIC medical school, and
several algorithm and math courses from Engineering campus. Currently there are 30
graduate bioinformatics students which will naturally included in the proposed ERC. The
students will participate with various components of the proposed research activities.
Each year, we have from 50 to 100 applicants with background from engineering to
physics chemistry to biology. We are able to recruit top level graduate students. The three
PhD graduated from our young program are in top labs for postdoc or in big
pharmaceutical companies. Besides the formal training in bioinformatics graduate
program, we also participated in teaching bioinformatics and computational biology in
UIC medical schools such as lectures in courses “Biology methods” and “medical
chemistry”. These activities will be expanded once the ERC has been setup.
Outreach Program, Bioinformatics usually has little exposure to high school student.
Built upon successful experience of UIC Bioengineering high school summer camp, we
will work closely with the Chicago Public School system to increase exposure of
bioinformatics research to high school and community college students from the Chicago
metropolitan area. The bioinformatics lectures and projects assigned to high school
students in the past three BIOE summer camp had very good responses from high school
students. Out future outreach plan will be achieved through: (1) Summer camp activities,
(2) Visits to local high school, (3) Hosting of interns in participating research labs, and (4)
Development of educational and career material on bioinformatics.
Diversity Program, The lead institution UIC is an urban research institution. It has a
much higher percentage of minority and women student bodies that are traditionally
under represented in engineering, providing great opportunity for our diversity program
in training students in teaching, curriculum development, and in research. Our outreach
efforts will also have a special focus to attract students not traditionally represented in
academic research. We will enhance our current collaboration with Truman college, part
of the City Colleges of Chicago. We will co-develop curriculum and train their
instructors for teaching Bioinformatics courses at the Associate Degree level.
4.e.
Industrial Collaboration and Technology Transfer Program. We plan to disseminate
our results through collaborations with industry and transfer technologies developed
during the funded project period. Currently there exist extensive industrial collaborations
and tech transfer activities at participating labs at UIC, Boston U, UIUC, UCSD. We will
continue our summer intern experience with Pfizer and explore additional opportunities
for our students and trainees in industry.