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Our general interests are mainly in the development of advanced computational
methods and their applications to the study of biomolecular structure,
dynamics and function. A particular emphasis has been on
understanding how intrinsic disorder of proteins mediates
function and how such functional mechanisms may be altered in human
diseases. Our research has been supported by NSF, NIH, AHA, and several
local and state funding agencies including the Johnson Center for
Basic Cancer Research. Specifically, we currently focus on:
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1. Development of advanced sampling techniques and accurate
implicit solvent models
Multi-scale enhanced sampling: Sufficient sampling of relevant
biomolecular conformations is crucial for achieving quantitative and
predictive simulations. We recently developed an effective approach where efficient
coarse-grained (CG) models are coupled with atomistic ones to
dramatically accelerate sampling at atomistic level. The bias from the
coupling is removed by Hamiltonian replica exchange, thus allowing one
to benefit simultaneously from faster transitions of CG modeling and
accuracy of atomistic force fields. Initial application of our
multi-scale enhanced sampling (MSES) method to nontrivial beta-hairpins
shows that MSES is up to >100-fold more efficient than temperature
replica exchange (T-RE). Importantly, by coupling the CG and atomistic models only
through "essential" degrees of freedom and using carefully designed
coupling potentials, MSES is highly tunable, scalable to larger
proteins, and relatively robust against artifacts of the CG model
without the later dictating the conformational diffusion. Furthermore,
MSES does not require pre-specified reaction coordinates, knowledge of the
density of states, or modifications to the atomistic energy
function. Further development of MSES is currently a key area of focus
for research in our lab. Specifically, we believe that the efficiency
of MSES can be further improved by developing better CG models and by
optimizing how conformational transitions are communicated between CG
and atomistic models.
Balanced implicit solvent force fields: In parallel, we are
leveraging much greater sampling efficiency of MSES to develop
balanced implicit solvent protein force fields, which can dramatically
extend the accessible simulation timescale by eliminating the explicit
representation of water. With efforts from my lab and others,
persisting problems with the arguably most advanced generalized Born
(GB) class of implicit solvent models are now relatively well
understood. A critical remaining limitation is in the treatment of
nonpolar solvation, where the popular surface area-based models are
insufficient in describing the conformational dependence of nonpolar
solvation. A key focus of our research is to develop better nonpolar
solvation models, through studying key physical properties of nonpolar
solvation, devising efficient numerical methods to model these
properties, and consistently optimizing the solvation model together
with the underlying protein force field.
Funding: These projects are currently supported by an NSF CAREER
award (MCB 0952514, 2010-16), NIH R01-GM114300 (2016-21), and NSF MCB 1817332 (2018-22).
2. Intrinsically disordered proteins: structure, function and disease
(Electrostatically accelerated coupled bindign and folding
of p27, Ganguly et al., JMB 2012)
Intrinsically disordered proteins (IDPs) are an important newly
recognized class of functional proteins that rely on a lack of stable
structures for function. They are highly abundant, play fundamental
roles in biology, and are extensively involved in human
diseases. We have been able to leverage our expertise
in advanced sampling, implicit solvent and NMR, and make progresses
toward establishing an effective computational framework for
reliable atomistic simulations of IDPs, and applying it to
understand the conformational properties of several IDPs and
determine how these properties may mediate IDP interaction and
regulation. A key lesson from our studies is that, although
preformed structures frequently exist in unbound IDPs, regulatory
IDPs tend to follow induced-folding like mechanisms. We
have shown that the somewhat surprising prevalence of
induced folding in IDPs is likely due to the constraint of fast
association required for cellular signaling and
regulation. Combining coarse-grained simulations and experiments, we
have further demonstrated that facile recognition of IDPs often involves a
novel electrostatically accelerated encounter and folding mechanism,
where long-range electrostatic forces not only accelerate protein
encounter, but also promote folding-competent encounter topologies
to enhance the efficiency of IDP folding upon encounter.
Besides efforts to understand the physical principles of IDP structure
and interaction, we have also been focusing on studies of the
structure-function-disease relationship of several important IDPs
including Bcl2 family proteins and tumor suppressor p53. In
particular, p53 is the most frequently mutated protein in
human cancers. Clinical studies have shown that the type of p53
mutation can be linked to cancer prognosis, response to treatment,
and patient survival. It is thus crucial to understand how various
mutations affect p53 at the molecular level, so as to understand the
biological outcomes and assess potential cancer intervention
strategies. The intrinsically disordered N-terminal transactivation
domain (TAD), in particular, is essential for p53's interactions
with multiple signaling networks that mediate the cellular response
to genotoxic stresses. We are combinging atomistic simulations, NMR
and other biophyscial approaches to determine the structural and
functional consequences of numerous cancer-associated mutations in
p53-TAD.
Funding: These projects have been supported by an NSF CAREER
award (MCB 0952514, 2010-16), NIH R01-GM114300 (2016-21), KINBRE (P20
GM103418, 2015-16), and NSF MCB 1817332 (2018-22).
3. Multi-scale simulation of fibril growth and nucleation
Experimental studies of protein aggregation utilize protocols that
accelerate aggregate formation by many orders of magnitude relative
to the multi-decade timescales that characterize the onset of dis-
eases like Alzheimer’s. The gap between in vivo and in vitro
aggregation timescales demands detailed theories of the aggregation
process in order to extrapolate experimental observations toward
physiological conditions. In collaboration with Jeremy Schmit from
Physics, we are developing a multi-scale computational algorithm for
simulating fibril growth and nucleation in atomistic
detail. The algorithm is motivated by a microscopic theory of fibril
elongation developed by Schmit, which identifies the
conformational search over H-bonding states as the slowest step in
the aggregation process (an observation that is in agreement with
recent simulations) and shows that this search can be efficiently
modeled as a random walk in a rugged one-dimensional potential.
Accordingly, a large number of small simulations can be
performed to compute the system diffusion tensor in the reaction
coordinate space predicted by the analytic theory. Ensemble
aggregation rates and pathways can be then computed from Markov
state trajectories. Feasibility of the algorithm has been
illustrated using a model peptide in implicit solvent. We are
currently further developing and validating the algorithma and will
apply it to investigate sequence dependent effects, for example the
difference between Abeta40 and Abeta42, on growth and nucleation rates.
Funding: This project is currently supported by NIH R01-GM107487 (2014-19).
4. Gating and pore-sensor coupling mechanisms of channel proteins.
Ion channels facilitate the flow of ions through cell membranes via opening of their pores in
response to various electrical and chemical signals. The transmembrane pore in ion channels
generally contain structures that control the types of permeating ions, known as the selectivity
filter, and those that open and close to control the flow of ions, known as the gate. Upon sensing
physiological stimulations, the channel proteins change conformation to open the gate and allow
ion passage. In collaboration with Jianmin Cui's lab at Washington
University at St Louis, we combine long-timescale atomistic
simulations and biophysical experiments and exploit the big current
calcium ativated potassium (BK) channels as a model system to
understand the mechanism of ion channel gating and allosteric
coupling between sensor domains and the ion-conducting pore. In
addition, we are working with experimental labs to study the
structure, dynamics and regulation of the recently discovered TMEM16
family of calcium activated chloride channels and lipid scramblases.
Funding: The BK channel project is currently supported by NIH R01-HL142301 (2018-22).
5. Computational characterization and design of novel functional peptides
My lab has also been performing multi-scale modeling to assist the
design and characterization of novel functional peptides including
synthetic ion channels and self-assembly peptide capsules. These
projects are collaborative efforts led by the Tomich lab at KSU. 1) We have
designed a series of pore-forming peptides derived from the
second transmembrane helix of glycine receptor (M2GlyR) alpha1
subunit with many desired properties (solubility, monomeric, spontaneous
insertion, large conductance, etc).
2) we have developed a new class of self-assembling branched
amphiphilic peptide capsules (BAPCs). These vesicles have
unusual but highly desirable properties that could address the
shortcoming of lipid-based carriers. These short vesicle-forming
peptides (15-23 residues) can be synthesized economically and
assembled easily by users. These peptides have already been tested
by collaborating labs to encapsulate a number of solutes and show
low cellular cytotoxicity.
3) In collaboration with the Thai lab at UMass, we are also designing
synthetic polymers for protein binding and sensing. These polymers
are inspired by intrinsically disordered proteins that nature has
evolved for versatile protein recognition and regulation.
Funding: These projects were previously supported by NIH R01
GM074096 (2010-14). We are seeking new funding supports from NSF and
NIH in order to continue to work on these exciting systems.
Want to find out more about our research? Check out our latest
publications, or, simply drop by if you can! We are in LSL S(outh)585.
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