Jianhan Chen Research Group
Computational Biophysics and Biomaterials

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:

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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