We care about three components of simulations: the development of accurate physics-based models, development of efficient sampling techniques, and application of the former to systems of broad biological interest.
Many groups have effectively used computational techniques to make important progress, yet the biomolecular simulation field is still in its infancy. There are many challenges in modeling the complex biophysics, as well as in the relatively short timescale accessible to direct simulation. We continue to develop more accurate models and novel methods that are specifically designed to harness the next generation of massively parallel supercomputers to solve biological problems such as drug discovery.
Simulations based on classical mechanical force fields can expose dynamics on the femtosecond to microsecond timescales. However, the force fields and solvent models used must be accurate enough for the conformations observed to correspond to those in reality. The Simmerling Lab is one of 6 that lead the development of the Amber molecular simulation software, which is in use worldwide by over 800 research labs. The Simmerling Lab also developed an atomic-detail energy function (ff99SB and ff14SB) that is widely considered to provide the most accurate model currently available for protein dynamics, and currently has over 6,000 citations.
GB-neck implicit solvent models
Molecular Dynamics simulations have the potential to supplement experimental studies on RNA. Currently, the quality of an RNA simulation is hard to assess, as RNA's slow folding dynamics and the need for explicit solvent impairs precision. We propose using the newly released GB-Neck2 implicit solvent model to greatly accelerate sampling, thus eliminating the precision problem. Our work involves benchmarking GB-Neck2 implicit solvent simulations against well converged explicit solvent simulations of small systems, and applying implicit solvent simulations to larger systems which are unfeasible in explicit solvent.
Efficient sampling techniques
Hybrid solvent REMD
The development of continuum implicit solvent models has made microsecond long protein and nucleic acid folding simulations accessible, thereby providing data to a degree of precision which is difficult to obtain with explicit solvent simulations. While this has been a significant step forward, unfortunately, the increase in precision comes at a loss of accuracy. We are currently working on improving the hybrid-solvent Replica Exchange Molecular Dynamics (HREMD) method which could potentially improve the accuracy of the ensembles generated via implicit solvent simulations.
Biomolecules undergo constant structural changes as they perform their functions. These changes range from small fluctuations of ligands bound tightly to a receptor, to larger but transient breathing events, and even adoption of completely different tertiary structures as occurs during protein folding. In the Simmerling group, we are interested in gaining insight into the biophysics of these changes, the interactions that drive them, and how they are modified in cases of disease or drug resistance. Since most experimental techniques provide averages over time and/or macroscopic numbers of molecules, we use a wide range of computer simulation methods to model these systems and understand the coupling between structure, energy, and dynamics. Each of our projects is closely coupled to experimental work by our collaborators.
Recognition and repair of damaged DNA
Although the integrity of DNA is essential to maintaining an organism’s genetic code, DNA is continually undergoing a process of damage and repair (thousands of times a day in each cell). While some types of damage involve large and bulky adducts, others involve minor chemical changes that do not appear at first to have a significant impact on DNA structure or stability. We use simulations to help understand how DNA repair enzymes can recognize damaged bases from a vast excess of normal DNA, bind to them with striking specificity, and repair the damage.
The role of protein dynamics in ligand binding and drug resistance
Many biological molecules, including important drug targets, change conformation as they perform their function. We aim to understand these dynamic events, and to investigate whether targeting the mechanism of conformational change may be more effective therapeutic than the design of inhibitors that mimic the substrate’s chemical properties. The is especially important in cases like HIV-1 protease, where it is believed that drug resistance arises from mutations that change the flexibility of the enzyme, making inhibitors less potent while maintaining function. We have published a series of papers on this important model system, including studies that revealed the opening mechanism, demonstrated that crystal packing can provide misleading structural data, showed how ligands can gain access to the binding site and inactivate the enzyme, and provided new insight into how multi-drug resistance arises from mutations that modulate protease dynamics.
Unfolded states and folding pathways
Understanding how and why proteins fold remains one of the grand challenges of science. Simulations have the potential to provide a unique perspective into this complex process, particularly for the disordered unfolded state that is difficult to characterize experimentally. Rather than focusing on predicting the native structure of a protein, our work emphasizes the folding process, and also on how residual interactions in the unfolded state can affect folding behavior. In 2002, we published the first successful simulations that were able to use atomic-detail dynamics simulations to predict an accurate structure for a small protein prior to the release of experimental coordinates. We continue to expand this work toward understanding the folding of other small model proteins and improving the methods used by many labs to study folding.
Private wiki for Simm members
Members of the Simmerling group can access our private wiki for more detail information on research, tutorials, advice and information to help them succeed at our lab and in their program at SBU. If you are new, email Prof. Simmerling for wiki access.