Thomas J. Lane

To Milliseconds and Beyond: Challenges in the Simulation of Protein Folding

Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.

MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

As molecular dynamics (MD) simulations continue to evolve into powerful computational tools for studying complex biomolecular systems, the necessity of flexible and easy-to-use software tools for the analysis of these simulations is growing. We have developed MDTraj, a modern, lightweight, and fast software package for analyzing MD simulations. MDTraj reads and writes trajectory data in a wide variety of commonly used formats. It provides a large number of trajectory analysis capabilities including minimal root-mean-square-deviation calculations, secondary structure assignment, and the extraction of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between MD data and the rapidly growing collection of industry-standard statistical analysis and visualization tools in Python. MDTraj is a powerful and user-friendly software package that simplifies the analysis of MD data and connects these datasets with the modern interactive data science software ecosystem in Python.

Markov State Model Reveals Folding and Functional Dynamics in Ultra-Long MD Trajectories

Two strategies have been recently employed to push molecular simulation to long, biologically relevant time scales: projection-based analysis of results from specialized hardware producing a small number of ultralong trajectories and the statistical interpretation of massive parallel sampling performed with Markov state models (MSMs). Here, we assess the MSM as an analysis method by constructing a Markov model from ultralong trajectories, specifically two previously reported 100 μs trajectories of the FiP35 WW domain (Shaw, D. E. Science 2010, 330, 341-346). We find that the MSM approach yields novel insights. It discovers new statistically significant folding pathways, in which either beta-hairpin of the WW domain can form first. The rates of this process approach experimental values in a direct quantitative comparison (time scales of 5.0 μs and 100 ns), within a factor of ∼2. Finally, the hub-like topology of the MSM and identification of a holo conformation predicts how WW domains may function through a conformational selection mechanism.

Structure of photosystem II and substrate binding at room temperature.

Light-induced oxidation of water by photosystem II (PS II) in plants, algae and cyanobacteria has generated most of the dioxygen in the atmosphere. PS II, a membrane-bound multi-subunit pigment protein complex, couples the one-electron photochemistry at the reaction centre with the four-electron redox chemistry of water oxidation at the Mn4CaO5 cluster in the oxygen-evolving complex (OEC). Under illumination, the OEC cycles through five intermediate S-states (S0 to S4), in which S1 is the dark-stable state and S3 is the last semi-stable state before O–O bond formation and O2 evolution. A detailed understanding of the O–O bond formation mechanism remains a challenge, and will require elucidation of both the structures of the OEC in the different S-states and the binding of the two substrate waters to the catalytic site. Here we report the use of femtosecond pulses from an X-ray free electron laser (XFEL) to obtain damage-free, room temperature structures of dark-adapted (S1), two-flash illuminated (2F; S3-enriched), and ammonia-bound two-flash illuminated (2F-NH3; S3-enriched) PS II. Although the recent 1.95 Å resolution structure of PS II at cryogenic temperature using an XFEL provided a damage-free view of the S1 state, measurements at room temperature are required to study the structural landscape of proteins under functional conditions, and also for in situ advancement of the S-states. To investigate the water-binding site(s), ammonia, a water analogue, has been used as a marker, as it binds to the Mn4CaO5 cluster in the S2 and S3 states. Since the ammonia-bound OEC is active, the ammonia-binding Mn site is not a substrate water site. This approach, together with a comparison of the native dark and 2F states, is used to discriminate between proposed O–O bond formation mechanisms.

Linac Coherent Light Source data analysis using psana

Psana (Photon Science Analysis) is a software package that is used to analyze data produced by the Linac Coherent Light Source X-ray free-electron laser at the SLAC National Accelerator Laboratory. The project began in 2011, is written primarily in C++ with some Python, and provides user interfaces in both C++ and Python. Most users use the Python interface. The same code can be run in real time while data are being taken as well as offline, executing on many nodes/cores using MPI for parallelization. It is publicly available and installable on the RHEL5/6/7 operating systems.

Observation of correlated X-ray scattering at atomic resolution.

Tools to study disordered systems with local structural order, such as proteins in solution, remain limited. Such understanding is essential for e.g. rational drug design. Correlated X-ray scattering (CXS) has recently attracted new interest as a way to leverage next-generation light sources to study such disordered matter. The CXS experiment measures angular correlations of the intensity caused by the scattering of X-rays from an ensemble of identical particles, with disordered orientation and position. Averaging over 15 496 snapshot images obtained by exposing a sample of silver nanoparticles in solution to a micro-focused synchrotron radiation beam, we report on experimental efforts to obtain CXS signal from an ensemble in three dimensions. A correlation function was measured at wide angles corresponding to atomic resolution that matches theoretical predictions. These preliminary results suggest that other CXS experiments on disordered ensembles—such as proteins in solution—may be feasible in the future.

OnDA : online data analysis and feedback for serial X-ray imaging

This article describes the software package OnDA: online data analysis and feedback for serial X-ray imaging.

Selenium single-wavelength anomalous diffraction de novo phasing using an X-ray-free electron laser

Structural information about biological macromolecules near the atomic scale provides important insight into the functions of these molecules. To date, X-ray crystallography has been the predominant method used for macromolecular structure determination. However, challenges exist when solving structures with X-rays, including the phase problem and radiation damage. X-ray-free electron lasers (X-ray FELs) have enabled collection of diffraction information before the onset of radiation damage, yet the majority of structures solved at X-ray FELs have been phased using external information via molecular replacement. De novo phasing at X-ray FELs has proven challenging due in part to per-pulse variations in intensity and wavelength. Here we report the solution of a selenobiotinyl-streptavidin structure using phases obtained by the anomalous diffraction of selenium measured at a single wavelength (Se-SAD) at the Linac Coherent Light Source. Our results demonstrate Se-SAD, routinely employed at synchrotrons for novel structure determination, is now possible at X-ray FELs.

Probing the origins of two-state folding

Many protein systems fold in a two-state manner. Random models, however, rarely display two-state kinetics and thus such behavior should not be accepted as a default. While theories for the prevalence of two-state kinetics have been presented, none sufficiently explain the breadth of experimental observations. A model, making minimal assumptions, is introduced that suggests two-state behavior is likely for any system with an overwhelmingly populated native state. We show two-state folding is a natural consequence of such two-state thermodynamics, and is strengthened by increasing the population of the native state. Further, the model exhibits hub-like behavior, with slow interconversions between unfolded states. Despite this, the unfolded state equilibrates quickly relative to the folding time. This apparent paradox is readily understood through this model. Finally, our results compare favorable with measurements of folding rates as a function of chain length and Keq, providing new insight into these relations.

A Simple Model Predicts Experimental Folding Rates and a Hub-Like Topology

A simple model is presented that describes general features of protein folding, in good agreement with experimental results and detailed all-atom simulations. Starting from microscopic physics, and with no free parameters, this model predicts that protein folding occurs remarkably quickly because native-like states are kinetic hubs. A hub-like network arises naturally out of microscopic physical concerns, specifically the kinetic longevity of native contacts during a search of globular conformations. The model predicts folding times scaling as τ(f) ~ e(ξN) in the number of residues, but because the model shows ξ is small, the folding times are much faster than Levinthals approximation. Importantly, the folding time scale is found to be small due to the topology and structure of the network. We show explicitly how our model agrees with generic experimental features of the folding process, including the scaling of τ(f) with N, two-state thermodynamics, a sharp peak in C(V), and native-state fluctuations.