Ali Dashti

Trajectories of the ribosome as a Brownian nanomachine

Significance Many functions in the cell are performed by Brownian machines, macromolecular assemblies that use energy from the thermal environment for many of the conformational changes involved in their work cycles. Here we present a new approach capable of mapping the continuous motions of such nanomachines along their trajectories in the free-energy landscape and demonstrate this capability in the context of experimental cryogenic electron microscope snapshots of the ribosome, the nanomachine responsible for protein synthesis in all living organisms. We believe our approach constitutes a universal platform for the analysis of free-energy landscapes and conformational motions of molecular nanomachines and their dependencies on temperature, buffer conditions, and regulatory factors. A Brownian machine, a tiny device buffeted by the random motions of molecules in the environment, is capable of exploiting these thermal motions for many of the conformational changes in its work cycle. Such machines are now thought to be ubiquitous, with the ribosome, a molecular machine responsible for protein synthesis, increasingly regarded as prototypical. Here we present a new analytical approach capable of determining the free-energy landscape and the continuous trajectories of molecular machines from a large number of snapshots obtained by cryogenic electron microscopy. We demonstrate this approach in the context of experimental cryogenic electron microscope images of a large ensemble of nontranslating ribosomes purified from yeast cells. The free-energy landscape is seen to contain a closed path of low energy, along which the ribosome exhibits conformational changes known to be associated with the elongation cycle. Our approach allows model-free quantitative analysis of the degrees of freedom and the energy landscape underlying continuous conformational changes in nanomachines, including those important for biological function.

High-resolution structure of viruses from random diffraction snapshots

The advent of the X-ray free-electron laser (XFEL) has made it possible to record diffraction snapshots of biological entities injected into the X-ray beam before the onset of radiation damage. Algorithmic means must then be used to determine the snapshot orientations and thence the three-dimensional structure of the object. Existing Bayesian approaches are limited in reconstruction resolution typically to 1/10 of the object diameter, with the computational expense increasing as the eighth power of the ratio of diameter to resolution. We present an approach capable of exploiting object symmetries to recover three-dimensional structure to high resolution, and thus reconstruct the structure of the satellite tobacco necrosis virus to atomic level. Our approach offers the highest reconstruction resolution for XFEL snapshots to date and provides a potentially powerful alternative route for analysis of data from crystalline and nano-crystalline objects.

Conformational landscape of a virus by single-particle X-ray scattering

Using a manifold-based analysis of experimental diffraction snapshots from an X-ray free electron laser, we determine the three-dimensional structure and conformational landscape of the PR772 virus to a detector-limited resolution of 9 nm. Our results indicate that a single conformational coordinate controls reorganization of the genome, growth of a tubular structure from a portal vertex and release of the genome. These results demonstrate that single-particle X-ray scattering has the potential to shed light on key biological processes.

Efficient computation of k-Nearest Neighbour Graphs for large high-dimensional data sets on GPU clusters.

This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG) construction for ultra-large high-dimensional data cloud. The proposed method uses Graphics Processing Units (GPUs) and is scalable with multi-levels of parallelism (between nodes of a cluster, between different GPUs on a single node, and within a GPU). The method is applicable to homogeneous computing clusters with a varying number of nodes and GPUs per node. We achieve a 6-fold speedup in data processing as compared with an optimized method running on a cluster of CPUs and bring a hitherto impossible -NNG generation for a dataset of twenty million images with 15 k dimensionality into the realm of practical possibility.

Fast k-NNG construction with GPU-based quick multi-select

In this paper, we describe a new brute force algorithm for building the -Nearest Neighbor Graph (k-NNG). The k-NNG algorithm has many applications in areas such as machine learning, bio-informatics, and clustering analysis. While there are very efficient algorithms for data of low dimensions, for high dimensional data the brute force search is the best algorithm. There are two main parts to the algorithm: the first part is finding the distances between the input vectors, which may be formulated as a matrix multiplication problem; the second is the selection of the k-NNs for each of the query vectors. For the second part, we describe a novel graphics processing unit (GPU)-based multi-select algorithm based on quick sort. Our optimization makes clever use of warp voting functions available on the latest GPUs along with user-controlled cache. Benchmarks show significant improvement over state-of-the-art implementations of the k-NN search on GPUs.

Single-particle structure determination by X-ray free-electron lasers: Possibilities and challenges

Single-particle structure recovery without crystals or radiation damage is a revolutionary possibility offered by X-ray free-electron lasers, but it involves formidable experimental and data-analytical challenges. Many of these difficulties were encountered during the development of cryogenic electron microscopy of biological systems. Electron microscopy of biological entities has now reached a spatial resolution of about 0.3 nm, with a rapidly emerging capability to map discrete and continuous conformational changes and the energy landscapes of biomolecular machines. Nonetheless, single-particle imaging by X-ray free-electron lasers remains important for a range of applications, including the study of large “electron-opaque” objects and time-resolved examination of key biological processes at physiological temperatures. After summarizing the state of the art in the study of structure and conformations by cryogenic electron microscopy, we identify the primary opportunities and challenges facing X-ray-based single-particle approaches, and possible means for circumventing them.

Conformational Dynamics and Energy Landscapes of Ligand Binding in RyR1

Using experimental single-particle cryo-EM snapshots of ryanodine receptor (RyR1), a Ca2+-channel involved in skeletal muscle excitation/contraction coupling, we present quantitative free-energy landscapes, reaction coordinates, and three-dimensional movies of the continuous conformational changes associated with the binding of activating ligands. Our results show multiple routes to ligand binding with comparable branching ratios. All high-probability routes involve significant conformational changes before and after the binding of ligands. We also present new insights into the local structural changes along the ligand-binding route, including accommodations at the calcium, ATP, and caffeine binding sites. These observations shed new light on the mechanisms and conformational routes to ligand binding.

Functional Pathways of Biomolecules Retrieved from Single-particle Snapshots

We present a new approach to determining the conformational changes associated with biological function, and demonstrate its capabilities in the context of experimental single-particle cryo-EM snapshots of ryanodine receptor (RyR1), a Ca2+-channel involved in skeletal muscle excitation/contraction coupling. These results include the detailed conformational motions associated with functional paths including transitions between energy landscapes. The functional motions differ substantially from those inferred from discrete structures, shedding new light on the gating mechanism in RyR1. The differences include the conformationally active structural domains, the nature, sequence, and extent of conformational motions involved in function, and the way allosteric signals are transduced within and between domains. The approach is general, and applicable to a wide range of systems and processes.