Building Proteins in a Day: Efficient 3D Molecular Reconstruction

Discovering the 3D atomic structure of molecules such as proteins and viruses is a fundamental research problem in biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D structures from 2D images. This paper addresses the challenging problem of 3D reconstruction from 2D Cryo-EM images. A new framework for estimation is introduced which relies on modern stochastic optimization techniques to scale to large datasets. We also introduce a novel technique which reduces the cost of evaluating the objective function during optimization by over five orders or magnitude. The net result is an approach capable of estimating 3D molecular structure from large scale datasets in about a day on a single workstation.Comment: To be presented at IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

In cryo-electron microscopy (EM), molecular structures are determined from large numbers of projection images of individual particles. To harness the full power of this single-molecule information, we use the Bayesian inference of EM (BioEM) formalism. By ranking structural models using posterior probabilities calculated for individual images, BioEM in principle addresses the challenge of working with highly dynamic or heterogeneous systems not easily handled in traditional EM reconstruction. However, the calculation of these posteriors for large numbers of particles and models is computationally demanding. Here we present highly parallelized, GPU-accelerated computer software that performs this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI parallelization combined with both CPU and GPU computing. The resulting BioEM software scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time. The general mathematical framework and robust algorithms are not limited to cryo-electron microscopy but can be generalized for electron tomography and other imaging experiments

pH-dependent gating mechanism of the Helicobacter pylori urea channel revealed by cryo-EM.

The urea channel of Helicobacter pylori (HpUreI) is an ideal drug target for preventing gastric cancer but incomplete understanding of its gating mechanism has hampered development of inhibitors for the eradication of H. pylori. Here, we present the cryo-EM structures of HpUreI in closed and open conformations, both at a resolution of 2.7 Å. Our hexameric structures of this small membrane protein (~21 kDa/protomer) resolve its periplasmic loops and carboxyl terminus that close and open the channel, and define a gating mechanism that is pH dependent and requires cooperativity between protomers in the hexamer. Gating is further associated with well-resolved changes in the channel-lining residues that modify the shape and length of the urea pore. Site-specific mutations in the periplasmic domain and urea pore identified key residues important for channel function. Drugs blocking the urea pore based on our structures should lead to a new strategy for H. pylori eradication

A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.Comment: 8 pages, 5 figures, 4 table

Cryo-EM structure of the potassium-chloride cotransporter KCC4 in lipid nanodiscs.

Cation-chloride-cotransporters (CCCs) catalyze transport of Cl- with K+ and/or Na+across cellular membranes. CCCs play roles in cellular volume regulation, neural development and function, audition, regulation of blood pressure, and renal function. CCCs are targets of clinically important drugs including loop diuretics and their disruption has been implicated in pathophysiology including epilepsy, hearing loss, and the genetic disorders Andermann, Gitelman, and Bartter syndromes. Here we present the structure of a CCC, the Mus musculus K+-Cl- cotransporter (KCC) KCC4, in lipid nanodiscs determined by cryo-EM. The structure, captured in an inside-open conformation, reveals the architecture of KCCs including an extracellular domain poised to regulate transport activity through an outer gate. We identify binding sites for substrate K+ and Cl- ions, demonstrate the importance of key coordinating residues for transporter activity, and provide a structural explanation for varied substrate specificity and ion transport ratio among CCCs. These results provide mechanistic insight into the function and regulation of a physiologically important transporter family