ARP/wARP is a software suite to build macromolecular models in X-ray crystallography electron density maps. Structural genomics initiatives and the study of complex macromolecular assemblies and membrane proteins all rely on advanced methods for 3D structure determination. ARP/wARP meets these needs by providing the tools to obtain a macromolecular model automatically, with a reproducible computational procedure. ARP/wARP 7.0 tackles several tasks: iterative protein model building including a high-level decision-making control module; fast construction of the secondary structure of a protein; building flexible loops in alternate conformations; fully automated placement of ligands, including a choice of the best-fitting ligand from a 'cocktail'; and finding ordered water molecules. All protocols are easy to handle by a nonexpert user through a graphical user interface or a command line. The time required is typically a few minutes although iterative model building may take a few hours.

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Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7.

As the endpoint for the ubiquitin-proteasome system, the 26S proteasome is the principal proteolytic machine responsible for regulated protein degradation in eukaryotic cells. The proteasome's cellular functions range from general protein homeostasis and stress response to the control of vital processes such as cell division and signal transduction. To reliably process all the proteins presented to it in the complex cellular environment, the proteasome must combine high promiscuity with exceptional substrate selectivity. Recent structural and biochemical studies have shed new light on the many steps involved in proteasomal substrate processing, including recognition, deubiquitination, and ATP-driven translocation and unfolding. In addition, these studies revealed a complex conformational landscape that ensures proper substrate selection before the proteasome commits to processive degradation. These advances in our understanding of the proteasome's intricate machinery set the stage for future studies on how the proteasome functions as a major regulator of the eukaryotic proteome.

Structure and Function of the 26S Proteasome.

Structural Genomics (SG) projects aim to expand our structural knowledge of biological macromolecules, while lowering the average costs of structure determination. We quantitatively analyzed the novelty, cost, and impact of structures solved by SG centers, and contrast these results with traditional structural biology. The first structure from a protein family is particularly important to reveal the fold and ancient relationships to other proteins. In the last year, approximately half of such structures were solved at a SG center rather than in a traditional laboratory. Furthermore, the cost of solving a structure at the most efficient U.S. center has now dropped to one-quarter the estimated cost of solving a structure by traditional methods. However, top structural biology laboratories are much more efficientthan the average, and comparable to SG centers despite working on very challenging structures. Moreover, traditional structural biology papers are cited significantly more often, suggesting greater current impact.

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The Impact of Structural Genomics: Expectations and Outcomes - eScholarship

Ubiquitylation is a major posttranslational modification that controls most complex aspects of cell physiology. It is reversed through the action of a large family of deubiquitylating enzymes (DUBs...

Deubiquitylases From Genes to Organism

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation.

We propose a framework that leverages deep residual CNNs pretrained on large, non-biomedical image data sets. These pretrained networks learn cross-domain features that improve low-level interpretation of images. We evaluate our model on brain imaging data and show that pretraining and the use of deep residual networks are crucial to seeing large improvements in Alzheimer's Disease diagnosis from brain MRIs.

/pdf/deep-residual-nets-for-improved-alzheimer-s-diagnosis-23gnk79nkk.pdf

Deep Residual Nets for Improved Alzheimer's Diagnosis

Uch37 is a de-ubiquitylating enzyme that is functionally linked with the 26S proteasome via Rpn13, and is essential for metazoan development. Here, we report the X-ray crystal structure of full-length human Uch37 at 2.95 A resolution. Uch37's catalytic domain is similar to those of all UCH enzymes characterized to date. The C-terminal extension is elongated, predominantly helical and contains coiled coil interactions. Additionally, we provide an initial characterization of Uch37's oligomeric state and identify a systematic error in previous analyses of Uch37 activity. Taken together, these data provide a strong foundation for further analysis of Uch37's several functions.

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Structural characterization of human Uch37.

In line with institutions across the United States, the Computer Science Department at Swarthmore College has faced the challenge of maintaining a demographic composition of students that matches the student body as a whole. To combat this trend, our department has made a concerted effort to revamp our introductory course sequence to both attract and retain more women and minority students. The focus of this paper is the changes instituted in our Introduction to Computer Science course (i.e., CS1) intended for both majors and non-majors. In addition to changing the content of the course, we introduced a new student mentoring program that is managed by a full-time coordinator and consists of undergraduate students who have recently completed the course. This paper describes these efforts in detail, including the extension of these changes to our CS2 course and the associated costs required to maintain these efforts. We measure the impact of these changes by tracking student enrollment and performance over 13 academic years. We show that, unlike national trends, enrollment from underrepresented groups has increased dramatically over this time period. Additionally, we show that the student mentoring program has increased both performance and retention of students, particularly from underrepresented groups, at statistically significant levels.

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A support program for introductory CS courses that improves student performance and retains students from underrepresented groups

Motivation: One bottleneck in high-throughput protein crystallography is interpreting an electron-density map, that is, fitting a molecular model to the 3D picture crystallography produces. Previously, we developed Acmi (Automatic Crystallographic Map Interpreter), an algorithm that uses a probabilistic model to infer an accurate protein backbone layout. Here, we use a sampling method known as particle filtering to produce a set of all-atom protein models. We use the output of Acmi to guide the particle filter's sampling, producing an accurate, physically feasible set of structures.

Results: We test our algorithm on 10 poor-quality experimental density maps. We show that particle filtering produces accurate all-atom models, resulting in fewer chains, lower sidechain RMS error and reduced R factor, compared to simply placing the best-matching sidechains on Acmi's trace. We show that our approach produces a more accurate model than three leading methods—Textal, Resolve and ARP/WARP—in terms of main chain completeness, sidechain identification and crystallographic R factor.

Availability: Source code and experimental density maps available at http://ftp.cs.wisc.edu/machine-learning/shavlik-group/programs/acmi/

Contact: dimaio@cs.wisc.edu

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Creating protein models from electron-density maps using particle-filtering methods

https://works.swarthmore.edu/cgi/viewcontent.cgi?article=1595&context=fac-biology

Ameet Soni

Papers

Deep Residual Nets for Improved Alzheimer's Diagnosis

Structural characterization of human Uch37.

A support program for introductory CS courses that improves student performance and retains students from underrepresented groups

Creating protein models from electron-density maps using particle-filtering methods

Dugesia japonica is the best suited of three planarian species for high-throughput toxicology screening