CanPredict: a computational tool for predicting cancer-associated missense mutations

Various cancer genome projects are underway to identify novel mutations that drive tumorigenesis. While these screens will generate large data sets, the majority of identified missense changes are likely to be innocuous passenger mutations or polymorphisms. As a result, it has become increasingly important to develop computational methods for distinguishing functionally relevant mutations from other variations. We previously developed an algorithm, and now present the web application, CanPredict (http://www.canpredict.org/ or http://www.cgl.ucsf.edu/Research/genentech/canpredict/), to allow users to determine if particular changes are likely to be cancer-associated. The impact of each change is measured using two known methods: Sorting Intolerant From Tolerant (SIFT) and the Pfam-based LogR.E-value metric. A third method, the Gene Ontology Similarity Score (GOSS), provides an indication of how closely the gene in which the variant resides resembles other known cancer-causing genes. Scores from these three algorithms are analyzed by a random forest classifier which then predicts whether a change is likely to be cancer-associated. CanPredict fills an important need in cancer biology and will enable a large audience of biologists to determine which mutations are the most relevant for further study

Production of C-2/C-3 Oxygenates from Planar Copper Nitride-Derived Mesoporous Copper via Electrochemical Reduction of CO2

Electrochemical reduction of CO2 provides an opportunity to produce fuels and chemicals in a carbon-neutral manner, assuming that CO2 can be captured from the atmosphere. To do so requires efficient, selective, and stable catalysts. In this study, we report a highly mesoporous metallic Cu catalyst prepared by electrochemical reduction of thermally nitrided Cu foil. Under aqueous saturated CO2 reduction conditions, the Cu3N-derived Cu electrocatalyst produces virtually no CH4, very little CO, and exhibits a faradaic efficiency of 68% in C2+ products (C2H4, C2H5OH, and C3H7OH) at a current density of ∼18.5 mA cm-2 and a cathode potential of -1.0 V versus the reversible hydrogen electrode. Under these conditions, the catalyst produces more oxygenated products than hydrocarbons. We show that surface roughness is a good descriptor of catalytic performance. The roughest surface reached 98% CO utilization efficiency for C2+ product formation from CO2 reduction and the ratio of oxygenated to hydrocarbon products correlates with the degree of surface roughness. These effects of surface roughness are attributed to the high population of undercoordinated sites as well as a high pH environment within the mesopores and adjacent to the surface of the catalyst

Learning to Count Isomorphisms with Graph Neural Networks

Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational costs. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting. Moreover, on an input graph, the space of possible query graphs is enormous, and different parts of the input graph will be triggered to match different queries. Thus, expecting a fixed representation of the input graph to match diversely structured query graphs is unrealistic. In this paper, we propose a novel GNN called Count-GNN for subgraph isomorphism counting, to deal with the above challenges. At the edge level, given that an edge is an atomic unit of encoding graph structures, we propose an edge-centric message passing scheme, where messages on edges are propagated and aggregated based on the edge adjacency to preserve fine-grained structural information. At the graph level, we modulate the input graph representation conditioned on the query, so that the input graph can be adapted to each query individually to improve their matching. Finally, we conduct extensive experiments on a number of benchmark datasets to demonstrate the superior performance of Count-GNN.Comment: AAAI-23 main trac

2,2,4,4-Tetra­phenyl-1,3-bis­(3,3,5,5-tetra­methyl-1,1-diphenyl-5-vinyl­trisilox­an-1-yl)­cyclo­disilazane

The title mol­ecule, C60H70N2O4Si8, lies on an inversion center. In the asymmetric unit, one of the phenyl rings is disordered over two sets of sites with refined occupancies 0.58 (2) and 0.42 (2). In addition, in two substitution sites of the terminal dimeth­yl(vin­yl)silyl unit, a methyl group and the vinyl group are disordered over the same site with refined occupancies 0.523 (13) and 0.477 (13)