A bibliometric analysis of the Journal of Molecular Graphics and Modelling

This paper reviews the articles published in Volumes 2-24 of the Journal of Molecular Graphics and Modelling (formerly the Journal of Molecular Graphics), focusing on the changes that have occurred in the subject over the years, and on the most productive and most cited authors and institutions. The most cited papers are those describing systems or algorithms, but the proportion of these types of article is decreasing as more applications of molecular graphics and molecular modelling are reported

Semi-Supervised Object Detection in the Open World

Existing approaches for semi-supervised object detection assume a fixed set of classes present in training and unlabeled datasets, i.e., in-distribution (ID) data. The performance of these techniques significantly degrades when these techniques are deployed in the open-world, due to the fact that the unlabeled and test data may contain objects that were not seen during training, i.e., out-of-distribution (OOD) data. The two key questions that we explore in this paper are: can we detect these OOD samples and if so, can we learn from them? With these considerations in mind, we propose the Open World Semi-supervised Detection framework (OWSSD) that effectively detects OOD data along with a semi-supervised learning pipeline that learns from both ID and OOD data. We introduce an ensemble based OOD detector consisting of lightweight auto-encoder networks trained only on ID data. Through extensive evalulation, we demonstrate that our method performs competitively against state-of-the-art OOD detection algorithms and also significantly boosts the semi-supervised learning performance in open-world scenarios

EigenFold: Generative Protein Structure Prediction with Diffusion Models

Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework for sampling a distribution of structures from a given protein sequence. We define a diffusion process that models the structure as a system of harmonic oscillators and which naturally induces a cascading-resolution generative process along the eigenmodes of the system. On recent CAMEO targets, EigenFold achieves a median TMScore of 0.84, while providing a more comprehensive picture of model uncertainty via the ensemble of sampled structures relative to existing methods. We then assess EigenFold's ability to model and predict conformational heterogeneity for fold-switching proteins and ligand-induced conformational change. Code is available at https://github.com/bjing2016/EigenFold.Comment: ICLR MLDD workshop 202

Structural basis for RING-Cys-Relay E3 ligase activity and its role in axon integrity.

MYCBP2 is a ubiquitin (Ub) E3 ligase (E3) that is essential for neurodevelopment and regulates axon maintenance. MYCBP2 transfers Ub to nonlysine substrates via a newly discovered RING-Cys-Relay (RCR) mechanism, where Ub is relayed from an upstream cysteine to a downstream substrate esterification site. The molecular bases for E2-E3 Ub transfer and Ub relay are unknown. Whether these activities are linked to the neural phenotypes is also unclear. We describe the crystal structure of a covalently trapped E2~Ub:MYCBP2 transfer intermediate revealing key structural rearrangements upon E2-E3 Ub transfer and Ub relay. Our data suggest that transfer to the dynamic upstream cysteine, whilst mitigating lysine activity, requires a closed-like E2~Ub conjugate with tempered reactivity, and Ub relay is facilitated by a helix-coil transition. Furthermore, neurodevelopmental defects and delayed injury-induced degeneration in RCR-defective knock-in mice suggest its requirement, and that of substrate esterification activity, for normal neural development and programmed axon degeneration

2021-2022 New Friends Dreams and Directions

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