Leveraging AlphaFold 2 as a general-purpose architecture for small molecule conformer prediction

Small molecules naturally exist in several three-dimensional conformations, and these conformations dictate a small molecule’s interactions and functions. Thus, learning the three-dimensional structure of small molecules is an important problem in research and drug discovery. Originally, the conformation of small molecules was determined through expensive experimental methods. The first computational approaches to small molecule conformer generation (MCG) relied on random or “knowledge-based” searches for low-energy conformations. More recently, deep learning approaches have become popular for MCG. The major advantage of deep learning methods is their relatively high speeds and low costs. Here, we build on AlphaFold2’s architecture for the purpose of MCG. We explore how various architectures and loss functions modify performance for MCG. Our highest performing model, termed OpenMol, achieves a mean (median) COV-R and COV-P of 81.4% (100%) and 83.1% (100%), respectively on the GEOM-QM9 dataset. Additionally, we introduce triangle-aligned point error (TAPE) loss, a novel loss function for MCG tasks

Dissecting mammalian spermatogenesis using spatial transcriptomics

Single-cell RNA sequencing has revealed extensive molecular diversity in gene programs governing mammalian spermatogenesis but fails to delineate their dynamics in the native context of seminiferous tubules, the spatially confined functional units of spermatogenesis. Here, we use Slide-seq, a spatial transcriptomics technology, to generate an atlas that captures the spatial gene expression patterns at near-single-cell resolution in the mouse and human testis. Using Slide-seq data, we devise a computational framework that accurately localizes testicular cell types in individual seminiferous tubules. Unbiased analysis systematically identifies spatially patterned genes and gene programs. Combining Slide-seq with targeted in situ RNA sequencing, we demonstrate significant differences in the cellular compositions of spermatogonial microenvironment between mouse and human testes. Finally, a comparison of the spatial atlas generated from the wild-type and diabetic mouse testis reveals a disruption in the spatial cellular organization of seminiferous tubules as a potential mechanism of diabetes-induced male infertility

Multisite study of the relationships between antemortem [11C]PIB-PET Centiloid values and postmortem measures of Alzheimer's disease neuropathology.

IntroductionWe sought to establish the relationships between standard postmortem measures of AD neuropathology and antemortem [11C]PIB-positron emission tomography ([11C]PIB-PET) analyzed with the Centiloid (CL) method, a standardized scale for Aβ-PET quantification.MethodsFour centers contributed 179 participants encompassing a broad range of clinical diagnoses, PET data, and autopsy findings.ResultsCL values increased with each CERAD neuritic plaque score increment (median -3 CL for no plaques and 92 CL for frequent plaques) and nonlinearly with Thal Aβ phases (increases were detected starting at phase 2) with overlap between scores/phases. PET-pathology associations were comparable across sites and unchanged when restricting the analyses to the 56 patients who died within 2 years of PET. A threshold of 12.2 CL detected CERAD moderate-to-frequent neuritic plaques (area under the curve = 0.910, sensitivity = 89.2%, specificity = 86.4%), whereas 24.4 CL identified intermediate-to-high AD neuropathological changes (area under the curve = 0.894, sensitivity = 84.1%, specificity = 87.9%).DiscussionOur study demonstrated the robustness of a multisite Centiloid [11C]PIB-PET study and established a range of pathology-based CL thresholds