High-throughput prediction of protein conformational distributions with subsampled AlphaFold2.

This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments.

Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold2.

This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' single ground state conformations and is limited in its ability to predict fold switching and the effects of mutations on conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different conformations of proteins and even accurately predict changes in those populations induced by mutations by subsampling multiple sequence alignments.

Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold 2.

This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments.

Molecular Level Insights Into the Structural and Dynamic Factors Driving Cytokine Function.

Cytokines are key mediators of cellular communication and regulators of biological advents. The timing, quantity and localization of cytokines are key features in producing specific biological outcomes, and thus have been thoroughly studied and reviewed while continuing to be a focus of the cytokine biology community. Due to the complexity of cellular signaling and multitude of factors that can affect signaling outcomes, systemic level studies of cytokines are ongoing.

Repeatability and accuracy of various region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification.

Purpose: To evaluate repeatability of ROI-sampling strategies for quantifying hepatic proton density fat fraction (PDFF) and to assess error relative to the 9-ROI PDFF.

Methods: This was a secondary analysis in subjects with known or suspected nonalcoholic fatty liver disease who underwent MRI for magnitude-based hepatic PDFF quantification. Each subject underwent three exams, each including three acquisitions (nine acquisitions total).

Mapping the Structural and Dynamic Determinants of pH-Sensitive Heparin Binding to Granulocyte Macrophage Colony Stimulating Factor.

Granulocyte macrophage colony stimulating factor (GMCSF) is an immunomodulatory cytokine that is harnessed as a therapeutic. GMCSF is known to interact with other clinically important molecules, such as heparin, suggesting that endogenous and administered GMCSF has the potential to modulate orthogonal treatment outcomes. Thus, molecular level characterization of GMCSF and its interactions with biologically active compounds is critical to understanding these mechanisms and predicting clinical consequences.

NMR and computational methods for molecular resolution of allosteric pathways in enzyme complexes.

Allostery is a ubiquitous biological mechanism in which a distant binding site is coupled to and drastically alters the function of a catalytic site in a protein. Allostery provides a high level of spatial and temporal control of the integrity and activity of biomolecular assembles composed of proteins, nucleic acids, or small molecules. Understanding the physical forces that drive allosteric coupling is critical to harnessing this process for use in bioengineering, de novo protein design, and drug discovery.

Hepatic steatosis and reduction in steatosis following bariatric weight loss surgery differs between segments and lobes.

Objectives: The purpose of this study was to (1) evaluate proton density fat fraction (PDFF) distribution across liver segments at baseline and (2) compare longitudinal segmental PDFF changes across time points in adult patients undergoing a very low-calorie diet (VLCD) and subsequent bariatric weight loss surgery (WLS).

Methods: We performed a secondary analysis of data from 118 morbidly obese adult patients enrolled in a VLCD-WLS program. PDFF was estimated using magnitude-based confounder-corrected chemical-shift-encoded (CSE) MRI in each hepatic segment and lobe at baseline (visit 1), after completion of VLCD (visit 2), and at 1, 3, and 6 months (visits 3-5) following WLS.

Liver fat imaging-a clinical overview of ultrasound, CT, and MR imaging.

Hepatic steatosis is a frequently encountered imaging finding that may indicate chronic liver disease, the most common of which is non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease is implicated in the development of systemic diseases and its progressive phenotype, non-alcoholic steatohepatitis, leads to increased liver-specific morbidity and mortality. With the rising obesity epidemic and advent of novel therapeutics aimed at altering metabolism, there is a growing need to quantify and monitor liver steatosis.