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. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.

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. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with accuracies in excess of 80%. Our method offers a fast and cost-effective way to predict protein conformations and their relative populations at even single point mutation resolution, making it a useful tool for pharmacology, analyzing NMR data, and studying the effects of evolution.

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. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, NMR analysis, and evolution.

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. Despite their small size, cytokines can exhibit structurally promiscuous and dynamic behavior that plays an equally important role in biological activity. In this review using case studies, we highlight the recent insight gained from observing cytokines through a molecular lens and how this may complement a system-level understanding of cytokine biology, explain diversity of downstream signaling events, and inform therapeutic and experimental development.

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). An ROI was placed in each hepatic segment on the first acquisition of the first exam and propagated to other acquisitions. PDFF was calculated for each of 511 sampling strategies using every combination of 1, 2, …, all 9 ROIs. Intra- and inter-exam intra-class correlation coefficients (ICCs) and repeatability coefficients (RCs) were estimated for each sampling strategy. Mean absolute error (MAE) was estimated relative to the 9-ROI PDFF. Strategies that sampled both lobes evenly ("balanced") were compared with those that did not ("unbalanced") using two-sample t tests. RESULTS: The 29 enrolled subjects (23 male, mean age 24 years) had mean 9-ROI PDFF 11.8% (1.1-36.3%). With more ROIs, ICCs increased, RCs decreased, and MAE decreased. Of the 60 balanced strategies with 4 ROIs, all (100%) achieved inter- and intra-exam ICCs > 0.998, 55 (92%) achieved intra-exam RC < 1%, 50 (83%) achieved inter-exam RC < 1%, and all (100%) achieved MAE < 1%. Balanced sampling strategies had higher ICCs and lower RCs, and lower MAEs than unbalanced strategies in aggregate (p < 0.001 for comparisons between balanced vs. unbalanced strategies). CONCLUSION: Repeatability improves and error diminishes with more ROIs. Balanced 4-ROI strategies provide high repeatability and low error.

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. Here, we dissect the biophysical factors that facilitate the GMCSF-heparin interaction, previously shown to be pH-dependent, using nuclear magnetic resonance spectroscopy, surface plasmon resonance, and molecular dynamics simulations. We find that the affinity of GMCSF for heparin increases not only with a transition to acidic pH but also with an increase in heparin chain length. Changes in local flexibility, including a disruption of the N-terminal helix at acidic pH, also accompany the binding of heparin to GMCSF. We use molecular dynamics simulations to propose a mechanism in which a positive binding pocket that is not fully solvent accessible at neutral pH becomes more accessible at acidic pH, facilitating the binding of heparin to the protein.

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. Current microscopic models of allostery highlight the importance of energetics, structural rearrangements, and conformational fluctuations, and in this review, we discuss the synergistic use of solution NMR spectroscopy and computational methods to probe these phenomena in allosteric systems, particularly protein-nucleic acid complexes. This combination of experimental and theoretical techniques facilitates an unparalleled detection of subtle changes to structural and dynamic equilibria in biomolecules with atomic resolution, and we provide a detailed discussion of specialized NMR experiments as well as the complementary methods that provide valuable insight into allosteric pathways in silico. Lastly, we highlight two case studies to demonstrate the adaptability of this approach to enzymes of varying size and mechanistic complexity.

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. Linear regressions were used to estimate the rate of PDFF change across visits. Lobar and segmental rates of change were compared pairwise. RESULTS: Baseline PDFF was significantly higher in the right lobe compared to the left lobe (p < 0.0001). Lobar and segmental PDFF declined by 3.9-4.5% per month between visits 1 and 2 (preoperative period) and by 4.3-4.8% per month between visits 1 and 3 (perioperative period), but no significant pairwise differences were found in slope between segments and lobes. For visits 3-5 (postoperative period), lobar and segmental PDFF reduction was much less overall (0.4-0.8% PDFF per month) and several pairwise differences were significant; in each case, a right-lobe segment had greater decline than a left-lobe segment. CONCLUSIONS: Baseline and longitudinal changes in fractional fat content in the 5-month postoperative period following WLS vary across segments, with right-lobe segments having higher PDFF at baseline and more rapid reduction in liver fat content. KEY POINTS: • Baseline and longitudinal changes in liver fat following bariatric weight loss surgery vary across liver segments. • Methods that do not provide whole liver fat assessment, such as liver biopsy, may be unreliable in monitoring longitudinal changes in liver fat following weight loss interventions.

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. Imaging methods for assessing steatosis range from simple and qualitative to complex and highly accurate metrics. Ultrasound may be appropriate in some clinical instances as a screening modality to identify the presence of abnormal liver morphology. However, it lacks sufficient specificity and sensitivity to constitute a diagnostic modality for instigating and monitoring therapy. Newer ultrasound techniques such as quantitative ultrasound show promise in turning qualitative assessment of steatosis on conventional ultrasound into quantitative measurements. Conventional unenhanced CT is capable of detecting and quantifying moderate to severe steatosis but is inaccurate at diagnosing mild steatosis and involves the use of radiation. Newer CT techniques, like dual energy CT, show potential in expanding the role of CT in quantifying steatosis. MRI proton-density fat fraction is currently the most accurate and precise imaging biomarker to quantify liver steatosis. As such, proton-density fat fraction is the most appropriate noninvasive end point for steatosis reduction in clinical trials and therapy response assessment.