This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.
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http://dx.doi.org/10.1016/j.jmb.2021.167375 | DOI Listing |
Naunyn Schmiedebergs Arch Pharmacol
January 2025
Hannover Medical School, Institute of Pharmacology, D-30625, Hannover, Germany.
The increasing supply shortages of antibacterial drugs presents significant challenges to public health in Germany. This study aims to predict the future consumption of the ten most prescribed antibacterial drugs in Germany up to 2040 using ARIMA (Auto Regressive Integrated Moving Average) models, based on historical prescription data. This analysis also evaluates the plausibility of the forecasts.
View Article and Find Full Text PDFEur J Haematol
January 2025
Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel.
Background: Bone marrow examination (BME) is the gold standard of diagnosing myelodysplastic syndromes (MDS).
Problems: it is invasive, painful, causing possible bleeding, inaccurate (aspirate hemodilution), and subjective (inter-observer interpretation discordance). We developed non-invasive diagnostic tools: A logistic regression formula [LeukRes 2018], then a web algorithm using 10 variables (age, gender, Hb, MCV, WBC, ANC, monocytes, PLT, glucose, creatinine) to diagnose/exclude MDS [BldAdv 2021].
Ther Apher Dial
January 2025
Department of Pediatric Rheumatology, Health Science University, Umraniye Research and Training Hospital, Istanbul, Turkey.
Introduction: Therapeutic plasma exchange (TPE) is crucial for saving lives when used appropriately. This study aimed to assess TPE's impact on tumor necrosis factor-like weak inducer of apoptosis (TWEAK) protein and IL-6 levels in critically ill pediatric patients.
Methods: Conducted between May 2022 and December 2022, the study observed pediatric intensive care unit (PICU) patients undergoing TPE, recording demographics, lab results, TWEAK, and IL-6 levels pre- and post-procedure.
J Chem Theory Comput
January 2025
Department of Physics, Clarendon Laboratory, University of Oxford, Oxford OX1 3PU, U.K.
Mechanisms of anion permeation within ion channels and nanopores remain poorly understood. Recent cryo-electron microscopy structures of the human bestrophin 1 Cl channel (hBest1) provide an opportunity to evaluate ion interactions predicted by molecular dynamics (MD) simulations against experimental observations. Here, we implement the fully polarizable force field AMOEBA in MD simulations on different conformations of hBest1.
View Article and Find Full Text PDFBMC Public Health
January 2025
Al-Barkaat Institute of Management Studies, Aligarh 202122, Dr. A. P. J. Abdul Kalam Technical University, Lucknow 226010, India.
Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the effectiveness of interventions, and predicting future disease trends. This study aims to investigate the modeling and forecasting of CVD mortality, specifically in the Sindh province of Pakistan.
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