Molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since ...they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein–ligand binding, protein–protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields.
Efficient and reliable calculation of protein–ligand binding free energy is a grand challenge in computational biology and is of critical importance in drug design and many other molecular ...recognition problems. The main challenge lies in the calculation of entropic contribution to protein–ligand binding or interaction systems. In this report, we present a new interaction entropy method which is theoretically rigorous, computationally efficient, and numerically reliable for calculating entropic contribution to free energy in protein–ligand binding and other interaction processes. Drastically different from the widely employed but extremely expensive normal mode method for calculating entropy change in protein–ligand binding, the new method calculates the entropic component (interaction entropy or −TΔS) of the binding free energy directly from molecular dynamics simulation without any extra computational cost. Extensive study of over a dozen randomly selected protein–ligand binding systems demonstrated that this interaction entropy method is both computationally efficient and numerically reliable and is vastly superior to the standard normal mode approach. This interaction entropy paradigm introduces a novel and intuitive conceptual understanding of the entropic effect in protein–ligand binding and other general interaction systems as well as a practical method for highly efficient calculation of this effect.
Abstract
Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural ...network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.
Accurate and efficient computation of protein–protein binding free energy remains a grand challenge. In this study, we develop a new strategy to achieve efficient calculation for total ...protein–protein binding free energies with improved accuracy. The new method combines the recently developed interaction entropy method for efficient computation of entropic change together with the use of residue type-specific dielectric constants in the framework of MM/GBSA to achieve optimal result for protein–protein binding free energies. The new strategy is shown to be computationally efficient and accurate than that using standard MM/GBSA methods in which the entropic computation is performed by the normal model approach and the protein interior is represented by the standard dielectric constant (typically set to 1), both in terms of accuracy and computational efficiency. Our study using the new strategy on a set of randomly selected 20 protein–protein binding systems produced an optimal dielectric constant of 2.7 for charged residues and 1.1 for noncharged residues. Using this new strategy, the mean absolute error in computed binding free energies for these 20 selected protein–protein systems is significantly reduced by more than 3-fold while the computational cost is reduced by more than 2 orders of magnitude, compared to the result using standard MM/GBSA method with the normal mode approach. A similar improvement in accuracy is confirmed for a test set consisting of 10 protein–protein systems.
Background and purpose
Although COVID‐19 predominantly affects the respiratory system, recent studies have reported the occurrence of neurological disorders such as stroke in relation to COVID‐19 ...infection. Encephalitis is an inflammatory condition of the brain that has been described as a severe neurological complication of COVID‐19. Despite a growing number of reported cases, encephalitis related to COVID‐19 infection has not been adequately characterised. To address this gap, this systematic review and meta‐analysis aims to describe the incidence, clinical course, and outcomes of patients who suffer from encephalitis as a complication of COVID‐19.
Methods
All studies published between 1 November 2019 and 24 October 2020 that reported on patients who developed encephalitis as a complication of COVID‐19 were included. Only cases with radiological and/or biochemical evidence of encephalitis were included.
Results
In this study, 610 studies were screened and 23 studies reporting findings from 129,008 patients, including 138 with encephalitis, were included. The average time from diagnosis of COVID‐19 to onset of encephalitis was 14.5 days (range = 10.8–18.2 days). The average incidence of encephalitis as a complication of COVID‐19 was 0.215% (95% confidence interval CI = 0.056%–0.441%). The average mortality rate of encephalitis in COVID‐19 patients was 13.4% (95% CI = 3.8%–25.9%). These patients also had deranged clinical parameters, including raised serum inflammatory markers and cerebrospinal fluid pleocytosis.
Conclusions
Although encephalitis is an uncommon complication of COVID‐19, when present, it results in significant morbidity and mortality. Severely ill COVID‐19 patients are at higher risk of suffering from encephalitis as a complication of the infection.
Ischemic stroke, which accounts for 75-80% of all strokes, is the predominant cause of morbidity and mortality worldwide. The post-stroke immune response has recently emerged as a new breakthrough ...target in the treatment strategy for ischemic stroke. Glial cells, including microglia, astrocytes, and oligodendrocytes, are the primary components of the peri-infarct environment in the central nervous system (CNS) and have been implicated in post-stroke immune regulation. However, increasing evidence suggests that glial cells exert beneficial and detrimental effects during ischemic stroke. Microglia, which survey CNS homeostasis and regulate innate immune responses, are rapidly activated after ischemic stroke. Activated microglia release inflammatory cytokines that induce neuronal tissue injury. By contrast, anti-inflammatory cytokines and neurotrophic factors secreted by alternatively activated microglia are beneficial for recovery after ischemic stroke. Astrocyte activation and reactive gliosis in ischemic stroke contribute to limiting brain injury and re-establishing CNS homeostasis. However, glial scarring hinders neuronal reconnection and extension. Neuroinflammation affects the demyelination and remyelination of oligodendrocytes. Myelin-associated antigens released from oligodendrocytes activate peripheral T cells, thereby resulting in the autoimmune response. Oligodendrocyte precursor cells, which can differentiate into oligodendrocytes, follow an ischemic stroke and may result in functional recovery. Herein, we discuss the mechanisms of post-stroke immune regulation mediated by glial cells and the interaction between glial cells and neurons. In addition, we describe the potential roles of various glial cells at different stages of ischemic stroke and discuss future intervention targets.
The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance ...computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔG
method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol
. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔG
method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.
COVID-19 has emerged as the most serious international pandemic in early 2020 and the lack of comprehensive knowledge in the recognition and transmission mechanisms of this virus hinders the ...development of suitable therapeutic strategies. The specific recognition during the binding of the spike glycoprotein (S protein) of coronavirus to the angiotensin-converting enzyme 2 (ACE2) in the host cell is widely considered the first step of infection. However, detailed insights on the underlying mechanism of dynamic recognition and binding of these two proteins remain unknown. In this work, molecular dynamics simulation and binding free energy calculation were carried out to systematically compare and analyze the receptor-binding domain (RBD) of six coronavirus’ S proteins. We found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses. The solvent-accessible surface area (SASA) and binding free energy of different RBD subunits indicate an “anchor-locker” recognition mechanism involved in the binding of the S protein to ACE2. Loop 2 (Y473-F490) acts as an anchor for ACE2 recognition, and Loop 3 (G496-V503) locks ACE2 at the other nonanchoring end. Then, the charged or long-chain residues in the β-sheet 1 (N450-F456) region reinforce this binding. The proposed binding mechanism was supported by umbrella sampling simulation of the dissociation process. The current computational study provides important theoretical insights for the development of new vaccines against SARS-CoV-2.
Abstract
The coronavirus disease 2019 (COVID-19) pandemic spread globally in the beginning of 2020. At present, predictors of severe disease and the efficacy of different treatments are not well ...understood. We conducted a systematic review and meta-analysis of all published studies up to 15 March 2020, which reported COVID-19 clinical features and/or treatment outcomes. Forty-five studies reporting 4203 patients were included. Pooled rates of intensive care unit (ICU) admission, mortality, and acute respiratory distress syndrome (ARDS) were 10.9%, 4.3%, and 18.4%, respectively. On meta-regression, ICU admission was predicted by increased leukocyte count (P < .0001), alanine aminotransferase (P = .024), and aspartate transaminase (P = .0040); elevated lactate dehydrogenase (LDH) (P < .0001); and increased procalcitonin (P < .0001). ARDS was predicted by elevated LDH (P < .0001), while mortality was predicted by increased leukocyte count (P = .0005) and elevated LDH (P < .0001). Treatment with lopinavir-ritonavir showed no significant benefit in mortality and ARDS rates. Corticosteroids were associated with a higher rate of ARDS (P = .0003).
Predictors of intensive care unit admission, mortality, and acute respiratory distress syndrome in patients with COVID-19 were identified. Lopinavir-ritonavir treatment did not show significant benefit, whereas corticosteroid use was associated with poorer outcome.
In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from ...docked decoy poses due to the lack of decoy structural information in their training data. Here, we developed a machine-learning model, named DeepBSP, that can directly predict the root mean square deviation (rmsd) of a ligand docking pose with reference to its native binding pose. Unlike the binding affinity, the rmsd between the docking poses with reference to their native structures can be straightforwardly determined. By training on a generated data set with 11,925 native complexes and more than 165,000 docked poses, our model shows excellent docking power on our test set and also on the CASF-2016 docking decoy set compared to other major scoring functions. Thus, by combining molecular dockings that generate many poses with the application of DeepBSP, one can more accurately predict the best binding pose that is closest to the native complex structure. This DeepBSP model shall be very useful in picking out poses close to their natives from many poses generated from a dock application.
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