In this work, a new ansatz is presented that combines molecular dynamics simulations with MM-PBSA (Molecular Mechanics Poisson−Boltzmann/surface area) to rank the binding affinities of 12 TIBO-like HIV-1 RT inhibitors. Encouraging results have been obtained not only for the relative binding free energies, but also for the absolute ones, which have a root-mean-square deviation of 1.0 kcal/mol (the maximum error is 1.89 kcal/mol). Since the root-mean-square error is rather small, this approach can be reliably applied in ranking the ligands from the databases for this important target. Encouraged by the results, we decided to apply MM-PBSA combined with molecular docking to determine the binding mode of efavirenz SUSTIVATM another promising HIV-1 RT inhibitor for which no ligand−protein crystal structure had been published at the time of this work. To proceed, we define the following ansatz:  Five hundred picosecond molecular dynamics simulations were first performed for the five binding modes suggested by DOCK 4.0, and then MM-PBSA was carried out for the collected snapshots. MM-PBSA successfully identified the correct binding mode, which has a binding free energy about 7 kcal/mol more favorable than the second best mode. Moreover, the calculated binding free energy (−13.2 kcal/mol) is in reasonable agreement with experiment (−11.6 kcal/mol). In addition, this procedure was also quite successful in modeling the complex and the structure of the last snapshot was quite close to that of the measured 2,3 Å resolution crystal (structure the root-mean-square deviation of the 54 Cα around the binding site and the inhibitor is 1.1 Å). We want to point out that this result was achieved without prior knowledge of the structure of the efavirenz/RT complex. Therefore, molecular docking combined with MD simulations followed by MM-PBSA analysis is an attractive approach for modeling protein complexes a priori.

In molecular docking, it is challenging to develop a scoring function which is accurate to conduct high throughput screenings (HTS). Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as Molecular Mechanics/ Poisson Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) in molecular docking studies. Here we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein/ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics (MD) simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric 2.0, produced a Spearman correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/ GBSA performs well for both binding pose predictions and binding free energy estimations and is efficient to re-score the top-hit poses produced by other less accurate scoring functions.

In Alzheimer’s disease, neurofibrillary lesions correlate with cognitive deficits and consist of inclusions of tau protein with cross-β structure. A stable dimeric form of soluble tau has been evidenced in the cells, but its high-resolution structure is missing in solution. We know, however, that cryo-electron microscopy (c-EM) of full-length tau in the brain of an individual with AD displays a core of eight β-sheets with a C-shaped architecture spanning the R3–R4 repeat domain, while the rest of the protein is very flexible. To address the conformational ensemble of the dimer, we performed atomistic replica exchange molecular dynamics simulations on the tau R3–R4 domain starting from the c-EM configuration. We find that the wild type tau R3–R4 dimer explores elongated, U-shaped, V-shaped, and globular forms rather than the C-shape. Phosphorylation of Ser356, pSer356, is known to block the interaction between the tau protein and the amyloid-β42 peptide. Standard molecular dynamics simulations of this phosphorylated sequence for a total of 5 μs compared to its wild type counterpart show a modulation of the population of β-helices and accessible topologies and a decrease of intermediates near the fibril-like conformers.

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

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