Here, we systematically investigated how the force fields and the partial charge models for ligands affect the ranking performance of the binding free energies predicted by the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) approaches. A total of 46 small molecules targeted to five different protein receptors were employed to test the following issues: (1) the impact of five AMBER force fields (ff99, ff99SB, ff99SB-ILDN, ff03, and ff12SB) on the performance of MM/GBSA, (2) the influence of the time scale of molecular dynamics (MD) simulations on the performance of MM/GBSA with different force fields, (3) the impact of five AMBER force fields on the performance of MM/PBSA, and (4) the impact of four different charge models (RESP, ESP, AM1-BCC, and Gasteiger) for small molecules on the performance of MM/PBSA or MM/GBSA. Based on our simulation results, the following important conclusions can be obtained: (1) for short time-scale MD simulations (1 ns or less), the ff03 force field gives the best predictions by both MM/GBSA and MM/PBSA; (2) for middle time-scale MD simulations (2–4 ns), MM/GBSA based on the ff99 force field yields the best predictions, while MM/PBSA based on the ff99SB force field does the best; however, longer MD simulations, for example, 5 ns or more, may not be quite necessary; (3) for most cases, MM/PBSA with the Tan’s parameters shows better ranking capability than MM/GBSA (GBOBC1); (4) the RESP charges show the best performance for both MM/PBSA and MM/GBSA, and the AM1-BCC and ESP charges can also give fairly satisfactory predictions. Our results provide useful guidance for the practical applications of the MM/GBSA and MM/PBSA approaches.

Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein–protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue–residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain–peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein–protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain–peptide interactions.

Macrophage migration inhibitory factor (MIF), an immunoregulatory protein, is a potential target for a number of inflammatory diseases. In the current work, the interactions between MIF and a series of phenolic hydrazones were studied by molecular docking, molecular dynamics (MD) simulations, binding free energy calculations, and binding energy decomposition analysis to determine the structural requirement for achieving favorable biological activity of phenolic hydrazones. First, molecular docking was used to predict the binding modes of inhibitors in the binding site of MIF. The good correlation between the predicted docking scores and the experimental activities shows that the binding conformations of the inhibitors in the active site of MIF are well predicted. Moreover, our results suggest that the flexibility of MIF is essential in ligand binding process. Then, MD simulations and MM/GBSA free energy calculations were employed to determine the dynamic binding process and compare the binding modes of the inhibitors with different activities. The predicted binding free energies given by MM/GBSA are not well correlated with the experimental activities for the two subsets of the inhibitors; however, for each subset, a good correlation between the predicted binding free energies and the experimental activities is achieved. The MM/GBSA free energy decomposition analysis highlights the importance of hydrophobic residues for the MIF binding of the studied inhibitors. Based on the essential factors for MIF-inhibitor interactions derived from the theoretical predictions, some derivatives were designed and the higher inhibitory activities of several candidates were confirmed by molecular docking studies. The structural insights obtained from our study are useful for designing potent inhibitors of MIF.

Molecular mechanical force field (FF) methods are useful in studying condensed phase properties. They are complementary to experiments and can often go beyond experiments in atomic details. Even if a FF is specific for studying structures, dynamics, and functions of biomolecules, it is still important for the FF to accurately reproduce the experimental liquid properties of small molecules that represent the chemical moieties of biomolecules. Otherwise, the force field may not describe the structures and energies of macromolecules in aqueous solutions properly. In this work, we have carried out a systematic study to evaluate the General AMBER Force Field (GAFF) in studying densities and heats of vaporization for a large set of organic molecules that covers the most common chemical functional groups. The latest techniques, such as the particle mesh Ewald (PME) for calculating electrostatic energies and Langevin dynamics for scaling temperatures, have been applied in the molecular dynamics (MD) simulations. For density, the average percent error (APE) of 71 organic compounds is 4.43% when compared to the experimental values. More encouragingly, the APE drops to 3.43% after the exclusion of two outliers and four other compounds for which the experimental densities have been measured with pressures higher than 1.0 atm. For the heat of vaporization, several protocols have been investigated, and the best one, P4/ntt0, achieves an average unsigned error (AUE) and a root-mean-square error (RMSE) of 0.93 and 1.20 kcal/mol, respectively. How to reduce the prediction errors through proper van der Waals (vdW) parametrization has been discussed. An encouraging finding in vdW parametrization is that both densities and heats of vaporization approach their “ideal” values in a synchronous fashion when vdW parameters are tuned. The following hydration free energy calculation using thermodynamic integration further justifies the vdW refinement. We conclude that simple vdW parametrization can significantly reduce the prediction errors. We believe that GAFF can greatly improve its performance in predicting liquid properties of organic molecules after a systematic vdW parametrization, which will be reported in a separate paper.

The cytokine macrophage migration inhibitory factor (MIF) is regarded as a major regulator of inflammation and a key mediator that counter-regulates the inhibitory effects of glucocorticoids within the immune system. Therefore, MIF is a therapeutic target for the treatment of inflammatory and autoimmune diseases. In addition, MIF was found to be implicated in cancer pathogenesis. Current therapeutic strategies for targeting MIF focus on inhibiting its signaling activity by small molecules or modulating its biological activities using anti-MIF neutralizing antibodies. In this review, the structure and biological functions of MIF are briefly outlined. Then, the available inhibitors of MIF are systematically summarized. Finally, the recent advances that have been made in the computer-aided drug design and molecular modeling studies of MIF are reviewed.Highlights► The structure and biological functions of MIF are briefly outlined. ► The available inhibitors of MIF are systematically summarized. ► The recent advances on the computer-aided drug design and molecular modeling studies for MIF are reviewed.

•The structures and therapeutic importance of ROCK are outlined.•The available inhibitors of ROCK are summarized.•The binding mechanisms of representative ROCK inhibitors are represented.•The applications of molecular modeling in the design of ROCK inhibitors are discussed.Rho-associated protein kinases (ROCK1 and ROCK2) belong to the AGC family of serine–threonine kinases, and regulate a wide range of fundamental cell functions. Inhibition of ROCK has been proven to be of potential therapeutic benefit for a variety of diseases. In this review, the structures and therapeutic importance of ROCK are discussed briefly. Then, the recent status of the development of ROCK inhibitors is also summarized. Our review offers a foundation outline from which strategies to design new leads against ROCK can be developed.

The impact of P-glycoprotein (P-gp) on the multidrug resistance and pharmacokinetics of clinically important drugs has been widely recognized. Here, we review in silico approaches and computational models for identifying substrates or inhibitors of P-gp. The advances in the datasets for model building and available computational models are summarized and the advantages and drawbacks of these models are outlined. We also discuss the impact of the recently reported crystal structures of P-gp on potential breakthroughs in the computational modeling of P-gp substrates. Finally, the challenges of developing reliable prediction models for P-gp inhibitors or substrates, as well as the strategies to surmount these challenges, are reviewed.