Discovery of Novel Anti-Resistance AR Antagonists Guided by Funnel Metadynamics Simulation.

Androgen receptor (AR) antagonists are widely used for the treatment of prostate cancer (PCa), but their therapeutic efficacy is usually compromised by the rapid emergence of drug resistance. However, the lack of the detailed interaction between AR and its antagonists poses a major obstacle to the design of novel AR antagonists. Here, funnel metadynamics is employed to elucidate the inherent regulation mechanisms of three AR antagonists (hydroxyflutamide, enzalutamide, and darolutamide) on AR. For the first time it is observed that the binding of antagonists significantly disturbed the C-terminus of AR helix-11, thereby disrupting the specific internal hydrophobic contacts of AR-LBD and correspondingly the communication between AR ligand binding pocket (AR-LBP), activation function 2 (AF2), and binding function 3 (BF3). The subsequent bioassays verified the necessity of the hydrophobic contacts for AR function. Furthermore, it is found that darolutamide, a newly approved AR antagonist capable of fighting almost all reported drug resistant AR mutants, can induce antagonistic binding structure. Subsequently, docking-based virtual screening toward the dominant binding conformation of AR for darolutamide is conducted, and three novel AR antagonists with favorable binding affinity and strong capability to combat drug resistance are identified by in vitro bioassays. This work provides a novel rational strategy for the development of anti-resistant AR antagonists.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention.

Inverse Protein Folding (IPF) is an important task of protein design, which aims to design sequences compatible with a given backbone structure. Despite the prosperous development of algorithms for this task, existing methods tend to rely on noisy predicted residues located in the local neighborhood when generating sequences. To address this limitation, we propose an entropy-based residue selection method to remove noise in the input residue context. Additionally, we introduce ProRefiner, a memory-efficient global graph attention model to fully utilize the denoised context. Our proposed method achieves state-of-the-art performance on multiple sequence design benchmarks in different design settings. Furthermore, we demonstrate the applicability of ProRefiner in redesigning Transposon-associated transposase B, where six out of the 20 variants we propose exhibit improved gene editing activity.

ComDock: A novel approach for protein-protein docking with an efficient fusing strategy.

Protein-protein interaction plays an important role in studying the mechanism of protein functions from the structural perspective. Molecular docking is a powerful approach to detect protein-protein complexes using computational tools, due to the high cost and time-consuming of the traditional experimental methods. Among existing technologies, the template-based method utilizes the structural information of known homologous 3D complexes as available and reliable templates to achieve high accuracy and low computational complexity. However, the performance of the template-based method depends on the quality and quantity of templates. When insufficient or even no templates, the ab initio docking method is necessary and largely enriches the docking conformations. Therefore, it's a feasible strategy to fuse the effectivity of the template-based model and the universality of ab initio model to improve the docking performance. In this study, we construct a new, diverse, comprehensive template library derived from PDB, containing 77,685 complexes. We propose a template-based method (named TemDock), which retrieves the evolutionary relationship between the target sequence and samples in the template library and transfers similar structural information. Then, the target structure is built by superposing on the homologous template complex with TM-align. Moreover, we develop a consensus-based method (named ComDock) to integrate our TemDock and an existing ab initio method (ZDOCK). On 105 targets with templates from Benchmark 5.0, the TemDock and ComDock achieve a success rate of 68.57 % and 71.43 % in the top 10 conformations, respectively. Compared with the HDOCK, ComDock obtains better I-RMSD of hit configurations on 9 targets and more hit models in the top 100 conformations. As an efficient method for protein-protein docking, the ComDock is expected to study protein-protein recognition and reveal the various biological passways that are critical for developing drug discovery. The final results are stored at https://github.com/guofei-tju/mqz_ComDock_docking.

Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions.

Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.

MetaRF: attention-based random forest for reaction yield prediction with a few trails.

Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection.

Learning on topological surface and geometric structure for 3D molecular generation.

Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes. The omission of these fine details leads to many models having difficulty in outputting reasonable molecules for a variety of therapeutic targets. Here, to address this challenge, we formulate a model, called SurfGen, that designs molecules in a fashion closely resembling the figurative key-and-lock principle. SurfGen comprises two equivariant neural networks, Geodesic-GNN and Geoatom-GNN, which capture the topological interactions on the pocket surface and the spatial interaction between ligand atoms and surface nodes, respectively. SurfGen outperforms other methods in a number of benchmarks, and its high sensitivity on the pocket structures enables an effective generative-model-based solution to the thorny issue of mutation-induced drug resistance.

Efficient and accurate large library ligand docking with KarmaDock.

Ligand docking is one of the core technologies in structure-based virtual screening for drug discovery. However, conventional docking tools and existing deep learning tools may suffer from limited performance in terms of speed, pose quality and binding affinity accuracy. Here we propose KarmaDock, a deep learning approach for ligand docking that integrates the functions of docking acceleration, binding pose generation and correction, and binding strength estimation. The three-stage model consists of the following components: (1) encoders for the protein and ligand to learn the representations of intramolecular interactions; (2) E(n) equivariant graph neural networks with self-attention to update the ligand pose based on both protein-ligand and intramolecular interactions, followed by post-processing to ensure chemically plausible structures; (3) a mixture density network for scoring the binding strength. KarmaDock was validated on four benchmark datasets and tested in a real-world virtual screening project that successfully identified experiment-validated active inhibitors of leukocyte tyrosine kinase (LTK).

Structural view on the role of the TRD loop in regulating DNMT3A activity: a molecular dynamics study.

DNA methyltransferase 3A (DNMT3A) has been regarded as a potential epigenetic target for the development of cancer therapeutics. A number of DNMT3A inhibitors have been reported, but most of them do not have good potency, high selectivity and/or low cytotoxicity. It has been suggested that a non-conserved region around the target recognition domain (TRD) loop is implicated in the DNMT3A activity under the allosteric regulation of the ATRX-DNMT3-DNMT3L (ADD) domain, but the molecular mechanism of the regulation of the TRD loop on the DNMT3A activity needs to be elucidated. In this study, based on the reported crystal structures, the dynamics of the TRD loop in different multimerization with/without the bound guest molecule, namely the ADD domain or the DNA molecule, was investigated using conventional molecular dynamics (MD) and umbrella sampling simulations. The simulation results illustrate that the TRD loop exhibits relatively higher flexibility than the other components in the whole catalytic domain (CD), which could be well stabilized into different local minima through the binding with either the ADD domain or the DNA molecule by forming tight hydrogen-bond and salt-bridge networks involving distinct residues. Moreover, the movement of the TRD loop away from the catalytic loop upon activation could be triggered simply by the detachment of the ADD domain, but not necessarily induced by the ADD domain relocation on the CD. All these dynamic structural details could be a supplement to the previously reported crystal structure, which underlines the importance of the structural flexibility for the critical residues in the TRD loop, arousing more interest in the rational design of novel DNMT3A inhibitors targeting this region.

Discovery of Novel GR Ligands toward Druggable GR Antagonist Conformations Identified by MD Simulations and Markov State Model Analysis.

Binding of different ligands to glucocorticoid receptor (GR) may induce different conformational changes and even trigger completely opposite biological functions. To understand the allosteric communication within the GR ligand binding domain, the folding pathway of helix 12 (H12) induced by the binding of the agonist dexamethasone (DEX), antagonist RU486, and modulator AZD9567 are explored by molecular dynamics simulations and Markov state model analysis. The ligands can regulate the volume of the activation function-2 through the residues Phe737 and Gln738. Without ligand or with agonist binding, H12 swings from inward to outward to visit different folding positions. However, the binding of RU486 or AZD9567 perturbs the structural state, and the passive antagonist state appears more stable. Structure-based virtual screening and in vitro bioassays are used to discover novel GR ligands that bias the conformation equilibria toward the passive antagonist state. HP-19 exhibits the best anti-inflammatory activity (IC50 = 0.041 ± 0.011 µm) in nuclear factor-kappa B signaling pathway, which is comparable to that of DEX. HP-19 also does not induce adverse effect-related transactivation functions of GR. The novel ligands discovered here may serve as promising starting points for the development of GR modulators.

InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions.

Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-dimensional graph representations limit their capability to learn the generalized molecular interactions in 3D space. Here, we proposed a novel deep graph representation learning framework named InteractionGraphNet (IGN) to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. In IGN, two independent graph convolution modules were stacked to sequentially learn the intramolecular and intermolecular interactions, and the learned intermolecular interactions can be efficiently used for subsequent tasks. Extensive binding affinity prediction, large-scale structure-based virtual screening, and pose prediction experiments demonstrated that IGN achieved better or competitive performance against other state-of-the-art ML-based baselines and docking programs. More importantly, such state-of-the-art performance was proven from the successful learning of the key features in protein-ligand interactions instead of just memorizing certain biased patterns from data.

Discovery of a Novel Fusarium Graminearum Mitogen-Activated Protein Kinase (FgGpmk1) Inhibitor for the Treatment of Fusarium Head Blight.

Mitogen-activated protein kinase FgGpmk1 plays vital roles in the development and virulence of Fusarium graminearum (F. graminearum), the causative agent of Fusarium head blight (FHB). However, to date, the druggability of FgGpmk1 still needs verification, and small molecules targeting FgGpmk1 have never been reported. Here, we reported the discovery of a novel inhibitor 94 targeting FgGpmk1. First, a novel hit (compound 21) with an EC50 value of 13.01 µg·mL-1 against conidial germination of F. graminearum was identified through virtual screening. Then, guided by molecular modeling, compound 94 with an EC50 value of 3.46 µg·mL-1 was discovered, and it can inhibit the phosphorylation level of FgGpmk1 and influence the nuclear localization of its downstream FgSte12. Moreover, 94 can inhibit deoxynivalenol biosynthesis without any damage to the host. This study reported a group of FgGpmk1 inhibitors with a novel scaffold, which paves the way for the development of potent fungicides to FHB management.

VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations.

The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein-ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at http://cadd.zju.edu.cn/vdgb.

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance of target-specific MLSFs on a relatively unbiased dataset, the MLSFs trained from three representative protein-ligand interaction representations were assessed on the LIT-PCBA dataset, and the classical Glide SP SF and three types of ligand-based quantitative structure-activity relationship (QSAR) models were also utilized for comparison. Two major aspects in virtual screening campaigns, including prediction accuracy and hit novelty, were systematically explored. The calculation results illustrate that the tested target-specific MLSFs yielded generally superior performance over the classical Glide SP SF, but they could hardly outperform the 2D fingerprint-based QSAR models. Although substantial improvements could be achieved by integrating multiple types of protein-ligand interaction features, the MLSFs were still not sufficient to exceed MACCS-based QSAR models. In terms of the correlations between the hit ranks or the structures of the top-ranked hits, the MLSFs developed by different featurization strategies would have the ability to identify quite different hits. Nevertheless, it seems that target-specific MLSFs do not have the intrinsic attributes of a traditional SF and may not be a substitute for classical SFs. In contrast, MLSFs can be regarded as a new derivative of ligand-based QSAR models. It is expected that our study may provide valuable guidance for the assessment and further development of target-specific MLSFs.

Pathway regulation mechanism revealed by cotranslational folding of villin headpiece subdomain HP35.

Cotranslational folding is one of the most important features of protein folding in vivo. Although many studies have shown that the folding pathways of cotranslational folding are different from free folding in vitro, the detailed mechanism of folding dynamics is lacking. Here we combine all-atom molecular simulations with an ideal ribosome tunnel model to investigate the cotranslational folding of villin headpiece subdomain HP35. By comparing the folding dynamics between cotranslational folding and free folding, we found that cotranslational folding tends to fold along the pathway that is easier to fold into native state in the latter. In addition, the roles of the ribosome tunnel and sequential folding are analyzed separately. Our results show that the ribosome can prevent the untimely folding of the C segment of HP35 to reduce the non-native interactions, while the translation speed can regulate the amounts of native and non-native interactions and the balance between them. Overall, these results give insights into the general mechanisms of cotranslational protein folding.

Comprehensive Evaluation of Fourteen Docking Programs on Protein-Peptide Complexes.

A large number of protein-protein interactions (PPIs) are mediated by the interactions between proteins and peptide segments binding partners, and therefore determination of protein-peptide interactions (PpIs) is quite crucial to elucidate important biological processes and design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, as a powerful computation tool, molecular docking has been widely utilized to predict the binding structures of protein-peptide complexes. However, although a number of docking programs have been available, the systematic study on the assessment of their performance for PpIs has never been reported. In this study, a benchmark data set called PepSet consisting of 185 protein-peptide complexes with peptide length ranging from 5 to 20 residues was employed to evaluate the performance of 14 docking programs, including three protein-protein docking programs (ZDOCK, FRODOCK, and HawkDock), three small molecule docking programs (GOLD, Surflex-Dock, and AutoDock Vina), and eight protein-peptide docking programs (GalaxyPepDock, MDockPeP, HPEPDOCK, CABS-dock, pepATTRACT, DINC, AutoDock CrankPep (ADCP), and HADDOCK peptide docking). A new evaluation parameter, named IL_RMSD, was proposed to measure the docking accuracy with fnat (the fraction of native contacts). In global docking, HPEPDOCK performs the best for the entire data set and yields the success rates of 4.3%, 24.3%, and 55.7% at the top 1, 10, and 100 levels, respectively. In local docking, overall, ADCP achieves the best predictions and reaches the success rates of 11.9%, 37.3%, and 70.3% at the top 1, 10, and 100 levels, respectively. It is expected that our work can provide some helpful insights into the selection and development of improved docking programs for PpIs. The benchmark data set is freely available at http://cadd.zju.edu.cn/pepset/.

Novel androgen receptor antagonist identified by structure-based virtual screening, structural optimization, and biological evaluation.

Androgen receptor (AR) plays important roles in the development of prostate cancer (PCa), and therefore it has been regarded as the most important therapeutic target for both hormone-sensitive prostate cancer (HSPC) and advanced PCa. In this study, a novel hit (C18) with IC50 of 2.4 µM against AR transcriptional activity in LNCaP cell was identified through structure-based virtual screening based on molecular docking and free energy calculations. The structure-activity relationship analysis and structural optimization of C18 resulted in the discovery of a structural analogue (AT2), a more potent AR antagonist with 16-fold improved anti-AR potency. Further assays indicated that AT2 was capable of effectively inhibiting the transcriptional function of AR and blocking the nuclear translocation of AR like the second-generation AR antagonists. The antagonists discovered in this study may be served as the promising lead compounds for the development of AR-driven PCa therapeutics.

Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities.

In structure-based drug design (SBDD), the molecular mechanics generalized Born surface area (MM/GBSA) approach has been widely used in ranking the binding affinity of small molecule ligands. However, an accurate estimation of protein-ligand binding affinity still remains a challenge due to the intrinsic limitation of the standard generalized Born (GB) model used in MM/GBSA. In this study, we proposed and evaluated the MM/GBSA approach based on a variable dielectric generalized Born (VDGB) model using residue-type-based dielectric constants. In the VDGB model, different dielectric values were assigned for the three types of protein residues, and the magnitude of the dielectric constants for residue types follows this order: charged ≥ polar ≥ nonpolar. We found that MM/GBSA based on a VDGB model (MM/GBSAVDGB) with an optimal dielectric constant of 4.0 for the charged residues and 1.0 for the noncharged residues together with a net-charge-dependent dielectric value for ligands achieved better predictions as judged by Pearson's correlation coefficient than the standard MM/GBSA with a uniform solute dielectric constant of 4.0 for the training set of 130 protein-ligand complexes. The prediction on the test set with 165 protein-ligand complexes also validated the better performance of MM/GBSAVDGB. Moreover, this method exhibited potential in predicting the relative binding free energies for multiple ligands against the same target. Furthermore, we found that rational truncation of protein residues far from the binding site can significantly speed up the MM/GBSAVDGB calculations, while it almost does not influence the prediction accuracy. Therefore, it is feasible to implement the system-truncated MM/GBSAVDGB as a scoring function for SBDD.

Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power.

Protein kinases have been regarded as important therapeutic targets for many diseases. Currently, a total of 41 kinase inhibitors have been approved by the Food and Drug Administration, along with a large number of kinase inhibitors being evaluated in clinical and preclinical trials. Among all, allosteric inhibitors, such as type II kinase inhibitors, have attracted extensive attention owing to their potential high selectivity. Nowadays, molecular docking has become a powerful tool to search for novel kinase inhibitors. However, as for type II kinase inhibitors, their allosteric characteristics may exert a deep influence on docking accuracy. In this study, a comprehensive assessment was conducted to evaluate the effectiveness of nine docking algorithms towards type II kinase inhibitors. The calculation results showed that most tested docking programs, especially Glide with XP scoring, LeDock and Surflex-Dock, succeeded in the accurate identification of near-native binding poses, with the success rates ranging from 0.80 to 0.90, and the scoring functions in GOLD and LeDock outperformed the others in the prediction of relative binding affinities. In terms of the P-values, areas under the curve and enrichment factors, Glide with XP scoring, Surflex-Dock, GOLD with Astex Statistical Potential scoring and LeDock had better screening power to discriminate between active compounds and decoys. However, the screening power is sensitive to different initial conformations of the same target. It is expected that our study can provide some guidance for docking-based virtual screening to discover novel type II kinase inhibitors, as well as other allosteric inhibitors.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions.

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (rp = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (rp = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.