Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.

Active learning (AL) has become a powerful tool in computational drug discovery, enabling the identification of top binders from vast molecular libraries. To design a robust AL protocol, it is important to understand the influence of AL parameters, as well as the features of the data sets on the outcomes. We use four affinity data sets for different targets (TYK2, USP7, D2R, Mpro) to systematically evaluate the performance of machine learning models [Gaussian process (GP) model and Chemprop model], sample selection protocols, and the batch size based on metrics describing the overall predictive power of the model (R2, Spearman rank, root-mean-square error) as well as the accurate identification of top 2%/5% binders (Recall, F1 score). Both models have a comparable Recall of top binders on large data sets, but the GP model surpasses the Chemprop model when training data are sparse. A larger initial batch size, especially on diverse data sets, increased the Recall of both models as well as overall correlation metrics. However, for subsequent cycles, smaller batch sizes of 20 or 30 compounds proved to be desirable. Furthermore, adding artificial Gaussian noise to the data up to a certain threshold still allowed the model to identify clusters with top-scoring compounds. However, excessive noise (<1σ) did impact the model's predictive and exploitative capabilities.

Tissue-specific transcriptional programming of macrophages controls the microRNA transcriptome targeting multiple functional pathways.

Recent interest in the biology and function of peritoneal tissue resident macrophages (pMΦ) has led to a better understanding of their cellular origin, programming, and renewal. The programming of pMΦ is dependent on microenvironmental cues and tissue-specific transcription factors, including GATA6. However, the contribution of microRNAs remains poorly defined. We conducted a detailed analysis of the impact of GATA6 deficiency on microRNA expression in mouse pMΦ. Our data suggest that for many of the pMΦ, microRNA composition may be established during tissue specialization and that the effect of GATA6 knockout is largely unable to be rescued in the adult by exogenous GATA6. The data are consistent with GATA6 modulating the expression pattern of specific microRNAs, directly or indirectly, and including miR-146a, miR-223, and miR-203 established by the lineage-determining transcription factor PU.1, to achieve a differentiated pMΦ phenotype. Lastly, we showed a significant dysregulation of miR-708 in pMΦ in the absence of GATA6 during homeostasis and in response to LPS/IFN-γ stimulation. Overexpression of miR-708 in mouse pMΦ in vivo altered 167 mRNA species demonstrating functional downregulation of predicted targets, including cell immune responses and cell cycle regulation. In conclusion, we demonstrate dependence of the microRNA transcriptome on tissue-specific programming of tissue macrophages as exemplified by the role of GATA6 in pMΦ specialization.

Association Between Clonal Hematopoiesis Driver Mutations, Immune Cell Function, and the Vasculometabolic Complications of Obesity.

BACKGROUND: Obesity is accompanied by dysregulated inflammation, which can contribute to vasculometabolic complications including metabolic syndrome and atherosclerosis. Recently, clonal hematopoiesis of indeterminate potential (CHIP) has emerged as a risk factor for cardiovascular diseases. We aimed to determine how CHIP is related to immune cell function, systemic inflammation, and vasculometabolic complications in obese individuals. METHODS AND RESULTS: Two hundred ninety-seven individuals with overweight and obesity, between the ages of 54 and 81 years, were recruited in a cross-sectional study. Clonal hematopoiesis driver mutations (CHDMs) were identified with an ultrasensitive targeted assay. Assessment of carotid artery atherosclerosis was performed with ultrasound. Detailed immunological parameters, including cytokine production capacity of peripheral blood mononuclear cells, and targeted plasma proteomics analysis, were studied. Adipose tissue inflammation was determined in subcutaneous fat biopsies. Individuals with CHIP had higher concentrations of circulating IL (interleukin)-6. Total number of leukocytes and neutrophils were higher in individuals with CHIP. In contrast, ex vivo cytokine production capacity of peripheral blood mononuclear cells was significantly lower in individuals with CHIP. Sex-stratified analysis showed that men with CHDMs had significantly higher leukocyte and neutrophil counts, and ex vivo cytokine production capacity was lower in women with CHDMs. Surprisingly, the presence of atherosclerotic plaques was significantly lower in individuals with CHDMs. There was no relation between CHIP and metabolic syndrome. CONCLUSIONS: In individuals with overweight or obesity, CHDMs are not associated with vasculometabolic complications, but rather with a lower presence of carotid plaques. CHDMs associate with increased circulating inflammatory markers and leukocyte numbers, but a lower peripheral blood mononuclear cell cytokine production capacity.

The effect of leptin on trained innate immunity and on systemic inflammation in subjects with obesity.

Leptin is associated with cardiometabolic complications of obesity, such as metabolic syndrome and atherosclerosis. In obese men, the presence of metabolic syndrome is associated with higher circulating leptin and interleukin (IL)-6 concentrations and increased monocyte cytokine production capacity. Here, we investigated the effects of leptin on monocyte function and systemic inflammatory markers in obese individuals. We specifically explored whether leptin can induce long-term changes in innate immune function by inducing innate immune memory (also called trained immunity). We exposed human primary monocytes for 24 h to relevant leptin concentrations in vitro and measured cytokine production. In addition, after removing leptin, we incubated monocytes for 5 d in culture medium, and we restimulated them on day 6 to assess cytokine production capacity, phagocytosis, and foam cell formation. Direct stimulation with leptin did not induce cytokine production, but exposure to 50 ng/mL leptin augmented lipopolysaccharide- and R848-induced tumor necrosis factor α (TNF-α) production after 1 wk. In a separate in vivo study in a cohort of 302 obese subjects (body mass index [BMI] >27 kg/m2, 55 to 81 yr), we measured circulating leptin, inflammatory markers, and cytokine production upon ex vivo stimulation of isolated peripheral blood mononuclear cells. Circulating leptin concentrations positively correlated with circulating IL-1ß and IL-6, which was more pronounced in men than in women. Four single nucleotide polymorphisms in the leptin gene influenced circulating IL-6 concentrations in men, suggesting a direct effect of leptin on IL-6. In conclusion, in vitro, leptin does not directly stimulate monocytes to produce cytokines, yet induces long-term monocyte hyperresponsiveness, i.e. trained immunity. In obese subjects, leptin is associated with circulating IL-6 in a sex-dependent manner. The underlying mechanisms of the sex-specific effect of leptin on innate immune cells remain to be further investigated.

Clonal Hematopoiesis Has Prognostic Value in Dilated Cardiomyopathy Independent of Age and Clone Size.

BACKGROUND: Clonal hematopoiesis (CH) gives rise to mutated leukocyte clones that induce cardiovascular inflammation and thereby impact the disease course in atherosclerosis and ischemic heart failure. CH of indeterminate potential refers to a variant allele frequency (VAF; a marker for clone size) in blood of ≥2%. The impact of CH clones-including small clone sizes (VAF <0.5%)-in nonischemic dilated cardiomyopathy (DCM) remains largely undetermined. OBJECTIVES: The authors sought to establish the prognostic impact of CH in DCM including small clones. METHODS: CH is determined using an ultrasensitive single-molecule molecular inversion probe technique that allows detection of clones down to a VAF of 0.01%. Cardiac death and all-cause mortality were analyzed using receiver-operating characteristic curve-optimized VAF cutoff values. RESULTS: A total of 520 DCM patients have been included. One hundred and nine patients (21%) had CH driver mutations, of which 45 had a VAF of ≥2% and 31 <0.5%. The median follow-up duration was 6.5 years [IQR: 4.7-9.7 years]. DCM patients with CH have a higher risk of cardiac death (HR: 2.33 using a VAF cutoff of 0.36%, 95% CI: 1.24-4.40) and all-cause mortality (HR: 1.72 using a VAF cutoff of 0.06%, 95% CI: 1.10-2.69), independent of age, sex, left ventricular ejection fraction, and New York Heart Association classification. CONCLUSIONS: CH predicts cardiac death and all-cause mortality in DCM patients with optimal thresholds for clone size of 0.36% and 0.06%, respectively. Therefore, CH is prognostically relevant, independent of clone size in patients with DCM.

Peripheral blood mononuclear cell hyperresponsiveness in patients with premature myocardial infarction without traditional risk factors.

An increasing number of patients develop an atherothrombotic myocardial infarction (MI) in the absence of standard modifiable risk factors (SMuRFs). Monocytes and macrophages regulate the development of atherosclerosis, and monocytes can adopt a long-term hyperinflammatory phenotype by epigenetic reprogramming, which can contribute to atherogenesis (called "trained immunity"). We assessed circulating monocyte phenotype and function and specific histone marks associated with trained immunity in SMuRFless patients with MI and matched healthy controls. Even in the absence of systemic inflammation, monocytes from SMuRFless patients with MI had an increased overall cytokine production capacity, with the strongest difference for LPS-induced interleukin-10 production, which was associated with an enrichment of the permissive histone marker H3K4me3 at the promoter region. Considering the lack of intervenable risk factors in these patients, trained immunity could be a promising target for future therapy.

Deconstructing allostery by computational assessment of the binding determinants of allosteric PTP1B modulators.

Fragment-based drug discovery is an established methodology for finding hit molecules that can be elaborated into lead compounds. However it is currently challenging to predict whether fragment hits that do not bind to an orthosteric site could be elaborated into allosteric modulators, as in these cases binding does not necessarily translate into a functional effect. We propose a workflow using Markov State Models (MSMs) with steered molecular dynamics (sMD) to assess the allosteric potential of known binders. sMD simulations are employed to sample protein conformational space inaccessible to routine equilibrium MD timescales. Protein conformations sampled by sMD provide starting points for seeded MD simulations, which are combined into MSMs. The methodology is demonstrated on a dataset of protein tyrosine phosphatase 1B ligands. Experimentally confirmed allosteric inhibitors are correctly classified as inhibitors, whereas the deconstructed analogues show reduced inhibitory activity. Analysis of the MSMs provide insights into preferred protein-ligand arrangements that correlate with functional outcomes. The present methodology may find applications for progressing fragments towards lead molecules in FBDD campaigns.

Th1 Cells Alter the Inflammatory Signature of IL-6 by Channeling STAT Transcription Factors to Alu-like Retroelements.

Cytokines that signal via STAT1 and STAT3 transcription factors instruct decisions affecting tissue homeostasis, antimicrobial host defense, and inflammation-induced tissue injury. To understand the coordination of these activities, we applied RNA sequencing, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin with high-throughput sequencing to identify the transcriptional output of STAT1 and STAT3 in peritoneal tissues from mice during acute resolving inflammation and inflammation primed to drive fibrosis. Bioinformatics focused on the transcriptional signature of the immunomodulatory cytokine IL-6 in both settings and examined how profibrotic IFN-γ-secreting CD4+ T cells altered the interpretation of STAT1 and STAT3 cytokine cues. In resolving inflammation, STAT1 and STAT3 cooperated to drive stromal gene expression affecting antimicrobial immunity and tissue homeostasis. The introduction of IFN-γ-secreting CD4+ T cells altered this transcriptional program and channeled STAT1 and STAT3 to a previously latent IFN-γ activation site motif in Alu-like elements. STAT1 and STAT3 binding to this conserved sequence revealed evidence of reciprocal cross-regulation and gene signatures relevant to pathophysiology. Thus, we propose that effector T cells retune the transcriptional output of IL-6 by shaping a regulatory interplay between STAT1 and STAT3 in inflammation.

Sex-specific Association Between Adipose Tissue Inflammation and Vascular and Metabolic Complications of Obesity.

CONTEXT: Adipose tissue (AT) inflammation predisposes to insulin resistance and metabolic syndrome in obesity. OBJECTIVE: To investigate the association between adipocyte size, AT inflammation, systemic inflammation, and metabolic and atherosclerotic complications of obesity in a sex-specific manner. DESIGN: Cross-sectional cohort study. SETTING: University hospital in the Netherlands. PARTICIPANTS: A total of 302 adult subjects with a body mass index (BMI) ≥ 27 kg/m2. MAIN OUTCOME MEASURES: We obtained subcutaneous abdominal fat biopsies and systematically assessed, in a sex-specific manner, associations of several parameters of AT inflammation (including adipocyte size, macrophage content, crown-like structures, and gene expression) to biomarkers of systemic inflammation, leukocyte number and function, and to the presence of metabolic syndrome, insulin resistance, and carotid atherosclerotic plaques, assessed with ultrasound. RESULTS: Adipocyte size was associated with metabolic syndrome and AT macrophage content with insulin resistance. In contrast, none of the AT parameters was associated with carotid atherosclerosis, although mRNA expression of the anti-inflammatory IL-37 was associated with a lower intima-media thickness. We revealed profound sex-specific differences, with an association between BMI and adipocyte size, and between adipocyte size and metabolic syndrome in men only. Also, only men showed an association between adipocyte size, AT expression of leptin and MCP-1, and AT macrophage numbers, and between AT inflammation (crown-like structure number) and several circulating inflammatory proteins, including high specificity C-reactive protein, and IL-6. CONCLUSIONS: Inflammation in abdominal subcutaneous adipose tissue is more related to the metabolic than the atherosclerotic complications of obesity, and there are profound sex-specific differences in the association between BMI, adipocyte size, AT inflammation, and systemic inflammation, which are much stronger in men than women.

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses.

Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.

Determination of a microRNA signature of protective kidney ischemic preconditioning originating from proximal tubules.

Ischemic preconditioning (IPC) is effective in limiting subsequent ischemic acute kidney injury in experimental models. MicroRNAs are an important class of post-transcriptional regulator and show promise as biomarkers of kidney injury. We evaluated the time- and dose-dependence of benefit from IPC in a rat model of functional (bilateral) ischemia-reperfusion injury (IRI). We found optimal protection from subsequent injury following short, repetitive sequences of preconditioning insult. We subsequently used hybridization array and microRNA sequencing to characterize microRNA signatures of protective IPC and of IRI. These approaches identified a profile of microRNA changes consequent on IRI, that were limited by prior IPC. To localize these signals within the kidney, we used laser capture microdissection and RT-qPCR to measure microRNA abundance in nephron segments, pinpointing microRNA changes principally to glomeruli and proximal tubules. Our data describe a unique microRNA signature for IRI in the rat kidney. Pulsatile IPC reduces kidney damage following IRI and diminishes this microRNA signal. We have also identified candidate microRNAs that may act as biomarkers of injury and therapeutic targets in this context.

Membrane Interactions of α-Synuclein Revealed by Multiscale Molecular Dynamics Simulations, Markov State Models, and NMR.

α-Synuclein (αS) is a presynaptic protein that binds to cell membranes and is linked to Parkinson's disease (PD). Binding of αS to membranes is a likely first step in the molecular pathophysiology of PD. The αS molecule can adopt multiple conformations, being largely disordered in water, adopting a ß-sheet conformation when present in amyloid fibrils, and forming a dynamic multiplicity of α-helical conformations when bound to lipid bilayers and related membrane-mimetic surfaces. Multiscale molecular dynamics simulations in conjunction with nuclear magnetic resonance (NMR) and cross-linking mass spectrometry (XLMS) measurements are used to explore the interactions of αS with an anionic lipid bilayer. The simulations and NMR measurements together reveal a break in the helical structure of the central non-amyloid-ß component (NAC) region of αS in the vicinity of residues 65-70, which may facilitate subsequent oligomer formation. Coarse-grained simulations of αS starting from the structure of αS when bound to a detergent micelle reveal the overall pattern of protein contacts to anionic lipid bilayers, while subsequent all-atom simulations provide details of conformational changes upon membrane binding. In particular, simulations and NMR data for liposome-bound αS indicate incipient ß-strand formation in the NAC region, which is supported by intramolecular contacts seen via XLMS and simulations. Markov state models based on the all-atom simulations suggest a mechanism of conformational change of membrane-bound αS via a dynamic helix break in the region of residue 65 in the NAC region. The emergent dynamic model of membrane-interacting αS advances our understanding of the mechanism of PD, potentially aiding the design of novel therapeutic approaches.

Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies.

Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on 18 chemically diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calculate, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal pathlike variable obtained using machine learning or with the Hamiltonian replica-exchange algorithm SWISH, this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.

Small molecules that inhibit TNF signalling by stabilising an asymmetric form of the trimer.

Tumour necrosis factor (TNF) is a cytokine belonging to a family of trimeric proteins; it has been shown to be a key mediator in autoimmune diseases such as rheumatoid arthritis and Crohn's disease. While TNF is the target of several successful biologic drugs, attempts to design small molecule therapies directed to this cytokine have not led to approved products. Here we report the discovery of potent small molecule inhibitors of TNF that stabilise an asymmetrical form of the soluble TNF trimer, compromising signalling and inhibiting the functions of TNF in vitro and in vivo. This discovery paves the way for a class of small molecule drugs capable of modulating TNF function by stabilising a naturally sampled, receptor-incompetent conformation of TNF. Furthermore, this approach may prove to be a more general mechanism for inhibiting protein-protein interactions.

Activation of naïve CD4+ T cells re-tunes STAT1 signaling to deliver unique cytokine responses in memory CD4+ T cells.

The cytokine IL-6 controls the survival, proliferation and effector characteristics of lymphocytes through activation of the transcription factors STAT1 and STAT3. While STAT3 activity is an ever-present feature of IL-6 signaling in CD4+ T cells, prior activation via the T cell antigen receptor limits IL-6's control of STAT1 in effector and memory populations. Here we found that phosphorylation of STAT1 in response to IL-6 was regulated by the tyrosine phosphatases PTPN2 and PTPN22 expressed in response to the activation of naïve CD4+ T cells. Transcriptomics and chromatin immunoprecipitation-sequencing (ChIP-seq) of IL-6 responses in naïve and effector memory CD4+ T cells showed how the suppression of STAT1 activation shaped the functional identity and effector characteristics of memory CD4+ T cells. Thus, tyrosine phosphatases induced by the activation of naïve T cells determine the way activated or memory CD4+ T cells sense and interpret cytokine signals.

Computational Exploration of Conformational Transitions in Protein Drug Targets.

Protein drug targets vary from highly structured to completely disordered; either way dynamics governs function. Hence, understanding the dynamical aspects of how protein targets function can enable improved interventions with drug molecules. Computational approaches offer highly detailed structural models of protein dynamics which are becoming more predictive as model quality and sampling power improve. However, the most advanced and popular models still have errors owing to imperfect parameter sets and often cannot access longer timescales of many crucial biological processes. Experimental approaches offer more certainty but can struggle to detect and measure lightly populated conformations of target proteins and subtle allostery. An emerging solution is to integrate available experimental data into advanced molecular simulations. In the future, molecular simulation in combination with experimental data may be able to offer detailed models of important drug targets such that improved functional mechanisms or selectivity can be accessed.

Quantifying Secondary Structure Changes in Calmodulin Using 2D-IR Spectroscopy.

Revealing the details of biomolecular processes in solution needs tools that can monitor structural dynamics over a range of time and length scales. We assess the ability of 2D-IR spectroscopy in combination with multivariate data analysis to quantify changes in secondary structure of the multifunctional calcium-binding messenger protein Calmodulin (CaM) as a function of temperature and Ca2+ concentration. Our approach produced quantitative agreement with circular dichroism (CD) spectroscopy in detecting the domain melting transitions of Ca2+-free (apo) CaM (reduction in α-helix structure by 13% (CD) and 15% (2D)). 2D-IR also allows accurate differentiation between melting transitions and generic heating effects observed in the more thermally stable Ca2+-bound (holo) CaM. The functionally relevant random-coil-α-helix transition associated with Ca2+ uptake that involves just 7-8 out of a total of 148 amino acid residues was clearly detected. Temperature-dependent Molecular Dynamics (MD) simulations show that apo-CaM exists in dynamic equilibrium with holo-like conformations, while Ca2+ uptake reduces conformational flexibility. The ability to combine quantitative structural insight from 2D-IR with MD simulations thus offers a powerful approach for measuring subtle protein conformational changes in solution.

Allosteric mechanism of action of the therapeutic anti-IgE antibody omalizumab.

Immunoglobulin E and its interactions with receptors FcϵRI and CD23 play a central role in allergic disease. Omalizumab, a clinically approved therapeutic antibody, inhibits the interaction between IgE and FcϵRI, preventing mast cell and basophil activation, and blocks IgE binding to CD23 on B cells and antigen-presenting cells. We solved the crystal structure of the complex between an omalizumab-derived Fab and IgE-Fc, with one Fab bound to each Cϵ3 domain. Free IgE-Fc adopts an acutely bent structure, but in the complex it is only partially bent, with large-scale conformational changes in the Cϵ3 domains that inhibit the interaction with FcϵRI. CD23 binding is inhibited sterically due to overlapping binding sites on each Cϵ3 domain. Studies of omalizumab Fab binding in solution demonstrate the allosteric basis for FcϵRI inhibition and, together with the structure, reveal how omalizumab may accelerate dissociation of receptor-bound IgE from FcϵRI, exploiting the intrinsic flexibility and allosteric potential of IgE.

Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations.

Cryptic pockets, that is, sites on protein targets that only become apparent when drugs bind, provide a promising alternative to classical binding sites for drug development. Here, we investigate the nature and dynamical properties of cryptic sites in four pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. We find that the studied cryptic sites do not correspond to local minima in the computed conformational free energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as Parallel Tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. Our observed mechanism of cryptic site formation is suggestive of an interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, we developed a novel Hamiltonian Replica Exchange-based method "SWISH" (Sampling Water Interfaces through Scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. We also addressed the issue of "false-positives" and propose a simple approach to distinguish them from druggable cryptic pockets. Our simulations, whose cumulative sampling time was more than 200 µs, help in clarifying the molecular mechanism of pocket formation, providing a solid basis for the choice of an efficient computational method.

Thermodynamics calculation of protein-ligand interactions by QM/MM polarizable charge parameters.

The calculation of protein-ligand binding free energy (ΔG) is of great importance for virtual screening and drug design. Molecular dynamics (MD) simulation has been an attractive tool to investigate this scientific problem. However, the reliability of such approach is affected by many factors including electrostatic interaction calculation. Here, we present a practical protocol using quantum mechanics/molecular mechanics (QM/MM) calculations to generate polarizable QM protein charge (QMPC). The calculated QMPC of some atoms in binding pockets was obviously different from that calculated by AMBER ff03, which might significantly affect the calculated ΔG. To evaluate the effect, the MD simulations and MM/GBSA calculation with QMPC for 10 protein-ligand complexes, and the simulation results were then compared to those with the AMBER ff03 force field and experimental results. The correlation coefficient between the calculated ΔΔG using MM/GBSA under QMPC and the experimental data is .92, while that with AMBER ff03 force field is .47 for the complexes formed by streptavidin or its mutants and biotin. Moreover, the calculated ΔΔG with QMPC for the complexes formed by ERß and five ligands is positively related to experimental result with correlation coefficient of .61, while that with AMBER ff03 charge is negatively related to experimental data with correlation coefficient of .42. The detailed analysis shows that the electrostatic polarization introduced by QMPC affects the electrostatic contribution to the binding affinity and thus, leads to better correlation with experimental data. Therefore, this approach should be useful to virtual screening and drug design.