Paeonol interferes with lupus nephritis by regulating M1/M2 polarization of macrophages.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease of unknown etiology. It is marked by the production of pathogenic autoantibodies and the deposition of immune complexes. Lupus nephritis (LN) is a prevalent and challenging clinical complications of SLE. Cortex Moutan contains paeonol as its main effective component. In this study, using the animal model of SLE induced by R848, it was found that paeonol could alleviate the lupus-like symptoms of lupus mouse model induced by R848 activating TLR7, reduce the mortality and ameliorate the renal damage of mice. In order to explore the mechanism of paeonol on lupus nephritis, we studied the effect of paeonol on the polarization of Raw264.7 macrophages in vitro. The experimental results show that paeonol can inhibit the polarization of macrophages to M1 and promote their polarization to M2, which may be related to the inhibition of MAPK and NF-κB signaling pathways. Our research provides a new insight into paeonol in the treatment of lupus nephritis, which is of great importance for the treatment of systemic lupus erythematosus and its complications.

Inhibitory selectivity to the AKR1B10 and aldose reductase (AR): insight from molecular dynamics simulations and free energy calculations.

AKR1B10 is over-expressed in many cancer types and is related to chemotherapy resistance, which makes AKR1B10 a potential anti-cancer target. The high similarity of the protein structure between AKR1B10 and AR makes it difficult to develop highly selective inhibitors against AKR1B10. Understanding the interaction between AKR1B10 and inhibitors is very important for designing selective inhibitors of AKR1B10. In this study, Fidarestat, Zopolrestat, MK184 and MK204 bound to AKR1B10 and AR were used to investigate the selectivity mechanism. The results of MM/PBSA calculations show that van der Waals and electrostatic interaction provide the main contributions of the binding free energy. The hydrogen bonding between residues Y49 and H111 and inhibitors plays a pivotal role in contributing to the high inhibitory activity of AKR1B10 inhibitors. The π-π stacking interaction between residue W112 and inhibitor also plays a key role in the stability of inhibitors and AKR1B10, but W112 should keep its natural conformation to stabilize the inhibitor-AKR1B10 complex. Highly selective AKR1B10 inhibitors should have a bulky moiety like a phenyl group, which can change its binding with ABP in binding with AR and cannot change its binding with AKR1B10. The free energy decomposition shows that residues W21, V48, Y49, K78, W80, H111, R298 and V302 are beneficial to the stability of the inhibitor-AKR1B10. Our work will provide an important in silico basis for researchers to develop highly selective inhibitors of AKR1B10.

Advances of computer-aided drug design (CADD) in the development of anti-Azheimer's-disease drugs.

Alzheimer's disease (AD) is a degenerative disease of the nervous system that progressively destroys memory and thinking skills. Currently there is no treatment to prevent or cure AD; targeting the direct cause of neuronal degeneration would constitute a rational strategy and hopefully offer better options for the treatment of AD. This paper first summarizes the physiological and pathological pathogenesis of AD and then discusses the representative drug candidates for targeted therapy of AD and their binding mode with their targets. Finally, the applications of computer-aided drug design in discovering anti-AD drugs are reviewed.

Transformer-based deep learning method for optimizing ADMET properties of lead compounds.

A successful drug needs to exhibit both effective pharmacodynamics (PD) and safe pharmacokinetics (PK). However, the coordinated optimization of PD and PK properties in molecule generation tasks remains a great challenge for most existing methods, especially when they focus on the pursuit of affinity and selectivity for the lead compound. Thus, molecular optimization for PK properties is a critical step in the drug discovery pipeline, in which absorption, distribution, metabolism, excretion and toxicity (ADMET) property predictive models play an increasingly important role by providing an effective method to assess multiple PK properties of compounds. Here, we proposed a Graph Bert-based ADMET prediction model that achieves state-of-the-art performance on the public dataset Therapeutics Data Commons (TDC) by combining molecular graph features and descriptor features, with 11 tasks ranked first and 20 tasks ranked in the top 3. Based on this prediction model, we trained a Transformer model with multiple properties as constraints for learning the structural transformations involved in MMP and the accompanying property changes. The experimental results show that the trained Constraints-Transformer can implement targeted modifications to the starting molecule, while preserving the core scaffold. Moreover, molecular docking and binding mode analysis demonstrate that the optimized molecules still retain the activity and selectivity for biological targets. Therefore, the proposed method accounts for biological activity and ADMET properties simultaneously. Finally, a webserver containing ADMET property prediction and molecular optimization functions is provided, enabling chemists to improve the properties of starting molecules individually.

Recurrent neural network (RNN) model accelerates the development of antibacterial metronidazole derivatives.

Metronidazole is a specific drug against trichomonas and anaerobic bacteria, and is widely used in the clinic. However, extensive clinical application is often accompanied by extensive side effects, so it is still of great significance to develop metronidazole derivatives with a new skeleton. Compared with other traditional receptor-based drug design methods, the computational model based on a neural network has higher accuracy and reliability. In this work, a Recurrent Neural Network (RNN) model is applied to the discovery of metronidazole drugs with a new skeleton. Firstly, the generation model based on a Gated Recurrent Unit (GRU) is trained to generate an effective Simplified Molecular-Input Line-Entry System (SMILES) string library with high precision. Then, transfer learning is introduced to fine-tune the GRU model, and many molecules with structures similar to known active drugs are generated. After cluster analysis of the structures of the new compounds, 20 small molecular compounds with metronidazole structures of all different categories were selected, of which 19 may not belong to any published patents or applications. Through prediction and personal experience, the difficulty of synthesizing these 20 new structures was analyzed, and compound 0001 was chosen as our synthetic target, and a series of structures (8a-l) similar to compound 0001 were synthesized. Finally, the inhibitory activities of these compounds against bacteria E. coli, P. aeruginosa, B. subtilis and S. aureus were determined. The results showed that compound 8a-l had obvious inhibitory activity against these four bacteria, which proved the accuracy of our compound generation model.

Accelerating the discovery of anticancer peptides targeting lung and breast cancers with the Wasserstein autoencoder model and PSO algorithm.

In the development of targeted drugs, anticancer peptides (ACPs) have attracted great attention because of their high selectivity, low toxicity and minimal non-specificity. In this work, we report a framework of ACPs generation, which combines Wasserstein autoencoder (WAE) generative model and Particle Swarm Optimization (PSO) forward search algorithm guided by attribute predictive model to generate ACPs with desired properties. It is well known that generative models based on Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN) are difficult to be used for de novo design due to the problems of posterior collapse and difficult convergence of training. Our WAE-based generative model trains more successfully (lower perplexity and reconstruction loss) than both VAE and GAN-based generative models, and the semantic connections in the latent space of WAE accelerate the process of forward controlled generation of PSO, while VAE fails to capture this feature. Finally, we validated our pipeline on breast cancer targets (HIF-1) and lung cancer targets (VEGR, ErbB2), respectively. By peptide-protein docking, we found candidate compounds with the same binding sites as the peptides carried in the crystal structure but with higher binding affinity and novel structures, which may be potent antagonists that interfere with these target-mediated signaling.

Current developments in extracellular-regulated protein kinase (ERK1/2) inhibitors.

Extracellular signal-regulated kinases 1 and 2 (ERK1/2) are important members of the RAS signaling pathway. They are abnormally expressed in many diseases and, thus, are considered key therapeutic targets of human diseases. In this review, we summarize the importance of the ERK1/2 signaling pathway in the treatment of different diseases and inhibitors of ERK1/2 in clinical or preclinical research. We also discuss the main approaches used to discover ERK inhibitors, including the application and advantages of computer-aided drug design (CADD) approaches.

Identification of an AXL kinase inhibitor in triple-negative breast cancer by structure-based virtual screening and bioactivity test.

Breast cancer is a malignant tumor that occurs in the glandular epithelium of the breast, and more than 15% of the patients are triple-negative breast cancer (TNBC). Therefore, finding new targets and targeted therapeutic drugs for TNBC is urgent. Overexpression of the AXL is associated with motility and invasiveness of the TNBC cells, which is a potential target for breast cancer therapy. A compound Y041-5921 (IC50  = 6.069 µm for AXL kinase and IC50  = 4.1 µm for MDA-MB-231 cell line) was identified through structure-based virtual screening and bioassay test for the first time. The compound Y041-5921 could significantly inhibit the proliferation and invasion of the TNBC cells and the toxicity of Y041-5921 to normal immortalized breast epithelial cells was far lower than that of commonly used clinical chemotherapy drugs. Besides, it also had well inhibitory effect on the proliferation of many other malignant tumor cell lines (the IC50  value are 10.0 m, 7.1 m, 10.3 m, 11.4 m and 5.8 m for U251 cell, COLO cell, PC-9 cell, CAKI-1 cell and MG63 cell, respectively). The interaction mechanism between Y041-5921 and AXL was studied by molecular dynamics (MD) simulations and binding free energy calculation, and the key residues whose energy contribution mainly comes from non-polar solvation interaction (such as Ala565, Lys567, Met598, Leu620, Pro621, Met623, Lys624, Arg676, Asn677 and Met679) were identified. The small molecule inhibitors Y041-5921 targeting AXL reported in this work will lay a foundation and provide a theoretical basis for the development of the TNBC.

Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks.

The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. However, this learning rule needs extra time for updating operations at each step, causing extra energy consumption and reducing the computational efficiency. The event-driven or spike-based coding strategy of spiking neural networks (SNNs), i.e., neurons will only be active if driven by continuous spiking trains, employs all-or-none pulses (spikes) to transmit information, contributing to sparseness in neuron activations. In this article, we propose two event-driven IP learning rules, namely, input-driven and self-driven IP, based on basic IP learning. Input-driven means that IP updating occurs only when the neuron receives spiking inputs from its presynaptic neurons, whereas self-driven means that IP updating only occurs when the neuron generates a spike. A spiking convolutional neural network (SCNN) is developed based on the ANN2SNN conversion method, i.e., converting a well-trained rate-based artificial neural network to an SNN via directly mapping the connection weights. By comparing the computational performance of SCNNs with different IP rules on the recognition of MNIST, FashionMNIST, Cifar10, and SVHN datasets, we demonstrate that the two event-based IP rules can remarkably reduce IP updating operations, contributing to sparse computations and accelerating the recognition process. This work may give insights into the modeling of brain-inspired SNNs for low-power applications.

Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors.

The de novo drug design based on SMILES format is a typical sequence-processing problem. Previous methods based on recurrent neural network (RNN) exhibit limitation in capturing long-range dependency, resulting in a high invalid percentage in generated molecules. Recent studies have shown the potential of Transformer architecture to increase the capacity of handling sequence data. In this work, the encoder module in the Transformer is used to build a generative model. First, we train a Transformer-encoder-based generative model to learn the grammatical rules of known drug molecules and a predictive model to predict the activity of the molecules. Subsequently, transfer learning and reinforcement learning were used to fine-tune and optimize the generative model, respectively, to design new molecules with desirable activity. Compared with previous RNN-based methods, our method has improved the percentage of generating chemically valid molecules (from 95.6 to 98.2%), the structural diversity of the generated molecules, and the feasibility of molecular synthesis. The pipeline is validated by designing inhibitors against the human BRAF protein. Molecular docking and binding mode analysis showed that our method can generate small molecules with higher activity than those carrying ligands in the crystal structure and have similar interaction sites with these ligands, which can provide new ideas and suggestions for pharmaceutical chemists.

Students' perceptions of school sugar-free, food and exercise environments enhance healthy eating and physical activity.

OBJECTIVE: The objective of this study was to examine the relationships between students' perceptions of their school policies and environments (i.e. sugar-sweetened beverages (SSB) free policy, plain water drinking, vegetables and fruit eating campaign, outdoor physical activity initiative, and the SH150 programme (exercise 150 min/week at school)) and their dietary behaviours and physical activity. DESIGN: Cross-sectional study. SETTING: Primary, middle and high schools in Taiwan. PARTICIPANTS: A nationally representative sample of 2433 primary school (5th-6th grade) students, 3212 middle school students and 2829 high school students completed the online survey in 2018. RESULTS: Multivariate analysis results showed that after controlling for school level, gender and age, the students' perceptions of school sugar-free policies were negatively associated with the consumption of SSB and positively associated with consumption of plain water. Schools' campaigns promoting the eating of vegetables and fruit were positively associated with students' consumption of vegetables. In addition, schools' initiatives promoting outdoor physical activity and the SH150 programme were positively associated with students' engagement in outdoor physical activities and daily moderate-to-vigorous physical activity. CONCLUSIONS: Students' perceptions of healthy school policies and environments promote healthy eating and an increase in physical activity for students.

The 5' and 3' Untranslated Regions of the Japanese Encephalitis Virus (JEV): Molecular Genetics and Higher Order Structures.

The untranslated region (UTRs) of viral genome are important for viral replication and immune modulation. Japanese encephalitis virus (JEV) is the most significant cause of epidemic encephalitis worldwide. However, little is known regarding the characterization of the JEV UTRs. Here, systematic analyses of the UTRs of JEVs isolated from a variety of hosts worldwide spanning about 80 years were made. All the important cis-acting elements and structures were compared with other mosquito-borne Flaviviruses [West Nile virus (WNV), Yellow fever virus (YFV), Zika virus (ZIKV), Dengue virus (DENV)] and annotated in detail in the UTRs of different JEV genotypes. Our findings identified the JEV-specific structure and the sequence motif with unique JEV feature. (i) The 3' dbsHP was identified as a small hairpin located in the DB region in the 3' UTR of JEV, with the structure highly conserved among the JEV genotypes. (ii) The spacer sequence UARs of JEV consist of four discrete spacer sequences, whereas the UARs of other mosquito-borne Flaviviruses are continuous sequences. In addition, repetitive elements have been discovered in the UTRs of mosquito-borne Flaviviruses. The lengths, locations, and numbers of the repetitive elements of JEV also differed from other Flaviviruses (WNV, YFV, ZIKV, DENV). A 300 nt-length region located at the beginning of the 3' UTR exhibited significant genotypic specificity. This study lays the basis for future research on the relationships between the JEV specific structures and elements in the UTRs, and their important biological function.

Deep Scoring Neural Network Replacing the Scoring Function Components to Improve the Performance of Structure-Based Molecular Docking.

Accurate prediction of protein-ligand interactions can greatly promote drug development. Recently, a number of deep-learning-based methods have been proposed to predict protein-ligand binding affinities. However, these methods independently extract the feature representations of proteins and ligands but ignore the relative spatial positions and interaction pairs between them. Here, we propose a virtual screening method based on deep learning, called Deep Scoring, which directly extracts the relative position information and atomic attribute information on proteins and ligands from the docking poses. Furthermore, we use two Resnets to extract the features of ligand atoms and protein residues, respectively, and generate an atom-residue interaction matrix to learn the underlying principles of the interactions between proteins and ligands. This is then followed by a dual attention network (DAN) to generate the attention for two related entities (i.e., proteins and ligands) and to weigh the contributions of each atom and residue to binding affinity prediction. As a result, Deep Scoring outperforms other structure-based deep learning methods in terms of screening performance (area under the receiver operating characteristic curve (AUC) of 0.901 for an unbiased DUD-E version), pose prediction (AUC of 0.935 for PDBbind test set), and generalization ability (AUC of 0.803 for the CHEMBL data set). Finally, Deep Scoring was used to select novel ERK2 inhibitor, and two compounds (D264-0698 and D483-1785) were obtained with potential inhibitory activity on ERK2 through the biological experiments.

HDR-GAN: HDR Image Reconstruction From Multi-Exposed LDR Images With Large Motions.

Synthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR) exposures in dynamic scenes is challenging. There are two major problems caused by the large motions of foreground objects. One is the severe misalignment among the LDR images. The other is the missing content due to the over-/under-saturated regions caused by the moving objects, which may not be easily compensated for by the multiple LDR exposures. Thus, it requires the HDR generation model to be able to properly fuse the LDR images and restore the missing details without introducing artifacts. To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images. To our best knowledge, this work is the first GAN-based approach for fusing multi-exposed LDR images for HDR reconstruction. By incorporating adversarial learning, our method is able to produce faithful information in the regions with missing content. In addition, we also propose a novel generator network, with a reference-based residual merging block for aligning large object motions in the feature domain, and a deep HDR supervision scheme for eliminating artifacts of the reconstructed HDR images. Experimental results demonstrate that our model achieves state-of-the-art reconstruction performance over the prior HDR methods on diverse scenes.

Boundary-aware RGBD Salient Object Detection with Cross-modal Feature Sampling.

Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper, we propose a boundary-aware cross-modal fusion network for RGBD salient object detection. In particular, to enhance the fusion of color and depth features, we present a cross-modal feature sampling module to balance the contribution of the RGB and depth features based on the statistics of their channel values. In addition, in our multi-scale dense fusion network architecture, we not only incorporate edge-sensitive losses to preserve the boundary of the detected salient region, but also refine its structure by merging the estimated saliency maps of different scales. We accomplish the multi-scale saliency map merging using two alternative methods which produce refined saliency maps via per-pixel weighted combination and an encoder-decoder network. Extensive experimental evaluations demonstrate that our proposed framework can achieve the state-of-the-art performance on several public RGBD-based datasets.

A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol.

DNA N6-methyladenine (6mA) is closely involved with various biological processes. Identifying the distributions of 6mA modifications in genome-scale is of great significance to in-depth understand the functions. In recent years, various experimental and computational methods have been proposed for this purpose. Unfortunately, existing methods cannot provide accurate and fast 6mA prediction. In this study, we present 6mAPred-FO, a bioinformatics tool that enables researchers to make predictions based on sequences only. To sufficiently capture the characteristics of 6mA sites, we integrate the sequence-order information with nucleotide positional specificity information for feature encoding, and further improve the feature representation capacity by analysis of variance-based feature optimization protocol. The experimental results show that using this feature protocol, we can significantly improve the predictive performance. Via further feature analysis, we found that the sequence-order information and positional specificity information are complementary to each other, contributing to the performance improvement. On the other hand, the improvement is also due to the use of the feature optimization protocol, which is capable of effectively capturing the most informative features from the original feature space. Moreover, benchmarking comparison results demonstrate that our 6mAPred-FO outperforms several existing predictors. Finally, we establish a web-server that implements the proposed method for convenience of researchers' use, which is currently available at http://server.malab.cn/6mAPred-FO.

Increased Time Outdoors Is Followed by Reversal of the Long-Term Trend to Reduced Visual Acuity in Taiwan Primary School Students.

PURPOSE: To investigate the change in the prevalence of reduced visual acuity (VA) in Taiwanese school children after a policy intervention promoting increased time outdoors. DESIGN: Prospective cohort study based on the Taiwan School Student Visual Acuity Screen (TSVAS) by the Ministry of Education in Taiwan. PARTICIPANTS: All school children from grades 1 through 6 were enrolled in the TSVAS from 2001 through 2015. METHODS: The TSVAS requires each school in Taiwan to perform measurements of uncorrected VA (UCVA) on all students in grades 1 through 6 every half year using a Tumbling E chart. Reduced VA was defined as UCVA of 20/25 or less. Data from 1.2 to 1.9 million primary school children each year were collected from 2001 through 2015. A policy program named Tian-Tian 120 encouraged schools to take students outdoors for 120 minutes every day for myopia prevention. It was instituted in September 2010. To investigate the impact of the intervention, a segmented regression analysis of interrupted time series was performed. MAIN OUTCOME MEASURES: Prevalence of reduced VA. RESULTS: From 2001 to 2011, the prevalence of reduced VA of school children from grades 1 through 6 increased from 34.8% (95% confidence interval [CI], 34.7%-34.9%) to 50.0% (95% CI, 49.9%-50.1%). After the implementation of the Tian-Tian 120 outdoor program, the prevalence decreased continuously from 49.4% (95% CI, 49.3%-49.5%) in 2012 to 46.1% (95% CI, 46.0%-46.2%) in 2015, reversing the previous long-term trend. For the segmented regression analysis controlling for gender and grade, a significant constant upward trend before the intervention in the mean annual change of prevalence was found (+1.58%; standard error [SE], 0.08; P < 0.001). After the intervention, the trend changed significantly, with a constant decrease by -2.34% annually (SE, 0.23; P < 0.001). CONCLUSIONS: Policy intervention to promote increased time outdoors in schools was followed by a reversal of the long-term trend toward increased low VA in school children in Taiwan. Because randomized trials have demonstrated outdoor exposure slowing myopia onset, interventions to promote increased time outdoors may be useful in other areas affected by an epidemic of myopia.

[Oral Health in Schools and the Process of School Nursing Care].

The decayed, missing, filled (DMF) index for permanent teeth among Taiwanese students remains above 2.0, which is the target standard established by the World Health Organization (WHO). Therefore, it is imperative that oral healthcare be promoted effectively in campus and community settings. This article conducts an analysis of relevant academic, education, and health authority survey statistics and discussions, and summarizes the three stages of oral health care from 1991 and the signing by the Ministry of Health and Welfare and the Ministry of Education of the plan for health promotion in schools in 2002. Based on the school hygiene law, although the incidence of dental cavities has been declining over the years due to campus oral healthcare promotion efforts, there remain issues in need of improvement. Oral health issues must be addressed through initiatives such as the school nurse health angel program, encouraging tooth cleaning after lunch, the National Dental Hygiene Tournament, implementing the use of fluoride mouthwash, regular oral exams, and implementing corrective measures during health screenings. The results of this empirical study offers policy advice on reducing the incidence of dental cavities among school-age children in Taiwan. In light of the deep relationships between school nurses and students, teachers, and parents, it is our mission to ensure that oral healthcare in Taiwan will soon reach WHO standards and meet the expectations of parents and society.

Importance of protein flexibility in ranking ERK2 Type I1/2 inhibitor affinities: a computational study.

Extracellular-regulated kinase (ERK2) has been regarded as an essential target for various cancers, especially melanoma. Recently, pyrrolidine piperidine derivatives were reported as Type I1/2 inhibitors of ERK2, which occupy both the ATP binding pocket and the allosteric pocket. Due to the dynamic behavior of ERK2 upon the binding of Type I1/2 inhibitors, it is difficult to predict the binding structures and relative binding potencies of these inhibitors with ERK2 accurately. In this work, the binding mechanism of pyrrolidine piperidines was discussed by using different simulation techniques, including molecular docking, ensemble docking based on multiple receptor conformation, molecular dynamics simulations and free energy calculations. Our computational results show that the traditional docking method cannot predict the relative binding ability of the studied inhibitors with high accuracy, but incorporating ERK2 protein flexibility into docking is an effective method to improve the prediction accuracy. It is worth noting that the binding free energies predicted by MM/GBSA or MM/PBSA based on the MD simulations for the docked poses have the highest correlation with the experimental data, which highlights the importance of protein flexibility for accurately predicting the binding ability of Type I1/2 inhibitors of ERK2. In addition, the comprehensive analysis of several representative inhibitors indicates that hydrogen bonds and hydrophobic interactions are of significance for improving the binding affinities of the inhibitors. We hope this work will provide valuable information for further design of novel and efficient Type I1/2 ERK2 inhibitors.

Resistance mechanism of the oncogenic ß3-αC deletion mutation in BRAF kinase to dabrafenib and vemurafenib revealed by molecular dynamics simulations and binding free energy calculations.

BRAF kinase is an essential target for anti-cancer drug development. Emergence of the ß3-αC loop deletion mutation (ΔNVTAP) in BRAF kinase frequently occurred in human cancers seriously compromises the therapeutic efficacy of some BRAF kinase inhibitors, such as dabrafenib and vemurafenib. However, the mechanism of this resistance is still not well understood. In this study, the influence of the ß3-αC deletion mutation on the binding profiles of three BRAF kinase inhibitors (AZ628, dabrafenib, and vemurafenib) with BRAFV600E or BRAFΔNVTAP was explored by conventional molecular dynamics (MD) simulations and binding free energy calculations. The simulation results indicated that the ß3-αC deletion mutation enhances the flexibility of the αC helix and alters their conformations, which amplify the conformational entropy change (-TΔS) and weaken the interactions between the inhibitors and BRAF. The further per-residue binding free energy decomposition analysis revealed that the ΔNVTAP mutation changed the contributions of a few key residues to the bindings of dabrafenib or vemurafenib, such as L57, L66, W83, C84, F135, G145, and F147, but did not have obvious impact on the contributions of these residues to AZ628. Our results provide valuable clues to understand the mechanisms of drug resistance conferred by the ß3-αC deletion mutation.