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1. Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

   https://doi.org/10.1186/s13321-021-00493-4  

   Ji, Beihong ; He, Xibing ; Zhang, Yuzhao ; Zhai, Jingchen ; Man, Viet Hoang ; Liu, Shuhan ; Wang, Junmei ( December 2021 , Journal of Cheminformatics)

   null (Ed.)

   Abstract In this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S 4 (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost. 

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2. In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function

   https://doi.org/10.1039/d2cp01727j  

   Zhai, Jingchen ; He, Xibing ; Sun, Yuchen ; Wan, Zhuoya ; Ji, Beihong ; Liu, Shuhan ; Li, Song ; Wang, Junmei ( August 2022 , Physical Chemistry Chemical Physics)

   Metabotropic glutamate receptors (mGluRs) play an important role in regulating glutamate signal pathways, which are involved in neuropathy and periphery homeostasis. mGluR4, which belongs to Group III mGluRs, is most widely distributed in the periphery among all the mGluRs. It has been proved that the regulation of this receptor is involved in diabetes, colorectal carcinoma and many other diseases. However, the application of structure-based drug design to identify small molecules to regulate the mGluR4 receptor is limited due to the absence of a resolved mGluR4 protein structure. In this work, we first built a homology model of mGluR4 based on a crystal structure of mGluR8, and then conducted hierarchical virtual screening (HVS) to identify possible active ligands for mGluR4. The HVS protocol consists of three hierarchical filters including Glide docking, molecular dynamic (MD) simulation and binding free energy calculation. We successfully prioritized active ligands of mGluR4 from a set of screening compounds using HVS. The predicted active ligands based on binding affinities can almost cover all the experiment-determined active ligands, with only one ligand missed. The correlation between the measured and predicted binding affinities is significantly improved for the MM-PB/GBSA-WSAS methods compared to the Glide docking method. More importantly, we have identified hotspots for ligand binding, and we found that SER157 and GLY158 tend to contribute to the selectivity of mGluR4 ligands, while ALA154 and ALA155 could account for the ligand selectivity to mGluR8. We also recognized other 5 key residues that are critical for ligand potency. The difference of the binding profiles between mGluR4 and mGluR8 can guide us to develop more potent and selective modulators. Moreover, we evaluated the performance of IPSF, a novel type of scoring function trained by a machine learning algorithm on residue–ligand interaction profiles, in guiding drug lead optimization. The cross-validation root-mean-square errors (RMSEs) are much smaller than those by the endpoint methods, and the correlation coefficients are comparable to the best endpoint methods for both mGluRs. Thus, machine learning-based IPSF can be applied to guide lead optimization, albeit the total number of actives/inactives are not big, a typical scenario in drug discovery projects. 

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3. Performance of human and server prediction in CAPRI rounds 38‐45

   https://doi.org/10.1002/prot.25956  

   Duan, Rui ; Qiu, Liming ; Xu, Xianjin ; Ma, Zhiwei ; Merideth, Benjamin Ryan ; Shyu, Chi‐Ren ; Zou, Xiaoqin ( July 2020 , Proteins: Structure, Function, and Bioinformatics)

   CAPRI challenges offer a variety of blind tests for protein‐protein interaction prediction. In CAPRI Rounds 38‐45, we generated a set of putative binding modes for each target with an FFT‐based docking algorithm, and then scored and ranked these binding modes with a proprietary scoring function, ITScorePP. We have also developed a novel web server, Rebipp. The algorithm utilizes information retrieval to identify relevant biological information to significantly reduce the search space for a particular protein. In parallel, we have also constructed a GPU‐based docking server, MDockPP, for protein‐protein complex structure prediction. Here, the performance of our protocol in CAPRI rounds 38‐45 is reported, which include 16 docking and scoring targets. Among them, three targets contain multiple interfaces: Targets 124, 125, and 136 have 2, 4, and 3 interfaces, respectively. In the predictor experiments, we predicted correct binding modes for nine targets, including one high‐accuracy interface, six medium‐accuracy binding modes, and six acceptable‐accuracy binding modes. For the docking server prediction experiments, we predicted correct binding modes for eight targets, including one high‐accuracy, three medium‐accuracy, and five acceptable‐accuracy binding modes.

    

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4. Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

   https://doi.org/10.1186/s12911-021-01462-5  

   Rabby, Md Fazle ; Tu, Yazhou ; Hossen, Md Imran ; Lee, Insup ; Maida, Anthony S. ; Hei, Xiali ( December 2021 , BMC Medical Informatics and Decision Making)

   Abstract Background Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. Methods In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. Results For the OhioT1DM (2018) dataset, containing eight weeks’ data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively. Conclusions To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings—the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management. 

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5. Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors

   https://doi.org/10.1093/bioinformatics/btz336  

   Wu, Jiansheng ; Liu, Ben ; Chan, Wallace K. B. ; Wu, Weijian ; Pang, Tao ; Hu, Haifeng ; Yan, Shancheng ; Ke, Xiaoyan ; Zhang, Yang ( July 2019 , Bioinformatics)

   Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities; this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key substructural features which determine ligand bioactivities are needed to address this issue.

   A new method, SED, was proposed to predict ligand bioactivities and to recognize key substructures associated with GPCRs through the coupling of screening for Lasso of long extended-connectivity fingerprints (ECFPs) with deep neural network training. The SED pipeline contains three successive steps: (i) representation of long ECFPs for ligand molecules, (ii) feature selection by screening for Lasso of ECFPs and (iii) bioactivity prediction through a deep neural network regression model. The method was examined on a set of 16 representative GPCRs that cover most subfamilies of human GPCRs, where each has 300–5000 ligand associations. The results show that SED achieves excellent performance in modelling ligand bioactivities, especially for those in the GPCR datasets without sufficient ligand associations, where SED improved the baseline predictors by 12% in correlation coefficient (r2) and 19% in root mean square error. Detail data analyses suggest that the major advantage of SED lies on its ability to detect substructures from long ECFPs which significantly improves the predictive performance.

   The source code and datasets of SED are freely available at https://zhanglab.ccmb.med.umich.edu/SED/.

   Supplementary data are available at Bioinformatics online.

    

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