1. Methods development for structure-based virtual screening

By taking advantage of the high-performance computers, structure-based virtual screening (SBVS) has become one of the core technologies in drug discovery. However, the prediction accuracy of SBVS may be impaired by some inherent defects, such as the ignorance of protein flexibility and the insufficient prediction accuracy of binding affinity. Our group has been focusing on the development of new methodologies to improve the accuracy and efficiency of SBVS. For example, we have developed the MIEC-SVM approach based on free energy decomposition and machine learning algorithm (Figure 1), which showed good capability to identify binding peptides of modular domains and small molecule inhibitors of drug targets [1~3].

Figure 1. Workflow of MIEC-SVM approach.

In order to account for protein flexibility in structure-based drug design (SBDD), we have developed a novel integrated VS strategy by combining molecular docking and complex-based pharmacophore mapping based on multiple protein structures (Figure 2), and this strategy was successfully employed to discover novel rho-associated kinase 1 (ROCK1) inhibitors [4~7].

Figure 2. Workflow of integrated VS protocol.

Furthermore, we have comprehensively evaluated the performance of MM/PBSA and MM/GBSA approaches at predicting binding affinities for extensive protein-ligand datasets (PDBbind), and examined their rescoring capabilities in molecular docking or docking-based VS. We found that MM/GBSA rescoring could achieve a good balance between computational burden and prediction accuracy for VS [8~15]. Recently, we present an easy-to-use pipeline tool named CaFE (Calculation of Free Energy) to conduct MM/GB(PB)SA and LIE calculations [16]. Powered by VMD and NAMD programs, CaFE is able to support various force field parameters and hence handle numerous static coordinate and molecular dynamics trajectory file formats generated by different molecular simulation packages.

References:

1. Tingjun Hou*, Nan Li, Youyong Li, Wei Wang*, Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models, Journal of Proteome Research, 2012, 11, 2982-2995.

2. Zheng Xu#, Tingjun Hou# (co-first author), Nan Li, Yang Xu, Wei Wang, Proteome-wide detection of Abl1 SH3 binding peptides by integrating computational prediction and peptide microarray, Molecular & Cellular Proteomics, 2012, 11, O111.010389.

3. Huiyong Sun, Peichen Pan, Sheng Tian, Lei Xu, Xiaotian Kong, Youyong Li, Dan Li, Tingjun Hou*, Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery, Scientific Reports, 2016, 6, 24817.

4. Shunye Zhou, Youyong Li, Tingjun Hou*, Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors, Journal of Chemical Information and Modeling, 2013, 53, 982-996.

5. Sheng Tian, Youyong Li, Dan Li, Xiaojie Xu, Junmei Wang, Qian Zhang, Tingjun Hou*, Modeling compound-target interaction network of Traditional Chinese Medicines for type II diabetes mellitus: insight for polypharmacology and drug design, Journal of Chemical Information and Modeling, 2013, 53, 1787–1803.

6. Sheng Tian, Huiyong Sun, Youyong Li, Dan Li, Tingjun Hou*, Development and evaluation of an integrated virtual screening strategy by combining molecular docking and pharmacophore searching based on multiple protein structures, Journal of Chemical Information and Modeling, 2013, 53, 2743-2756.

7. Xiaotian Kong, Peichen Pan, Dan Li, Sheng Tian, Youyong Li*, Tingjun Hou*, Importance of Protein Flexibility in Ranking Inhibitor Affinities: Modeling the Binding Mechanisms of Piperidine Carboxamides as Type I1/2 ALK Inhibitors, Physical Chemistry Chemical Physics, 2015, 17, 6098-6113.

8. Fu Chen, Hui Liu, Huiyong Sun, Peichen Pan, Youyong Li, Dan Li, Tingjing Hou*, Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking, Physical Chemistry Chemical Physics, 2016, 18, 22129-22139.

9. Huiyong Sun, Youyong Li, Mingyun Shen, Sheng Tian, Lei Xu, Peichen Pan, Yan Guan, Tingjun Hou*, Assessing the Performance of MM/PBSA and MM/GBSA Methods. 5. Improved Docking Performance by Using High Solute Dielectric Constant MM/GBSA and MM/PBSA Rescoring, Physical Chemistry Chemical Physics, 2014, 16, 22035-22045.

10. Huiyong Sun, Youyong Li, Sheng Tian, Lei Xu, Tingjun Hou*, Assessing the Performance of MM/PBSA and MM/GBSA Methods. 4. Accuracies of MM/PBSA and MM/GBSA Methodologies Evaluated by Various Simulation Protocols using PDBbind Data Set, Physical Chemistry Chemical Physics, 2014, 16, 16719-16729.

11. Lei Xu, Huiyong Sun, Youyong Li, Junmei Wang, Tingjun Hou*, Assessing the Performance of MM/PBSA and MM/GBSA Methods. 3. The Impact of Force Fields and Ligand Charge Models, Journal of Physical Chemistry B, 2013, 117, 8408–8421.

12. Tingjun Hou*, Junmei Wang, Youyong Li, Wei Wang*, Assessing the performance of the Molecular Mechanics/Poisson Boltzmann Surface Area and Molecular Mechanics/ Generalized Born Surface Area methods. II. The accuracy of ranking poses generated from docking, Journal of Computational Chemistry, 2011, 32, 866-877.

13. Tingjun Hou*, Junmei Wang, Youyong Li, Wei Wang*,Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations, Journal of Chemical Information and Modeling, 2011, 51, 69-82.

14. Junmei Wang*, Tingjun Hou*, Application of molecular dynamics simulations in molecular property prediction I: density and heat of vaporization, Journal of Chemical Theory and Computation, 2011, 7, 2151-2165.

15. Junmei Wang*, Tingjun Hou*, Application of molecular dynamics simulations in molecular property prediction II: diffusion coefficients, Journal of Computational Chemistry, 2011, 32, 3505-3519.

16. Hui Liu, Tingjun Hou*, CaFE: a tool for binding affinity prediction using end-point free energy methods, Bioinformatics, 2016, 32, 2216-2218.


2. Predictions of ADMET and drug-likeness

In drug discovery, the importance of optimizing Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties for potential drug candidates has been widely recognized. We have developed a variety of theoretical models for predicting important ADMET properties and drug-likeness, such as aqueous solubility (logS), octanol/water partition coefficient (logP), Caco-2 permeability, intestinal absorption, oral bioavailability, hERG blockade, blood-brain barrier (BBB), P-glycoprotein transport, pregnane X receptor activation, drug-likeness, etc. [1~14]. Continuous efforts are put on developing more reliable and accurate models. Moreover, a software platform to predict the ADME and toxicity properties is now being arranged.

Figure 3. ADMET Datasets, models and programs developed in our group

Poor pharmacokinetic properties (absorption, distribution, metabolism and excretion, ADME) and toxicity is one of the main reasons for the failure of drug development,and thus the ADMET evaluations for drug candidates at the early stage of drug development is very important.We have developed PharmacoKinetics Knowledge Base (PKKB) to compile comprehensive information about ADMET properties into a single electronic repository, and more than 10000 experimental ADMET measurements of 1685 drugs were incorporated into PKKB [11]. As far as we know, PKKB is currently the most extensive ADMET experimental information system for the public.

Figure 4. The graphical interfaces of PKKB.

References:

1. Shuangquan Wang, Huiyong Sun, Hui Liu, Dan Li, Youyong Li, Tingjun Hou*, ADMET evaluation in drug discovery. 16. Predicting hERG blockers by combining multiple pharmacophores and machine learning approaches, Molecular Pharmaceutics, 2016, 13, 2855-2866.

2. Tailong Lei, Dan Li, Youyong Li, Huiyong Sun, Tingjun Hou*, ADMET Evaluation in Drug Discovery. 15. Accurate Prediction of Rat Oral Acute Toxicity Using Relevance Vector Machine and Consensus Modeling, Journal of Cheminformatics, 2016, 8, 1.

3. Sheng Tian, Junmei Wang, Youyong Li, Dan Li, Lei Xu, Tingjun Hou*, The application of in silico drug-likeness predictions in pharmaceutical research, Advanced Drug Delivery Reviews, 2015, 86, 2-10.

4. Huali Shi, Sheng Tian, Youyong Li, Dan Li, Huidong Yu*, Xuechu Zhen*, Tingjun Hou*, Absorption, Distribution, Metabolism, Excretion, and Toxicity Evaluation in Drug Discovery. 14. Prediction of Human Pregnane X Receptor Activators by Using Naive Bayesian Classification Technique, Chemical Research in Toxicology, 2015, 28, 116-125.

5. Dan Li, Lei Xu, Youyong Li, Sheng Tian, Huiyong Sun, Tingjun Hou*, ADMET Evaluation in Drug Discovery. 13. Development of in silico Prediction Models for P-glycoprotein Substrates, Molecular Pharmaceutics, 2014, 11, 716-726.

6. Sichao Wang, Youyong Li, Lei Xu, Dan Li, Tingjun Hou*, Recent developments in computational prediction of hERG blockage, Current Topics in Medicinal Chemistry, 2013, 13, 1317-1326.

7. Sheng Tian, Youyong Li, Junmei Wang, Xiaojie Xu, Lei Xu, Xiaohong Wang, Lei Chen, Tingjun Hou*, Drug-likeness analysis of Traditional Chinese Medicines: 2. characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from Traditional Chinese Medicines, Journal of Cheminformatics, 2013, 5, 5.

8. Mingyun Shen, Sheng Tian, Youyong Li, Qian Li, Xiaojie Xu, Junmei Wang, Tingjun Hou*, Drug-likeness analysis of Traditional Chinese Medicines: 1. Property distributions of Drug-like compounds, Non-drug-like compounds and Natural compounds from Traditional Chinese Medicines, Journal of Cheminformatics, 2012, 4, 31.

9. Drug-likeness analysis of Traditional Chinese Medicines: 3. prediction of drug-likeness using machine learning approaches, Molecular Pharmaceutics, 2012, 9, 2875-2886.

10. Sichao Wang, Youyong Li, Junmei Wang, Lei Chen, Liling Zhang, Huidong Yu, Tingjun Hou*, ADME evaluation in drug discovery. 12. development of binary classification models for prediction of hERG potassium channel blockage, Molecular Pharmaceutics, 2012, 9, 996-1010.

11. Dongyue Cao, Junmei Wang, Rui Zhou, Youyong Li, Huidong Yu, Tingjun Hou*, ADMET evaluation in drug discovery. 11. Pharmaco Kinetics Knowledge Base (PKKB) - a comprehensive database of pharmacokinetic and toxic properties for drugs, Journal of Chemical Information and Modeling, 2012, 52, 1132-1137.

12. Lei Chen, Youyong Li, Huidong Yu, Liling Zhang, Tingjun Hou*, Computational models for predicting substrates and inhibitors of P-glycoprotein, Drug Discovery Today, 2012, 17, 343-351.

13. Lei Chen, Youyong Li, Qin Zhao, Hui Peng*, Tingjun Hou*, ADME evaluation in drug discovery. 10. predictions of P-glycoprotein inhibitors using recursive partitioning and naïve Bayesian classification techniques, Molecular Pharmaceutics, 2011, 8, 889-900.

14. Sheng Tian, Youyong Li, Junmei Wang, Jian Zhang*, Tingjun Hou*, ADME evaluation in drug discovery. 9. prediction of oral bioavailability in human based on molecular properties and structural fingerprints, Molecular Pharmaceutics, 2011, 8, 841-851.


3. Structure-based drug design for important drug targets

Identification and optimization of lead compound are inalienable parts of new drug discovery pipelines. With the purpose of providing hits with novel chemical scaffolds, computational virtual screening (VS) has exhibited a remarkably increasing influence on the early phase of drug discovery. We tried to discover leads for important drug targets by combining in silico, in vitro and in vivo assays. Our strength rests in a full spectrum of lead discovery, chemistry optimization, ADMET evaluation and chemistry/biology solution, which can expedite drug discovery and development programs at fairly lower costs. Our efforts involve a wide range of biological targets, including ROCK [1~5], PI3K [6~8], MIF [9~11], ALK [12], Tie-2 [13] and A2A adenosine receptor. For example, by combining docking-based virtual screening and in vitro bioassays (Figure 5), we identified a number of novel small-molecule inhibitors of MIF. The most potent inhibitor (IC50=0.55 μM) is 26-fold more active than the reference compound ISO-1 [10].

Figure 5. Protocol to discover novel MIF inhibitors.

References:

1. Peichen Pan, Mingyun Shen, Huidong Yu, Youyong Li, Dan Li, Tingjun Hou*, Advances in the development of Rho-associated protein kinase (ROCK) inhibitors, Drug Discovery Today, 2013, 18, 1323–1333.

2. Cheong-Meng Chong, Man-Teng Kou, Peichen Pan, Hefeng Zhou, Nana Ai, Chuwen Li, Hai-Jing Zhong, Chung-Hang Leung, Tingjun Hou*, Simon Ming-Yuen Lee*, Discovery of a novel ROCK2 inhibitor with anti-migration effect via docking and high-content drug screening, Molecular Biosystems, 2016, 12, 2713-2721.

3. Mingyun Shen, Sheng Tian, Peichen Pan, Huiyong Sun, Dan Li, Youyong Li, Hefeng Zhou, Chuwen Li, Simon Ming-Yuen Lee*, Tingjun Hou*, Discovery of Novel ROCK1 Inhibitors via Integrated Virtual Screening Strategy and Bioassays, Scientific Reports, 2015, 5, 16749.

4. Cheong Meng Chong, Mingyun Shen, Zhongyan Zhou, Peichen Pan, Puiman Hoi, Shang Li, Liang Wang, Nana Ai, Lunqing Zhang, Cheuk-Wing Li, Huidong Yu, Tingjun Hou*, Simon Mingyuen Lee*, Discovery of a benzofuran derivative (MBPTA) as a novel ROCK inhibitor in protecting against MPP+-induced oxidative stress and cell death in SH-SY5Y cells, Free Radical Biology & Medicine, 2014, 74, 283-293.

5. Mingyun Shen, Huidong Yu, Youyong Li, Pixu Li, Peichen Pan, Shunye Zhou, Liling Zhang, Shang Li, Simon Ming-Yuen Lee*, Tingjun Hou*, Discovery of Rho-kinase inhibitors by docking-based virtual screening, Molecular Biosystems, 2013, 9, 1511-1521.

6. Jingyu Zhu, Man Wang, Yu Yang, Kunkun Han, Juan Tang, Lei Xu, Zubing Zhang, Guodong Chen, Jie Li, ChunhuaQiao, Tingjun Hou*, Xinliang Mao*, A novel PI3K inhibitor identified by a virtual screen displays potent activity against multiple myeloma, Oncotarget, 2015, 5, 3836-3848.

7. Jingyu Zhu, Tingjun Hou*, Xinliang Mao*, Discovery of selective phosphatidylinositol 3-kinase inhibitors to treat hematological malignancies, Drug Discovery Today, 2015, 20, 988-994.

8. Juan Tang, Jingyu Zhu, Yu Yang, Zubing Zhang, Guodong Chen, Jie Li, Chunhua Qiao, Tingjun Hou*, Xinliang Mao*, A virtual screen identified C96 as a novel inhibitor of phosphatidylinositol 3-kinase that displays potent preclinical activity in multiple myeloma in vitro and in vivo, Oncotarget, 2014, 5, 3836-3848.

9. Lei Xu, Youyong Li, Huiyong Sun, Xuechu Zhen, Chunhua Qiao, Sheng Tian, Tingjun Hou*, Current developments of macrophage migration inhibitory factor (MIF) inhibitors, Drug Discovery Today, 2013, 18, 592-600.

10. Lei Xu, Yu Zhang, Longtai Zheng, Chunhua Qiao,Youyong Li, Dan Li, Xuechu Zhen*, Tingjun Hou*, Discovery of novel inhibitors targeting macrophage migration inhibitory factor via structure-based virtual screening and bioassays, Journal of Medicinal Chemistry, 2014, 57, 3737-3745.

11. Yu Zhang, Lei Xu, Zhiqiang Zhang, Zhiyu Zhang, Longtai Zheng, Dan Li, Youyong Li, Feng Liu, Kunqian Yu, Tingjun Hou*, Xuechu Zhen*, Structure-activity relationships and anti-inflammatotry activities of N-carbamothioylformamide analogues as MIF tautomerase inhibitors, Journal of Chemical Information and Modeling, 2015, 55, 1994-2004.

12. Huiyong Sun, Peichen Pan, Sheng Tian, Lei Xu, Xiaotian Kong, Youyong Li, Dan Li, Tingjun Hou*, Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery, Scientific Reports, 2016, 6, 24817.

13. Peichen Pan, Sheng Tian, Huiyong Sun, Xiaotian Kong, Wenfang Zhou, Dan Li, Youyong Li, Tingjun Hou*, Identification and Preliminary SAR Analysis of Novel Type-I Inhibitors of TIE-2 via Structure-Based Virtual Screening and Biological Evaluation in in vitro Models, Journal of Chemical Information and Modeling, 2015, 55, 2693-2704.


4. Large-scale simulations of target-ligand recognition.

Conventional methods in structural biology (X-ray and NMR) are useful to provide static snapshots of protein or protein/ligand conformations, but they are not fine enough to describe the dynamic processes involved in target-ligand recognition. In this context, atomistic molecular dynamics (MD) simulations (traditional MD and enhanced sampling MD), served as a “computational microscope”, may complement such experimental techniques by providing mechanical and thermodynamic information at the atomic level. In our group, conventional and advanced all-atom MD simulations, such as metadynamics, umbrella sampling (US), adaptive biasing force (ABF), etc., were employed to provide clues to understand the complexity in target-ligand recognition. We are especially interested in understanding the mechanisms of drug resistance, deciphering the working mechanisms of ABC transporters and predicting drug-target residence time by characterizing the free energy surfaces [1~14]. For example, based on the free energy surfaces through advanced MD techniques, it was revealed that the more rigid P-loop region in the G2032R-mutated ROS1 was primarily responsible for the crizotinib resistance, which impaired the binding of crizotinib directly and shortened the residence time induced by the flattened free energy surface (Figure 6) [6].

Figure 6. Free energy surface of crizotinib separated from G2032R-ROS1

Recently, we performed long time unbiased MD simulations and enhanced sampling MD simulations to explore the ligand binding domain of AR (AR-LBD) in complex with various ligands, and further constructed their free energy profiles by metadynamics simulations, which revealed the transition process between the antagonistic form and agonistic form of AR-LBD [13]. Interestingly, many intermediate states characterized by the free energy profile of the antagonistic form of AR-LBD could provide potential targets for the design of efficient drugs to inhibit the activity of ARs (Figure 7).

Figure 7. Free energy profiles of AR-LBD in complex with agonist or antagonist.

References:

1. Jing Zhang#, Tingjun Hou# (Co-first author), Wei Wang, Jun S. Liu, Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance, Proceedings of the National Academy of Sciences, 2010, 107, 1321-1326.

2. Lin Li, Youyong Li, Liling Zhang, Tingjun Hou*, Theoretical studies on the susceptibility of oseltamivir against variants of 2009 A/H1N1 influenza neuraminidase, Journal of Chemical Information and Modeling, 2012, 52, 2715-2729.

3. Peichen Pan, Youyong Li, Huidong Yu, Tingjun Hou*, Molecular principle of topotecan resistance by topoisomerase I mutations through molecular modeling approaches, Journal of Chemical Information and Modeling, 2013, 53, 997-1006.

4. Huiyong Sun, Youyong Li*, Dan Li, Tingjun Hou*, Insight into Crizotinib Resistance Mechanisms Caused by Three Mutations in ALK Tyrosine Kinase using Free Energy Calculation Approaches, Journal of Chemical Information and Modeling, 2013, 53, 2376–2389.

5. Peichen Pan, Lin Li, Youyong Li, Dan Li*, Tingjun Hou*, Insights into susceptibility of antiviral drugs against the E119G mutant of 2009 influenza A (H1N1) neuraminidase by molecular dynamics simulations and free energy calculations, Antiviral Research, 2013, 100, 356–364.

6. Huiyong Sun, Youyong Li, Sheng Tian, Junmei Wang, Tingjun Hou*, P-loop Conformation Governed Crizotinib Resistance in G2032R-mutated ROS1 Tyrosine Kinase: Clues from Free Energy Landscape, PLoS Computational Biology, 2014, 10, e1003729.

7. Yan Guan, Huiyong Sun, Youyong Li, Peichen Pan, Dan Li, Tingjun Hou*, The competitive binding between inhibitors and substrates of HCV NS3/4A protease: a general mechanism of drug resistance, Antiviral Research, 2014, 103, 60-70.

8. Yan Guan, Huiyong Sun, Peichen Pan, Youyong Li, Dan Li*, Tingjun Hou*, Exploring resistance mechanisms of HCV NS3/4A protease mutations to MK5172: insight from molecular dynamics simulations and free energy calculations, Molecular Biosystems, 2015, 11, 2568-2578.

9. Nan Li, Richard I. Ainsworth, Bo Ding, Tingjun Hou*, and Wei Wang*, Using Hierarchical Virtual Screening to Combat Drug Resistance of the HIV-1 Protease, Journal of Chemical Information and Modeling, 2015, 55, 1400–1412.

10. Hui Liu, Dan Li, Youyong Li, Tingjun Hou*, Atomistic molecular dynamics simulations of ATP-binding cassette transporters, WIREs Computational Molecular Science, 2016, 6, 255-265.

11. Wei Cui, Yuanhua Cheng, LinglingGeng, Densheng Liang, Tingjun Hou*, MingjuanJi*, Unraveling the Mechanism of PTP1B by Free Energy Calculation Based on Umbrella Sampling, Journal of Chemical Information and Modeling, 2013, 53, 1157-1167.

12. Huiyong Sun, Sheng Tian, Youyong Li, Dan Li, Peichen Pan, Tingjun Hou*, Revealing the favorable dissociation pathway of type II kinase inhibitors via enhanced sampling simulations and two-end-state calculations, Scientific Reports, 2015, 5, 8457.

13. Mojie Duan, Na Liu, Wenfang Zhou, Dan Li, Minghui Yang*, Tingjun Hou*, Structural diversity of ligand-binding androgen receptors revealed by microsecond long molecular dynamics simulations and enhanced sampling, Journal of Chemical Theory and Computation, 2016, 12, 4611-4619.

14. Huiyong Sun, Pengcheng Chen, Dan Li, Youyong Li, Tingjun Hou*, Directly-binding rather than induced-fit dominated binding affinity difference in (S) and (R)-crizotinib bound MTH1, Journal of Chemical Theory and Computation, 2016, 12, 851-860.