A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.

Virtual screening (VS) is a routine method to evaluate chemical libraries for lead identification. Therefore, the selection of appropriate protein structures for VS is an essential prerequisite to identify true actives during docking. But the presence of several crystal structures of the same protein makes it difficult to select one or few structures rationally for screening. Therefore, a computational prioritization protocol has been developed for shortlisting crystal structures that identify true active molecules with better efficiency. As identification of small-molecule inhibitors is an important clinical requirement for the T790M/L858R (TMLR) EGFR mutant, it has been selected as a case study. The approach involves cross-docking of 21 co-crystal ligands with all the structures of the same protein to select structures that dock non-native ligands with lower RMSD. The cross docking performance was then correlated with ligand similarity and binding-site conformational similarity. Eventually, structures were shortlisted by integrating cross-docking performance, and ligand and binding-site similarity. Thereafter, binding pose metadynamics was employed to identify structures having stable co-crystal ligands in their respective binding pockets. Finally, different enrichment metrics like BEDROC, RIE, AUAC, and EF1% were evaluated leading to the identification of five TMLR structures (5HCX, 5CAN, 5CAP, 5CAS, and 5CAO). These structures docked a number of non-native ligands with low RMSD, contain structurally dissimilar ligands, have conformationally dissimilar binding sites, harbor stable co-crystal ligands, and also identify true actives early. The present approach can be implemented for shortlisting protein targets of any other important therapeutic kinases.

Revisiting the inhibitory potential of protein kinase inhibitors against NEK7 protein via comprehensive computational investigations.

The NEK7 protein is required for spindle formation, cell division, and the activation of the NLRP3 inflammasome receptor. The aberrant expression of NEK7 has been implicated to the growth of metastasis and severe inflammatory conditions like rheumatoid arthritis, liver cirrhosis, and gout. An emergent target for the development of anti-cancer drugs is NEK7. In this context, the PubChem database was used to retrieve the 675 compound library and FDA-approved protein kinase inhibitors, which were then thoroughly examined via in-silico experiments. Computational studies investigated the binding orientation, electronic, and thermodynamic characteristics of drug candidates related to target protein. Drugs were investigated using density functional theory and molecular docking to find binding interactions with NEK7. Molecular dynamic simulations assessed interactions and stability of protein-ligand complex. DFT analyses showed that selected compounds maintained a significant amount of chemical reactivity in both liquid and gaseous states. Alectinib, Crizotinib, and compound 146476703 all displayed promising molecular interactions, according to molecular docking studies, with docking scores of - 32.76, - 30.54, and - 34.34 kJ/mol, respectively. Additionally, MD simulations determined the stability and dynamic characteristics of the complex over a 200 ns production run. The current study's findings indicate that the drugs Alectinib, Crizotinib, and compound 146476703 can successfully inhibit the overexpression of the NEK7 protein. To discover more potent drugs against NEK7, it is recommended to synthesize the derivatives of Alectinib and Crizotinib and carry out additional in-vitro and in-vivo studies at the molecular level.

DNALI1 deficiency causes male infertility with severe asthenozoospermia in humans and mice by disrupting the assembly of the flagellar inner dynein arms and fibrous sheath.

The axonemal dynein arms (outer (ODA) and inner dynein arms (IDAs)) are multiprotein structures organized by light, intermediate, light intermediate (LIC), and heavy chain proteins. They hydrolyze ATP to promote ciliary and flagellar movement. Till now, a variety of dynein protein deficiencies have been linked with asthenospermia (ASZ), highlighting the significance of these structures in human sperm motility. Herein, we detected bi-allelic DNALI1 mutations [c.663_666del (p.Glu221fs)], in an ASZ patient, which resulted in the complete loss of the DNALI1 in the patient's sperm. We identified loss of sperm DNAH1 and DNAH7 rather than DNAH10 in both DNALI1663_666del patient and Dnali1-/- mice, demonstrating that mammalian DNALI1 is a LIC protein of a partial IDA subspecies. More importantly, we revealed that DNALI1 loss contributed to asymmetries in the most fibrous sheath (FS) of the sperm flagellum in both species. Immunoprecipitation revealed that DNALI1 might interact with the cytoplasmic dynein complex proteins in the testes. Furthermore, DNALI1 loss severely disrupted the transport and assembly of the FS proteins, especially AKAP3 and AKAP4, during flagellogenesis. Hence, DNALI1 may possess a non-classical molecular function, whereby it regulates the cytoplasmic dynein complex that assembles the flagella. We conclude that a DNALI deficiency-induced IDAs injury and an asymmetric FS-driven tail rigid structure alteration may simultaneously cause flagellum immotility. Finally, intracytoplasmic sperm injection (ICSI) can effectively resolve patient infertility. Collectively, we demonstrate that DNALI1 is a newly causative gene for AZS in both humans and mice, which possesses multiple crucial roles in modulating flagellar assembly and motility.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery.

BACKGROUND: CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE: This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD: We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS: Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION: All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Multi-Targeted Molecular Docking, Pharmacokinetics, and Drug-Likeness Evaluation of Okra-Derived Ligand Abscisic Acid Targeting Signaling Proteins Involved in the Development of Diabetes.

Diabetes mellitus is a global threat affecting millions of people of different age groups. In recent years, the development of naturally derived anti-diabetic agents has gained popularity. Okra is a common vegetable containing important bioactive components such as abscisic acid (ABA). ABA, a phytohormone, has been shown to elicit potent anti-diabetic effects in mouse models. Keeping its anti-diabetic potential in mind, in silico study was performed to explore its role in inhibiting proteins relevant to diabetes mellitus- 11ß-hydroxysteroid dehydrogenase (11ß-HSD1), aldose reductase, glucokinase, glutamine-fructose-6-phosphate amidotransferase (GFAT), peroxisome proliferator-activated receptor-gamma (PPAR-gamma), and Sirtuin family of NAD(+)-dependent protein deacetylases 6 (SIRT6). A comparative study of the ABA-protein docked complex with already known inhibitors of these proteins relevant to diabetes was compared to explore the inhibitory potential. Calculation of molecular binding energy (ΔG), inhibition constant (pKi), and prediction of pharmacokinetics and pharmacodynamics properties were performed. The molecular docking investigation of ABA with 11-HSD1, GFAT, PPAR-gamma, and SIRT6 revealed considerably low binding energy (ΔG from -8.1 to -7.3 Kcal/mol) and predicted inhibition constant (pKi from 6.01 to 5.21 µM). The ADMET study revealed that ABA is a promising drug candidate without any hazardous effect following all current drug-likeness guidelines such as Lipinski, Ghose, Veber, Egan, and Muegge.

In silico-determined compound from the root of Pueraria lobate alleviates synaptic plasticity injury induced by Alzheimer's disease via the p38MAPK-CREB signaling pathway.

Pueraria lobata is utilized as a food source in China. The aim of this study is to combine virtual screening and molecular dynamics predictive model to screen out the potential synaptic plasticity-maintaining components from the root of P. lobate and to verify it by employing the amyloid ß-injected rats' model. Eighteen compounds were identified by HPLC-MS/MS; puerarin manifested the most potential to form a stable complex with calcium/calmodulin kinase IIα (CaMK IIα), which is the key protein in synaptic plasticity by the in silico study. The further in vivo assay showed that puerarin could elevate the synaptic thickness, density, and length, relieve calcium overload, regulate the expression of CaMK IIα, and other p38MAPK-CREB signaling pathway-related biochemical criteria. The behavioral test also verified the results. Results have confirmed that the root of P. lobate can work anti-AD by maintaining the synaptic plasticity and proved the reliability of using the in silico predictive model to determine active ingredients from the natural product.

Comprehensive computational target fishing approach to identify Xanthorrhizol putative targets.

Xanthorrhizol (XNT), is a bioactive compound found in Curcuma xanthorrhiza Roxb. This study aimed to determine the potential targets of the XNT via computational target fishing method. This compound obeyed Lipinski's and Veber's rules where it has a molecular weight (MW) of 218.37 gmol-1, TPSA of 20.23, rotatable bonds (RBN) of 4, hydrogen acceptor and donor ability is 1 respectively. Besides, it also has half-life (HL) values 3.5 h, drug-likeness (DL) value of 0.07, oral bioavailability (OB) of 32.10, and blood-brain barrier permeability (BBB) value of 1.64 indicating its potential as therapeutic drug. Further, 20 potential targets were screened out through PharmMapper and DRAR-CPI servers. Co-expression results derived from GeneMANIA revealed that these targets made connection with a total of 40 genes and have 744 different links. Four genes which were RXRA, RBP4, HSD11B1 and AKR1C1 showed remarkable co-expression and predominantly involved in steroid metabolic process. Furthermore, among these 20 genes, 13 highly expressed genes associated with xenobiotics by cytochrome P450, chemical carcinogenesis and steroid metabolic pathways were identified through gene ontology (GO) and KEGG pathway analysis. In conclusion, XNT is targeting multiple proteins and pathways which may be exploited to shape a network that exerts systematic pharmacological effects.

GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking.

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, GWOVina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side-chain sampling. The new method was validated for rigid and flexible-receptor docking using four independent datasets. In rigid docking, GWOVina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible-receptor docking, GWOVina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, GWOVina can play a role in solving the complex flexible-receptor docking cases and is suitable for virtual screening of compound libraries. GWOVina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.

Benzenesulfonamide Derivatives as Calcium/Calmodulin-Dependent Protein Kinase Inhibitors and Antiviral Agents against Dengue and Zika Virus Infections.

Emerging and resurging mosquito-borne flaviviruses are an important public health challenge. The increased prevalence of dengue virus (DENV) infection has had a significant socioeconomic impact on epidemic countries. The recent outbreak of Zika virus (ZIKV) has created an international public health emergency because ZIKV infection has been linked to congenital defects and Guillain-Barré syndrome. To develop potentially prophylactic antiviral drugs for combating these acute infectious diseases, we have targeted the host calcium/calmodulin-dependent kinase II (CaMKII) for inhibition. By using CaMKII structure-guided inhibitor design, we generated four families of benzenesulfonamide (BSA) derivatives for SAR analysis. Among these substances, N-(4-cycloheptyl-4-oxobutyl)-4-methoxy-N-phenylbenzenesulfonamide (9) showed superior properties as a lead CaMKII inhibitor and antiviral agent. BSA 9 inhibited CaMKII activity with an IC50 value of 0.79 µM and displayed EC50 values of 1.52 µM and 1.91 µM against DENV and ZIKV infections of human neuronal BE(2)C cells, respectively. Notably, 9 significantly reduced the viremia level and increased animal survival time in mouse-challenge models.

Overcoming Taxol-resistance in A549 cells: A comprehensive strategy of targeting P-gp transporter, AKT/ERK pathways, and cytochrome P450 enzyme CYP1B1 by 4-hydroxyemodin.

Taxol-based chemotherapy is widely used as the first-line treatment for non-small cell lung cancer (NSCLC), however, the subsequent development of taxol-resistance is a major concern and challenge, resulting in tumor relapse and poor prognosis. Given the complex nature of taxol-resistance, we further delved into its mechanisms and demonstrated that CYP1B1 was associated to taxol response in taxol-resistant A549/Taxol cells. Compared to its parent A549 counterpart, A549/Taxol presented much higher level of CYP1B1, which was paralleled by increased aryl hydrocarbon receptor (AhR) expressions likely due to the long term taxol exposure and thereby allowed a subsequent up-regulation of CYP1B1. Inhibition of CYP1B1 by TMS [(E)-2,3',4,5'-tetramethoxystilbene], the specific CYP1B1 inhibitor, remarkably enhanced the sensitivity of A549/Taxol to taxol. Moreover, pre-incubation of taxol with human recombinant CYP1B1 did not affect drug toxicity in A549 cells, precluding the possibility of drug resistance ascribed to CYP1B1 due to directly inactivating taxol. Indeed, CYP1B1 is responsible for bio-transforming estrogen (E2) into the carcinogenetic metabolite that would inhibit microtubule stabilization induced by taxol and thereby compromising treatment efficacy. Remarkably, our data revealed potent CYP1B1 inhibition efficacy of 4-hydroxyemodin (HEM) as reflected by both molecular docking simulations and EROD assay, which posed HEM the advantage of breaking the vicious circle between E2 and CYP1B1, not only favoring to overcome taxol-resistance, but also offering long term benefit via circumventing carcinogenesis and tumor progression induced by E2. In addition to CYP1B1 inhibition, HEM notably inhibited P-gp activity and expression, a common feature of drug resistance, as well as significantly inactivated AKT/ERK pathways that contributed to the cell proliferation, migration, and drug resistance. Thus, HEM may act in concert to overcome taxol-resistance through comprehensive targeting three considered arms of drug-resistance mechanisms. Moreover, HEM profoundly resisted E2-stimulated cell migration in both A549 and A549/Taxol cells, a primary reason for tumor patients' mortality, as well as inflicted selective injury to A549/Taxol cells rather than normal lung cells, supporting HEM to be a promising agent for overcoming taxol-resistance in A549 cells.

A-Kinase Anchoring Protein 150 and Protein Kinase A Complex in the Basolateral Amygdala Contributes to Depressive-like Behaviors Induced by Chronic Restraint Stress.

BACKGROUND: The basolateral amygdala (BLA) has been widely implicated in the pathophysiology of major depressive disorder. A-kinase anchoring protein 150 (AKAP150) directs kinases and phosphatases to synaptic glutamate receptors, controlling synaptic transmission and plasticity. However, the role of the AKAP150 in the BLA in major depressive disorder remains poorly understood. METHODS: Depressive-like behaviors in C57BL/6J mice were developed by chronic restraint stress (CRS). Mice received either intra-BLA injection of lentivirus-expressing Akap5 short hairpin RNA or Ht-31, a peptide to disrupt the interaction of AKAP150 and protein kinase A (PKA), followed by depressive-like behavioral tests. Alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid glutamate receptor (AMPAR)-mediated miniature excitatory postsynaptic currents were recorded by whole-cell patch-clamp techniques. RESULTS: Chronic stress exposure induced depressive-like behaviors, which were accompanied by an increase in total and synaptic AKAP150 expression in the BLA. Accordingly, CRS facilitated the association of AKAP150 with PKA, but not of calcineurin in the BLA. Intra-BLA infusion of lentivirus-expressing Akap5 short hairpin RNA or Ht-31 prevented depressive-like behaviors and normalized phosphorylation of serine 845 and surface expression of AMPAR subunit 1 (GluA1) in the BLA of CRS mice. Finally, blockage of AKAP150-PKA complex signaling rescued the changes in AMPAR-mediated miniature excitatory postsynaptic currents in depressive-like mice. CONCLUSIONS: These results suggest that AKAP150-PKA directly modulates BLA neuronal synaptic strength, and that AKAP150-PKA-GluA1 streamline signaling complex is responsible for CRS-induced disruption of synaptic AMPAR-mediated transmission and depressive-like behaviors in mice.

In silico identification of natural product inhibitors for γ-secretase activating protein, a therapeutic target for Alzheimer's disease.

Alzheimer's disease (AD) is clinically characterized by the aggregation of neurotoxic amyloid-ß (Aß) peptides in the brain. γ-Secretase catalyzes the reaction of Aß formation. Inhibition of γ-secretase activating protein (GSAP) reduces Aß production without disrupting other molecular functions and serves as a promising therapeutic target for lowering Aß and curing AD. Till date, no proven drug is available for curing AD because of the nonexistence of crystal/NMR structure of GSAP. Thus in the present study, for the first time, we adopted in silico method to predict the 3D structure of GSAP via comparative modeling and studied the architecture and function of GSAP through simulation studies. Docking studies with 4153 phytochemicals revealed that GSAP having a better binding affinity with macaflavanone C, (E)-1-[2,4-dihydroxy-3-(3-methylbut-2-enyl)phenyl]-3-(2,2-dimethyl-8-hydroxy-2H-benzopyran-6-yl)prop-2-en-1-one, and monachosorin B as compared with the standard drug, imatinib. Further, the molecular dynamics analysis suggested that only two phytochemicals, namely, macaflavanone C and (E)-1-[2,4-dihydroxy-3-(3-methylbut-2-enyl)phenyl]-3-(2,2-dimethyl-8-hydroxy-2H-benzopyran-6-yl)prop-2-en-1-one) significantly disrupt the original property of GSAP and also cleared the absorption, distribution, metabolism, and excretion test. These natural compounds may be utilized in future for curing AD after further investigations.

Generation of a biotinylatable Sox2 mouse model to identify Sox2 complexes in vivo.

Sox2 is a Sry-box containing family member of related transcription factors sharing homology in their DNA binding domain. Sox2 is important during different stages of development, and previously we showed that Sox2 plays an important role in branching morphogenesis and epithelial cell differentiation in lung development. The transcriptional activity of Sox2 depends on its interaction with other proteins, leading to 'complex-specific' DNA binding and transcriptional regulation. In this study, we generated a mouse model containing a biotinylatable-tag targeted at the translational start site of the endogenous Sox2 gene (bioSox2). This tag was biotinylated by the bacterial birA protein and the resulting bioSox2 protein was used to identify associating partners of Sox2 at different phases of lung development in vivo (the Sox2 interactome). Homozygous bioSox2 mice are viable and fertile irrespective of the biotinylation of the bio tag, indicating that the bioSox2 gene is normally expressed and the protein is functional in all tissues. This suggests that partners of Sox2 are most likely able to associate with the bioSox2 protein. BioSox2 complexes were isolated with high affinity using streptavidin beads and analysed by MALDI-ToF mass spectrometry analysis. Several of the identified binding partners are already shown to have a respiratory phenotype. Two of these partners, Wdr5 and Tcf3, were validated to confirm their association in Sox2 complexes. This bioSox2 mouse model will be a valuable tool for isolating in vivo Sox2 complexes from different tissues.

Exploring the Stability of Ligand Binding Modes to Proteins by Molecular Dynamics Simulations: A Cross-docking Study.

Docking has become an indispensable approach in drug discovery research to predict the binding mode of a ligand. One great challenge in docking is to efficiently refine the correct pose from various putative docking poses through scoring functions. We recently examined the stability of self-docking poses under molecular dynamics (MD) simulations and showed that equilibrium MD simulations have some capability to discriminate between correct and decoy poses. Here, we have extended our previous work to cross-docking studies for practical applications. Three target proteins (thrombin, heat shock protein 90-alpha, and cyclin-dependent kinase 2) of pharmaceutical interest were selected. Three comparable poses (one correct pose and two decoys) for each ligand were then selected from the docking poses. To obtain the docking poses for the three target proteins, we used three different protocols, namely: normal docking, induced fit docking (IFD), and IFD against the homology model. Finally, five parallel MD equilibrium runs were performed on each pose for the statistical analysis. The results showed that the correct poses were generally more stable than the decoy poses under MD. The discrimination capability of MD depends on the strategy. The safest way was to judge a pose as being stable if any one run among five parallel runs was stable under MD. In this case, 95% of the correct poses were retained under MD, and about 25-44% of the decoys could be excluded by the simulations for all cases. On the other hand, if we judge a pose as being stable when any two or three runs were stable, with the risk of incorrectly excluding some correct poses, approximately 31-53% or 39-56% of the two decoys could be excluded by MD, respectively. Our results suggest that simple equilibrium simulations can serve as an effective filter to exclude decoy poses that cannot be distinguished by docking scores from the computationally expensive free-energy calculations.

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations.

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.

Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints.

BACKGROUND: In structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193-208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively. RESULTS: In this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases. CONCLUSIONS: KGS2 can be applied as a convenient "add-on" to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors.

Protein-Ligand Empirical Interaction Components for Virtual Screening.

A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations.

Ligand docking is a widely used tool for lead discovery and binding mode prediction based drug discovery. The greatest challenges in docking occur when the receptor significantly reorganizes upon small molecule binding, thereby requiring an induced fit docking (IFD) approach in which the receptor is allowed to move in order to bind to the ligand optimally. IFD methods have had some success but suffer from a lack of reliability. Complementing IFD with all-atom molecular dynamics (MD) is a straightforward solution in principle but not in practice due to the severe time scale limitations of MD. Here we introduce a metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein-ligand complexes at a practically useful computational cost. Our strategy allows treating this problem in full atomistic detail and in a computationally efficient manner and enhances the predictive power of IFD methods. We significantly increase the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand-receptor systems. We expect this approach to be of significant value in computationally driven drug design.

Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise.

The CSAR 2014 exercise provided an important benchmark for testing current approaches for pose identification and ligand ranking using three X-ray characterized proteins: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA Methyltransferase (TRMD). In Phase 1 of the exercise, we employed Glide and MedusaDock docking software, both individually and in combination, with the special target-specific pose classifier trained to discriminate native-like from decoy poses. All approaches succeeded in the accurate detection of native and native-like poses. We then used Glide SP and MedusaScore scoring functions individually and in combination with the pose-scoring approach to predict relative binding affinities of the congeneric series of ligands in Phase 2 of the exercise. Similar to other participants in the CSAR 2014 exercise, we found that our models showed modest prediction accuracy. Quantitative structure-activity relationship (QSAR) models developed for the FXa ligands using available bioactivity data from ChEMBL showed relatively low prediction accuracy for the CSAR 2014 ligands of the same target. Interestingly, QSAR models built with CSAR data only yielded Spearman correlation coefficients as high as ρ = 0.69 for FXa and ρ = 0.79 for SYK based on 5-fold cross-validation. Virtual screening of the DUD library using the FXa structure was successful in discriminating between active compounds and decoys in spite of poor ranking accuracy of the underlying scoring functions. Our results suggest that two of the three common tasks associated with molecular docking, i.e., native-like pose identification and virtual screening, but not binding affinity prediction, could be accomplished successfully for the CSAR 2014 challenge data set.