Distinguishing Binders from False Positives by Free Energy Calculations: Fragment Screening Against the Flap Site of HIV Protease

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand–receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets

Flaring Behavior of the Quasar 3C~454.3 across the Electromagnetic Spectrum

We analyze the behavior of the parsec-scale jet of the quasar 3C~454.3 during pronounced flaring activity in 2005-2008. Three major disturbances propagated down the jet along different trajectories with Lorentz factors $\Gamma>$10. The disturbances show a clear connection with millimeter-wave outbursts, in 2005 May/June, 2007 July, and 2007 December. High-amplitude optical events in the $R$-band light curve precede peaks of the millimeter-wave outbursts by 15-50 days. Each optical outburst is accompanied by an increase in X-ray activity. We associate the optical outbursts with propagation of the superluminal knots and derive the location of sites of energy dissipation in the form of radiation. The most prominent and long-lasting of these, in 2005 May, occurred closer to the black hole, while the outbursts with a shorter duration in 2005 Autumn and in 2007 might be connected with the passage of a disturbance through the millimeter-wave core of the jet. The optical outbursts, which coincide with the passage of superluminal radio knots through the core, are accompanied by systematic rotation of the position angle of optical linear polarization. Such rotation appears to be a common feature during the early stages of flares in blazars. We find correlations between optical variations and those at X-ray and $\gamma$-ray energies. We conclude that the emergence of a superluminal knot from the core yields a series of optical and high-energy outbursts, and that the mm-wave core lies at the end of the jet's acceleration and collimation zone.Comment: 57 pages, 23 figures, 8 tables (submitted to ApJ

Mechanism of MicroRNA-Target Interaction: Molecular Dynamics Simulations and Thermodynamics Analysis

MicroRNAs (miRNAs) are endogenously produced ∼21-nt riboregulators that associate with Argonaute (Ago) proteins to direct mRNA cleavage or repress the translation of complementary RNAs. Capturing the molecular mechanisms of miRNA interacting with its target will not only reinforce the understanding of underlying RNA interference but also fuel the design of more effective small-interfering RNA strands. To address this, in the present work the RNA-bound (Ago-miRNA, Ago-miRNA-target) and RNA-free Ago forms were analyzed by performing both molecular dynamics simulations and thermodynamic analysis. Based on the principal component analysis results of the simulation trajectories as well as the correlation analysis in fluctuations of residues, we discover that: 1) three important (PAZ, Mid and PIWI) domains exist in Argonaute which define the global dynamics of the protein; 2) the interdomain correlated movements are so crucial for the interaction of Ago-RNAs that they not only facilitate the relaxation of the interactions between residues surrounding the RNA binding channel but also induce certain conformational changes; and 3) it is just these conformational changes that expand the cavity of the active site and open putative pathways for both the substrate uptake and product release. In addition, by thermodynamic analysis we also discover that for both the guide RNA 5′-end recognition and the facilitated site-specific cleavage of the target, the presence of two metal ions (of Mg2+) plays a predominant role, and this conclusion is consistent with the observed enzyme catalytic cleavage activity in the ternary complex (Ago-miRNA-mRNA). Our results find that it is the set of arginine amino acids concentrated in the nucleotide-binding channel in Ago, instead of the conventionally-deemed seed base-paring, that makes greater contributions in stabilizing the binding of the nucleic acids to Ago

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014–2015)

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (<i>Mtb</i>) infection has resulted in large-scale phenotypic screening and thousands of new active compounds <i>in vitro</i>. The next challenge is to identify candidates to pursue in a mouse <i>in vivo</i> efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse <i>in vivo</i> efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with <i>in vivo</i> data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM <i>t</i>_1/2) and <i>in vitro</i>-based <i>Mtb</i> models incorporating cytotoxicity data that were used to predict <i>in vivo</i> activity for comparison. Our best <i>Mtb</i> <i>in vivo</i> models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM <i>t</i>_1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and <i>in vitro</i> <i>Mtb</i> models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature <i>in vivo</i> <i>Mtb</i> data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM <i>t</i>_1/2 and <i>in vitro</i>-based <i>Mtb</i> models to assist in selecting antitubercular compounds with desirable <i>in vivo</i> efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict <i>in vivo</i> activity for <i>Mtb</i>. In addition, we describe a new clustering method for data visualization and apply this to the <i>in vivo</i> training and test data, ultimately making the method accessible in a mobile app

Robust Scoring Functions for Protein–Ligand Interactions with Quantum Chemical Charge Models

Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein–ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin-model 1-bond charge correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein–ligand free energy regression model with AM1-BCC charges for ligands and Amber99SB charges for proteins achieve lowest root-mean-squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted root-mean-squared deviation of less than 2 Å. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed)

Comparing and Validating Machine Learning Models for <i>Mycobacterium tuberculosis</i> Drug Discovery

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>) has led to many large-scale phenotypic screens and many thousands of new active compounds identified <i>in vitro</i>. However, with limited funding, efforts to discover new active molecules against <i>Mtb</i> needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule <i>Mtb</i> data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One <i>Mtb</i> model, a combined <i>in vitro</i> and <i>in vivo</i> data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set (<i>n</i> = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature <i>Mtb</i> data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such <i>Mtb</i> machine learning models could help prioritize compounds for testing <i>in vitro</i> and <i>in vivo</i>