Jeffrey L. Mendenhall

Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database

With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed.

Identification of specific ligand-receptor interactions that govern binding and cooperativity of diverse modulators to a common metabotropic glutamate receptor 5 allosteric site

A common metabotropic glutamate receptor 5 (mGlu5) allosteric site is known to accommodate diverse chemotypes. However, the structural relationship between compounds from different scaffolds and mGlu5 is not well understood. In an effort to better understand the molecular determinants that govern allosteric modulator interactions with mGlu5, we employed a combination of site-directed mutagenesis and computational modeling. With few exceptions, six residues (P654, Y658, T780, W784, S808, and A809) were identified as key affinity determinants across all seven allosteric modulator scaffolds. To improve our interpretation of how diverse allosteric modulators occupy the common allosteric site, we sampled the wealth of mGlu5 structure-activity relationship (SAR) data available by docking 60 ligands (actives and inactives) representing seven chemical scaffolds into our mGlu5 comparative model. To spatially and chemically compare binding modes of ligands from diverse scaffolds, the ChargeRMSD measure was developed. We found a common binding mode for the modulators that placed the long axes of the ligands parallel to the transmembrane helices 3 and 7. W784 in TM6 not only was identified as a key NAM cooperativity determinant across multiple scaffolds, but also caused a NAM to PAM switch for two different scaffolds. Moreover, a single point mutation in TM5, G747V, altered the architecture of the common allosteric site such that 4-nitro-N-(1,3-diphenyl-1H-pyrazol-5-yl)benzamide (VU29) was noncompetitive with the common allosteric site. Our findings highlight the subtleties of allosteric modulator binding to mGlu5 and demonstrate the utility in incorporating SAR information to strengthen the interpretation and analyses of docking and mutational data.

Documentation of an Imperative To Improve Methods for Predicting Membrane Protein Stability

There is a compelling and growing need to accurately predict the impact of amino acid mutations on protein stability for problems in personalized medicine and other applications. Here the ability of 10 computational tools to accurately predict mutation-induced perturbation of folding stability (ΔΔG) for membrane proteins of known structure was assessed. All methods for predicting ΔΔG values performed significantly worse when applied to membrane proteins than when applied to soluble proteins, yielding estimated concordance, Pearson, and Spearman correlation coefficients of <0.4 for membrane proteins. Rosetta and PROVEAN showed a modest ability to classify mutations as destabilizing (ΔΔG < −0.5 kcal/mol), with a 7 in 10 chance of correctly discriminating a randomly chosen destabilizing variant from a randomly chosen stabilizing variant. However, even this performance is significantly worse than for soluble proteins. This study highlights the need for further development of reliable and reproducible methods for predicting thermodynamic folding stability in membrane proteins.

Predicting the Functional Impact of KCNQ1 Variants of Unknown SignificanceCLINICAL PERSPECTIVE

Background— An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity. Methods and Results— In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew’s correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred. Conclusions— Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools.

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein-membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein-membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein-protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org .

Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints: Structure Prediction of Membrane Proteins Aided by Contact Numbers

One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α‐helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue–residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best‐sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best‐sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix–helix packing. Proteins 2017; 85:1212–1221.