Towards generalizable predictions for G protein-coupled receptor variant expression.

Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of membrane proteins.

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble).

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps.

The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure-activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein-ligand atomic property correlations.

Efficient Sampling of Protein Loop Regions Using Conformational Hashing Complemented with Random Coordinate Descent.

construction of loop regions is an important problem in computational structural biology. Compared to regions with well-defined secondary structure, loops tend to exhibit significant conformational heterogeneity. As a result, their structures are often ambiguous when determined using experimental data obtained by crystallography, cryo-EM, or NMR.

BCL::Conf: Improved Open-Source Knowledge-Based Conformation Sampling Using the Crystallography Open Database.

We previously described BCL::Conf, a knowledge-based conformation sampling algorithm utilizing a small molecule fragment rotamer library derived from the Cambridge Structural Database (CSD, license required), as a component of the BioChemical Library (BCL) cheminformatics toolkit. This paper describes substantial improvements made to the BCL::Conf algorithm and a transition to a rotamer library derived from molecules in the Crystallography Open Database (COD, no license required). We demonstrate the performance of the new BCL::Conf on native conformer recovery in the Platinum dataset of high-quality protein-ligand complexes.

Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking.

Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes.

BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization.

Comparing fragment based molecular fingerprints of drug-like molecules is one of the most robust and frequently used approaches in computer-assisted drug discovery. Molprint2D, a popular atom environment (AE) descriptor, yielded the best enrichment of active compounds across a diverse set of targets in a recent large-scale study. We present here BCL::Mol2D descriptors that outperformed Molprint2D on nine PubChem datasets spanning a wide range of protein classes.

Protein structure aids predicting functional perturbation of missense variants in and .

Rare variants in the cardiac potassium channel K7.1 () and sodium channel Na1.5 () are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations.

BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping.

Small molecule flexible alignment is a critical component of both ligand- and structure-based methods in computer-aided drug discovery. Despite its importance, the availability of high-quality flexible alignment software packages is limited. Here, we present BCL::MolAlign, a freely available property-based molecular alignment program.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.

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.

Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.

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.

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.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets.

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.

Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign.

Quantitative structure-activity relationship (QSAR) is a branch of computer aided drug discovery that relates chemical structures to biological activity. Two well established and related QSAR descriptors are two- and three-dimensional autocorrelation (2DA and 3DA). These descriptors encode the relative position of atoms or atom properties by calculating the separation between atom pairs in terms of number of bonds (2DA) or Euclidean distance (3DA).

BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library.

The interaction of a small molecule with a protein target depends on its ability to adopt a three-dimensional structure that is complementary. Therefore, complete and rapid prediction of the conformational space a small molecule can sample is critical for both structure- and ligand-based drug discovery algorithms such as small molecule docking or three-dimensional quantitative structure-activity relationships. Here we have derived a database of small molecule fragments frequently sampled in experimental structures within the Cambridge Structure Database and the Protein Data Bank.

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.

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.

Calcium-activated nonspecific cation current and synaptic depression promote network-dependent burst oscillations.

Central pattern generators (CPGs) produce neural-motor rhythms that often depend on specialized cellular or synaptic properties such as pacemaker neurons or alternating phases of synaptic inhibition. Motivated by experimental evidence suggesting that activity in the mammalian respiratory CPG, the preBötzinger complex, does not require either of these components, we present and analyze a mathematical model demonstrating an unconventional mechanism of rhythm generation in which glutamatergic synapses and the short-term depression of excitatory transmission play key rhythmogenic roles. Recurrent synaptic excitation triggers postsynaptic Ca(2+)-activated nonspecific cation current (I(CAN)) to initiate a network-wide burst.

4-Aminopyridine-sensitive outward currents in preBötzinger complex neurons influence respiratory rhythm generation in neonatal mice.

We measured a low-threshold, inactivating K+ current, i.e. A-current (I(A)), in respiratory neurons of the preBötzinger complex (preBötC) in rhythmically active slice preparations from neonatal C57BL/6 mice.