Understanding structure-guided variant effect predictions using 3D convolutional neural networks

Predicting pathogenicity of missense variants in molecular diagnostics remains a challenge despite the available wealth of data, such as evolutionary information, and the wealth of tools to integrate that data. We describe DeepRank-Mut, a configurable framework designed to extract and learn from physicochemically relevant features of amino acids surrounding missense variants in 3D space. For each variant, various atomic and residue-level features are extracted from its structural environment, including sequence conservation scores of the surrounding amino acids, and stored in multi-channel 3D voxel grids which are then used to train a 3D convolutional neural network (3D-CNN). The resultant model gives a probabilistic estimate of whether a given input variant is disease-causing or benign. We find that the performance of our 3D-CNN model, on independent test datasets, is comparable to other widely used resources which also combine sequence and structural features. Based on the 10-fold cross-validation experiments, we achieve an average accuracy of 0.77 on the independent test datasets. We discuss the contribution of the variant neighborhood in the model’s predictive power, in addition to the impact of individual features on the model’s performance. Two key features: evolutionary information of residues in the variant neighborhood and their solvent accessibilities were observed to influence the predictions. We also highlight how predictions are impacted by the underlying disease mechanisms of missense mutations and offer insights into understanding these to improve pathogenicity predictions. Our study presents aspects to take into consideration when adopting deep learning approaches for protein structure-guided pathogenicity predictions

GPCRDB: information system for G protein-coupled receptors

The GPCRDB is a Molecular Class-Specific Information System (MCSIS) that collects, combines, validates and disseminates large amounts of heterogeneous data on G protein-coupled receptors (GPCRs). The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data such as multiple sequence alignments and homology models. The GPCRDB provides access to the data via a number of different access methods. It offers visualization and analysis tools, and a number of query systems. The data is updated automatically on a monthly basis. The GPCRDB can be found online at http://www.gpcr.org/7tm/

GPCRDB: information system for G protein-coupled receptors

The GPCRDB is a Molecular Class-Specific Information System (MCSIS) that collects, combines, validates and disseminates large amounts of heterogeneous data on G protein-coupled receptors (GPCRs). The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data such as multiple sequence alignments and homology models. The GPCRDB provides access to the data via a number of different access methods. It offers visualization and analysis tools, and a number of query systems. The data is updated automatically on a monthly basis. The GPCRDB can be found online at http://www.gpcr.org/7tm/

Homology modelling and spectroscopy, a never-ending love story

Homology modelling is normally the technique of choice when experimental structure data are not available but three-dimensional coordinates are needed, for example, to aid with detailed interpretation of results of spectroscopic studies. Herein, the state of the art of homology modelling will be described in the light of a series of recent developments, and an overview will be given of the problems and opportunities encountered in this field. The major topic, the accuracy and precision of homology models, will be discussed extensively due to its influence on the reliability of conclusions drawn from the combination of homology models and spectroscopic data. Three real-world examples will illustrate how both homology modelling and spectroscopy can be beneficial for (bio)medical research

Systematic generation of in vivo G protein-coupled receptor mutants in the rat

G-protein-coupled receptors (GPCRs) constitute a large family of cell surface receptors that are involved in a wide range of physiological and pathological processes, and are targets for many therapeutic interventions. However, genetic models in the rat, one of the most widely used model organisms in physiological and pharmacological research, are largely lacking. Here, we applied N-ethyl-N-nitrosourea (ENU)-driven target-selected mutagenesis to generate an in vivo GPCR mutant collection in the rat. A pre-selected panel of 250 human GPCR homologs was screened for mutations in 813 rats, resulting in the identification of 131 non-synonymous mutations. From these, seven novel potential rat gene knockouts were established as well as 45 lines carrying missense mutations in various genes associated with or involved in human diseases. We provide extensive in silico modeling results of the missense mutations and show experimental data, suggesting loss-of-function phenotypes for several models, including Mc4r and Lpar1. Taken together, the approach used resulted not only in a set of novel gene knockouts, but also in allelic series of more subtle amino acid variants, similar as commonly observed in human disease. The mutants presented here may greatly benefit studies to understand specific GPCR function and support the development of novel therapeutic strategies

No synergistic effect of subtherapeutic doses of donepezil and EVP-6124 in healthy elderly subjects in a scopolamine challenge model

Introduction: Donepezil is a widely used cholinesterase inhibitor in the management of Alzheimer's disease. Despite large-scaled evidence for its efficacy, elevated peripheral ACh levels often lead to side effects and are dose limiting. The present exploratory study is designed to determine the potentiation of the effects of donepezil by cotreatment with EVP-6124, an alpha-7 nicotinic agonist, to reduce scopolamine-induced cognitive deficits in healthy elderly subjects. Secondary objectives are to explore safety and pharmacokinetic and pharmacodynamics effects of EVP-6124 alone and in combination with donepezil compared to placebo. Methods: A phase I randomized, single-center, placebo-controlled, double-blind, five-way, partial crossover study was performed with donepezil 2.5, 5 mg or placebo combined with EVP-6124 0.3, 1, 2, 4 mg or placebo in three cohorts of healthy elderly subjects in a scopolamine (0.3 mg i.v.) challenge test. Safety, pharmacokinetic, and pharmacodynamics outcomes were assessed. Results: A total of 36 subjects completed the study. Donepezil pharmacokinetic parameters were similar with and without EVP-6124. Effective dose combinations were donepezil/EVP-6124(5/2 mg) and donepezil/EVP-6124 (5/0.3 mg) and showed improvements of the delayed recall of the Visual Verbal Learning Test (1.2; CI = 0.1–2.3) and reaction time during the two-back condition of the N-back (−42; CI = −77, −8), respectively. Overall, no marked reversal of scopolamine effects was observed. Discussion: This study shows no synergistic effect of subtherapeutic doses of donepezil and EVP-6124 in a scopolamine challenge model in healthy elderly subjects. Dosing of scopolamine and the combination of donepezil and EVP-6124 requires further study

GPCRDB: information system for G protein-coupled receptors

Contains fulltext : 91844.pdf (publisher's version ) (Open Access

Deeprank2

What's Changed Fix fix: check only 1 pssm for variant queries by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/430 fix: pdb files with underscore in the filename gives unexpected query ids by @joyceljy in https://github.com/DeepRank/deeprank2/pull/447 fix: dataset_train inheritance warnings by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/461 fix: cast hse feature to float64 by @DanLep97 in https://github.com/DeepRank/deeprank2/pull/465 fix: readthedocs after deeprank2 renaming by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/472 fix: force scipy version for fixing deeprank2 installation by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/478 fix: warning messages for invalid data in test_dataset.py by @joyceljy in https://github.com/DeepRank/deeprank2/pull/442 fix: make scipy 1.11.2 work by @cbaakman in https://github.com/DeepRank/deeprank2/pull/482 Refactor refactor: inherit information from training set for valid/test sets by @joyceljy in https://github.com/DeepRank/deeprank2/pull/446 refactor: rename deeprankcore to deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/469 Build build: improve installation making use of pyproject.toml file only and setuptools by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/491 CI CI: decrease sensitivity of test_graph_augmented_write_as_grid_to_hdf5 by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/445 CI: fewer triggers by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/457 Docs docs: update README.md by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/443 docs: create tutorial README by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/455 docs: improve installation instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/452 docs: add tutorials for PPIs by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/434 docs: add tutorials for variants by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/459 docs: minor improvements to install instructions by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/484 docs: type hinting and docstrings in molstruct by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/497 docs: joss paper by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/423 docs: clarify ppi scoring metrics and add doc strings and tests by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/498 docs: add performances table for deeprank2 by @gcroci2 in https://github.com/DeepRank/deeprank2/pull/493 Style style: auto-scrape trailing whitespace upon save in VS code by @DaniBodor in https://github.com/DeepRank/deeprank2/pull/483 Full Changelog: https://github.com/DeepRank/deeprank2/compare/v2.0.0...v2.1.

Pharmacokinetics and pharmacodynamics of oral mecamylamine - Development of a nicotinic acetylcholine receptor antagonist cognitive challenge test using modelling and simulation

A pharmacologic challenge model with a nicotinic antagonist could be an important tool not only to understand the complex role of the nicotinic cholinergic system in cognition, but also to develop novel compounds acting on the nicotinic acetylcholine receptor. The objective was to develop a pharmacokinetic-pharmacodynamic (PKPD) model using nonlinear mixed effects (NLME) methods to quantitate the pharmacokinetics of three oral mecamylamine doses (10, 20 and 30 mg) and correlate the plasma concentrations to the pharmacodynamic effects on a cognitive and neurophysiologic battery of tests in healthy subjects. A one-compartment linear kinetic model best described the plasma concentrations of mecamylamine. Mecamylamine's estimated clearance was 0.28 ± 0.015 L min-1. The peripheral volume of distribution (291 ± 5.15 L) was directly related to total body weight. Mecamylamine impaired the accuracy and increased the reaction time in tests evaluating short term working memory with a steep increase in the concentration-effect relationship at plasma concentrations below 100 μg L-1. On the other hand, mecamylamine induced a decrease in performance of tests evaluating visual and fine motor coordination at higher plasma concentrations (EC50 97 μg L-1). Systolic and diastolic blood pressure decreased exponentially after a plasma mecamylamine concentration of 80 μg L-1, a known effect previously poorly studied in healthy subjects. The developed mecamylamine PKPD model was used to quantify the effects of nicotinic blockade in a set of neurophysiological tests in humans with the goal to provide insight into the physiology and pharmacology of the nicotinic system in humans and the possibility to optimize future trials that use mecamylamine as a pharmacological challenge