PheSA: An Open-Source Tool for Pharmacophore-Enhanced Shape Alignment.

PheSA is an open-source pharmacophore- and shape-based screening and molecular alignment tool that is fully open-source as part of OpenChemLib. Supporting standard ligand-based screening, flexible refinement of alignments, and receptor-guided shape docking, PheSA is a very flexible tool and can be used for different use cases in structure-based drug design. We present the algorithm and different benchmark studies that investigate the screening performance and also the quality of the generated alignments and the pose prediction performance of the receptor-guided PheSA algorithm.

Impact of preprocessing methods on the Raman spectra of brain tissue.

Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification.

Correlation properties of a spatially quasi-incoherent imaging interferometer.

The depth-gating capacity of a spatially quasi-incoherent imaging interferometer is investigated in relation to the 3D correlation properties of diffraction field laser speckles. The system exploits a phase-stepped imaging Michelson-type interferometer in which spatially quasi-incoherent illumination is generated by passing an unexpanded laser beam through a rotating diffuser. Numerical simulations and optical experiments both verify that the depth-gating capacity of the imaging interferometer scales as /22, where is the wavelength of the laser and is the numerical aperture of the illumination.

Fully Automated Creation of Virtual Chemical Fragment Spaces Using the Open-Source Library OpenChemLib.

Synthetically accessible chemical spaces provide a valuable source to search for small-molecule analogues or new starting points in drug discovery projects. Having a toolbox at hand that can automatically create searchable representations of such spaces using reaction definitions and building blocks as inputs is a prerequisite to put this approach into practice. Herein, we present a tool kit to create such virtual chemical spaces.

A Microfluidic System for Simultaneous Raman Spectroscopy, Patch-Clamp Electrophysiology, and Live-Cell Imaging to Study Key Cellular Events of Single Living Cells in Response to Acute Hypoxia.

The ability to sense changes in oxygen availability is fundamentally important for the survival of all aerobic organisms. However, cellular oxygen sensing mechanisms and pathologies remain incompletely understood and studies of acute oxygen sensing, in particular, have produced inconsistent results. Current methods cannot simultaneously measure the key cellular events in acute hypoxia (i.

Bypassing mitochondrial complex III using alternative oxidase inhibits acute pulmonary oxygen sensing.

Mitochondria play an important role in sensing both acute and chronic hypoxia in the pulmonary vasculature, but their primary oxygen-sensing mechanism and contribution to stabilization of the hypoxia-inducible factor (HIF) remains elusive. Alteration of the mitochondrial electron flux and increased superoxide release from complex III has been proposed as an essential trigger for hypoxic pulmonary vasoconstriction (HPV). We used mice expressing a tunicate alternative oxidase, AOX, which maintains electron flux when respiratory complexes III and/or IV are inhibited.

Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks.

Preprocessing of Raman spectra is generally done in three separate steps: (1) cosmic ray removal, (2) signal smoothing, and (3) baseline subtraction. We show that a convolutional neural network (CNN) can be trained using simulated data to handle all steps in one operation. First, synthetic spectra are created by randomly adding peaks, baseline, mixing of peaks and baseline with background noise, and cosmic rays.

Accuracy evaluation and addition of improved dihedral parameters for the MMFF94s.

The Platinum dataset of protein-bound ligand conformations was used to benchmark the ability of the MMFF94s force field to generate bioactive conformations by minimization of randomly generated conformers. Torsion angle parameters that generally caused wrong geometries were reparameterized by conducting dihedral scans using ab initio calculations at the MP2 level. This reparameterization resulted in a systematic improvement of generated conformations.

Assessing the Predictive Power of Relative Binding Free Energy Calculations for Test Cases Involving Displacement of Binding Site Water Molecules.

Improved sampling methodologies, more accurate force fields, and access to longer simulation time scales have led to an increased application of Relative Binding Free Energy (RBFE) calculations in drug discovery projects. In order to assess the strengths and limitations of such tools, adequate benchmark sets are required that challenge the methodology in certain well-defined aspects. We applied Free Energy Perturbation (FEP) calculations to six congeneric ligand pairs taken from the literature, in which addition of a functional group resulted in the displacement of buried binding site water molecules and compared the calculated relative binding free energies with the experimental ones.

Development of a Gas-Tight Microfluidic System for Raman Sensing of Single Pulmonary Arterial Smooth Muscle Cells Under Normoxic/Hypoxic Conditions.

Acute hypoxia changes the redox-state of pulmonary arterial smooth muscle cells (PASMCs). This might influence the activity of redox-sensitive voltage-gated K⁺-channels (Kv-channels) whose inhibition initiates hypoxic pulmonary vasoconstriction (HPV). However, the molecular mechanism of how hypoxia-or the subsequent change in the cellular redox-state-inhibits Kv-channels remains elusive.

Endocrine Disruption at the Androgen Receptor: Employing Molecular Dynamics and Docking for Improved Virtual Screening and Toxicity Prediction.

The androgen receptor (AR) is a key target for the development of drugs targeting hormone-dependent prostate cancer, but has also an important role in endocrine disruption. Reliable prediction of the binding of ligands towards the AR is therefore of great relevance. Molecular docking is a powerful computational method for exploring small-ligand binding to proteins.

Thermodynamic Insight into the Effects of Water Displacement and Rearrangement upon Ligand Modifications using Molecular Dynamics Simulations.

Computational methods, namely molecular dynamics (MD) simulations in combination with inhomogeneous fluid solvation theory (IFST) were used to retrospectively investigate various cases of ligand structure modifications that led to the displacement of binding site water molecules. Our findings are that water displacement per se is energetically unfavorable in the discussed examples, and that it is merely the fine balance between change in protein-ligand interaction energy, ligand solvation free energies, and binding site solvation free energies that determine if water displacement is favorable or not. We furthermore evaluated if we can reproduce experimental binding affinities by a computational approach combining changes in solvation free energies with changes in protein-ligand interaction energies and entropies.

Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives.

The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, as well as aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects. The simulations are conducted at the atomic level and explicitly allow for a mechanistic interpretation of the results (in real-time 3D/4D), thereby complying with the Setubal principles put forward in 2002 for computational approaches to toxicology.