Pocket shapes of the -conformation with 1ads/1el3/2acs and 2acq/2acr/2acu forming similar subsets

<p><b>Copyright information:</b></p><p>Taken from "PocketPicker: analysis of ligand binding-sites with shape descriptors"</p><p>http://journal.chemistrycentral.com/content/1/1/7</p><p>Chemistry Central Journal 2007;1():7-7.</p><p>Published online 13 Mar 2007</p><p>PMCID:PMC1994066.</p><p></p

Shapes of pocket conformations induced by IDD594 (), zenarestat () and tolrestat ()

<p><b>Copyright information:</b></p><p>Taken from "PocketPicker: analysis of ligand binding-sites with shape descriptors"</p><p>http://journal.chemistrycentral.com/content/1/1/7</p><p>Chemistry Central Journal 2007;1():7-7.</p><p>Published online 13 Mar 2007</p><p>PMCID:PMC1994066.</p><p></p> Binding sites are given in PocketPicker representation with darker spheres indicating greater buriedness

Recurrent Neural Network Model for Constructive Peptide Design

We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries

Recurrent Neural Network Model for Constructive Peptide Design

We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries

Computer-Assisted Discovery of Retinoid X Receptor Modulating Natural Products and Isofunctional Mimetics

Natural products (NPs) are progressively recognized as invaluable source of pharmacological tools and lead structures. To enable NP-inspired retinoid X receptor (RXR) modulator design, three novel RXR-targeting NPs were computationally identified. Among them, valerenic acid was found to be selective for RXRβ, rendering it a unique pharmacological tool compound. The NPs then served as templates for automated, ligand-based de novo design of innovative, easily accessible mimetics that inherited the biological activities of their natural templates

Computer-Assisted Discovery of Retinoid X Receptor Modulating Natural Products and Isofunctional Mimetics

Natural products (NPs) are progressively recognized as invaluable source of pharmacological tools and lead structures. To enable NP-inspired retinoid X receptor (RXR) modulator design, three novel RXR-targeting NPs were computationally identified. Among them, valerenic acid was found to be selective for RXRβ, rendering it a unique pharmacological tool compound. The NPs then served as templates for automated, ligand-based de novo design of innovative, easily accessible mimetics that inherited the biological activities of their natural templates

Native Electrospray Ionization Mass Spectrometry Reveals Multiple Facets of Aptamer–Ligand Interactions: From Mechanism to Binding Constants

Aptamers are oligonucleotide receptors obtained through an iterative selection process from random-sequence libraries. Though many aptamers for a broad range of targets with high affinity and selectivity have been generated, a lack of high-resolution structural data and the limitations of currently available biophysical tools greatly impede understanding of the mechanisms of aptamer–ligand interactions. Here we demonstrate that an approach based on native electrospray ionization mass spectrometry (ESI-MS) can be successfully applied to characterize aptamer–ligand complexes in all details. We studied an adenosine-binding aptamer (ABA), a l-argininamide-binding aptamer (LABA), and a cocaine-binding aptamer (CBA) and their noncovalent interactions with ligands by native ESI-MS and complemented these measurements by ion mobility spectrometry (IMS), isothermal titration calorimetry (ITC), and circular dichroism (CD) spectroscopy. The ligand selectivity of the aptamers and the respective complex stoichiometry could be determined by the native ESI-MS approach. The ESI-MS data can also help refining the binding model for aptamer–ligand complexes and deliver accurate aptamer–ligand binding affinities for specific and nonspecific binding events. For specific ligands, we found <i>K</i>_d1 = 69.7 μM and <i>K</i>_d2 = 5.3 μM for ABA (two binding sites); <i>K</i>_d1 = 22.04 μM for LABA; and <i>K</i>_d1 = 8.5 μM for CBA

MOESM1 of Calcium binding protects E-cadherin from cleavage by Helicobacter pylori HtrA

Additional file 1. Supplementary information

Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

Lipophilicity, as measured by the partition coefficient between octanol and water (log P), is a key parameter in early drug discovery research. However, measuring log P experimentally is difficult for specific compounds and log P ranges. The resulting lack of reliable experimental data impedes development of accurate in silico models for such compounds. In certain discovery projects at Novartis focused on such compounds, a quantum mechanics (QM)-based tool for log P estimation has emerged as a valuable supplement to experimental measurements and as a preferred alternative to existing empirical models. However, this QM-based approach incurs a substantial computational cost, limiting its applicability to small series and prohibiting quick, interactive ideation. This work explores a set of machine learning models (Random Forest, Lasso, XGBoost, Chemprop, and Chemprop3D) to learn calculated log P values on both a public data set and an in-house data set to obtain a computationally affordable, QM-based estimation of drug lipophilicity. The message-passing neural network model Chemprop emerged as the best performing model with mean absolute errors of 0.44 and 0.34 log units for scaffold split test sets of the public and in-house data sets, respectively. Analysis of learning curves suggests that a further decrease in the test set error can be achieved by increasing the training set size. While models directly trained on experimental data perform better at approximating experimentally determined log P values than models trained on calculated values, we discuss the potential advantages of using calculated log P values going beyond the limits of experimental quantitation. We analyze the impact of the data set splitting strategy and gain insights into model failure modes. Potential use cases for the presented models include pre-screening of large compound collections and prioritization of compounds for full QM calculations

Distribution of the first position of the PEXEL motif.

<p>A) position of the PEXEL motif in sequences of the PfEMP1 protein family (red), exported proteins with a predicted signal peptide (blue), and exported proteins lacking a predicted signal peptide (gray). In B) the blue bars show the positions of the PEXEL motif after cleaving off the predicted signal peptide. Only sequences with a predicted signal peptidase cleavage site (<i>score</i> >0.5 according to SignalP <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001560#pone.0001560-Bendtsen1" target="_blank">[38]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001560#pone.0001560-Nielsen1" target="_blank">[39]</a>) are included. Gray bars in B) represent the unchanged distribution of PEXEL in proteins lacking a predicted signal sequence. Note that all bars are displayed on top of each other.</p