AggreProt: a web server for predicting and engineering aggregation prone regions in proteins.

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.

pyCaverDock: Python implementation of the popular tool for analysis of ligand transport with advanced caching and batch calculation support.

SUMMARY: Access pathways in enzymes are crucial for the passage of substrates and products of catalysed reactions. The process can be studied by computational means with variable degrees of precision. Our in-house approximative method CaverDock provides a fast and easy way to set up and run ligand binding and unbinding calculations through protein tunnels and channels. Here we introduce pyCaverDock, a Python3 API designed to improve user experience with the tool and further facilitate the ligand transport analyses. The API enables users to simplify the steps needed to use CaverDock, from automatizing setup processes to designing screening pipelines. AVAILABILITY AND IMPLEMENTATION: pyCaverDock API is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://loschmidt.chemi.muni.cz/caverdock/.

Special function form solutions of multi-parameter generalized Mittag-Leffler kernel based bio-heat fractional order model subject to thermal memory shocks.

The primary objective of this research is to develop a mathematical model, analyze the dynamic occurrence of thermal shock and exploration of how thermal memory with moving line impact of heat transfer within biological tissues. An extended version of the Pennes equation as its foundational framework, a new fractional modelling approach called the Prabhakar fractional operator to investigate and a novel time-fractional interpretation of Fourier's law that incorporates its historical behaviour. This fractional operator has multi parameter generalized Mittag-Leffler kernel. The fractional formulation of heat flow, achieved through a generalized fractional operator with a non-singular type kernel, enables the representation of the finite propagation speed of heat waves. Furthermore, the dynamics of thermal source continually generates a linear thermal shock at predefined locations within the tissue. Introduced the appropriate set of variables to transform the governing equations into dimensionless form. Laplace transform (LT) is operated on the fractional system of equations and results are presented in series form and also expressed the solution in the form of special functions. The article derives analytical solutions for the heat transfer phenomena of both the generalized model, in the Laplace domain, and the ordinary model in the real domain, employing Laplace inverse transformation. The pertinent parameter's influence, such as α, ß, γ, a0, b0, to gain insights into the impact of the thermal memory parameter on heat transfer, is brought under consideration to reveal the interesting results with graphical representations of the findings.

Effects of Pregnanolone Glutamate and Its Metabolites on GABAA and NMDA Receptors and Zebrafish Behavior.

Multiple molecular targets have been identified to mediate membrane-delimited and nongenomic effects of natural and synthetic steroids, but the influence of steroid metabolism on neuroactive steroid signaling is not well understood. To begin to address this question, we set out to identify major metabolites of a neuroprotective synthetic steroid 20-oxo-5ß-pregnan-3α-yl l-glutamyl 1-ester (pregnanolone glutamate, PAG) and characterize their effects on GABAA and NMDA receptors (GABARs, NMDARs) and their influence on zebrafish behavior. Gas chromatography-mass spectrometry was used to assess concentrations of PAG and its metabolites in the hippocampal tissue of juvenile rats following intraperitoneal PAG injection. PAG is metabolized in the peripheral organs and nervous tissue to 20-oxo-17α-hydroxy-5ß-pregnan-3α-yl l-glutamyl 1-ester (17-hydroxypregnanolone glutamate, 17-OH-PAG), 3α-hydroxy-5ß-pregnan-20-one (pregnanolone, PA), and 3α,17α-dihydroxy-5ß-pregnan-20-one (17-hydroxypregnanolone, 17-OH-PA). Patch-clamp electrophysiology experiments in cultured hippocampal neurons demonstrate that PA and 17-OH-PA are potent positive modulators of GABARs, while PAG and 17-OH-PA have a moderate inhibitory effect at NMDARs. PAG, 17-OH-PA, and PA diminished the locomotor activity of zebrafish larvae in a dose-dependent manner. Our results show that PAG and its metabolites are potent modulators of neurotransmitter receptors with behavioral consequences and indicate that neurosteroid-based ligands may have therapeutic potential.

Industry-scale application and evaluation of deep learning for drug target prediction.

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.