KiSSim: Predicting off-targets from structural similarities in the kinome

Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory, and degenerative diseases. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help to explain and predict off-targets among the kinase family. Here, we present the kinase-focused and subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. Thereby, we could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib’s known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group) though belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as conda package: https://github.com/volkamerlab/kissim

The Digital Urban Experience of a Lost City Using Mixed Methods to Depict the Historical Street Life of Edo/Tokyo

This paper digitally reconstructs street life in Edo (present-day Tokyo), the largest lost city of the pre-modern world. The ephemeral character of the Edo makes the historic urban experience extremely difficult to capture. We argue that the hypothetical digital reconstructions should incorporate evidence on human agency and spatial properties for a holistic simulation of historic street life. We develop a 3D hypothetical reconstruction based on multi-layered historical evidence to unlock the lost character of the Edo streets. It reveals the streets of Edo, including the rhythms of everyday life and the impact of the material culture.

Place Matters

This article positions place within a phenomenological framework. Current philosophical inquiry shows little interest in place, yet academic disciplines concerned with spatial properties look to philosophy—and in particular phenomenology—to provide important contributions to overcome the limitations of quantitative methodologies, particularly with respect to sentiments of attachment to and identification with places. Seemingly, however, philosophy offers little support in this field. Place disappears from philosophical investigations during the Middle Ages and is replaced by considerations on space. Keeping the employment of phenomenological approaches in other academic disciplines in mind, this article sets out to explore traces of a re-emerging interest in place among twentieth-century phenomenologists. It proposes a phenomenological approach to place in which there is a shift from regarding place in terms of a where to understanding what a place is in human experience.

Phragmén-Lindelöf Alternative Results for a Class of Thermoelastic Plate

The spatial properties of solutions for a class of thermoelastic plate with biharmonic operator were studied. The energy method was used. We constructed an energy expression. A differential inequality which the energy expression was controlled by a second-order differential inequality is deduced. The Phragme´n-Lindelo¨f alternative results of the solutions were obtained by solving the inequality. These results show that the Saint-Venant principle is also valid for the hyperbolic–hyperbolic coupling equations. Our results can been seen as a version of symmetry in inequality for studying the Phragme´n-Lindelo¨f alternative results.

Probing Electron Properties in ECR Plasmas Using X-ray Bremsstrahlung and Fluorescence Emission

A quantitative analysis of X-ray emission from an electron cyclotron resonance (ECR) plasma was performed to probe the spatial properties of electrons having energy for effective ionisation. A series of measurements were taken by INFN-LNS and ATOMKI, capturing spatially and spectrally resolved X-ray maps as well as volumetric emissions from argon plasma. Comparing the former with model generated maps (involving space-resolved phenomenological electron energy distribution function and geometrical efficiency calculated using ray-tracing Monte Carlo (MC) routine) furnished information on structural aspects of the plasma. Similarly, fitting a model composed of bremsstrahlung and fluorescence to the volumetric X-ray spectrum provided valuable insight into the density and temperature of confined and lost electrons. The latter can be fed back to existing electron kinetics models for simulating more relevant energies, consequently improving theoretical X-ray maps and establishing the method as an excellent indirect diagnostic tool for warm electrons, required for both fundamental and applied research in ECR plasmas.

Modeling the Ventral and Dorsal Cortical Visual Pathways Using Artificial Neural Networks

Although in conventional models of cortical processing, object recognition and spatial properties are processed separately in ventral and dorsal cortical visual pathways respectively, some recent studies have shown that representations associated with both objects' identity (of shape) and space are present in both visual pathways. However, it is still unclear whether the presence of identity and spatial properties in both pathways have functional roles. In our study, we have tried to answer this question through computational modeling. Our simulation results show that both a model ventral and dorsal pathway, separately trained to do object and spatial recognition, respectively, each actively retained information about both identity and space. In addition, we show that these networks retained different amounts and kinds of identity and spatial information. As a result, our modeling suggests that two separate cortical visual pathways for identity and space (1) actively retain information about both identity and space (2) retain information about identity and space differently and (3) that this differently retained information about identity and space in the two pathways may be necessary to accurately and optimally recognize and localize objects. Further, modeling results suggests these findings are robust and do not strongly depend on the specific structures of the neural networks.

Stochastic Downscaling to Chaotic Weather Regimes using Spatially Conditioned Gaussian Random Fields with Adaptive Covariance

Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at a lower computational and storage cost than numerical simulation alone. This is especially appealing for targeting convective scales, which are at the edge of what is possible to simulate operationally. Since convective scale weather has a high degree of independence from larger scales, a generative approach is essential. We here propose a statistical method for downscaling moist variables to convective scales using conditional Gaussian random fields, with an application to wet bulb potential temperature (WBPT) data over the UK. Our model uses an adaptive covariance estimation to capture the variable spatial properties at convective scales. We further propose a method for the validation, which has historically been a challenge for generative models.