Sander Roet

Abstract Covalent inhibition has become more accepted in the past two decades, as illustrated by the clinical approval of several irreversible inhibitors designed to covalently modify their target. Elucidation of the structure‐activity relationship and potency of such inhibitors requires a detailed kinetic evaluation. Here, we elucidate the relationship between the experimental read‐out and the underlying inhibitor binding kinetics. Interactive kinetic simulation scripts are employed to highlight the effects of in vitro enzyme activity assay conditions and inhibitor binding mode, thereby showcasing which assumptions and corrections are crucial. Four stepwise protocols to assess the biochemical potency of (ir)reversible covalent enzyme inhibitors targeting a nucleophilic active site residue are included, with accompanying data analysis tailored to the covalent binding mode. Together, this will serve as a guide to make an educated decision regarding the most suitable method to assess covalent inhibition potency. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol I : Progress curve analysis of substrate association competition Basic Data Analysis Protocol 1A : Two‐step irreversible covalent inhibition Basic Data Analysis Protocol 1B : One‐step irreversible covalent inhibition Basic Data Analysis Protocol 1C : Two‐step reversible covalent inhibition Basic Data Analysis Protocol 1D : Two‐step irreversible covalent inhibition with substrate depletion Basic Protocol II : Incubation time–dependent potency IC 50 ( t ) Basic Data Analysis Protocol 2 : Two‐step irreversible covalent inhibition Basic Protocol III : Preincubation time–dependent inhibition without dilution Basic Data Analysis Protocol 3 : Preincubation time–dependent inhibition without dilution Basic Data Analysis Protocol 3Ai : Two‐step irreversible covalent inhibition Alternative Data Analysis Protocol 3Aii : Two‐step irreversible covalent inhibition Basic Data Analysis Protocol 3Bi : One‐step irreversible covalent inhibition Alternative Data Analysis Protocol 3Bii : One‐step irreversible covalent inhibition Basic Data Analysis Protocol 3C : Two‐step reversible covalent inhibition Basic Protocol IV : Preincubation time–dependent inhibition with dilution/competition Basic Data Analysis Protocol 4 : Preincubation time–dependent inhibition with dilution Basic Data Analysis Protocol 4Ai : Two‐step irreversible covalent inhibition Alternative Data Analysis Protocol 4Aii : Two‐step irreversible covalent inhibition Basic Data Analysis Protocol 4Bi : One‐step irreversible covalent inhibition Alternative Data Analysis Protocol 4Bii : One‐step irreversible covalent inhibition

Cryo-electron tomography provides 3D images of macromolecules in their cellular context. To detect macromolecules in tomograms, template matching (TM) is often used, which uses 3D models that are often reliable for substantial parts of the macromolecules. However, the extent of rotational searches in particle detection has not been investigated due to computational limitations. Here, we provide a GPU implementation of TM as part of the PyTOM software package, which drastically speeds up the orientational search and allows for sampling beyond the Crowther criterion within a feasible timeframe. We quantify the improvements in sensitivity and false-discovery rate for the examples of ribosome identification and detection. Sampling at the Crowther criterion, which was effectively impossible with CPU implementations due to the extensive computation times, allows for automated extraction with high sensitivity. Consequently, we also show that an extensive angular sample renders 3D TM sensitive to the local alignment of tilt series and damage induced by focused ion beam milling. With this new release of PyTOM, we focused on integration with other software packages that support more refined subtomogram-averaging workflows. The automated classification of ribosomes by TM with appropriate angular sampling on locally corrected tomograms has a sufficiently low false-discovery rate, allowing for it to be directly used for high-resolution averaging and adequate sensitivity to reveal polysome organization.

The algorithmic development in the field of path sampling has made tremendous progress in recent years. Although the original transition path sampling method was mostly used as a qualitative tool to sample reaction paths, the more recent family of interface-based path sampling methods has paved the way for more quantitative rate calculation studies. Of the exact methods, the replica exchange transition interface sampling (RETIS) method is the most efficient, but rather difficult to implement. This has been the main motivation to develop the open-source Python-based computer library PyRETIS that was released in 2017. PyRETIS is designed to be easily interfaced with any molecular dynamics (MD) package using either classical or ab initio MD. In this study, we report on the principles and the software enhancements that are now included in PyRETIS 2, as well as the recent developments on the user interface, improvements of the efficiency via the implementation of new shooting moves, easier initialization procedures, analysis methods, and supported interfaced software. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Permeation of compounds through membranes is important in biological and engineering processes, e.g., drug delivery through lipid bilayers, anesthetics, or chemical reactor design. Simulations at the atomic scale can provide insight in the diffusive pathways and they give estimates of the membrane permeability based on counting membrane transitions or on the inhomogeneous solubility-diffusivity model described by the Smoluchowski equation. For many permeants, permeation through a membrane is too slow to gather sufficient statistics with conventional molecular dynamics simulations, i.e., permeation is a rare event. Recent attempts to improve the description of the dynamics of such rare permeation events have been based on milestoning, which allows the study of processes at timescales beyond those achievable by straightforward molecular dynamics. The approach is not relying on an overdamped description, but, still, it uses a Markovian approximation which is only valid for small permeants that are not disruptive to the membrane structure. To overcome this fundamental limitation, we show here how replica exchange transition interface sampling (RETIS) can effectively be used on this problem by deriving an effective set of equations that relate the outcome of RETIS simulations and the permeability coefficient. In addition, we introduce two new path Monte Carlo (MC) moves specifically for permeation dynamics, that are used in combination with the ordinary path generating moves, which considerably increase the efficiency. The advantage of our method is that it gives exact results, identical to brute force molecular dynamics, but orders of magnitude faster.

• A new software for template matching (TM) in cryo-electron tomography (cryo-ET), pytom-match-pick, is introduced and its implementation of point spread function weighting and background normalization are validated. • TM in cryo-ET is often prone to false positives when using it for automated annotation, hence a novel application of a tophat transform on score maps is tested and proven effective at filtering steep local maxima. • The tophat transform is integrated into a dual-constraint thresholding strategy and improves automated classification of macromolecules in a simulated benchmark and, in experimental data, leading to improved subtomogram averages. Template matching (TM) in cryo-electron tomography (cryo-ET) enables in situ detection and localization of known macromolecules. However, TM faces challenges of weak signal of the macromolecules and interfering features with a high signal-to-noise ratio, which are often addressed by time-consuming, subjective manual curation of results. To improve the detection performance we introduce pytom-match-pick, a GPU-accelerated, open-source command line interface for enhanced TM in cryo-ET. Using pytom-match-pick, we first quantify the effects of point spread function (PSF) weighting and show that a tilt-weighted PSF outperforms a binary wedge with a single defocus estimate. We also assess previously introduced background normalization methods for classification performance. This indicates that phase randomization is more effective than spectrum whitening in reducing false positives. Furthermore, a novel application of the tophat transform on score maps, combined with a dual-constraint thresholding strategy, reduces false positives and improves precision. We benchmarked pytom-match-pick on public datasets, demonstrating improved classification and localization of macromolecules like ribosomal subunits and proteasomes that led to fewer artifacts in subtomogram averages. This tool promises to advance visual proteomics by improving the efficiency and accuracy of macromolecule detection in cellular contexts.