Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale.

The development of accurate transferable force fields is key to realizing the full potential of atomistic modeling in the study of biological processes such as protein-ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to yield accuracy improvements. However, force field torsion parameters, which must account for many stereoelectronic and steric effects, are considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for druglike fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root-mean-square error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.560.390.77 to 0.420.280.59 kcal/mol and R2 correlation improved from 0.720.350.87 to 0.930.840.97).

Monitoring hippocampal glycine with the computationally designed optical sensor GlyFS.

Fluorescent sensors are an essential part of the experimental toolbox of the life sciences, where they are used ubiquitously to visualize intra- and extracellular signaling. In the brain, optical neurotransmitter sensors can shed light on temporal and spatial aspects of signal transmission by directly observing, for instance, neurotransmitter release and spread. Here we report the development and application of the first optical sensor for the amino acid glycine, which is both an inhibitory neurotransmitter and a co-agonist of the N-methyl-D-aspartate receptors (NMDARs) involved in synaptic plasticity. Computational design of a glycine-specific binding protein allowed us to produce the optical glycine FRET sensor (GlyFS), which can be used with single and two-photon excitation fluorescence microscopy. We took advantage of this newly developed sensor to test predictions about the uneven spatial distribution of glycine in extracellular space and to demonstrate that extracellular glycine levels are controlled by plasticity-inducing stimuli.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.

Computational design and experimental characterisation of a stable human heparanase variant.

Heparanase is the only human enzyme known to hydrolyse heparin sulfate and is involved in many important physiological processes. However, it is also unregulated in many disease states, such as cancer, diabetes and Covid-19. It is thus an important drug target, yet the heterologous production of heparanase is challenging and only possible in mammalian or insect expression systems, which limits the ability of many laboratories to study it. Here we describe the computational redesign of heparanase to allow high yield expression in Escherchia coli. This mutated form of heparanase exhibits essentially identical kinetics, inhibition, structure and protein dynamics to the wild type protein, despite the presence of 26 mutations. This variant will facilitate wider study of this important enzyme and contributes to a growing body of literature that shows evolutionarily conserved and functionally neutral mutations can have significant effects on protein folding and expression.

A Rationally and Computationally Designed Fluorescent Biosensor for d-Serine.

Solute-binding proteins (SBPs) have evolved to balance the demands of ligand affinity, thermostability, and conformational change to accomplish diverse functions in small molecule transport, sensing, and chemotaxis. Although the ligand-induced conformational changes that occur in SBPs make them useful components in biosensors, they are challenging targets for protein engineering and design. Here, we have engineered a d-alanine-specific SBP into a fluorescence biosensor with specificity for the signaling molecule d-serine (D-serFS). This was achieved through binding site and remote mutations that improved affinity (KD = 6.7 ± 0.5 µM), specificity (40-fold increase vs glycine), thermostability (Tm = 79 °C), and dynamic range (∼14%). This sensor allowed measurement of physiologically relevant changes in d-serine concentration using two-photon excitation fluorescence microscopy in rat brain hippocampal slices. This work illustrates the functional trade-offs between protein dynamics, ligand affinity, and thermostability and how these must be balanced to achieve desirable activities in the engineering of complex, dynamic proteins.

Structural and evolutionary approaches to the design and optimization of fluorescence-based small molecule biosensors.

Biosensors that selectively report on the presence of specific small molecule analytes have applications in many fields of research, medicine and biotechnology. Here, we review recent advances and emerging approaches in the design and optimisation of genetically encoded fluorescence-based small molecule biosensors. We discuss how natural sensory proteins can be exploited to produce novel biosensors and the strategies for optimizing ligand specificity and fluorescence readout. Finally, we provide insight into high-throughput sensor optimisation and discuss the challenges that are faced when designing novel biosensors.

Capturing the Dynamics of a Spring-Loaded Protein.

Skp and other holdase chaperones bind unfolded bacterial outer membrane proteins, preventing premature folding until they insert into the membrane. In this issue of Structure, Holdbrook et al. (2017) use a combination of NMR, SAXS, ensemble optimization, and MD simulations to show that the Skp chaperone samples a much wider range of conformations than suggested by its structure alone.

Method for Developing Optical Sensors Using a Synthetic Dye-Fluorescent Protein FRET Pair and Computational Modeling and Assessment.

Biosensors that exploit Förster resonance energy transfer (FRET) can be used to visualize biological and physiological processes and are capable of providing detailed information in both spatial and temporal dimensions. In a FRET-based biosensor, substrate binding is associated with a change in the relative positions of two fluorophores, leading to a change in FRET efficiency that may be observed in the fluorescence spectrum. As a result, their design requires a ligand-binding protein that exhibits a conformational change upon binding. However, not all ligand-binding proteins produce responsive sensors upon conjugation to fluorescent proteins or dyes, and identifying the optimum locations for the fluorophores often involves labor-intensive iterative design or high-throughput screening. Combining the genetic fusion of a fluorescent protein to the ligand-binding protein with site-specific covalent attachment of a fluorescent dye can allow fine control over the positions of the two fluorophores, allowing the construction of very sensitive sensors. This relies upon the accurate prediction of the locations of the two fluorophores in bound and unbound states. In this chapter, we describe a method for computational identification of dye-attachment sites that allows the use of cysteine modification to attach synthetic dyes that can be paired with a fluorescent protein for the purposes of creating FRET sensors.