Machine Learning of Reactive Potentials.

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach.

Machine learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems solely through ML methods (such as those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical force fields (MM), in the same spirit as the successful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is an adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so-called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x and ANI-2x databases, solvated in water. The results suggest that the minimal basis iterative stockholder (MBIS) atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction.

Novel Lennard-Jones Parameters for Cysteine and Selenocysteine in the AMBER Force Field.

Cysteine is a common amino acid with a thiol group that plays a pivotal role in a variety of scenarios in redox biochemistry. In contrast, selenocysteine, the 21st amino acid, is only present in 25 human proteins. Classical force-field parameters for cysteine and selenocysteine are still scarce. In this context, we present a methodology to obtain Lennard-Jones parameters for cysteine and selenocysteine in different physiologically relevant oxidation and protonation states. The new force field parameters obtained in this work are available at https://github.com/MALBECC/AMBER-parameters-database. The parameters were adjusted to reproduce water radial distribution functions obtained by density functional theory ab initio molecular dynamics. We validated the results by evaluating the impact of the choice of parameters on the structure and dynamics in classical molecular dynamics simulations of representative proteins containing catalytic cysteine/selenocysteine residues. There are significant changes in protein structure and dynamics depending on the parameters choice, specifically affecting the residues close to the catalytic sites.

Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials.

Optically active molecular materials, such as organic conjugated polymers and biological systems, are characterized by strong coupling between electronic and vibrational degrees of freedom. Typically, simulations must go beyond the Born-Oppenheimer approximation to account for non-adiabatic coupling between excited states. Indeed, non-adiabatic dynamics is commonly associated with exciton dynamics and photophysics involving charge and energy transfer, as well as exciton dissociation and charge recombination. Understanding the photoinduced dynamics in such materials is vital to providing an accurate description of exciton formation, evolution, and decay. This interdisciplinary field has matured significantly over the past decades. Formulation of new theoretical frameworks, development of more efficient and accurate computational algorithms, and evolution of high-performance computer hardware has extended these simulations to very large molecular systems with hundreds of atoms, including numerous studies of organic semiconductors and biomolecules. In this Review, we will describe recent theoretical advances including treatment of electronic decoherence in surface-hopping methods, the role of solvent effects, trivial unavoided crossings, analysis of data based on transition densities, and efficient computational implementations of these numerical methods. We also emphasize newly developed semiclassical approaches, based on the Gaussian approximation, which retain phase and width information to account for significant decoherence and interference effects while maintaining the high efficiency of surface-hopping approaches. The above developments have been employed to successfully describe photophysics in a variety of molecular materials.

Exploring Coupled Redox and pH Processes with a Force-Field-Based Approach: Applications to Five Different Systems.

Coupled redox and pH-driven processes are at the core of many important biological mechanisms. As the distribution of protonation and redox states in a system is associated with the pH and redox potential of the solution, having efficient computational tools that can simulate under these conditions becomes very important. Such tools have the potential to provide information that complement and drive experiments. In previous publications we have presented the implementation of the constant pH and redox potential molecular dynamics (C(pH,E)MD) method in AMBER and we have shown how multidimensional replica exchange can be used to significantly enhance the convergence efficiency of our simulations. In the current work, after an improvement in our C(pH,E)MD approach that allows a given residue to be simultaneously pH- and redox-active, we have employed our methodologies to study five different systems of interest in the literature. We present results for capped tyrosine dipeptide, two maquette systems containing one pH- and redox-active tyrosine (α3Y and peptide A), and two proteins that contain multiple heme groups (diheme cytochrome c from Rhodobacter sphaeroides and Desulfovibrio vulgaris Hildenborough cytochrome c3). We show that our results can provide new insights into previous theoretical and experimental findings by using a fully force-field-based and GPU-accelerated approach, which allows the simulations to be executed with high computational performance.

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials.

This paper presents TorchANI, a PyTorch-based program for training/inference of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical properties of molecular systems. ANI is an accurate neural network potential originally implemented using C++/CUDA in a program called NeuroChem. Compared with NeuroChem, TorchANI has a design emphasis on being lightweight, user friendly, cross platform, and easy to read and modify for fast prototyping, while allowing acceptable sacrifice on running performance. Because the computation of atomic environmental vectors and atomic neural networks are all implemented using PyTorch operators, TorchANI is able to use PyTorch's autograd engine to automatically compute analytical forces and Hessian matrices, as well as do force training without requiring any additional codes. TorchANI is open-source and freely available on GitHub: https://github.com/aiqm/torchani.

The generalized Boltzmann distribution is the only distribution in which the Gibbs-Shannon entropy equals the thermodynamic entropy.

We show that the generalized Boltzmann distribution is the only distribution for which the Gibbs-Shannon entropy equals the thermodynamic entropy. This result means that the thermodynamic entropy and the Gibbs-Shannon entropy are not generally equal, but rather the equality holds only in the special case where a system is in equilibrium with a reservoir.

A Coupled Ionization-Conformational Equilibrium Is Required To Understand the Properties of Ionizable Residues in the Hydrophobic Interior of Staphylococcal Nuclease.

Ionizable residues in the interior of proteins play essential roles, especially in biological energy transduction, but are relatively rare and seem incompatible with the complex and polar environment. We perform a comprehensive study of the internal ionizable residues on 21 variants of staphylococcal nuclease with internal Lys, Glu, or Asp residues. Using pH replica exchange molecular dynamics simulations, we find that, in most cases, the pKa values of these internal ionizable residues are shifted significantly from their values in solution. Our calculated results are in excellent agreement with the experimental observations of the Garcia-Moreno group. We show that the interpretation of the experimental pKa values requires the study of not only protonation changes but also conformational changes. The coupling between the protonation and conformational equilibria suggests a mechanism for efficient pH-sensing and regulation in proteins. This study provides new physical insights into how internal ionizable residues behave in the hydrophobic interior of proteins.

Probing the Structures of Solvent-Complexed Ions Formed in Electrospray Ionization Using Cryogenic Infrared Photodissociation Spectroscopy.

The gas-phase infrared photodissociation (IRPD) spectra of solvent-tagged small biomolecules are studied in a cryogenic ion trap at 17 K. In this study para-aminobenzoic acid (PABA) and tyramine molecules are noncovalently tagged with water or acetonitrile in the electrospray ionization (ESI) source. The complexes are then cooled in the cryogenic trap prior to spectroscopic measurements. These molecules provide two putative sites for solvent attachment: the protonated amine (NH3+) and the OH groups. Comparisons of the experimental IR spectra to theoretical spectra obtained with density functional theory show that the NH3+ site is mainly favored. Evidence for the formation of both NH3-bound and OH-bound conformers is found only in tyramine, despite having similar solution- and gas-phase energetics to that of PABA. Since the structures cannot interconvert in the gas phase, this suggests an isomerization during the electrospray process.

Cation-Dependent Conformations in 25-Hydroxyvitamin D3-Cation Adducts Measured by Ion Mobility-Mass Spectrometry and Theoretical Modeling.

Ion mobility-mass spectrometry is a useful tool in separation of biological isomers, including clinically relevant analytes such as 25-hydroxyvitamin D3 (25OHD3) and its epimer, 3-epi-25-hydroxyvitamin D3 (epi25OHD3). Previous research indicates that these epimers adopt different gas-phase sodiated monomer structures, either the "open" or "closed" conformer, which allow 25OHD3 to be readily resolved in mixtures. In the current work, alternative metal cation adducts are investigated for their relative effects on the ratio of "open" and "closed conformers. Alkali and alkaline earth metal adducts caused changes in the 25OHD3 conformer ratio, where the proportion of the "open" conformer generally increases with the size of the metal cation in a given group. As such, the ratio of the "open" conformer, which is unique to 25OHD3 and absent for its epimer, can be increased from approximately 1:1 for the sodiated monomer to greater than 8:1 for the barium adduct. Molecular modeling and energy calculations agree with the experimental results, indicating that the Gibbs free energy of conversion from the "closed" to the "open" conformation decreased with increasing cation size, correlating with the variation in ratio between the conformers. This work demonstrates the effect of cation adducts on gas-phase conformations of small, flexible molecules and offers an additional strategy for resolution of clinically relevant epimers.

Redox potential replica exchange molecular dynamics at constant pH in AMBER: Implementation and validation.

Redox processes are important in chemistry, with applications in biomedicine, chemical analysis, among others. As many redox experiments are also performed at a fixed value of pH, having an efficient computational method to support experimental measures at both constant redox potential and pH is very important. Such computational techniques have the potential to validate experimental observations performed under these conditions and to provide additional information unachievable experimentally such as an atomic level description of macroscopic measures. We present the implementation of discrete redox and protonation states methods for constant redox potential Molecular Dynamics (CEMD), for coupled constant pH and constant redox potential MD (C(pH,E)MD), and for Replica Exchange MD along the redox potential dimension (E-REMD) on the AMBER software package. Validation results are presented for a small system that contains a single heme group: N-acetylmicroperoxidase-8 (NAcMP8) axially connected to a histidine peptide. The methods implemented allow one to make standard redox potential (Eo) predictions with the same easiness and accuracy as pKa predictions using the constant pH molecular dynamics and pH-REMD methods currently available on AMBER. In our simulations, we can correctly describe, in agreement also with theoretical predictions, the following behaviors: when a redox-active group is reduced, the pKa of a near pH-active group increases because it becomes easier for a proton to be attached; equivalently, when a pH-active group is protonated, the standard redox potential (Eo) of an adjacent redox-active group rises. Furthermore, our results also show that E-REMD is able to achieve faster statistical convergence than CEMD or C(pH,E)MD. Moreover, computational benchmarks using our methodologies show high-performance of GPU (Graphics Processing Unit) accelerated calculations in comparison to conventional CPU (Central Processing Unit) calculations.

Less is more: Sampling chemical space with active learning.

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

Interpretation of pH-activity profiles for acid-base catalysis from molecular simulations.

The measurement of reaction rate as a function of pH provides essential information about mechanism. These rates are sensitive to the pK(a) values of amino acids directly involved in catalysis that are often shifted by the enzyme active site environment. Experimentally observed pH-rate profiles are usually interpreted using simple kinetic models that allow estimation of "apparent pK(a)" values of presumed general acid and base catalysts. One of the underlying assumptions in these models is that the protonation states are uncorrelated. In this work, we introduce the use of constant pH molecular dynamics simulations in explicit solvent (CpHMD) with replica exchange in the pH-dimension (pH-REMD) as a tool to aid in the interpretation of pH-activity data of enzymes and to test the validity of different kinetic models. We apply the methods to RNase A, a prototype acid-base catalyst, to predict the macroscopic and microscopic pK(a) values, as well as the shape of the pH-rate profile. Results for apo and cCMP-bound RNase A agree well with available experimental data and suggest that deprotonation of the general acid and protonation of the general base are not strongly coupled in transphosphorylation and hydrolysis steps. Stronger coupling, however, is predicted for the Lys41 and His119 protonation states in apo RNase A, leading to the requirement for a microscopic kinetic model. This type of analysis may be important for other catalytic systems where the active forms of the implicated general acid and base are oppositely charged and more highly correlated. These results suggest a new way for CpHMD/pH-REMD simulations to bridge the gap with experiments to provide a molecular-level interpretation of pH-activity data in studies of enzyme mechanisms.

Dynamics of Energy Transfer in a Conjugated Dendrimer Driven by Ultrafast Localization of Excitations.

Solar energy conversion starts with the harvest of light, and its efficacy depends on the spatial transfer of the light energy to where it can be transduced into other forms of energy. Harnessing solar power as a clean energy source requires the continuous development of new synthetic materials that can harvest photon energy and transport it without significant losses. With chemically-controlled branched architectures, dendrimers are ideally suited for these initial steps, since they consist of arrays of chromophores with relative positioning and orientations to create energy gradients and to spatially focus excitation energies. The spatial localization of the energy delimits its efficacy and has been a point of intense research for synthetic light harvesters. We present the results of a combined theoretical experimental study elucidating ultrafast, unidirectional, electronic energy transfer on a complex molecule designed to spatially focus the initial excitation onto an energy sink. The study explores the complex interplay between atomic motions, excited-state populations, and localization/delocalization of excitations. Our findings show that the electronic energy-transfer mechanism involves the ultrafast collapse of the photoexcited wave function due to nonadiabatic electronic transitions. The localization of the wave function is driven by the efficient coupling to high-frequency vibrational modes leading to ultrafast excited-state dynamics and unidirectional efficient energy funneling. This work provides a long-awaited consistent experiment-theoretical description of excited-state dynamics in organic conjugated dendrimers with atomistic resolution, a phenomenon expected to universally appear in a variety of synthetic conjugated materials.

Nonadiabatic excited-state molecular dynamics: modeling photophysics in organic conjugated materials.

To design functional photoactive materials for a variety of technological applications, researchers need to understand their electronic properties in detail and have ways to control their photoinduced pathways. When excited by photons of light, organic conjugated materials (OCMs) show dynamics that are often characterized by large nonadiabatic (NA) couplings between multiple excited states through a breakdown of the Born-Oppenheimer (BO) approximation. Following photoexcitation, various nonradiative intraband relaxation pathways can lead to a number of complex processes. Therefore, computational simulation of nonadiabatic molecular dynamics is an indispensable tool for understanding complex photoinduced processes such as internal conversion, energy transfer, charge separation, and spatial localization of excitons. Over the years, we have developed a nonadiabatic excited-state molecular dynamics (NA-ESMD) framework that efficiently and accurately describes photoinduced phenomena in extended conjugated molecular systems. We use the fewest-switches surface hopping (FSSH) algorithm to treat quantum transitions among multiple adiabatic excited state potential energy surfaces (PESs). Extended molecular systems often contain hundreds of atoms and involve large densities of excited states that participate in the photoinduced dynamics. We can achieve an accurate description of the multiple excited states using the configuration interaction single (CIS) formalism with a semiempirical model Hamiltonian. Analytical techniques allow the trajectory to be propagated "on the fly" using the complete set of NA coupling terms and remove computational bottlenecks in the evaluation of excited-state gradients and NA couplings. Furthermore, the use of state-specific gradients for propagation of nuclei on the native excited-state PES eliminates the need for simplifications such as the classical path approximation (CPA), which only uses ground-state gradients. Thus, the NA-ESMD methodology offers a computationally tractable route for simulating hundreds of atoms on ~10 ps time scales where multiple coupled excited states are involved. In this Account, we review recent developments in the NA-ESMD modeling of photoinduced dynamics in extended conjugated molecules involving multiple coupled electronic states. We have successfully applied the outlined NA-ESMD framework to study ultrafast conformational planarization in polyfluorenes where the rate of torsional relaxation can be controlled based on the initial excitation. With the addition of the state reassignment algorithm to identify instances of unavoided crossings between noninteracting PESs, NA-ESMD can now be used to study systems in which these so-called trivial unavoided crossings are expected to predominate. We employ this technique to analyze the energy transfer between poly(phenylene vinylene) (PPV) segments where conformational fluctuations give rise to numerous instances of unavoided crossings leading to multiple pathways and complex energy transfer dynamics that cannot be described using a simple Förster model. In addition, we have investigated the mechanism of ultrafast unidirectional energy transfer in dendrimers composed of poly(phenylene ethynylene) (PPE) chromophores and have demonstrated that differential nuclear motion favors downhill energy transfer in dendrimers. The use of native excited-state gradients allows us to observe this feature.

Applicability of fluorescence-based sensors to the determination of kinetic parameters for O2 in oxygenases.

Optical methods for O2 determination based on dynamic fluorescence quenching have been applied to measure oxygen uptake rates in cell culture and to determine intracellular oxygen levels. Here we demonstrate the applicability of fluorescence-based probes in determining kinetic parameters for O2 using as an example catalysis by a cofactor-independent oxygenase (DpgC). Fluorescence-based sensors provide a direct assessment of enzyme-catalyzed O2 consumption using commercially available, low-cost instrumentation that is easily customizable and, thus, constitutes a convenient alternative to the widely used Clark-type electrode, especially in cases where chemical interference is expected to be problematic.

Insights into the mechanism of oxidation of dihydroorotate to orotate catalysed by human class 2 dihydroorotate dehydrogenase: a QM/MM free energy study.

The dihydroorotate dehydrogenase (DHOD) enzyme catalyzes the unique redox reaction in the de novo pyrimidine biosynthesis pathway. In this reaction, the oxidation of dihydroorotate (DHO) to orotate (OA) and reduction of the flavin mononucleotide (FMN) cofactor is catalysed by DHOD. The class 2 DHOD, to which the human enzyme belongs, was experimentally shown to follow a stepwise mechanism but the data did not allow the determination of the order of bond-breaking in a stepwise oxidation of DHO. The goal of this study is to understand the reaction mechanism at the molecular level of class 2 DHOD, which may aid in the design of inhibitors that selectively impact the activity of only certain members of the enzyme family. In this paper, the catalytic mechanism of oxidation of DHO to OA in human DHOD was studied using a hybrid Quantum Mechanical/Molecular Mechanical (QM/MM) approach and Molecular Dynamics (MD) simulations. The free energy barriers calculated reveal that the mechanism in human DHOD occurs via a stepwise reaction pathway. In the first step, a proton is abstracted from the C5 of DHO to the deprotonated Ser215 side chain. Whereas, in the second step, the transfer of the hydride or hydride equivalent from the C6 of DHO to the N5 of FMN, where free energy barrier calculated by the DFT/MM level is 10.84 kcal mol(-1). Finally, a residual decomposition analysis was carried out in order to elucidate the influence of the catalytic region residues during DHO oxidation.

On the analysis and comparison of conformer-specific essential dynamics upon ligand binding to a protein.

The native state of a protein consists of an equilibrium of conformational states on an energy landscape rather than existing as a single static state. The co-existence of conformers with different ligand-affinities in a dynamical equilibrium is the basis for the conformational selection model for ligand binding. In this context, the development of theoretical methods that allow us to analyze not only the structural changes but also changes in the fluctuation patterns between conformers will contribute to elucidate the differential properties acquired upon ligand binding. Molecular dynamics simulations can provide the required information to explore these features. Its use in combination with subsequent essential dynamics analysis allows separating large concerted conformational rearrangements from irrelevant fluctuations. We present a novel procedure to define the size and composition of essential dynamics subspaces associated with ligand-bound and ligand-free conformations. These definitions allow us to compare essential dynamics subspaces between different conformers. Our procedure attempts to emphasize the main similarities and differences between the different essential dynamics in an unbiased way. Essential dynamics subspaces associated to conformational transitions can also be analyzed. As a test case, we study the glutaminase interacting protein (GIP), composed of a single PDZ domain. Both GIP ligand-free state and glutaminase L peptide-bound states are analyzed. Our findings concerning the relative changes in the flexibility pattern upon binding are in good agreement with experimental Nuclear Magnetic Resonance data.

Hydrophobic effect drives oxygen uptake in myoglobin via histidine E7.

Since the elucidation of the myoglobin (Mb) structure, a histidine residue on the E helix (His-E7) has been proposed to act as a gate with an open or closed conformation controlling access to the active site. Although it is believed that at low pH, the His-E7 gate is in its open conformation, the full relationship between the His-E7 protonation state, its conformation, and ligand migration in Mb is hotly debated. We used molecular dynamics simulations to first address the effect of His-E7 protonation on its conformation. We observed the expected shift from the closed to the open conformation upon protonation, but more importantly, noted a significant difference between the conformations of the two neutral histidine tautomers. We further computed free energy profiles for oxygen migration in each of the possible His-E7 states as well as in two instructive Mb mutants: Ala-E7 and Trp-E7. Our results show that even in the closed conformation, the His-E7 gate does not create a large barrier to oxygen migration and permits oxygen entry with only a small rotation of the imidazole side chain and movement of the E helix. We identify, instead, a hydrophobic site in the E7 channel that can accommodate an apolar diatomic ligand and enhances ligand uptake particularly in the open His-E7 conformation. This rate enhancement is diminished in the closed conformation. Taken together, our results provide a new conceptual framework for the histidine gate hypothesis.

Kinetics and computational studies of ligand migration in nitrophorin 7 and its Δ1-3 mutant.

Nitrophorins (NPs) are nitric oxide (NO)-carrying heme proteins found in the saliva of the blood-sucking insect Rhodnius prolixus. Though NP7 exhibits a large sequence resemblance with other NPs, two major differential features are the ability to interact with negatively charged cell surfaces and the presence of a specific N-terminus composed of three extra residues (Leu1-Pro2-Gly3). The aim of this study is to examine the influence of the N-terminus on the ligand binding, and the topological features of inner cavities in closed and open states of NP7, which can be associated to the protein structure at low and high pH, respectively. Laser flash photolysis measurements of the CO rebinding kinetics to NP7 and its variant NP7(Δ1-3), which lacks the three extra residues at the N-terminus, exhibit a similar pattern and support the existence of a common kinetic mechanism for ligand migration and binding. This is supported by the existence of a common topology of inner cavities, which consists of two docking sites in the heme pocket and a secondary site at the back of the protein. The ligand exchange between these cavities is facilitated by an additional site, which can be transiently occupied by the ligand in NP7, although it is absent in NP4. These features provide a basis to explain the enhanced internal gas hosting capacity found experimentally in NP7 and the absence of ligand rebinding from secondary sites in NP4. The current data allow us to speculate that the processes of docking to cell surfaces and NO release may be interconnected in NP7, thereby efficiently releasing NO into a target cell. This article is part of a Special Issue entitled: Oxygen Binding and Sensing Proteins.