Report of the First ONTOX Stakeholder Network Meeting: Digging Under the Surface of ONTOX Together With the Stakeholders.

The first Stakeholder Network Meeting of the EU Horizon 2020-funded ONTOX project was held on 13-14 March 2023, in Brussels, Belgium. The discussion centred around identifying specific challenges, barriers and drivers in relation to the implementation of non-animal new approach methodologies (NAMs) and probabilistic risk assessment (PRA), in order to help address the issues and rank them according to their associated level of difficulty. ONTOX aims to advance the assessment of chemical risk to humans, without the use of animal testing, by developing non-animal NAMs and PRA in line with 21st century toxicity testing principles. Stakeholder groups (regulatory authorities, companies, academia, non-governmental organisations) were identified and invited to participate in a meeting and a survey, by which their current position in relation to the implementation of NAMs and PRA was ascertained, as well as specific challenges and drivers highlighted. The survey analysis revealed areas of agreement and disagreement among stakeholders on topics such as capacity building, sustainability, regulatory acceptance, validation of adverse outcome pathways, acceptance of artificial intelligence (AI) in risk assessment, and guaranteeing consumer safety. The stakeholder network meeting resulted in the identification of barriers, drivers and specific challenges that need to be addressed. Breakout groups discussed topics such as hazard versus risk assessment, future reliance on AI and machine learning, regulatory requirements for industry and sustainability of the ONTOX Hub platform. The outputs from these discussions provided insights for overcoming barriers and leveraging drivers for implementing NAMs and PRA. It was concluded that there is a continued need for stakeholder engagement, including the organisation of a 'hackathon' to tackle challenges, to ensure the successful implementation of NAMs and PRA in chemical risk assessment.

Quantitative phosphoproteomics reveal cellular responses from caffeine, coumarin and quercetin in treated HepG2 cells.

Protein phosphorylation is the most common type of post-translational modification where serine, threonine or tyrosine are reversibly bound to the phosphate group of ATP in a reaction catalyzed by protein kinases. Phosphorylation plays an important role in regulation of cell homeostasis, including but not limited to signal perception and transduction, gene expression and function of proteins. Protein phosphorylation happens on a fast time scale and represents an energy-efficient way for the cell to adapt to exposure to chemical stressors. To understand the cascade of cellular signaling induced by exposure to chemicals, we have exposed HepG2 cells to three chemicals with different modes of action, namely, caffeine, coumarin, and quercetin in a concentration and time response manner. Significantly upregulated and downregulated phosphosites were screened to analyze the activation/deactivation of signaling pathways by protein kinases. In total, 69, 44 and 12 signaling pathways were found enriched in caffeine, coumarin and quercetin treated cells, respectively, of which 9 pathways were co-enriched with 11 jointly responded kinases. Among identified co-responded kinases, CDK1, MAPK1 and MAPK3 play important roles in cell cycle and insulin signaling pathways. Quantitative phosphoproteomics can sensitively distinguish the effects of different chemicals on cells, allowing the assessment of chemical safety through changes in substrates and metabolic pathways at the cellular level, which is important for the development of non-animal approaches for chemical safety assessment.

Confidence in Inactive and Active Predictions from Structural Alerts.

Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.

Structural characterization of the interaction of α-synuclein nascent chains with the ribosomal surface and trigger factor.

The ribosome is increasingly becoming recognized as a key hub for integrating quality control processes associated with protein biosynthesis and cotranslational folding (CTF). The molecular mechanisms by which these processes take place, however, remain largely unknown, in particular in the case of intrinsically disordered proteins (IDPs). To address this question, we studied at a residue-specific level the structure and dynamics of ribosome-nascent chain complexes (RNCs) of α-synuclein (αSyn), an IDP associated with Parkinson's disease (PD). Using solution-state nuclear magnetic resonance (NMR) spectroscopy and coarse-grained molecular dynamics (MD) simulations, we find that, although the nascent chain (NC) has a highly disordered conformation, its N-terminal region shows resonance broadening consistent with interactions involving specific regions of the ribosome surface. We also investigated the effects of the ribosome-associated molecular chaperone trigger factor (TF) on αSyn structure and dynamics using resonance broadening to define a footprint of the TF-RNC interactions. We have used these data to construct structural models that suggest specific ways by which emerging NCs can interact with the biosynthesis and quality control machinery.

MOAG-4 promotes the aggregation of α-synuclein by competing with self-protective electrostatic interactions.

Aberrant protein aggregation underlies a variety of age-related neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. Little is known, however, about the molecular mechanisms that modulate the aggregation process in the cellular environment. Recently, MOAG-4/SERF has been identified as a class of evolutionarily conserved proteins that positively regulates aggregate formation. Here, by using nuclear magnetic resonance (NMR) spectroscopy, we examine the mechanism of action of MOAG-4 by characterizing its interaction with α-synuclein (α-Syn). NMR chemical shift perturbations demonstrate that a positively charged segment of MOAG-4 forms a transiently populated α-helix that interacts with the negatively charged C terminus of α-Syn. This process interferes with the intramolecular interactions between the N- and C-terminal regions of α-Syn, resulting in the protein populating less compact forms and aggregating more readily. These results provide a compelling example of the complex competition between molecular and cellular factors that protect against protein aggregation and those that promote it.

Structural Characterization of the Early Events in the Nucleation-Condensation Mechanism in a Protein Folding Process.

The nucleation-condensation mechanism represents a major paradigm to understand the folding process of many small globular proteins. Although substantial evidence has been acquired for this mechanism, it has remained very challenging to characterize the initial events leading to the formation of a folding nucleus. To achieve this goal, we used a combination of relaxation dispersion NMR spectroscopy and molecular dynamics simulations to determine ensembles of conformations corresponding to the denatured, transition, and native states in the folding of the activation domain of human procarboxypeptidase A2 (ADA2h). We found that the residues making up the folding nucleus tend to interact in the denatured state in a transient manner and not simultaneously, thereby forming incomplete and distorted versions of the folding nucleus. Only when all the contacts between these key residues are eventually formed can the protein reach the transition state and continue folding. Overall, our results elucidate the mechanism of formation of the folding nucleus of a protein and provide insights into how its folding rate can be modified during evolution by mutations that modulate the strength of the interactions between the residues forming the folding nucleus.

Investigation of Ionization Pattern of the Adjacent Acidic Residues in the DXDXE Motif of GH-18 Chitinases Using Theoretical pKa Calculations.

GH-18 chitinases are chitinolytic enzymes, primarily responsible for the recycling of insoluble chitin biomaterials. These enzymes contain three invariant acidic active-site residues within a DXDXE motif, which play a synergistic role in the catalytic cycle of chitin degradation. We employed a pKa calculation approach to approximate the protonation states of residues D1, D2, and E in the DXDXE motif of 75 GH-18 chitinases. Theoretical pH-activity profiles of these enzymes were subsequently constructed and compared with the experimentally determined pH-activity profiles. Theoretical pKa data indicate that in the majority of chitinases the D1 side-chain is in the "up" and the E side-chain in the "down" position, while the position of the D2 side-chain is versatile and depends on the state of the enzyme. The pKa values in 75 GH-18 chitinases were predicted to be <0 for D1, 8-13 for D2, and 6-9 for E, indicating that the D1-D2 pair holds exactly one net negative charge. On the other hand, the catalytic acid E is protonated over the active pH-range, agreeing with the pH-activity curves reported previously for most chitinases. The results obtained from this study help to elucidate the mechanistic details of the concerted participation of D1, D2, and E in the catalytic cycle of chitin hydrolysis by GH-18 chitinases.

Structural Insights into the Calcium-Mediated Allosteric Transition in the C-Terminal Domain of Calmodulin from Nuclear Magnetic Resonance Measurements.

Calmodulin is a two-domain signaling protein that becomes activated upon binding cooperatively two pairs of calcium ions, leading to large-scale conformational changes that expose its binding site. Despite significant advances in understanding the structural biology of calmodulin functions, the mechanistic details of the conformational transition between closed and open states have remained unclear. To investigate this transition, we used a combination of molecular dynamics simulations and nuclear magnetic resonance (NMR) experiments on the Ca(2+)-saturated E140Q C-terminal domain variant. Using chemical shift restraints in replica-averaged metadynamics simulations, we obtained a high-resolution structural ensemble consisting of two conformational states and validated such an ensemble against three independent experimental data sets, namely, interproton nuclear Overhauser enhancements, (15)N order parameters, and chemical shift differences between the exchanging states. Through a detailed analysis of this structural ensemble and of the corresponding statistical weights, we characterized a calcium-mediated conformational transition whereby the coordination of Ca(2+) by just one oxygen of the bidentate ligand E140 triggers a concerted movement of the two EF-hands that exposes the target binding site. This analysis provides atomistic insights into a possible Ca(2+)-mediated activation mechanism of calmodulin that cannot be achieved from static structures alone or from ensemble NMR measurements of the transition between conformations.

Mapping the Protein Fold Universe Using the CamTube Force Field in Molecular Dynamics Simulations.

It has been recently shown that the coarse-graining of the structures of polypeptide chains as self-avoiding tubes can provide an effective representation of the conformational space of proteins. In order to fully exploit the opportunities offered by such a 'tube model' approach, we present here a strategy to combine it with molecular dynamics simulations. This strategy is based on the incorporation of the 'CamTube' force field into the Gromacs molecular dynamics package. By considering the case of a 60-residue polyvaline chain, we show that CamTube molecular dynamics simulations can comprehensively explore the conformational space of proteins. We obtain this result by a 20 µs metadynamics simulation of the polyvaline chain that recapitulates the currently known protein fold universe. We further show that, if residue-specific interaction potentials are added to the CamTube force field, it is possible to fold a protein into a topology close to that of its native state. These results illustrate how the CamTube force field can be used to explore efficiently the universe of protein folds with good accuracy and very limited computational cost.

Probing the Residual Structure of the Low Populated Denatured State of ADA2h under Folding Conditions by Relaxation Dispersion Nuclear Magnetic Resonance Spectroscopy.

The structural characterization of low populated states of proteins with accuracy comparable to that achievable for native states is important for understanding the mechanisms of protein folding and function, as well as misfolding and aggregation. Because of the transient nature of these low populated states, they are seldom detected directly under conditions that favor folding. The activation domain of human procarboxypeptidase A2 (ADA2h) is an α/ß-protein that forms amyloid fibrils at low pH, presumably initiated from a denatured state with a considerable amount of residual structure. Here we used Carr-Parcell-Meiboom-Gill relaxation dispersion (CPMG RD) nuclear magnetic resonance (NMR) spectroscopy to characterize the structure of the denatured state of the ADA2h I71V mutant under conditions that favor folding. Under these conditions, the lifetime of the denatured state of I71V ADA2h is on the order of milliseconds and its population is approximately several percent, which makes this mutant amenable to studies by CPMG RD methods. The nearly complete set of CPMG RD-derived backbone (15)N, (13)C, and (1)H NMR chemical shifts in the I71V ADA2h denatured state reveals that it retains a significant fraction (up to 50-60%) of nativelike α-helical structure, while the regions encompassing native ß-strands are structured to a much lesser extent. The nativelike α-helical structure of the denatured state can bring together hydrophobic residues on the same sides of α-helices, making them available for intra- or intermolecular interactions. CPMG RD data analysis thus allowed a detailed structural characterization of the ADA2h denatured state under folding conditions not previously achieved for this protein.

A Phosphorylation-Induced Turn Defines the Alzheimer's Disease AT8 Antibody Epitope on the Tau Protein.

Post mortem biochemical staging of Alzheimer's disease is currently based on immunochemical analysis of brain slices with the AT8 antibody. The epitope of AT8 is described around the pSer202/pThr205 region of the hyperphosphorylated form of the neuronal protein tau. In this study, NMR spectroscopy was used to precisely map the AT8 epitope on phosphorylated tau, and derive its defining structural features by a combination of NMR analyses and molecular dynamics. A particular turn conformation is stabilized by a hydrogen bond of the phosphorylated Thr205 residue to the amide proton of Gly207, and is further stabilized by the two Arg residues opposing the pSer202/pThr205.

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks.

BACKGROUND: Protein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure.In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past. RESULTS: We initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å.After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å.Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server. CONCLUSIONS: The methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints.

Next generation risk assessment for occupational chemical safety - A real world example with sodium-2-hydroxyethane sulfonate.

Next Generation Risk Assessment (NGRA) is an exposure-led approach to safety assessment that uses New Approach Methodologies (NAMs). Application of NGRA has been largely restricted to assessments of consumer use of cosmetics and is not currently implemented in occupational safety assessments, e.g. under EU REACH. By contrast, a large proportion of regulatory worker safety assessments are underpinned by toxicological studies using experimental animals. Consequently, occupational safety assessment represents an area that would benefit from increasing application of NGRA to safety decision making. Here, a workflow for conducting NGRA under an occupational safety context was developed, which is illustrated with a case study chemical; sodium 2-hydroxyethane sulphonate (sodium isethionate or SI). Exposures were estimated using a standard occupational exposure model following a comprehensive life cycle assessment of SI and considering factory-specific data. Outputs of this model were then used to estimate internal exposures using a Physiologically Based Kinetic (PBK) model, which was constructed with SI specific Absorption, Distribution, Metabolism and Excretion (ADME) data. PBK modelling indicated a worst-case plasma maximum concentration (Cmax) of 0.8 µM across the SI life cycle. SI bioactivity was assessed in a battery of NAMs relevant to systemic, reproductive, and developmental toxicity; a cell stress panel, high throughput transcriptomics in three cell lines (HepG2, HepaRG and MCF-7 cells), pharmacological profiling and specific assays relating to developmental toxicity (Reprotracker and devTOX quickPredict). Points of Departure (PoDs) for SI ranged from 104 to 5044 µM. Cmax values obtained from PBK modelling of occupational exposures to SI were compared with PoDs from the bioactivity assays to derive Bioactivity Exposure Ratios (BERs) which demonstrated the safety for workers exposed to SI under current levels of factory specific risk management. In summary, the tiered and iterative workflow developed here represents an opportunity for integrating non animal approaches for a large subset of substances for which systemic worker safety assessment is required. Such an approach could be followed to ensure that animal testing is only conducted as a "last resort" e.g. under EU REACH.

Protein dielectric constants determined from NMR chemical shift perturbations.

Understanding the connection between protein structure and function requires a quantitative understanding of electrostatic effects. Structure-based electrostatic calculations are essential for this purpose, but their use has been limited by a long-standing discussion on which value to use for the dielectric constants (ε(eff) and ε(p)) required in Coulombic and Poisson-Boltzmann models. The currently used values for ε(eff) and ε(p) are essentially empirical parameters calibrated against thermodynamic properties that are indirect measurements of protein electric fields. We determine optimal values for ε(eff) and ε(p) by measuring protein electric fields in solution using direct detection of NMR chemical shift perturbations (CSPs). We measured CSPs in 14 proteins to get a broad and general characterization of electric fields. Coulomb's law reproduces the measured CSPs optimally with a protein dielectric constant (ε(eff)) from 3 to 13, with an optimal value across all proteins of 6.5. However, when the water-protein interface is treated with finite difference Poisson-Boltzmann calculations, the optimal protein dielectric constant (ε(p)) ranged from 2 to 5 with an optimum of 3. It is striking how similar this value is to the dielectric constant of 2-4 measured for protein powders and how different it is from the ε(p) of 6-20 used in models based on the Poisson-Boltzmann equation when calculating thermodynamic parameters. Because the value of ε(p) = 3 is obtained by analysis of NMR chemical shift perturbations instead of thermodynamic parameters such as pK(a) values, it is likely to describe only the electric field and thus represent a more general, intrinsic, and transferable ε(p) common to most folded proteins.

Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment.

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.

The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration.

Next-generation risk assessment (NGRA) for environmental chemicals involves a weight of evidence (WoE) framework integrating a suite of new approach methodologies (NAMs) based on points of departure (PoD) obtained from in vitro assays. Among existing NAMs, the omic-based technologies are of particular importance based on the premise that any apical endpoint change indicative of impaired health must be underpinned by some alterations at the omics level, such as transcriptome, proteome, metabolome, epigenome and genome. Transcriptomic assay plays a leading role in providing relatively conservative PoDs compared with apical endpoints. However, it is unclear whether and how parameters measured with other omics techniques predict the cellular response to chemical perturbations, especially at exposure levels below the transcriptomically defined PoD. Multi-omics coverage may provide additional sensitive or confirmative biomarkers to complement and reduce the uncertainty in safety decisions made using targeted and transcriptomics assays. In the present study, we conducted multi-omics studies of transcriptomics, proteomics and phosphoproteomics on two prototype compounds, coumarin and 2,4-dichlorophenoxyacetic acid (2,4-D), with multiple chemical concentrations and time points, to understand the sensitivity of the three omics techniques in response to chemically-induced changes in HepG2. We demonstrated that, phosphoproteomics alterations occur not only earlier in time, but also more sensitive to lower concentrations than proteomics and transcriptomics when the HepG2 cells were exposed to various chemical treatments. The phosphoproteomics changes appear to approach maximum when the transcriptomics alterations begin to initiate. Therefore, it is proximal to the very early effects induced by chemical exposure. We concluded that phosphoproteomics can be utilized to provide a more complete coverage of chemical-induced cellular alteration and supplement transcriptomics-based health safety decision making.

Beyond AOPs: A Mechanistic Evaluation of NAMs in DART Testing.

New Approach Methodologies (NAMs) promise to offer a unique opportunity to enable human-relevant safety decisions to be made without the need for animal testing in the context of exposure-driven Next Generation Risk Assessment (NGRA). Protecting human health against the potential effects a chemical may have on embryo-foetal development and/or aspects of reproductive biology using NGRA is particularly challenging. These are not single endpoint or health effects and risk assessments have traditionally relied on data from Developmental and Reproductive Toxicity (DART) tests in animals. There are numerous Adverse Outcome Pathways (AOPs) that can lead to DART, which means defining and developing strict testing strategies for every AOP, to predict apical outcomes, is neither a tenable goal nor a necessity to ensure NAM-based safety assessments are fit-for-purpose. Instead, a pragmatic approach is needed that uses the available knowledge and data to ensure NAM-based exposure-led safety assessments are sufficiently protective. To this end, the mechanistic and biological coverage of existing NAMs for DART were assessed and gaps to be addressed were identified, allowing the development of an approach that relies on generating data relevant to the overall mechanisms involved in human reproduction and embryo-foetal development. Using the knowledge of cellular processes and signalling pathways underlying the key stages in reproduction and development, we have developed a broad outline of endpoints informative of DART. When the existing NAMs were compared against this outline to determine whether they provide comprehensive coverage when integrated in a framework, we found them to generally cover the reproductive and developmental processes underlying the traditionally evaluated apical endpoint studies. The application of this safety assessment framework is illustrated using an exposure-led case study.

Are Non-animal Systemic Safety Assessments Protective? A Toolbox and Workflow.

An important question in toxicological risk assessment is whether non-animal new approach methodologies (NAMs) can be used to make safety decisions that are protective of human health, without being overly conservative. In this work, we propose a core NAM toolbox and workflow for conducting systemic safety assessments for adult consumers. We also present an approach for evaluating how protective and useful the toolbox and workflow are by benchmarking against historical safety decisions. The toolbox includes physiologically based kinetic (PBK) models to estimate systemic Cmax levels in humans, and 3 bioactivity platforms, comprising high-throughput transcriptomics, a cell stress panel, and in vitro pharmacological profiling, from which points of departure are estimated. A Bayesian model was developed to quantify the uncertainty in the Cmax estimates depending on how the PBK models were parameterized. The feasibility of the evaluation approach was tested using 24 exposure scenarios from 10 chemicals, some of which would be considered high risk from a consumer goods perspective (eg, drugs that are systemically bioactive) and some low risk (eg, existing food or cosmetic ingredients). Using novel protectiveness and utility metrics, it was shown that up to 69% (9/13) of the low risk scenarios could be identified as such using the toolbox, whilst being protective against all (5/5) the high-risk ones. The results demonstrated how robust safety decisions could be made without using animal data. This work will enable a full evaluation to assess how protective and useful the toolbox and workflow are across a broader range of chemical-exposure scenarios.

Improving the analysis of NMR spectra tracking pH-induced conformational changes: removing artefacts of the electric field on the NMR chemical shift.

pH-induced chemical shift perturbations (CSPs) can be used to study pH-dependent conformational transitions in proteins. Recently, an elegant principal component analysis (PCA) algorithm was developed and used to study the pH-dependent structural transitions in bovine beta-lactoglobulin (betaLG) by analyzing its NMR pH-titration spectra. Here, we augment this analysis method by filtering out changes in the NMR chemical shift that stem from effects that are electrostatic in nature. Specifically, we examine how many CSPs can be explained by purely electrostatic effects arising from titrational events in betaLG. The results show that around 20% of the amide nuclei CSPs in betaLG originate exclusively from "through-space" electric field effects. A PCA of NMR data where electric field artefacts have been removed gives a different picture of the pH-dependent structural transitions in betaLG. The method implemented here is well suited to be applied on a whole range of proteins, which experience at least one pH-dependent conformational change. Proteins 2010. (c) 2009 Wiley-Liss, Inc.