Meta-analysis of animal studies applied to short-term inhalation exposure levels of hazardous chemicals.

For short-term chemical inhalation exposures to hazardous chemicals, the incidence of a health effect in biological testing usually conforms to a general linear model with a probit link function dependent on inhalant concentration C and the duration of exposure t. The National Academy's Acute Exposure Guideline Levels (AEGLs) Committee relies on these models when establishing AEGLs. Threshold concentrations at AEGL durations are established by the toxic load equation Cn x t = constant, which toxic load exponent n (TLE or n-value) directly follows from the bivariate probit model. When multiple probit datasets are available, the AEGL Committee routinely pools studies' incidence data. Such meta-analytical models are valid only when the pooled data are homogeneous, with similar sensitivities and equivalent responses to exposure concentrations and durations. In the present study, the homogeneity of datasets meta-analyzed by the AEGL Committee was examined, finding that 70% of datasets pooled by the AEGL Committee are heterogeneous. In these instances, data pooling leads to a statistically invalid model and TLE estimate, potentially resulting in under- or over-estimated inhalation guidance levels. When data pooling is inappropriate, other meta-analysis options include categorical regression, fixed-effect and random-effects models, or even designation of a key study based on scientific judgement. In the present work, options of TLE meta-analysis are summarized in a decision tree contingent on statistical testing.

Concentration-time extrapolation of short-term inhalation exposure levels: dimethyl sulfide, a case study using a chemical-specific toxic load exponent.

OBJECTIVE: Dimethyl sulfide (DMS, CAS 75-18-3) is an industrial chemical. It is both an irritant and neurotoxicant that may be life-threatening because of accidental release. The effects of DMS on public health and associated public health response depend on the exposure concentration and duration. However, currently, public health advisory information exists for only a 1 h exposure duration, developed by the American Industrial Hygiene Association (AIHA). In the present work, the AIHA-reviewed data were computationally extrapolated to other common short-term durations. METHODS: The extrapolation was carried out using the toxic load equation, Cn × t = TL, where C and t are exposure concentration and duration, TL is toxic load, and n is a chemical-specific toxic load exponent derived in the present work using probit meta-analysis. The developed threshold levels were vetted against the AIHA database of clinical and animal health effects induced by DMS. RESULTS: Tier-1 levels were derived based on human exposures that resulted in an easily detectable odor, because DMS is known to have a disagreeable odor that may cause nausea. Tier-2 levels were derived from the lower 95% confidence bounds on a benchmark concentration that caused 10% incidence (BMCL10) of coma in rats during a 15 min inhalation exposure to DMS. Tier-3 levels were based on a BMCL05 for mortality in rats. CONCLUSION: Emergency responders and health assessors may consider these computationally derived threshold levels as a supplement to traditional chemical risk assessment procedures in instances where AIHA developed public health advisory levels do not exist.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals--drugs, pesticides, and environmental pollutants--interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹5N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹5N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme.

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2-3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹5N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

SAR/QSAR methods in public health practice.

Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

Computational toxicology methods in public health practice.

ABSTRACT Hazard identification and health risk assessment traditionally rely on results of experimental testing in laboratory animals. It is a lengthy and expensive process, which at the end still involves large uncertainty because the sensitivity of animals is unequal to the sensitivity of humans. The Agency for Toxic Substances and Disease Registry (ATSDR) Computational Toxicology and Method Development Laboratory develops and applies advanced computational models that augment the traditional toxicological approach with multilevel cross-extrapolation techniques. On the one hand, these techniques help to reduce the uncertainty associated with experimental testing, and on the other, they encompass yet untested chemicals, which otherwise would be left out of public health assessment. Computational models also improve understanding of the mode of action of toxic agents, and fundamental mechanisms by which they may cause injury to the people. The improved knowledge is incorporated in scientific health guidance documents of the Agency, including the Toxicological Profiles, which are used as the basis for scientifically defensible public health assessments.

Impact of negatively charged patches on the surface of MHC class II antigen-presenting proteins on risk of chronic beryllium disease.

Chronic beryllium disease (CBD) is a granulomatous lung disease that occurs primarily in workers who are exposed to beryllium dust or fumes. Although exposure to beryllium is a necessary factor in the pathobiology of CBD, alleles that code for a glutamic acid residue at the 69th position of the HLA-DPbeta1 gene have previously been found to be associated with CBD. To date, 43 HLA-DPbeta1 alleles that code for glutamic acid 69 (E69) have been described. Whether all of these E69 coding alleles convey equal risk of CBD is unknown. The present study demonstrates that, on the one hand, E69 alleloforms of major histocompatibility complex class II antigen-presenting proteins with the greatest negative surface charge convey the highest risk of CBD, and on the other hand, irrespective of allele, they convey equal risk of beryllium sensitization (BeS). In addition, the data suggest that the same alleles that cause the greatest risk of CBD are also important for the progression from BeS to CBD. Alleles convey the highest risk code for E26 in a constant region and for E69, aspartic acid 55 (D55), E56, D84 and E85 in hypervariable regions of the HLA-DPbeta1 chain. Together with the calculated high binding affinities for beryllium, these results suggest that an adverse immune response, leading to CBD, is triggered by chemically specific metal-protein interactions.

A statistical model for assessing genetic susceptibility as a risk factor in multifactorial diseases: lessons from occupational asthma.

BACKGROUND: Incorporating the influence of genetic variation in the risk assessment process is often considered, but no generalized approach exists. Many common human diseases such as asthma, cancer, and cardiovascular disease are complex in nature, as they are influenced variably by environmental, physiologic, and genetic factors. The genetic components most responsible for differences in individual disease risk are thought to be DNA variants (polymorphisms) that influence the expression or function of mediators involved in the pathological processes. OBJECTIVE: The purpose of this study was to estimate the combinatorial contribution of multiple genetic variants to disease risk. METHODS: We used a logistic regression model to help estimate the joint contribution that multiple genetic variants would have on disease risk. This model was developed using data collected from molecular epidemiology studies of allergic asthma that examined variants in 16 susceptibility genes. RESULTS: Based on the product of single gene variant odds ratios, the risk of developing asthma was assigned to genotype profiles, and the frequency of each profile was estimated for the general population. Our model predicts that multiple disease variants broaden the risk distribution, facilitating the identification of susceptible populations. This model also allows for incorporation of exposure information as an independent variable, which will be important for risk variants associated with specific exposures. CONCLUSION: The present model provided an opportunity to estimate the relative change in risk associated with multiple genetic variants. This will facilitate identification of susceptible populations and help provide a framework to model the genetic contribution in probabilistic risk assessment.

Nearest-neighbor nonparametric method for estimating the configurational entropy of complex molecules.

A method for estimating the configurational (i.e., non-kinetic) part of the entropy of internal motion in complex molecules is introduced that does not assume any particular parametric form for the underlying probability density function. It is based on the nearest-neighbor (NN) distances of the points of a sample of internal molecular coordinates obtained by a computer simulation of a given molecule. As the method does not make any assumptions about the underlying potential energy function, it accounts fully for any anharmonicity of internal molecular motion. It provides an asymptotically unbiased and consistent estimate of the configurational part of the entropy of the internal degrees of freedom of the molecule. The NN method is illustrated by estimating the configurational entropy of internal rotation of capsaicin and two stereoisomers of tartaric acid, and by providing a much closer upper bound on the configurational entropy of internal rotation of a pentapeptide molecule than that obtained by the standard quasi-harmonic method. As a measure of dependence between any two internal molecular coordinates, a general coefficient of association based on the information-theoretic quantity of mutual information is proposed. Using NN estimates of this measure, statistical clustering procedures can be employed to group the coordinates into clusters of manageable dimensions and characterized by minimal dependence between coordinates belonging to different clusters.

Impact of silanol surface density on the toxicity of silica aerosols measured by erythrocyte haemolysis.

Exposures to silica-containing dusts are associated with a risk of developing life-threatening lung diseases. However, the mechanism of silica toxicity is poorly understood. In this work the atomic structure of the surfaces of different silica polymorphs was determined, and a relationship with in vitro silica toxicity was examined. The density of geminal and single silanol groups was quantitatively estimated for different silica polymorphs using a novel molecular modeling method. An association was found between the reported haemolytic activity and modeled densities of surface geminal (but not single) silanol groups on several silica polymorphs. These findings suggest a new view of aerosol toxicity based on the estimation of surface site densities. The results can be used in the development of new toxicological assays for respirable particulates, including nanomaterials.

Development of an empirical force field for silica. Application to the quartz-water interface.

Interactions of pulverized crystalline silica with biological systems, including the lungs, cause cell damage, inflammation, and apoptosis. To allow computational atomistic modeling of these pathogenic processes, including interactions between silica surfaces and biological molecules, new parameters for quartz, compatible with the CHARMM empirical force field were developed. Parameters were optimized to reproduce the experimental geometry of alpha-quartz, ab initio vibrational spectra, and interactions between model compounds and water. The newly developed force field was used to study interactions of water with two singular surfaces of alpha-quartz, (011) and (100). Properties monitored and analyzed include the variation of the density of water molecules in the plane perpendicular to the surface, disruption of the water H-bond network upon adsorption, and space-time correlations of water oxygen atoms in terms of Van Hove self-correlation functions. The vibrational density of states spectra of water in confined compartments were also computed and compared with experimental neutron-scattering results. Both the attenuation and shifting to higher frequencies of the hindered translational peaks upon confinement are clearly reproduced by the model. However, an upshift of librational peaks under the conditions of model confinement still remains underrepresented at the current empirical level.

Immunogenetic factors in beryllium sensitization and chronic beryllium disease.

Exposure to beryllium in the workplace can cause beryllium sensitization and chronic beryllium disease. Sensitization to beryllium can be detected in the laboratory using the beryllium lymphocyte proliferation test. It was shown that anti-HLA antibodies could block the beryllium-specific response in the beryllium lymphocyte proliferation test, thereby implicating HLA genes in chronic beryllium disease. A supratypic genetic marker, HLA-DPB1*E69, was found to be strongly associated with immunologic sensitization to beryllium and chronic beryllium disease in beryllium workers. However, there are 40 HLA-DPB1 gene variants that have E69 but that also have other DNA sequence variations. The purpose of the study was to evaluate the evidence for potential differential susceptibility that may be associated with the physical characteristics of HLA protein molecules for which different HLA-DPB1*E69 variants code; that is, do some HLA-DPB1*E69 variants convey higher risk of beryllium sensitization and chronic beryllium disease than others. To do this, two approaches were pursued: first, detailed analysis of the findings from the published literature was performed, and second, computational chemistry was used to seek clues concerning the physical properties of the HLA protein molecules for which these alleles code. Among the 40 HLA-DPB1 gene variants that code for E69, molecular epidemiological studies have suggested a risk hierarchy, where some variants appear to convey low to moderate risk of chronic beryllium disease (e.g., HLA-DPB1*0201, approximately 3-fold increased risk), some convey an intermediate risk (e.g., HLA-DPB1*1901, approximately 5-fold) and others convey high risk (e.g., HLA-DPB1*1701, >10-fold). Molecular modeling has been used to further investigate a potential mechanistic basis for these observations. We found a strong correlation between the hierarchical order of risk of chronic beryllium disease associated with specific alleles and the predicted surface electrostatic potential and charge of the corresponding isotypes. Therefore, when alleles were grouped by the relative negative charge on the molecules for which they code, the data suggest that those alleles associated with the most negatively charged proteins carry the greatest risk of beryllium sensitization and disease.

Structure-activity models for contact sensitization.

Allergic contact dermatitis (ACD) is a widespread cause of workers' disabilities. Although some substances found in the workplace are rigorously tested, the potential of the vast majority of chemicals to cause skin sensitization remains unknown. At the same time, exhaustive testing of all chemicals in workplaces is costly and raises ethical concerns. New approaches to developing information for risk assessment based on computational (quantitative) structure-activity relationship [(Q)SAR] methods may be complementary to and reduce the need for animal testing. Virtually any number of existing, de novo, and even preconceived compounds can be screened in silico at a fraction of the cost of animal testing. This work investigates the utility of ACD (Q)SAR modeling from the occupational health perspective using two leading software products, DEREK for Windows and TOPKAT, and an original method based on logistic regression methodology. It is found that the correct classification of (Q)SAR predictions for guinea pig data achieves values of 73.3, 82.9, and 87.6% for TOPKAT, DEREK for Windows, and the logistic regression model, respectively. The correct classification using LLNA data equals 73.0 and 83.2% for DEREK for Windows and the logistic regression model, respectively.

Estimation of the absolute internal-rotation entropy of molecules with two torsional degrees of freedom from stochastic simulations.

A method of statistical estimation is applied to the problem of evaluating the absolute entropy of internal rotation in a molecule with two torsional degrees of freedom. The configurational part of the entropy is obtained as that of the joint probability density of an arbitrary form represented by a two-dimensional Fourier series, the coefficients of which are statistically estimated using a sample of the torsional angles of the molecule obtained by a stochastic simulation. The internal rotors in the molecule are assumed to be attached to a common frame, and their reduced moments of inertia are initially calculated as functions of the two torsional angles, but averaged over all the remaining internal degrees of freedom using the stochastic-simulation sample of the atomic configurations of the molecule. The torsional-angle dependence of the reduced moments of inertia can be also averaged out, and the absolute internal-rotation entropy of the molecule is obtained in a good approximation as the sum of the configurational entropy and a kinetic contribution fully determined by the averaged reduced moments of inertia. The method is illustrated using Monte Carlo simulations of isomers of stilbene and halogenated derivatives of propane. The two torsional angles in cis-stilbene are found to be much more strongly correlated than those in trans-stilbene, while the degree of the angular correlation in propane increases strongly on substitution of hydrogen atoms with chlorine.

A comparative study of unrelaxed surfaces on quartz and kaolinite, using the periodic density functional theory.

To investigate surface properties of fractured silica particles, which are commonly connected to the etiology of silica toxicity, models of low-index unrelaxed surfaces of quartz and kaolinite were constructed and analyzed using the periodic density functional theory calculations. The models were used to investigate surface sites that emerge in the processes of heterolytic and homolytic cleavage of quartz. It is found that the quartz surface is stabilized by two types of interactions. One, due to a more even charge distribution of sites, was characterized by surface energies of up to 0.025 eV x A(-2) and the other, due to a more even oxygen distribution between complementary surfaces, was up to 0.036 eV x A(-2). The total specific surface energies of unrelaxed surfaces ranged from 0.161 to 0.200 eV x A(-2) for quartz and from 0.017 to 0.158 eV x A(-2) for kaolinite. For the conchoidal fracture of quartz an average specific surface energy of 0.187 eV x A(-2) was obtained. These results provide a foundation for further characterization of the surface properties of mechanically comminuted respirable silica particulate and for reduction of occupational health hazards due to pulverized silica.

Electrostatic potential on human leukocyte antigen: implications for putative mechanism of chronic beryllium disease.

The pathobiology of chronic beryllium disease (CBD) involves the major histocompatibility complex class II human leukocyte antigen (HLA). Although occupational exposure to beryllium is the cause of CBD, molecular epidemiologic studies suggest that specific (Italic)HLA-DPB1(/Italic) alleles may be genetic susceptibility factors. We have studied three-dimensional structural models of HLA-DP proteins encoded by these genes. The extracellular domains of HLA-DPA1*0103/B1*1701, *1901, *0201, and *0401, and HLA-DPA1*0201/B1*1701, *1901, *0201, and *0401 were modeled from the X-ray coordinates of an HLA-DR template. Using these models, the electrostatic potential at the molecular surface of each HLA-DP was calculated and compared. These comparisons identify specific characteristics in the vicinity of the antigen-binding pocket that distinguish the different HLA-DP allotypes. Differences in electrostatics originate from the shape, specific disposition, and variation in the negatively charged groups around the pocket. The more negative the pocket potential, the greater the odds of developing CBD estimated from reported epidemiologic studies. Adverse impact is caused by charged substitutions in positions 55, 56, 69, 84, and 85, namely, the exact same loci identified as genetic markers of CBD susceptibility as well as cobalt-lung hard metal disease. These findings suggest that certain substitutions may promote an involuntary cation-binding site within a putatively metal-free peptide-binding pocket and therefore change the innate specificity of antigen recognition.

Statistical thermodynamics of internal rotation in a hindering potential of mean force obtained from computer simulations.

A method of statistical estimation is applied to the problem of one-dimensional internal rotation in a hindering potential of mean force. The hindering potential, which may have a completely general shape, is expanded in a Fourier series, the coefficients of which are estimated by fitting an appropriate statistical-mechanical distribution to the random variable of internal rotation angle. The function of reduced moment of inertia of an internal rotation is averaged over the thermodynamic ensemble of atomic configurations of the molecule obtained in stochastic simulations. When quantum effects are not important, an accurate estimate of the absolute internal rotation entropy of a molecule with a single rotatable bond is obtained. When there is more than one rotatable bond, the "marginal" statistical-mechanical properties corresponding to a given internal rotational degree of freedom are reduced. The method is illustrated using Monte Carlo simulations of two public health relevant halocarbon molecules, each having a single internal-rotation degree of freedom, and a molecular dynamics simulation of an immunologically relevant polypeptide, in which several dihedral angles are analyzed.

Topographical organization of the N-terminal segment of lung pulmonary surfactant protein B (SP-B(1-25)) in phospholipid bilayers.

The location and depth of each residue of lung pulmonary surfactant protein B (SP-B(1-25)) in a phospholipid bilayer (PB) was determined by fluorescence quenching using synthesized single-residue-substituted peptides that were reconstituted into 1,2-dipalmitoyl phosphatidylcholine (DPPC)-enriched liposomes. The single-residue substitutions in peptides were either aspartate or tryptophan. The aspartate was subsequently labeled with the N-cyclohexyl-N'-(4-(dimethylamino)naphthyl)carbodiimide (NCD-4) fluorophore, whereas tryptophan is autofluorescent. Spin-labeled compounds, 5-doxylstearic acid (5-DSA), 7-doxylstearic acid (7-DSA), 12-doxylstearic acid (12-DSA), 4-(N,N-dimethyl-N-hexadecyl)ammonium-2,2,6,6-tetramethylpiperidine-1-oxyl iodide (CAT-16), and 4-trimethylammonium-2,2,6,6-tetramethylpiperidine-1-oxy iodide (CAT-1), were used in the quenching experiments. The effective quenching order is determined by the accessibility of the quencher to a fluorescent group on the peptide. The order of quenching efficiency provides information about the relative locations of individual residues in the PB. Our data indicate that residues Phe1-Pro6 are located at the surface of PB, residues Tyr7-Trp9 are embedded in PB, and residues Leu10-Ile22 are involved in an amphipathic alpha-helix with its axis parallel to the surface of PB; residues Pro23-Gly25 reside at the surface. The effects of intermolecular disulfide bond formation in the SP-B(1-25) dimer were also investigated. The experiments suggest that the SP-B helix A has to rotate at an angle to form a disulfide bond with the neighboring cysteine, which makes the hydrophobic sides of the amphipathic helices face each other, thus forming a hydrophobic domain. The detailed topographical mapping of SP-B(1-25) and its dimer in PB provides new insights into the conformational organization of the lung pulmonary surfactant proteins in the environment that mimics the native state. The environment-specific conformational flexibility of the hydrophobic domain created by SP-B folding may explain the key functional properties of SP-B including their impact on phospholipid transport between the lipid phases and in modulating the cell inflammatory response during respiratory distress syndrome.