Individual Residues Research Articles

Evaluation of Brazilian pesticide residue reported by monitoring programs considering foods offered to childrens

The consumption of fruits and vegetables, which are the basis of infant nutrition, carries a high risk of pesticide residue contamination. With higher food intake and lower body weight, infants can be considered more sensitive to contamination than adults, which justifies the growing concern about the diet of this population in recent decades. In 2006 in the European Union (EU), processed baby foods were regulated and should not contain individual pesticide residues at levels higher than 0.01 mg/kg, to protect children from ingesting harmful substances. Considering the reference pesticide residues for infant food in the EU and the pesticide residue monitoring programs in Brazil (from the National Health Surveillance Agency and the Ministry of Agriculture, Livestock and Supply), this study compared the results of pesticide residues in some vegetables and fruits with the maximum residue level (MRL) for infant foods established by the EU. 10,174 vegetable samples were analyzed by the monitoring programs, and 80.3% were in accordance with their respective MRLs. Some foods, such as rice, potatoes, and chayote, have average pesticide residue levels equal to or lower than the EU-baby-MRL. However, only 44.0% of the analyzed foods presented residues with results lower than 0.01 mg/kg of food. It can be concluded that most fruits and vegetables contain pesticide residues above the EU recommendations for infant food.

Guided diffusion for molecular generation with interaction prompt.

Molecular generative models have exhibited promising capabilities in designing molecules from scratch with high binding affinities in a predetermined protein pocket, offering potential synergies with traditional structural-based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies and have been recognized as appealing targets for designed molecules. In this work, we develop an interaction prompt guided diffusion model, InterDiff to deal with the challenges. Four kinds of atomic interactions are involved in our model and represented as learnable vector embeddings. These embeddings serve as conditions for individual residue to guide the molecular generative process. Comprehensive in silico experiments evince that our model could generate molecules with desired ligand-protein interactions in a guidable way. Furthermore, we validate InterDiff on two realistic protein-based therapeutic agents. Results show that InterDiff could generate molecules with better or similar binding mode compared to known targeted drugs.

Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity.

We studied the effect of the column temperature on the selectivity of reversed-phase peptide separation in bottom-up proteomics. The number of peptide identifications from 2 h liquid chromatography with tandem mass spectrometry (LC-MS/MS) acquisitions reaches a plateau at 45-55 °C, driven simultaneously by improved separation efficiency, a gradual decrease in peptide retention, and possible on-column degradation of peptides at elevated temperatures. Performing 2D LC-MS/MS acquisitions at 25, 35, 45, and 55 °C resulted in the identification of ∼100,000 and ∼120,000 unique peptides for nonmodified and tandem mass tags (TMT)-labeled samples, respectively. These peptide collections were used to investigate the temperature-driven retention features. The latter is governed by the specific temperature response of individual residues, peptide hydrophobicity and length, and amphipathic helicity. On average, peptide retention decreased by 0.56 and 0.5% acetonitrile for each 10 °C increase for label-free and TMT-labeled peptides, respectively. This generally linear response of retention shifts allowed the extrapolation of predictive models beyond the studied temperature range. Thus, (trap) column cooling from room temperature to 0 °C will allow the retention of an additional 3% of detectable tryptic peptides. Meanwhile, the application of 90 °C would result in the loss of ∼20% of tryptic peptides that were amenable to MS/MS-based identification.

Differences in the Membrane-Binding Properties of Flaviviral Nonstructural 1 (NS1) Protein: Comparative Simulations of Zika and Dengue Virus NS1 Proteins in Explicit Bilayers.

NS1 in flaviviruses is the only nonstructural protein that is secretory and interacts with different cellular components of the host cell membrane. NS1 is localized in the ER as a dimer to facilitate viral replication. Crystal structures of NS1 homologues from zika (ZIKV) and dengue (DENV) viruses have revealed the organization of different domains in NS1 dimers. The β-roll and the connector and intertwined loop regions of wing domains of NS1 have been shown to interact with the membranes. In this study, we have performed multiple molecular dynamics (MD) simulations of ZIKV and DENV NS1 systems in apo and in POPE bilayers with different cholesterol concentrations (0, 20 and 40%). The NS1 protein was placed just above the membrane surface, and for each NS1-membrane system two to three independent simulations with 600 ns production run were performed. At the end of the production runs, ZIKV NS1 inserts deeper inside the membrane compared to the DENV counterpart. Unlike ZIKV NS1, the orientation of DENV NS1 is asymmetric in which one of the chains in the dimer interacts with the membrane while the other is more exposed to the solvent. The β-roll region in ZIKV NS1 penetrates beyond the headgroup region and interacts with the lipid acyl chains while the C-terminal region barely interacts with the headgroup. Specific residues in the intertwined region deeply penetrate inside the membrane. The role of charged and aromatic residues of ZIKV NS1 in strongly interacting with the membrane components is revealed. The presence of cholesterol affects the extent of insertion in the membrane and interaction of individual residues. Overall, membrane-binding properties of ZIKV NS1 significantly differ from its counterpart in DENV. The differences found in the binding and insertion of NS1 can be used to design drugs and novel antibodies that can be flavivirus specific.

Molecular Dynamics Simulations of Native Protein Charging via Proton Transfer during Electrospray Ionization with Grotthuss Diffuse H3O.

Unraveling the mechanism by which native proteins are charged through electrospray ionization (ESI) has been the focus of considerable research because observable charge states can be correlated to biophysical characteristics, such as protein folding and, thus, solution conformation. Difficulties in characterizing electrosprayed droplets have catalyzed the use of molecular dynamics (MD) to provide insights into the mechanisms by which proteins are charged and transferred to the gas phase. However, prior MD studies have utilized metal ions, primarily Na+, as charge carriers, even though proteins are primarily detected as protonated ions in the mass spectra. Here, we propose a modified MD protocol for simulating discrete Grotthuss diffuse H3O+ that is capable of dynamically altering amino-acid protonation states to model electrospray charging and gaseous ion formation of model proteins, ubiquitin, and myoglobin. Application of the protocol to the evaporation of acidic droplets enables a molecular perspective of H3O+ coordination and proton transfer to/from proteins, which is unfeasible with the metal charge carriers used in previous MD studies of ESI. Our protocol recreates experimentally observed charge-state distributions and supports the charge residue model (CRM) as the dominant mechanism of native protein ionization during ESI. Additionally, our results suggest that protonation is highly specific to individual residues and is correlated to the formation of localized hydrated regions on the protein surface as droplets desolvate. Considering the use of discrete H3O+ instead of Na+, the developed protocol is a necessary step toward developing a more comprehensive model of protein ionization during ESI.

Protein design using structure-based residue preferences

Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at individual residues—without accounting for mutation interactions—explain much and sometimes virtually all of the combinatorial mutation effects across 8 datasets (R2 ~ 78-98%). Hence, few observations (~100 times the number of mutated residues) enable accurate prediction of held-out variant effects (Pearson r > 0.80). We hypothesized that the local structural contexts around a residue could be sufficient to predict mutation preferences, and develop an unsupervised approach termed CoVES (Combinatorial Variant Effects from Structure). Our results suggest that CoVES outperforms not just model-free methods but also similarly to complex models for creating functional and diverse protein variants. CoVES offers an effective alternative to complicated models for identifying functional protein mutations.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1.

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Insights into the Adsorption Mechanisms of the Antimicrobial Peptide CIDEM-501 on Membrane Models.

CIDEM-501 is a hybrid antimicrobial peptide rationally designed based on the structure of panusin and panulirin template peptides. The new peptide exhibits significant antibacterial activity against multidrug-resistant pathogens (MIC = 2-4 μM) while conserving no toxicity in human cell lines. We conducted molecular dynamics (MD) simulations using the CHARMM-36 force field to explore the CIDEM-501 adsorption mechanism with different membrane compositions. Several parameters that characterize these interactions were analyzed to elucidate individual residues' structural and thermodynamic contributions. The membrane models were constructed using CHARMM-GUI, mimicking the bacterial and eukaryotic phospholipid compositions. Molecular dynamics simulations were conducted over 500 ns, showing rapid and highly stable peptide adsorption to bacterial lipids components rather than the zwitterionic eucaryotic model membrane. A predominant peptide orientation was observed in all models dominated by an electric dipole. The peptide remained parallel to the membrane surface with the center loop oriented to the lipids. Our findings shed light on the antibacterial activity of CIDEM-501 on bacterial membranes and yield insights valuable for designing potent antimicrobial peptides targeting multi- and extreme drug-resistant bacteria.

CD11B/CD18 SIALYLATION REGULATES NEUTROPHIL TRAFFICKING AND INFLAMMATORY FUNCTION IN THE INTESTINE

Abstract Pathogen-triggered neutrophil (PMN) recruitment is critical for innate immunity, but excessive PMN influx and associated mucosal tissue damage is implicated in the pathogenesis of numerous human inflammatory disorders including inflammatory bowel disease (IBD). Critically in both ulcerative colitis and Crohn’s disease, disease flares requiring medical intervention are associated with migration of large numbers of PMNs across colonic epithelium resulting in mucosal injury/ulceration. Recent studies have shown that specific PMN glycans can be targeted to reduce transepithelial migration (TEpM) the critical final step in PMN intestinal trafficking. However the role of Sialic acid (Sia), the most abundant PMN expressed terminal glycan, in regulating PMN migration and inflammatory function in the gut is not well understood. Using sophisticated models of human and murine PMN intestinal migration, we show that blocking sialidase-mediated removal of alpha 2-3 linked sialic acid from CD11b/CD18 inhibits PMN TEpM in vitro and in vivo.Measurement of cytochrome C reduction revealed that inhibition of sialidases reduced bacterial peptide (fMLF) stimulated superoxide release by human and murine PMN. Flow cytometry analyses revealed that sialidase inhibition decreased fMLF mediated PMN degranulation and fMLF mediated CD11b/CD18 conformational activation. Highlighting the functional importance of individual Sia residues, inhibition of alpha 2-3 sialidase activity with 3’ Sialyllactose (3’SL) was found to inhibit PMN intestinal trafficking in vitro and in vivo and reduce PMN degranulation responses. Immunoblotting studies showed that sialidase inhibition deactivates spleen tyrosine kinase (Syk) and p38 MAP kinase, key intracellular mediators that signal downstream of PMN CD11b/CD18. Finally, inhibition of sialidase activity reduced adhesion of PMN to ICAM-1 (a major physiological substrate of CD11b/CD18) demonstrating that endogenous PMN sialidases regulate binding interactions between migrating PMN and intestinal epithelial receptors. Taken together data show that upon activation PMN sialidases remove alpha 2-3 Sia from CD11b/CD18 which leads to integrin activation and subsequent outside in signaling mediated by Syk to drive PMN inflammatory effector functions including migration, degranulation, and superoxide release. As such targeting CD11b/CD18 sialylation represents a promising new therapeutic strategy for reducing dysregulated PMN influx and associated mucosal tissue damage in IBD and other inflammatory disorders.

Influence of Thermal Pretreatment on Lignin Destabilization in Harvest Residues: An Ensemble Machine Learning Approach

The research on lignocellulose pretreatments is generally performed through experiments that require substantial resources, are often time-consuming and are not always environmentally friendly. Therefore, researchers are developing computational methods which can minimize experimental procedures and save money. In this research, three machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM), as well as their ensembles were evaluated to predict acid-insoluble detergent lignin (AIDL) content in lignocellulose biomass. Three different types of harvest residue (maize stover, soybean straw and sunflower stalk) were first pretreated in a laboratory oven with hot air under two different temperatures (121 and 175 °C) at different duration (30 and 90 min) with the aim of disintegration of the lignocellulosic structure, i.e., delignification. Based on the leave-one-out cross-validation, the XGB resulted in the highest accuracy for all individual harvest residues, achieving the coefficient of determination (R2) in the range of 0.756–0.980. The relative variable importances for all individual harvest residues strongly suggested the dominant impact of pretreatment temperature in comparison to its duration. These findings proved the effectiveness of machine learning prediction in the optimization of lignocellulose pretreatment, leading to a more efficient lignin destabilization approach.

Significance of the Disulfide Bridge in the Structure and Stability of Metalloprotein Azurin.

Metalloproteins make up a class of proteins that incorporate metal ions into their structures, enabling them to perform essential functions in biological systems, such as catalysis and electron transport. Azurin is one such metalloprotein with copper cofactor, having a β-barrel structure with exceptional thermal stability. The copper metal ion is coordinated at one end of the β-barrel structure, and there is a disulfide bond at the opposite end. In this study, we explore the effect of this disulfide bond in the high thermal stability of azurin by analyzing both the native S-S bonded and S-S nonbonded (S-S open) forms using temperature replica exchange molecular dynamics (REMD). Similar to experimental observations, we find a 35 K decrease in denaturation temperature for S-S open azurin compared to that of the native holo form (420 K). As observed in the case of native holo azurin, the unfolding process of the S-S open form also started with disruptions of the α-helix. The free energy surfaces of the unfolding process revealed that the denaturation event of the S-S open form progresses through different sets of conformational ensembles. Subsequently, we compared the stabilities of individual β-sheet strands of both the S-S bonded and the S-S nonbonded forms of azurin. Further, we examined the contacts between individual residues for the central structures from the free energy surfaces of the S-S nonbonded form. The microscopic origin of the lowering in the denaturation temperature is further supplemented by thermodynamic analysis.

Exploring Residue-Level Interactions between the Biofilm-Driving R2ab Protein and Polystyrene Nanoparticles.

In biological systems, proteins can bind to nanoparticles to form a "corona" of adsorbed molecules. The nanoparticle corona is of significant interest because it impacts an organism's response to a nanomaterial. Understanding the corona requires knowledge of protein structure, orientation, and dynamics at the surface. A residue-level mapping of protein behavior on nanoparticle surfaces is needed, but this mapping is difficult to obtain with traditional approaches. Here, we have investigated the interaction between R2ab and polystyrene nanoparticles (PSNPs) at the level of individual residues. R2ab is a bacterial surface protein from Staphylococcus epidermidis and is known to interact strongly with polystyrene, leading to biofilm formation. We have used mass spectrometry after lysine methylation and hydrogen-deuterium exchange (HDX) NMR spectroscopy to understand how the R2ab protein interacts with PSNPs of different sizes. Lysine methylation experiments reveal subtle but statistically significant changes in methylation patterns in the presence of PSNPs, indicating altered protein surface accessibility. HDX rates become slower overall in the presence of PSNPs. However, some regions of the R2ab protein exhibit faster than average exchange rates in the presence of PSNPs, while others are slower than the average behavior, suggesting conformational changes upon binding. HDX rates and methylation ratios support a recently proposed "adsorbotope" model for PSNPs, wherein adsorbed proteins consist of unfolded anchor points interspersed with partially structured regions. Our data also highlight the challenges of characterizing complex protein-nanoparticle interactions using these techniques, such as fast exchange rates. While providing insights into how R2ab adsorbs onto PSNP surfaces, this research emphasizes the need for advanced methods to comprehend residue-level interactions in the nanoparticle corona.

Substitution of acidic residues near the catalytic Glu131 leads to human HYAL1 activity at neutral pH via charge-charge interactions.

Human hyaluronidase 1 (HYAL1) and PH20 play vital roles in degrading hyaluronic acids through the substrate-assisted double displacement mechanism. While HYAL1, a lysosomal enzyme, functions optimally under acidic conditions, PH20, a sperm surface hyaluronidase, displays a broader pH range, from acidic to neutral. Our objective was to extend HYAL1's pH range towards neutral pH by introducing repulsive charge-charge interactions involving the catalytic Glu131, increasing its pKa as the proton donor. Substituting individual acidic residues in the β3-loop (S77D), β3'-β3″ hairpin (T86D and P87E), and at Ala132 (A132D and A132E) enabled HYAL1 to demonstrate enzyme activity at pH 7, with the mutants S77D, P87E, and A132E showing the highest activity in the substrate gel assay. However, double and triple substitutions, including S77D/T86D/A132E as found in the PH20 configuration, did not result in enhanced activity compared to single substitutions. Conversely, PH20 mutants with non-acidic substitutions, such as D94S in the β3-loop and D103T in the β3'-β3″ hairpin, significantly reduced activity within the pH range of 4 to 7. However, the PH20 mutant E149A, reciprocally substituted compared to A132E in HYAL1, exhibited activity similar to PH20 wild-type (WT) at pH 7. In a turbidimetric assay, HYAL1 mutants with single acidic substitutions exhibited activity similar to that of PH20 WT at pH 7. These results suggest that substituting acidic residues near Glu131 results in HYAL1 activity at neutral pH through electrostatic repulsion. This study highlights the significance of charge-charge interactions in both HYAL1 and PH20 in regulating the pH-dependent activity of hyaluronidases.

From covalent transition states in chemistry to noncovalent in biology: from β- to Φ-value analysis of protein folding.

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the β-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of β- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

PH dependent physicochemical signatures of amino acids in aqueous buffers: Thermodynamic analysis

The physicochemical signatures such as apparent molar volume (V2,Φ), isoentropic compressibilities (KS,2,φ), and dilution enthalpies of a series of amino acids (AAs) glycine (Gly), L-alanine (Ala), DL-α-amino-n-butyric acid (ABA), L-valine (Val) and L-leucine (Leu) have been measured at various pH values (pH = 2, 5 7 and 9) at a buffer molality of 0.1 mol kg−1 of water at 298.15 K. The data obtained from high sensitivity densimeter and sound analyser along with ultrasensitive isothermal titration calorimetry has been analyzed to evaluate the values of standard partial molar quantities such as for volume at infinite dilution (V2,m0), compressibility KS, 2,m0 and dilution enthalpy (ΔdilH0) of the AAs together with standard partial molar quantities of transfer of these solutes from water to aqueous buffer systems at these pH values. The linear correlation of V2,m0 for this series of AAs against number of carbon atoms in their alkyl chain has been used to evaluate the contribution of zwitterionic end groups (NH3+, COO–), the CH2 group, and other parts of alkyl chains of the AAs to the overall value of V2,m0. The results of standard partial molar transfer properties on volumes, compressibilities and enthalpies of dilution from water to aqueous buffer systems at various pH values have been connected and explained in terms of ion-polar, ion-hydrophilic, hydrophobic-hydrophobic and ion-hydrophobic interactions of the participating groups. These thermodynamic signatures when compared at different pH values suggest that at electrostriction of water dominates at pH 2. Such physicochemical properties of constituents of proteins such as amino acids have not been reported earlier. Thus, the results demonstrate that the interaction of individual amino acid residues with the buffer components does not lead to significant change in their physicochemical properties thereby permitting their use in obtaining mechanistic insights into systems of biological interest consisting of these constituents.

A conserved electrostatic membrane-binding surface in synaptotagmin-like proteins revealed using molecular phylogenetic analysis and homology modeling.

Protein structure prediction has emerged as a core technology for understanding biomolecules and their interactions. Here, we combine homology-based structure prediction with molecular phylogenetic analysis to study the evolution of electrostatic membrane binding among the vertebrate synaptotagmin-like protein (Slp) family. Slp family proteins play key roles in the membrane trafficking of large dense-core secretory vesicles. Our previous experimental and computational study found that the C2A domain of Slp-4 (also called granuphilin) binds with high affinity to anionic phospholipids in the cytoplasmic leaflet of the plasma membrane through a large positively charged protein surface centered on a cluster of phosphoinositide-binding lysine residues. Because this surface contributes greatly to Slp-4 C2A domain membrane binding, we hypothesized that the net charge on the surface might be evolutionarily conserved. To test this hypothesis, the known C2A sequences of Slp-4 among vertebrates were organized by class (from mammalia to pisces) using molecular phylogenetic analysis. Consensus sequences for each class were then identified and used to generate homology structures, from which Poisson-Boltzmann electrostatic potentials were calculated. For comparison, homology structures and electrostatic potentials were also calculated for the five human Slp protein family members. The results demonstrate that the charge on the membrane-binding surface is highly conserved throughout the evolution of Slp-4, and more highly conserved than many individual residues among the human Slp family paralogs. Such molecular phylogenetic-driven computational analysis can help to describe the evolution of electrostatic interactions between proteins and membranes which are crucial for their function.

Hidden risk of terrestrial food chain contamination from organochlorine insecticides in a vegetable cultivation area of Northwest Bangladesh

Organochlorine insecticide (OCI) exposures in terrestrial food chains from historical or current applications were studied in a vegetable production area in northwest Bangladesh. A total of 57 subsoil, 57 topsoil, and 57 vegetable samples, as well as 30 cow's milk samples, were collected from 57 farms. Multiple OCI residues were detected using GC–MS/MS with modified QuEChERS in 20 % of subsoils, 21 % of topsoils, 23 % of vegetables, and 7 % of cow's milk samples. Diversified OCI residues were detected in subsoils (17 residues with a concentration of 179.15 ± 148.61 μg kg−1) rather than in topsoils (3 DDT residues with a concentration of 25.76 ± 20.19 μg kg−1). Isomeric ratios indicate intensive historical applications of OCIs. According to Dutch and Chinese standards, the lower concentrations of individual OCI residues in the soil indicate negligible to slight soil pollution, assuming local farmers follow local pesticide use regulations. However, a maximum of 78.24 μg kg−1 ΣAldrines and 35.57 μg kg−1 ΣHCHs were detected (1–4 residues) in 60 % of brinjal, 28 % of cucumber, 29 % of sponge gourd, and 20 % of lady's finger samples, which could be a result of either historical or current OCI applications, or both. A strong positive correlation between aldrines in subsoils and cucurbit vegetables indicates greater bioaccumulation. Cow milk samples contained up to 6.96 μg kg−1 ΣDDTs, which resulted either from rationing contaminated vegetables or grazing on contaminated land. Individual OCI in both vegetables and cow's milk was below the respective maximum residue limits of US and FAO/WHO CODEX and poses little or no risk to human health. However, combined exposure to multiple pesticides could increase human health risks. A cumulative health risk assessment of multiple pesticide residues is suggested to assess the suitability of those soils for cultivation and grazing, as well as the safety of vegetables and cow's milk for human consumption.

SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances.

Protein functions are tightly related to the fine details of their 3D structures. To understand protein structures, computational prediction approaches are highly needed. Recently, protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning techniques. Most of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential function. Here, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue's pose according to all other residues' features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at https://github.com/gongtiansu/SASA-Net/.

Energy coupling and stoichiometry of Zn2+/H+ antiport by the prokaryotic cation diffusion facilitator YiiP

YiiP from Shewanella oneidensis is a prokaryotic Zn2+/H+ antiporter that serves as a model for the Cation Diffusion Facilitator (CDF) superfamily, members of which are generally responsible for homeostasis of transition metal ions. Previous studies of YiiP as well as related CDF transporters have established a homodimeric architecture and the presence of three distinct Zn2+ binding sites named A, B, and C. In this study, we use cryo-EM, microscale thermophoresis and molecular dynamics simulations to address the structural and functional roles of individual sites as well as the interplay between Zn2+ binding and protonation. Structural studies indicate that site C in the cytoplasmic domain is primarily responsible for stabilizing the dimer and that site B at the cytoplasmic membrane surface controls the structural transition from an inward facing conformation to an occluded conformation. Binding data show that intramembrane site A, which is directly responsible for transport, has a dramatic pH dependence consistent with coupling to the proton motive force. A comprehensive thermodynamic model encompassing Zn2+ binding and protonation states of individual residues indicates a transport stoichiometry of 1 Zn2+ to 2–3 H+ depending on the external pH. This stoichiometry would be favorable in a physiological context, allowing the cell to use the proton gradient as well as the membrane potential to drive the export of Zn2+.

Energy coupling and stoichiometry of Zn2+/H+ antiport by the prokaryotic cation diffusion facilitator YiiP.

YiiP from Shewanella oneidensis is a prokaryotic Zn2+/H+ antiporter that serves as a model for the Cation Diffusion Facilitator (CDF) superfamily, members of which are generally responsible for homeostasis of transition metal ions. Previous studies of YiiP as well as related CDF transporters have established a homodimeric architecture and the presence of three distinct Zn2+ binding sites named A, B, and C. In this study, we use cryo-EM, microscale thermophoresis and molecular dynamics simulations to address the structural and functional roles of individual sites as well as the interplay between Zn2+ binding and protonation. Structural studies indicate that site C in the cytoplasmic domain is primarily responsible for stabilizing the dimer and that site B at the cytoplasmic membrane surface controls the structural transition from an inward facing conformation to an occluded conformation. Binding data show that intramembrane site A, which is directly responsible for transport, has a dramatic pH dependence consistent with coupling to the proton motive force. A comprehensive thermodynamic model encompassing Zn2+ binding and protonation states of individual residues indicates a transport stoichiometry of 1 Zn2+ to 2-3 H+ depending on the external pH. This stoichiometry would be favorable in a physiological context, allowing the cell to use the proton gradient as well as the membrane potential to drive the export of Zn2+.