On the folding of a structurally complex protein to its metastable active state.

For successful protease inhibition, the reactive center loop (RCL) of the two-domain serine protease inhibitor, α1-antitrypsin (α1-AT), needs to remain exposed in a metastable active conformation. The α1-AT RCL is sequestered in a ß-sheet in the stable latent conformation. Thus, to be functional, α1-AT must always fold to a metastable conformation while avoiding folding to a stable conformation. We explore the structural basis of this choice using folding simulations of coarse-grained structure-based models of the two α1-AT conformations. Our simulations capture the key features of folding experiments performed on both conformations. The simulations also show that the free energy barrier to fold to the latent conformation is much larger than the barrier to fold to the active conformation. An entropically stabilized on-pathway intermediate lowers the barrier for folding to the active conformation. In this intermediate, the RCL is in an exposed configuration, and only one of the two α1-AT domains is folded. In contrast, early conversion of the RCL into a ß-strand increases the coupling between the two α1-AT domains in the transition state and creates a larger barrier for folding to the latent conformation. Thus, unlike what happens in several proteins, where separate regions promote folding and function, the structure of the RCL, formed early during folding, determines both the conformational and the functional fate of α1-AT. Further, the short 12-residue RCL modulates the free energy barrier and the folding cooperativity of the large 370-residue α1-AT. Finally, we suggest experiments to test the predicted folding mechanism for the latent state.

In the multi-domain protein adenylate kinase, domain insertion facilitates cooperative folding while accommodating function at domain interfaces.

Having multiple domains in proteins can lead to partial folding and increased aggregation. Folding cooperativity, the all or nothing folding of a protein, can reduce this aggregation propensity. In agreement with bulk experiments, a coarse-grained structure-based model of the three-domain protein, E. coli Adenylate kinase (AKE), folds cooperatively. Domain interfaces have previously been implicated in the cooperative folding of multi-domain proteins. To understand their role in AKE folding, we computationally create mutants with deleted inter-domain interfaces and simulate their folding. We find that inter-domain interfaces play a minor role in the folding cooperativity of AKE. On further analysis, we find that unlike other multi-domain proteins whose folding has been studied, the domains of AKE are not singly-linked. Two of its domains have two linkers to the third one, i.e., they are inserted into the third one. We use circular permutation to modify AKE chain-connectivity and convert inserted-domains into singly-linked domains. We find that domain insertion in AKE achieves the following: (1) It facilitates folding cooperativity even when domains have different stabilities. Insertion constrains the N- and C-termini of inserted domains and stabilizes their folded states. Therefore, domains that perform conformational transitions can be smaller with fewer stabilizing interactions. (2) Inter-domain interactions are not needed to promote folding cooperativity and can be tuned for function. In AKE, these interactions help promote conformational dynamics limited catalysis. Finally, using structural bioinformatics, we suggest that domain insertion may also facilitate the cooperative folding of other multi-domain proteins.

Modelling of lindane transport in groundwater of metropolitan city Vadodara, Gujarat, India.

Migration pattern of organochloro pesticide lindane has been studied in groundwater of metropolitan city Vadodara. Groundwater flow was simulated using the groundwater flow model constructed up to a depth of 60 m considering a three-layer structure with grid size of 40 × 40 × 40 m(3). The general groundwater flow direction is from northeast to south and southwest. The river Vishwamitri and river Jambua form natural hydrologic boundary. The constant head in the north and south end of the study area is taken as another boundary condition in the model. The hydraulic head distribution in the multilayer aquifer has been computed from the visual MODFLOW groundwater flow model. TDS has been computed though MT3D mass transport model starting with a background concentration of 500 mg/l and using a porosity value of 0.3. Simulated TDS values from the model matches well with the observed data. Model MT3D was run for lindane pesticide with a background concentration of 0.5 µg/l. The predictions of the mass transport model for next 50 years indicate that advancement of containment of plume size in the aquifer system both spatially and depth wise as a result of increasing level of pesticide in river Vishwamitri. The restoration of the aquifer system may take a very long time as seen from slow improvement in the groundwater quality from the predicted scenarios, thereby, indicating alarming situation of groundwater quality deterioration in different layers. It is recommended that all the industries operating in the region should install efficient effluent treatment plants to abate the pollution problem.

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques.

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization.

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

Assessment of geochemical processes occurring in groundwaters in the coastal alluvial aquifer.

Groundwater samples are collected from 30 observation wells in the study area to analyze the hydrochemical quality for determining the seawater encroachment in the part of Central Godavari Delta, Bay of Bengal, India. In order to establish the baseline hydrochemical conditions and processes determining the groundwater quality, an integrated investigation coupled with multivariate statistical analysis and hydrochemical methods are used to identify and interpret the groundwater chemistry of the aquifer system. The major land use is irrigated agriculture and aquaculture in the study area. The ground waters affected by the seawater intrusion featured high levels of sodium (Na(+)), chloride (Ca(+)), and TDS, which are the simplest common indicators for seawater influence. The elevated levels of NO3-N at some monitoring wells indicate nitrate pollution of groundwater due to anthropogenic origin such as septic effluents or chemical fertilizers. Besides the major chemical compositions, it was also demonstrated that ionic ratios would be useful to delineate seawater intrusion and they include Na(+)/Ca(2+), Mg(2+)/Ca(2+), SO4 (2-)/Ca(2+), Na(+)/(Na(+) + Cl(-)), and Ca(-)/sum of anions. This paper demonstrates the variations in hydrochemical quality of groundwater and its evolution processes in two different seasons in the coastal aquifer alluvial settings.

Vaccination saves lives: a real-time study of patients with chronic diseases and severe COVID-19 infection.

OBJECTIVES: This study aims to describe the demographic and clinical profile and ascertain the determinants of outcome among hospitalized coronavirus disease 2019 (COVID-19) adult patients enrolled in the National Clinical Registry for COVID-19 (NCRC). METHODS: NCRC is an on-going data collection platform operational in 42 hospitals across India. Data of hospitalized COVID-19 patients enrolled in NCRC between 1st September 2020 to 26th October 2021 were examined. RESULTS: Analysis of 29 509 hospitalized, adult COVID-19 patients [mean (SD) age: 51.1 (16.2) year; male: 18 752 (63.6%)] showed that 15 678 (53.1%) had at least one comorbidity. Among 25 715 (87.1%) symptomatic patients, fever was the commonest symptom (72.3%) followed by shortness of breath (48.9%) and dry cough (45.5%). In-hospital mortality was 14.5% (n = 3957). Adjusted odds of dying were significantly higher in age group ≥60 years, males, with diabetes, chronic kidney diseases, chronic liver disease, malignancy and tuberculosis, presenting with dyspnoea and neurological symptoms. WHO ordinal scale 4 or above at admission carried the highest odds of dying [5.6 (95% CI: 4.6-7.0)]. Patients receiving one [OR: 0.5 (95% CI: 0.4-0.7)] or two doses of anti-SARS CoV-2 vaccine [OR: 0.4 (95% CI: 0.3-0.7)] were protected from in-hospital mortality. CONCLUSIONS: WHO ordinal scale at admission is the most important independent predictor for in-hospital death in COVID-19 patients. Anti-SARS-CoV2 vaccination provides significant protection against mortality.

Integrated hydrochemical and geophysical studies for assessment of groundwater pollution in basaltic settings in Central India.

The Pithampur Industrial sectors I, II, and III, located approximately, 45 km from Indore in Central India have emerged as one of the largest industrial clusters in the region. Various types of industries ranging from automobiles to chemicals and pharmaceuticals have been set up in the region since 1990. Most of the industries have effluent treatment plants (ETP) for treating wastewater before its disposal on land and/or in water body. The present study is an attempt to assess the groundwater quality in the watersheds surrounding these industrial sectors to develop the baseline groundwater quality in order to enable the policy makers to facilitate decisions on the development of industries in this region. The industries are located in two sub-watersheds, namely, Gambhir river sub-watershed and Chambal river sub-watershed. Geologically, the study area is located in the Deccan traps of Cretaceous to Paleocene age. The different basaltic flow units underlie clayey soils varying in thickness from 2-3 m. The aquifer is mostly of unconfined nature. Samples have been collected from a network of observation wells set up in the watersheds. The water quality analysis of the groundwater samples has been carried out six times during three hydrological cycles of 2004, 2005, and 2006. The results indicate that a few observation wells in the vicinity of the industrial clusters have very high TDS concentration and exceed the Bureau of Indian Standards (BIS) guideline for TDS concentration. The contamination of groundwater has been more severe in the Gambhir watershed as compared to the Chambal watershed. The presence of the impermeable clay layers has resulted in a slow migration of contaminants from the sources. The findings reveal that there is no significant groundwater contamination in the Pithampur industrial sectors except in the vicinity of the industrial clusters, which indicates that there is good environmental space available for the expansion of industrial units in the Pithampur industrial hub.

Effectiveness of two flavored topical anesthetic agents in reducing injection pain in children: a comparative study.

UNLABELLED: Topical anesthesia is widely advocated in pediatric dentistry practice to reduce pain and anxiety produced by administration of local anesthesia. There are different combinations of topical anesthetic agents that are marketed worldwide. However, sparse literature reports exist regarding clinical efficacy of these agents. AIM: To compare the clinical effectiveness of two strawberry flavored topical anesthetics viz. Precaine (8% Lidocaine + 0.8% Dibucaine) and Precaine B (20% Benzocaine) in children before intra oral local anesthetic injections and for extraction of mobile primary teeth. STUDY DESIGN: This triple blind clinical study included sixty children divided equally under three techniques--palatal injections, inferior alveolar nerve block and extraction of mobile primary teeth. Both the products were used alternately using split mouth design in two visits and the child's pain response was assessed using VAS and SEM pain scale. The scores obtained were subjected to statistical analysis. RESULTS: Precaine has shown lower mean scores in all the techniques under both the pain scales, but were statistically insignificant. Gender wise comparison has also shown lower mean scores for Precaine for both males and females, however these were statistically insignificant. On visit wise comparison, Precaine B reported significant lower scores (p < 0.05) in visit 2 compared to visit 1 for inferior alveolar nerve block and extraction of mobile primary teeth under SEM pain scale. CONCLUSION: Precaine (8% Lidocaine + 0.8% Dibucaine) can be used as effectively as Precaine B (20% Benzocaine).

Particle size dependence on magnetic and electrical properties of (Ni0.8Fe0.2)10C90 granular composites.

The granular structure and electrical transport behavior of ball milled magnetic permalloy particles and graphite forming (Ni0.8Fe0.2)10C90 granular composites have been reported. Retaining the composite composition to be 90:10 and varying the particle size of permalloy, the electronic transport properties have been carried out down to 4 K under the external applied magnetic field of 50 kOe. All the samples show semiconducting like behavior and positive magnetoresistance (MR) in the temperature range 4-300 K. A strong anisotropic magnetoresistance in these samples has also been observed. The highest 31% longitudinal and 6.8% transverse magnetoresistance values have been observed in 40 hrs ball milled (Ni0.8Fe0.2)10C90 composite. From these studies, we suggest that the magnetic component present in the sample may not be playing a major role in obtaining large positive MR values, which is in deviation with the earlier reports.

Metal fractionation study on bed sediments of Hussainsagar Lake, Hyderabad, India.

Hussainsagar Lake in the heart of Hyderabad City (India) receives toxic substances through five streams draining from a catchment area of 245 km(2). Of particular interest are heavy metals received from urban runoff as well as municipal sewage and industrial effluents. Heavy metals entering the lake get adsorbed onto the suspended sediments, which eventually settle down in the bottom of the lake. In this study, fractionation of metal ions has been studied on the bed sediments of Hussainsagar Lake in order to determine the ecotoxic potential of metal ions. Comparison of sediments with average shale values indicated anthropogenic enrichment with copper, nickel, lead, cadmium, and zinc. The risk assessment code as applied to the present study reveals that 10-17% of manganese, 10-18% of nickel, 14-24% of chromium, 10-19% of lead, 21-30% of cadmium, and 18-28% of zinc exist in exchangeable fraction and, therefore, comes under medium risk category and may enter into food chain. The association of these metals with the exchangeable fraction may cause deleterious effects to aquatic life. The present database will help in formulating guidelines for carrying out dredging operations under restoration programs in the Hussainsagar Lake.

An In-vitro Comparison of the Shear Bond Strength of Three Different Composite Materials with Three Different Etchants for the Bonding of Orthodontic Brackets.

Introduction Esthetics, being the major concern of today's treatments, has led to numerous innovations, including composites, for different treatment options. Esthetic orthodontics requires the use of composites for bonding orthodontic brackets to the teeth. Aims To identify which combination of composites has the highest shear bond strength at the tooth-bracket interface. Materials and methods Three different composite kits were selected for each group (n=42) and were further divided into three subgroups (n=14), where the bonding agents and/or primer were interchanged to find the best combination. Results Sub-group B2 (Orthofix + Eazetch + Universal Bond) showed the highest shear bond strength (10.74 ± 3.45 MPa), which was highly significant at p =/<0.0001. Conclusion The highest shear bond strength was found with the combination of 37% phosphoric acid (Eazetch), GC Universal Bond (GC Corporation, Tokyo, Japan), and Orthofix composite material (Anabond Stedman, Chennai, India). As this study is an in-vitro study, we need longitudinal in-vivo studies to establish the best combination for the bonding of orthodontic brackets.

Evaluation of the Clinical and Radiological Outcomes of Pulpotomized Primary Molars Treated with Three Different Materials: Mineral Trioxide Aggregate, Biodentine, and Pulpotec. An In-vivo Study.

Introduction The main treatment objective of pediatric dentistry lies in maintaining the integrity of the arches. The loss of primary teeth at an early age causes malocclusion. Primary teeth are considered the best space maintainers in the arch. Hence, every effort should be directed to preserve these teeth as far as possible. One of the most important goals of pediatric dentistry is the restoration of carious primary teeth and the maintenance of optimal oral health. Aim The aim of this study is to compare and evaluate different pulpotomy materials like mineral trioxide aggregate (MTA), Biodentine, and Pulpotec in primary molars. Materials and methods In the present study, 84 primary molars were selected for the pulpotomy procedure and randomly assigned to one of the three groups of MTA, Biodentine, and Pulpotec, allocating 28 primary molars to each group. The pulpotomy procedure was performed on all selected teeth and followed by permanent restoration with stainless steel crowns. All the molars were evaluated, both clinically and radiographically, at an interval of one, three, and six months. Results At the end of the first month, there were no adverse clinical and radiographical findings observed in all three groups. At the end of the third month, Group I showed 96% clinical and radiographical success, Group II showed 100% clinical and 96% radiographical success, and Group III showed 100% clinical and radiographical success. At the end of the sixth month, Group I showed 96% clinical and radiographical success, Group II showed 100% clinical and 90% radiographical success and Group III showed 100% clinical and radiographical success. The observations were subjected to statistical analysis using Fisher's exact test and the Chi-square test. Conclusion MTA, Biodentine, and Pulpotec can be used as materials of choice for pulpotomy. Furthermore, Pulpotec appeared to be clinically and radiographically more successful than MTA and Biodentine.

Effect of nanometric grain size on electronic-transport, magneto-transport and magnetic properties of La(0.7)Ba(0.3)MnO(3) nanoparticles.

We have investigated the effect of nanometric grain size on electronic-transport, magneto-transport and magnetic properties of single-phase La(0.7)Ba(0.3)MnO(3) (LBMO) nanoparticles having an average grain size in the nanometric regime (21-35 nm). We have observed that both the metal-insulator transition temperature (T(P)) and para-ferromagnetic transition temperature (T(C)) shift to lower temperature with a decrease in average grain size. For the entire series of samples, a distinct minima in resistivity at a temperature (T(min)) followed by an upturn at a very low temperature (≤47 K) is observed. We have attributed the steeper low temperature (∼47 K) resistivity upturn in the smaller grain size sample than that in the larger grain size sample below T(min) to the increased value of charging energy (E(C)). E(C) has been estimated to be 1.3, 0.56 and 0.04 K for an average grain size of 21, 25 and 30 nm, respectively. Magneto-transport measurements show that the magnitude of low field MR (LFMR) varies with average grain size. In order to investigate the MR behavior of LBMO nanoparticles, we have analyzed our data in the light of a phenomenological model, based on spin-polarized transport of conduction electrons at the grain boundaries. Magneto-transport measurements show that the magnitude of low field MR (LFMR), as well as of high field MR (HFMR), remains constant up to sufficiently high temperature (∼50 K) and then drops sharply with temperature. We found that this strange temperature dependence of MR is decided predominantly by the nature of the temperature response of the surface magnetization (M(S)) of nanosized magnetic particles.

Genotyping of Hepatitis C virus (HCV) in infected patients from South India.

Hepatitis C virus shows substantial nucleotide sequence diversity distributed throughout the viral genome. In the present study genotyping for Hepatitis C virus (HCV) infected patients was based on RFLP analysis of 5' UTR and using type specific primers of NS5B regions. It was observed that 60% of the patients (30 patients with chronic hepatitis) were infected with variants of genotype 1 and 40% of the patients (4 chronic hepatitis patients, 12 patients with chronic renal failure and 4 cirrhosis) were infected with variants of type 3 of HCV. None of the cirrhotic patients and patients with chronic renal failure, in the present study, were infected with type 1 of HCV. While PCR-RFLP, typing was rapid in conjunction with the primers used for RT-PCR, NS5 typing was helpful in determining the subtype. There was good correlation between the two typing methods and this method can be used as a cost-effective method for studying large number of samples. The study shows that predominant genotypes of HCV in South India include type 1 and 3. Type 3 seems to be transmitted nosocomially as suggested by the results in patients with chronic renal failure, as these patients are exposed to multiple medical interventions.

Synthesis and Structure of Environmentally Relevant Perfluorinated Sulfonamides.

Alkylated perfluorooctanesulfonamides are compounds of environmental concern. To make these compounds available for environmental and toxicological studies, a series of N-alkylated perfluorooctanesulfonamides and structurally related compounds were synthesized by reaction of the corresponding perfluoroalkanesulfonyl fluoride with a suitable primary or secondary amine. Perfluoroalkanesulfonamidoethanols were obtained from the N-alkyl perfluoroalkanesulfonamides either by direct alkylation with bromoethanol or alkylation with acetic acid 2-bromo-ethyl ester followed by hydrolysis of the acetate. N-Alkyl perfluorooctanesulfonamidoacetates were synthesized in an analogous way by alkylation of N-alkyl perfluoroalkanesulfonamides with a bromo acetic acid ester, followed by basic ester hydrolysis. Alternatively, N-alkyl perfluoroalkanesulfonamides can be alkylated with an appropriate alcohol using the Mitsunobu reaction. Perfluorooctanesulfonamide was synthesized from the perfluorooctanesulfonyl fluoride via the azide by reduction with Zn/HCl. All perfluorooctanesulfonamides contained linear as well as branched C(8)F(17) isomers, typically in a 20:1 to 30:1 ratio. The crystal structures of N-ethyl and N,N-diethyl perfluorooctanesulfonamide show that the S-N bond has considerable double bond character. This double bond character results in a significant rotational barrier around the S-N bond (ΔG(≠) = 62-71 kJ mol(-1)) and a preferred solid state and solution conformation in which the N-alkyl groups are oriented opposite to the perfluorooctyl group to minimize steric crowding around the S-N bond.

MDR1 gene-specific monoclonal antibody C494 cross-reacts with pyruvate carboxylase.

Overexpression of P-glycoprotein, the plasma membrane protein product of the MDR1 gene, is a major determinant in the development of resistance to a large number of cancer chemotherapeutic agents. A battery of antibodies, including the MDR1 gene-specific monoclonal antibody (mAb) C494, is used to evaluate human tissues in clinical multidrug resistance surveillance and modulation trials. In rat liver fractions, we report that mAb C494 strongly cross-reacted with a nonmembranous M(r) approximately 130,000 protein, comigrating with core-glycosylated human MDR1 on 7% sodium dodecyl sulfate-polyacrylamide gel electrophoresis. By immunoblotting and microsequence analysis, this protein was identified as pyruvate carboxylase (PC), an abundant mitochondrial enzyme. A search of the National Center for Biotechnology Information data base, using the epitope-specific sequence of mAb C494, revealed that PC (mouse) contains four of the five most reactive amino acids (TLEG), located near the COOH-terminal end of PC at positions 1167-1170. mAb C494 specifically reacted with PC purified from bovine liver; immunoreactivity was completely abolished by preincubating mAb C494 in the presence of excess synthetic C494 epitope-specific peptide. Furthermore, in cryosections of human skeletal muscle, a tissue known not to express P-glycoprotein, peptide-displaceable immunohistochemical staining with mAb C494 showed a distinct mitochondrial pattern specific to type 1 fibers. Variable immunostaining results were obtained with formaldehyde-fixed, paraffin-embedded muscle and isolated liver mitochondrial preparations. In summary, mAb C494 cross-reacted strongly with rat, bovine, and human PC. Caution is warranted in interpretation of immunoblots and immunohistochemical sections with this putative MDR1 gene-specific mAb.

Using the folding landscapes of proteins to understand protein function.

Proteins fold on a biologically-relevant timescale because of a funnel-shaped energy landscape. This landscape is sculpted through evolution by selecting amino-acid sequences that stabilize native interactions while suppressing stable non-native interactions that occur during folding. However, there is strong evolutionary selection for functional residues and these cannot be chosen to optimize folding. Their presence impacts the folding energy landscape in a variety of ways. Here, we survey the effects of functional residues on folding by providing several examples. We then review how such effects can be detected computationally and be used as assays for protein function. Overall, an understanding of how functional residues modulate folding should provide insights into the design of natural proteins and their homeostasis.

Capturing the Membrane-Triggered Conformational Transition of an α-Helical Pore-Forming Toxin.

Escherichia coli cytolysin A (ClyA) is an α-helical pore-forming toxin (PFT) which lyses target cells by forming membrane permeabilizing pores. The rate-determining step of this process is the conversion of the soluble ClyA monomer into a membrane inserted protomer. We elucidate the mechanism of this conformational transition using molecular dynamics simulations of coarse-grained models of ClyA and a membrane. We find that a membrane is necessary for the conformational conversion because membrane-protein interactions counteract the loss of the many intraprotein hydrophobic interactions that stabilize the membrane-inserting segments in the ClyA monomer. Of the two membrane-inserting segments, the flexible and highly hydrophobic ß-tongue inserts first while the insertion of helix αA1 is membrane assisted. We conclude that the ß-tongue is designed to behave as a quick-response membrane sensor, while helix αA1 improves target selectivity for cholesterol-containing cell membranes by acting as a fidelity check.