Protein-protein interactions

Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease

Huntington disease (HD) is a degenerative brain disease caused by the expansion of CAG (cytosine-adenine-guanine) repeats, which is inherited as a dominant trait and progressively worsens over time possessing threat. Although HD is monogenetic, the specific pathophysiology and biomarkers are yet unknown specifically, also, complex to diagnose at an early stage, and identification is restricted in accuracy and precision. This study combined bioinformatics analysis and network-based system biology approaches to discover the biomarker, pathways, and drug targets related to molecular mechanism of HD etiology. The gene expression profile data sets GSE64810 and GSE95343 were analyzed to predict the molecular markers in HD where 162 mutual differentially expressed genes (DEGs) were detected. Ten hub genes among them (DUSP1, NKX2-5, GLI1, KLF4, SCNN1B, NPHS1, SGK2, PITX2, S100A4, and MSX1) were identified from protein-protein interaction (PPI) network which were mostly expressed as down-regulated. Following that, transcription factors (TFs)-DEGs interactions (FOXC1, GATA2, etc), TF-microRNA (miRNA) interactions (hsa-miR-340, hsa-miR-34a, etc), protein-drug interactions, and disorders associated with DEGs were predicted. Furthermore, we used gene set enrichment analysis (GSEA) to emphasize relevant gene ontology terms (eg, TF activity, sequence-specific DNA binding) linked to DEGs in HD. Disease interactions revealed the diseases that are linked to HD, and the prospective small drug molecules like cytarabine and arsenite was predicted against HD. This study reveals molecular biomarkers at the RNA and protein levels that may be beneficial to improve the understanding of molecular mechanisms, early diagnosis, as well as prospective pharmacologic targets for designing beneficial HD treatment.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Progress in micellar casein concentrate: Production and applications

Micellar casein concentrate (MCC) is a novel ingredient with high casein content. Over the past decade, MCC has emerged as one of the most promising dairy ingredients having applications in beverages, yogurt, cheese, and process cheese products. Industrially, MCC is manufactured by microfiltration (MF) of skim milk and is commercially available as a liquid, concentrated, or dried containing ≥9, ≥22, and ≥80% total protein, respectively. As an ingredient, MCC not only imparts a bland flavor but also offers unique functionalities such as foaming, emulsifying, wetting, dispersibility, heat stability, and water-binding ability. The high protein content of MCC represents a valuable source of fortification in a number of food formulations. For the last 20 years, MCC is utilized in many applications due to the unique physiochemical and functional characteristics. It also has promising applications to eliminate the cost of drying by producing concentrated MCC. This work aims at providing a succinct overview of the historical progress of the MCC, a review on the manufacturing methods, a discussion of MCC properties, varieties, and applications.

Transcriptome analysis of Takifugu obscurus liver in response to acute retene exposure

Retene (1-methyl-7-isopropyl-phenanthrene, RET) is an alkyl polycyclic aromatic hydrocarbon (PAH) with environmental risk to aquatic animals. Takifugu obscurus is a migratory fish species with high economic and ecological value. To assess the toxic effects of RET on molecular metabolism, juvenile T. obscurus in this study were acutely exposed to 44.30 µg/L of RET for four days. The transcriptome profiles of livers were compared between RET treatment group and the control, and the results revealed that 1,897 genes were significantly differentially expressed (DEGs) after exposure to RET, which enriched 17 KEGG pathways. Among these, glycerolipid metabolism, glycerophospholipid metabolism, insulin signaling pathway, and FOXO signaling pathways were significantly activated. Further exploration indicated that RET exposure disrupted glucose metabolism, stimulated insulin metabolism, and activated cell proliferation genes. Overall, these findings help explain the molecular mechanisms underlying RET toxicity, and may offer evidence to support T. obscurus protection.

An unmet clinical need: The history of thrombus imaging

Robust thrombus imaging is an unresolved clinical unmet need dating back to the mid 1970s. While early molecular imaging approaches began with nuclear SPECT imaging, contrast agents for virtually all biomedical imaging modalities have been demonstrated in vivo with unique strengths and common weaknesses. Two primary molecular imaging targets have been pursued for thrombus imaging: platelets and fibrin. Some common issues noted over 40 years ago persist today. Acute thrombus is readily imaged with all probes and modalities, but aged thrombus remains a challenge. Similarly, anti-coagulation continues to interfere with and often negate thrombus imaging efficacy, but heparin is clinically required in patients suspected of pulmonary embolism, deep venous thrombosis or coronary ruptured plaque prior to confirmatory diagnostic studies have been executed and interpreted. These fundamental issues can be overcome, but an innovative departure from the prior approaches will be needed.

A two-step framework for inferring direct protein-protein interaction network from AP-MS data

Background

Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have been developed to identify genuine interactions from AP-MS data sets. However, most of these methods aim at removing false positives that contain contaminants, ignoring the distinction between direct interactions and indirect interactions.

Results

In this paper, we present an initialization-and-refinement framework for inferring direct PPI networks from AP-MS data, in which an initial network is first generated with existing scoring methods and then a refined network is constructed by the application of indirect association removal methods. Experimental results on several real AP-MS data sets show that our method is capable of identifying more direct interactions than traditional scoring methods.

Conclusions

The proposed framework is sufficiently general to incorporate any feasible methods in each step so as to have potential for handling different types of AP-MS data in the future applications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0452-y) contains supplementary material, which is available to authorized users.

Synthetic biology outside the cell: linking computational tools to cell-free systems

As mathematical models become more commonly integrated into the study of biology, a common language for describing biological processes is manifesting. Many tools have emerged for the simulation of in vivo synthetic biological systems, with only a few examples of prominent work done on predicting the dynamics of cell-free synthetic systems. At the same time, experimental biologists have begun to study dynamics of in vitro systems encapsulated by amphiphilic molecules, opening the door for the development of a new generation of biomimetic systems. In this review, we explore both in vivo and in vitro models of biochemical networks with a special focus on tools that could be applied to the construction of cell-free expression systems. We believe that quantitative studies of complex cellular mechanisms and pathways in synthetic systems can yield important insights into what makes cells different from conventional chemical systems.

Comparative Study on Sequence-Structure-Function Relationship of the Human Short-chain Dehydrogenases/Reductases Protein Family

Human short-chain dehydrogenases/reductases (SDRs) protein family has been the subject of recent studies for its critical role in human metabolism. Studies also found that single nucleotide polymorphisms of the SDR protein family were responsible for a variety of genetic diseases, including type II diabetes. This study reports the effect of sequence variation on the structural and functional integrities of human SDR protein family using phylogenetics and correlated mutation analysis tools. Our results indicated that (i) tyrosine, serine, and lysine are signature protein residues that have direct contribution to the structural and functional stabilities of the SDR protein family, (ii) subgroups of SDR protein family have their own signature protein combination that represent their unique functionality, and (iii) mutations of the human SDR protein family showed high correlation in terms of evolutionary history. In combination, the results inferred that over evolutionary history, the SDR protein family was able to diverge itself in order to adapt with the changes in human nutritional demands. Our study reveals understanding of structural and functional scaffolds of specific SDR subgroups that may facilitate the design of specific inhibitor.

Discriminating lysosomal membrane protein types using dynamic neural network

This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

Microscopic observation of the effect of some reagents on the physical state of casein

Observations were made by anoptral phase-contrast microscopy of the effect of various reagents on the state of dispersion of casein micelles in skim-milk and in lactic acid and rennet coagula. Among the hydrogen-bond-breaking reagents, urea at higher (4M) concentration dispersed the casein micelles in skim-milk and in acid and rennet coagula. Sodium salicylate was effective at low (M) concentration. Guanidine hydrochloride at low concentration caused aggregation of the micelles in skim-milk but at high concentration caused dispersion, while at both concentrations it dispersed acid and rennet coagula. Lithium iodide and potassium thiocyanate flocculated the casein in skim-milk, but dispersed it in both types of coagula. Flocculation in skim-milk by these reagents was followed by slow coalescence of the casein.

At substantially lower molar concentrations, sodium dodecyl sulphate, an anionic detergent, which attacks either the hydrophobic bonds or the salt linkages in proteins, dispersed micelles and both types of coagula. Sodium thioglycollate, a disulphide-bond-breaking reagent, had no visible effect on the casein.

In rennet coagulum in which the calcium bridges have been destroyed by the sequestering action of EDTA or in lactic acid coagulum, urea at low concentrations caused a quick and almost complete coalescence of casein. Fibres were formed in the rennet coagulum when the coalesced material, which presumably consists of randomly coiled chains of unfolded casein macromolecules, was subjected to stretching or flowing. Similar chemical and physical conditions would appear to prevail when fibre formation takes place in cheddaring cheese curd.

Surface charge of polyoxometalates modulates polymerization of the scrapie prion protein

Prions are composed solely of an alternatively folded isoform of the prion protein (PrP), designated PrP(Sc). N-terminally truncated PrP(Sc), denoted PrP 27-30, retains infectivity and polymerizes into rods with the ultrastructural and tinctorial properties of amyloid. We report here that some polyoxometalates (POMs) favor polymerization of PrP 27-30 into prion rods, whereas other POMs promote assembly of the protein into 2D crystals. Antibodies reacting with epitopes in denatured PrP 27-30 also bound to 2D crystals treated with 3 M urea. These same antibodies did not bind to either native PrP(Sc) or untreated 2D crystals. By using small, spherical POMs with Keggin-type structures, the central heteroatom was found to determine whether prion rods or 2D crystals were preferentially formed. An example of a Keggin-type POM with a phosphorous heteroatom is the phosphotungstate anion (PTA). Both PTA and a Keggin-type POM with a silicon heteratom have low-charge densities and favor formation of prion rods. In contrast, POMs with boron or hydrogen heteroatoms exhibiting higher negative charges encouraged 2D crystal formation. The 2D crystals of PrP 27-30 produced by selective precipitation with POMs were larger and more well ordered than those obtained by sucrose gradient centrifugation. Our findings argue that the negative charge of Keggin-type POMs determines the quaternary structure adopted by PrP 27-30. The mechanism by which POMs function in competing prion polymerization pathways--one favoring 2D crystals and the other, amyloid fibrils--remains to be established.

Optimization of linear disorder predictors yields tight association between crystallographic disorder and hydrophobicity

X-ray crystallographic protein structures often contain disordered regions that are observed as missing electron density. Diffraction data may give little or no direct evidence as to the specific nature of disordered regions. We have developed a weighted window-based disorder predictor optimized using crystallographic data. Performance of a predictor is strongly influenced by chain termini. Optimized score adjustment values for amino- and carboxy-terminal positions demonstrate a simple, monotonic relationship between disorder and residue distance from termini. This optimized disorder predictor performs similarly to DISOPRED2 on crystallographically disordered regions. Data-optimized residue disorder propensities show strong linear correlation with experimentally determined amino acid transfer energies between water and hydrogen-bonding organic solvents, which primarily reflect residue hydrophobicity (exemplified by the Nozaki-Tanford hydrophobicity scale). Disorder propensities do not correlate as well with transfer energies between water and apolar solvents, which primarily reflect a different hydropathic property: residue hydrophilicity (also reflected by the Kyte-Doolittle hydropathy scale). Our results suggest that while hydrophobic side-chain interactions are primarily involved in determining stability of the folded conformation, hydrogen bonding, and similar polar interactions are primarily involved in conformational and interaction specificity.

J-Aggregation of Anionic Ethyl meso-Thiacarbocyanine Dyes Induced by Binding to Proteins

The effects of ribonuclease A (RNase), lysozyme, trypsin, and bovine serum albumin (BSA) on the J-aggregation behavior of 3,3'-bis[sulfopropyl]-5-methoxy-4',5'-benzo-9-ethylthiacarbocyanine (1), 3,3'-bis[sulfopropyl]-4,5,4',5'-dibenzo-9-ethylthiacarbocyanine (2), and 3,3'-bis[sulfopropyl]-5,5'-dimethoxy-9-ethylthiacarbocyanine (3) were studied in aqueous solution. The formation of J-aggregates at pH 6 is induced by RNase for 1-3, by lysozyme for 1 and 2, and by trypsin for 2. The formation of J-aggregates correlates with decay of the dimers and is supported by induced circular dichroism spectra. The concentration of J-aggregates for lysozyme/1 increases with an increase in the protein/dye concentration ratio, reaches a plateau, and then gradually decreases. J-aggregates are characterized by relatively weak fluorescence; e.g., Phi(f) = 0.01 for lysozyme/1, and by a small Stokes shift of 6-8 nm, indicating almost resonance fluorescence. J-aggregation proceeds in the range of seconds to minutes with sigmoidal type kinetic curves for trypsin/2 and nonsigmoidal kinetics in the other cases. The presence of BSA, in contrast to RNase, lysozyme, and trypsin, results in deaggregation of dimers of 1-3 and formation of bound monomers and exhibits intense fluorescence from the trans-monomer; e.g., Phi(f) = 0.22 for BSA/1. Generally, the binding of 1-3 to the proteins is a cooperative process, where the number of binding sites changes from n = 15 for lysozyme/1 to n = 6 for trypsin/2 and n = 0.3 and 1 for BSA/3.

Current insights on protein behaviour in hydrophobic interaction chromatography

This paper gives a summary of different aspects for predicting protein behaviour in hydrophobic interaction chromatography (HIC). First, a brief description of HIC, hydrophobic interactions, amino acid and protein hydrophobicity is presented. After that, several factors affecting protein chromatographic behaviour in HIC are described. Finally, different approaches for predicting protein retention time in HIC are shown. Using all this information, it could be possible to carry out computational experiments by varying the different operating conditions for the purification of a target protein; and then selecting the best conditions in silico and designing a rational protein purification process involving an HIC step.

Early events in insulin fibrillization studied by time-lapse atomic force microscopy

The importance of understanding the mechanism of protein aggregation into insoluble amyloid fibrils lies not only in its medical consequences, but also in its more basic properties of self-organization. The discovery that a large number of uncorrelated proteins can form, under proper conditions, structurally similar fibrils has suggested that the underlying mechanism is a general feature of polypeptide chains. In this work, we address the early events preceding amyloid fibril formation in solutions of zinc-free human insulin incubated at low pH and high temperature. Here, we show by time-lapse atomic force microscopy that a steady-state distribution of protein oligomers with a quasiexponential tail is reached within a few minutes after heating. This metastable phase lasts for a few hours, until fibrillar aggregates are observable. Although for such complex systems different aggregation mechanisms can occur simultaneously, our results indicate that the prefibrillar phase is mainly controlled by a simple coagulation-evaporation kinetic mechanism, in which concentration acts as a critical parameter. These experimental facts, along with the kinetic model used, suggest a critical role for thermal concentration fluctuations in the process of fibril nucleation.

Prediction of protein retention in hydrophobic interaction chromatography

Hydrophobic interaction chromatography (HIC) is a powerful technique for protein separation. This review examines methodologies for predicting protein retention time in HIC involving elution with salt gradients. The methodologies discussed consider three-dimensional structure data of the protein and its surface hydrophobicity. Despite their limitations, the methods discussed are useful in designing purification processes for proteins and easing the tedious experimental work that is currently required for developing purification protocols.

Dynamic covariation between gene expression and proteome characteristics

Background

Cells react to changing intra- and extracellular signals by dynamically modulating complex biochemical networks. Cellular responses to extracellular signals lead to changes in gene and protein expression. Since the majority of genes encode proteins, we investigated possible correlations between protein parameters and gene expression patterns to identify proteome-wide characteristics indicative of trends common to expressed proteins.

Results

Numerous bioinformatics methods were used to filter and merge information regarding gene and protein annotations. A new statistical time point-oriented analysis was developed for the study of dynamic correlations in large time series data. The method was applied to investigate microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle.

Conclusion

We show that the properties of proteins synthesized correlate dynamically with the gene expression profile, indicating that not only is the actual identity and function of expressed proteins important for cellular responses but that several physicochemical and other protein properties correlate with gene expression as well. Gene expression correlates strongly with amino acid composition, composition- and sequence-derived variables, functional, structural, localization and gene ontology parameters. Thus, our results suggest that a dynamic relationship exists between proteome properties and gene expression in many biological systems, and therefore this relationship is fundamental to understanding cellular mechanisms in health and disease.

Scooby-domain: prediction of globular domains in protein sequence

Scooby-domain (sequence hydrophobicity predicts domains) is a fast and simple method to identify globular domains in protein sequence, based on the observed lengths and hydrophobicities of domains from proteins with known tertiary structure. The prediction method successfully identifies sequence regions that will form a globular structure and those that are likely to be unstructured. The method does not rely on homology searches and, therefore, can identify previously unknown domains for structural elucidation. Scooby-domain is available as a Java applet at http://ibivu.cs.vu.nl/programs/scoobywww. It may be used to visualize local properties within a protein sequence, such as average hydrophobicity, secondary structure propensity and domain boundaries, as well as being a method for fast domain assignment of large sequence sets.

The fibrinogen molecule: its size, shape, and mode of polymerization

Improved electron micrographs of the shadow-cast bovine fibrinogen molecule have been obtained establishing its general morphology and dimensions in the dry state. It consists of a linear array of 3 nodules held together by a very thin thread which is estimated to have a diameter of from 8 to 15 A, though it is not clearly resolved. The two end nodules are alike but the center one is slightly smaller. Measurements of shadow lengths indicate that nodule diameters are in the range 50 to 70 A. The length of the dried molecule is 475 ± 25 A. Adopting the molecular volume from previous physical chemical data and the general morphological features and length from electron microscopy, we calculate the diameters of the end nodules to be 65 A and the center one as 50 A. The model of the molecule so obtained is consistent with the electron microscopical observations and the data from physical chemistry. The intermediate polymers formed when fibrinogen is activated with thrombin were also examined and found to be end-to-end aggregates of altered fibrinogen molecules which shrink in length during the process. Intermediate polymer lengths are from 1000 to 5000 A. The nodular nature of fibrinogen, its shrinkage and end-to-end aggregation on polymerization permits us to deduce an explanation for the system of cross-bands previously observed in stained fibrin fibrils.

How protein chemists learned about the hydrophobic factor

It is generally accepted today that the hydrophobic force is the dominant energetic factor that leads to the folding of polypeptide chains into compact globular entities. This principle was first explicitly introduced to protein chemists in 1938 by Irving Langmuir, past master in the application of hydrophobicity to other problems, and was enthusiastically endorsed by J.D. Bernal. But both proposal and endorsement came in the course of a debate about a quite different structural principle, the so-called "cyclol hypothesis" proposed by D. Wrinch, which soon proved to be theoretically and experimentally unsupportable. Being a more tangible idea, directly expressed in structural terms, the cyclol hypothesis received more attention than the hydrophobic principle and the latter never actually entered the mainstream of protein science until 1959, when it was thrust into the limelight in a lucid review by W. Kauzmann. A theoretical paper by H.S. Frank and M. Evans, not itself related to protein folding, probably played a major role in the acceptance of the hydrophobicity concept by protein chemists because it provided a crude but tangible picture of the origin of hydrophobicity per se in terms of water structure.

Toward understanding insulin fibrillation

Formation of insulin fibrils is a physical process by which partially unfolded insulin molecules interact with each other to form linear aggregates. Shielding of hydrophobic domains is the main driving force for this process, but formation of intermolecular beta-sheet may further stabilize the fibrillar structure. Conformational displacement of the B-chain C-terminal with exposure of nonpolar, aliphatic core residues, including A2, A3, B11, and B15, plays a crucial role in the fibrillation process. Recent crystal analyses and molecular modeling studies have suggested that when insulin fibrillates this exposed domain interacts with a hydrophobic surface domain formed by the aliphatic residues A13, B6, B14, B17, and B18, normally buried when three insulin dimers form a hexamer. In rabbit immunization experiments, insulin fibrils did not elicit an increased immune response with respect to formation of IgG insulin antibodies when compared with native insulin. In contrast, the IgE response increased with increasing content of insulin in fibrillar form. Strategies and practical approaches to prevent insulin from forming fibrils are reviewed. Stabilization of the insulin hexameric structure and blockage of hydrophobic interfaces by addition of surfactants are the most effective means of counteracting insulin fibrillation.

Probing the determinants of protein stability: comparison of class A beta-lactamases

Five class A beta-lactamases produced by various mesophilic bacterial species have been compared. Although closely related in primary and overall structures, these enzymes exhibit very different stabilities. In order to investigate the factors responsible for these differences, several features deduced from the amino acid composition and three-dimensional structures were studied for the five proteins. This analysis revealed that higher stability appeared to correlate with increased numbers of intramolecular hydrogen bonds and of salt bridges. By contrast, the global hydrophobicity of the protein seemed to play a relatively minor role. A strongly unfavourable balance between charged residues and the presence of a cis-peptide bond preceding a non-proline residue might also contribute to the particularly low stability of two of the enzymes.

Long-term implantation of a new implantable programmable insulin pump in two diabetic dogs

A positive-pressure implantable programmable pump with an integrated sideport and a detachable catheter was tested for long-term intraperitoneal insulin delivery in 2 diabetic dogs. Programming and refilling of the pump was simple and presented no problems during 28 months of pump function. During pump therapy an acceptable degree of glycemic control was maintained. No catheter occlusion occurred, and no skin erosions or infections were observed. Insulin in the pump reservoir was stable. After 9.5 months of pump function, flow progressively decreased because of insulin precipitation in the valved accumulator. Solubilization of these precipitates using 0.1 N NaOH injected in the reservoir and drained through the sideport normalized pump delivery rates. We conclude that this device is safe for long-term intraperitoneal insulin delivery. The sideport enables discrimination between different causes of pump malfunction and allows for the safe use of an alkaline solution to remove insulin precipitates, thus avoiding pump explantation.

Hydrophobicity of amino acid residues: differential scanning calorimetry and synthesis of the aromatic analogues of the polypentapeptide of elastin

Relative hydrophobicities of aromatic amino acid residues are investigated by using differential scanning calorimetry (DSC) on 10 synthetic copolypentapeptides of poly(VPGVG) of elastin. Utilizing the hydrophobic-driven process of the inverse temperature transition exhibited by these polypentapeptides in aqueous solution, the relative hydrophobicities of Phe, Trp, and Tyr residues are determined by the critical temperature and heat of the transition. The DSC data for the aromatic residue containing copolypentapeptide aqueous solution indicate that tryptophan is the most hydrophobic amino acid residue, phenylalanine the third most hydrophobic on basis of transition temperature and the second on basis of transition heat. For tyrosine, significant differences are observed between the phenolic and the phenoxide anionic states. At pH 7, where tyrosine is protonated, it is found to be the second most hydrophobic amino acid residue on the basis of the transition temperature, whereas on the basis of the heat of transition, it is less hydrophobic than both tryptophan and phenylalanine. Changing the pH from pH 7 to pH 12, for example, for poly[0.8(VPGVG), 0.2(VPGYG)] in aqueous solution shifts the transition temperature from 7 to 49 degrees C with a dramatically reduced heat. On the basis of both the transition temperature scale and the heat of transition, the hydroxylated tyrosine appears less hydrophobic than glycine.

Purification and properties of a 75-kilodalton major protein, an immunodominant surface antigen, from the oral anaerobe Bacteroides gingivalis

A 75-kilodalton major protein (75K protein) was purified to homogeneity from the cell lysate fraction and the envelope of Bacteroides gingivalis 381. The 75K protein was originally present in the outer membrane or the outermost part of this organism as a large, stable complex with an apparent molecular weight of about 2,000,000. Heating at 80 degrees C and at higher temperatures in the presence of sodium dodecyl sulfate was needed to completely dissociate it to monomers. Amino acid analysis revealed that the 75K protein had about 50% nonpolar amino acids. Various strains of B. gingivalis but not other bacteria, including oral Bacteroides species tested, contained serologically related 75K proteins when tested in Western blotting (immunoblotting) analysis. The abundance and localization of the 75K protein in this organism suggest that it has the potential to participate in the host-parasite interaction in infection. The 75K protein was, indeed, strongly recognized in patients with adult periodontal diseases. Immunoblotting with sera from patients and with rabbit antisera generated by intravenous inoculations of whole B. gingivalis cells revealed that the 75K protein was an immunodominant antigen on the surface of B. gingivalis.

Characterization of the carbamino adducts of insulin

Carbon-13 (13C) nuclear magnetic resonance spectroscopy (NMR) is performed to characterize the formation of carbamino adducts between insulin and (13C) carbon dioxide over a range of pH values in the presence of a physiological concentration (23 mM) of sodium bicarbonate. The peaks from two of the carbamino adducts resonate at higher frequencies than the signal from bicarbonate, at 164.6 and 165.3 ppm, and are attributed to the adducts with the terminal amino groups of phenylalanine B1 and glycine A1. The intensities of these signals vary with the pH, with unique patterns. Over 6% of each terminal amino group exists as the carbamino adduct at the optimum pH values of 7.8 and 8.3. A unique third adduct resonates at 159.3 ppm, and is attributed to lysine B29. This adduct is present on 2% of the insulin molecules at pH 8.2, but has minimal intensity at pH 7.4. No signals from adducts are detected below pH 6.2, where the amino groups exist predominantly in the protonated form. Creation of the adducts is rapid and they are stable for over 4 wk at 37 degrees C. The narrow bandwidth of the resonance of the adduct (4.0-4.5 Hz) relative to the irreversible cyanate adduct is consistent with molecular forms of the carbamino adduct smaller than the 2-Zn-hexamer which is the preponderate form of clinically utilized U-100 insulin (i.e., 100 U/ml).

Preparation and some properties of carp heavy meromyosin

Trypsin fragments carp myosin heavy chain into components of lower molecular weight. These changes are concomitant with the loss of Ca2+-ATPase and a weakening in actin-myosin interaction. As heavy meromyosin (HMM) prepared from myofibrils is more homogeneous, the above changes are due to overfragmentation of heavy chain mass. Amino-acid composition of carp and rabbit HMM's are similar, but differences exist in response to DTNB and incorporation.