Kyte-Doolittle Hydrophobicity Research Articles

Modeling Chromatography Binding through Molecular Dynamics Simulations with Resin Fragments.

Accurate atomistic modeling of the interactions of a chromatography resin with a solute can inform the selection of purification conditions for a product, an important problem in the biotech and pharmaceutical industries. We present a molecular dynamics simulation-based approach for the qualitative prediction of interaction sites (specificity) and retention times (affinity) of a protein for a given chromatography resin. We mimicked the resin with an unrestrained ligand composed of the resin headgroup coupled with successively larger fragments of the agarose backbone. The interactions of the ligand with the protein are simulated in an explicit solvent using the Replica Exchange Molecular Dynamics enhanced sampling approach in conjunction with Hydrogen Mass Repartitioning (REMD-HMR). We computed the ligand interaction surface from the simulation trajectories and correlated the features of the interaction surface with experimentally determined retention times. The simulation and analysis protocol were first applied to a series of ubiquitin mutants for which retention times on Capto MMC resin are available. The ubiquitin simulations helped identify the optimal ligand that was used in subsequent simulations on six proteins for which Capto MMC elution times are available. For each of the six proteins, we computed the interaction surface and characterized it in terms of a range of simulation-averaged residue-level physicochemical descriptors. Modeling of the salt concentrations required for elution with respect to the descriptors resulted in a linear fit in terms of aromaphilicity and Kyte-Doolittle hydrophobicity that was robust to outliers, showed high correlation, and correctly ranked the protein elution order. The physics-based model building approach described here does not require a large experimental data set and can be readily applied to different resins and diverse biomolecules.

Solvated protein–protein docking using Kyte‐Doolittle‐based water preferences

HADDOCK is one of the few docking programs that can explicitly account for water molecules in the docking process. Its solvated docking protocol starts from hydrated molecules and a fraction of the resulting interfacial waters is subsequently removed in a biased Monte Carlo procedure based on water-mediated contact probabilities. The latter were derived from an analysis of water contact frequencies from high-resolution crystal structures. Here, we introduce a simple water-mediated amino acid-amino acid contact probability scale derived from the Kyte-Doolittle hydrophobicity scale and assess its performance on the largest high-resolution dataset developed to date for solvated docking. Both scales yield high-quality docking results. The novel and simple hydrophobicity scale, which should reflect better the physicochemical principles underlying contact propensities, leads to a performance improvement of around 10% in ranking, cluster quality and water recovery at the interface compared with the statistics-based original solvated docking protocol.

Prediction of the Types of Membrane Proteins Based on Discrete Wavelet Transform and Support Vector Machines

Membrane proteins are crucial for many biological functions and have become attractive targets for both basic research and drug discovery. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is both time-consuming and expensive to determine the types of newly found membrane proteins solely with traditional experiment, and so it is highly demanded to develop an automatic method for fast and accurately identifying the type of membrane proteins according to their amino acid sequences. In this study, the discrete wavelet transform (DWT) and support vector machine (SVM) have been used for the prediction of the types of membrane proteins. Maximum accuracy has been obtained using SVM with a wavelet function of bior2.4, a decomposition scale j = 4, and Kyte-Doolittle hydrophobicity scales. The results indicate that the proposed method may play an important complementary role to the existing methods in this area.

Using support vector machines to distinguish enzymes: Approached by incorporating wavelet transform

The enzymatic attributes of newly found protein sequences are usually determined either by biochemical analysis of eukaryotic and prokaryotic genomes or by microarray chips. These experimental methods are both time-consuming and costly. With the explosion of protein sequences registered in the databanks, it is highly desirable to develop an automated method to identify whether a given new sequence belongs to enzyme or non-enzyme. The discrete wavelet transform (DWT) and support vector machine (SVM) have been used in this study for distinguishing enzyme structures from non-enzymes. The networks have been trained and tested on two datasets of proteins with different wavelet basis functions, decomposition scales and hydrophobicity data types. Maximum accuracy has been obtained using SVM with a wavelet function of Bior2.4, a decomposition scale j=5, and Kyte–Doolittle hydrophobicity scales. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, which indicates that the proposed method can be employed as a useful assistant technique for distinguishing enzymes from non-enzymes.

Nonlinear signal analysis to understand the dynamics of the protein sequences

Recurrence plots are a useful tool to identify structure in a data set in a time resolved way qualitatively. Recurrence plots and its quantification has become an important research tool in the analysis of nonlinear dynamical systems. In the present work, we utilize the recurrence property to study the protein sequences. The sequences that we analyze belong to two distinct classes, viz., soluble proteins and proteins that form inclusion bodies when over expressed in Escherichia coli. We use Kyte-Doolittle hydrophobicity scale in the analysis. We study the underlying dynamics and extract the information which codes the essential class of a protein using simple statistical and global characteristics based features as well as some advanced features based on recurrence quantification. The extracted features are used in probability estimation using Gaussian Process Classification technique. The results give meaningful insights to the level of understanding the protein sequence dynamics.

NIPA1(SPG6), the Basis for Autosomal Dominant Form of Hereditary Spastic Paraplegia, Encodes a Functional Mg2+ Transporter

Mutations in the NIPA1(SPG6) gene, named for "nonimprinted in Prader-Willi/Angelman" has been implicated in one form of autosomal dominant hereditary spastic paraplegia (HSP), a neurodegenerative disorder characterized by progressive lower limb spasticity and weakness. However, the function of NIPA1 is unknown. Here, we show that reduced magnesium concentration enhances expression of NIPA1 suggesting a role in cellular magnesium metabolism. Indeed NIPA1 mediates Mg2+ uptake that is electrogenic, voltage-dependent, and saturable with a Michaelis constant of 0.69+/-0.21 mM when expressed in Xenopus oocytes. Subcellular localization with immunofluorescence showed that endogenous NIPA1 protein associates with early endosomes and the cell surface in a variety of neuronal and epithelial cells. As expected of a magnesium-responsive gene, we find that altered magnesium concentration leads to a redistribution between the endosomal compartment and the plasma membrane; high magnesium results in diminished cell surface NIPA1 whereas low magnesium leads to accumulation in early endosomes and recruitment to the plasma membrane. The mouse NIPA1 mutants, T39R and G100R, corresponding to the respective human mutants showed a loss-of-function when expressed in oocytes and altered trafficking in transfected COS7 cells. We conclude that NIPA1 normally encodes a Mg2+ transporter and the loss-of function of NIPA1(SPG6) due to abnormal trafficking of the mutated protein provides the basis of the HSP phenotype.

The Major Aeromonas veronii Outer Membrane Protein: Gene Cloning and Sequence Analysis

The gene encoding the major outer membrane protein (OMP) from Aeromonas veronii, Omp38, was cloned and characterized. Sequence analysis revealed an open reading frame of 1,047 nucleotides coding for a primary protein of 349 amino acids with a 20-amino-acid signal peptide at the N-terminal and the consensus sequence Ala-X-Ala (Ala-Asn-Ala) as the signal peptidase I recognition site. The mature protein is composed of 329 amino acids with a calculated molecular mass of 36,327 Da. The degree of identity of the deduced Omp38 amino acid sequence to porins from enteric bacteria (OmpF, PhoE, and OmpC) was only 30%. Nevertheless, Omp38 possesses typical features of Gram-negative porins, including acidic pI, high glycine and low proline content, no cysteine residues, and a carboxy-terminal Phe. On the basis of PhoE-OmpF three-dimensional structure and the Kyte-Doolittle hydrophobicity analysis, it seems likely that Omp38 secondary structure consists of 16 antiparallel beta-strands and 8 loops. Phylogenetic analyses among Omp38 and related porins from Gram-negative bacteria originate well-defined clusters that agree with the taxonomy of the corresponding organisms.

Inferring ideal amino acid interaction forms from statistical protein contact potentials

We have analyzed 29 different published matrices of protein pairwise contact potentials (CPs) between amino acids derived from different sets of proteins, either crystallographic structures taken from the Protein Data Bank (PDB) or computer-generated decoys. Each of the CPs is similar to 1 of the 2 matrices derived in the work of Miyazawa and Jernigan (Proteins 1999;34:49-68). The CP matrices of the first class can be approximated with a correlation of order 0.9 by the formula e(ij) = h(i) + h(j), 1 <or= i, j <or= 20, where the residue-type dependent factor h is highly correlated with the frequency of occurrence of a given amino acid type inside proteins. Electrostatic interactions for the potentials of this class are almost negligible. In the potentials belonging to this class, the major contribution to the potentials is the one-body transfer energy of the amino acid from water to the protein environment. Potentials belonging to the second class can be approximated with a correlation of 0.9 by the formula e(ij) = c(0) - h(i)h(j) + q(i)q(j), where c(0) is a constant, h is highly correlated with the Kyte-Doolittle hydrophobicity scale, and a new, less dominant, residue-type dependent factor q is correlated ( approximately 0.9) with amino acid isoelectric points pI. Including electrostatic interactions significantly improves the approximation for this class of potentials. While, the high correlation between potentials of the first class and the hydrophobic transfer energies is well known, the fact that this approximation can work well also for the second class of potentials is a new finding. We interpret potentials of this class as representing energies of contact of amino acid pairs within an average protein environment.

Wavelet transforms for the characterization and detection of repeating motifs

The role of repeating motifs in protein structures is thought to be as modular building blocks which allow an economic way of constructing complex proteins. In this work novel wavelet transform analysis techniques are used to detect and characterize repeating motifs in protein sequence and structure data, where the Kyte-Doolittle hydrophobicity scale (HΦ) and relative accessible surface area (rASA) data provide residue information about the protein sequence and structure, respectively. We analyze a variety of repeating protein motifs, TIM barrels, propellor blades, coiled coils and leucine-rich repeat structures. Detection and characterization of these motifs is performed using techniques based on the continuous wavelet transform (CWT). Results indicate that the wavelet transform techniques developed herein are a promising approach for the detection and characterization of repeating motifs for both structural and in some instances sequence data.

Stage-dependent Localization of a Novel Gene Product of the Malaria Parasite, Plasmodium falciparum

A novel Plasmodium falciparum gene, MB2, was identified by screening a sporozoite cDNA library with the serum of a human volunteer protected experimentally by the bites of P. falciparum-infected and irradiated mosquitoes. The single-exon, single-copy MB2 gene is predicted to encode a protein with an M(r) of 187,000. The MB2 protein has an amino-terminal basic domain, a central acidic domain, and a carboxyl-terminal domain with similarity to the GTP-binding domain of the prokaryotic translation initiation factor 2. MB2 is expressed in sporozoites, the liver, and blood-stage parasites and gametocytes. The MB2 protein is distributed as a approximately 120-kDa moiety on the surface of sporozoites and is imported into the nucleus of blood-stage parasites as a approximately 66-kDa species. Proteolytic processing is favored as the mechanism regulating the distinct subcellular localization of the MB2 protein. This differential localization provides multiple opportunities to exploit the MB2 gene product as a vaccine or therapeutic target.

The Membrane-Associated Protein pKe#192/MAP17 in Human Keratinocytes

In order to isolate genes that are upregulated in human keratinocytes upon loss of cell/matrix contact, a subtractive cDNA library was constructed from dispase-treated versus untreated keratinocytes. Among the cloned cDNAs one was pKe#192 having an open reading frame of 411 bp. By database analysis pKe#192 was found to be identical with the gene "MAP17" previously isolated from human kidney. Kyte-Doolittle hydrophobicity analyzes showed a hydrophobic amino terminus of 13 amino acids, a transmembrane region and a 61 amino acid hydrophilic carboxy-terminus and two potential phosphorylation sites. In order to study regulation of pKe#192/MAP17 expression, RNA was extracted from resting human keratinocytes and from keratinocytes stimulated by dispase-induced detachment from the growth substratum. Reverse transcription polymerase chain reaction did not reveal specific mRNA in resting keratinocytes, whereas mRNA was detectable after detachment. For further characterization poly- and monoclonal antibodies were generated against a recombinant fusion protein. Immunohistologic studies using the mono- and polyclonal antibodies showed staining of the upper layers of the stratum granulosum in normal human epidermis. The staining was colocalized with involucrin. Immunhistologic staining of frozen sections derived from lesional skin of bullous pemphigoid und pemphigus vulgaris indicated that pKe#192/MAP17 was upregulated in the epidermis adjacent to the blister. Taken together, the data demonstrate that pKe#192/MAP17 is expressed in keratinocytes and may be involved in epidermal physiology and pathology. bullous diseases/differentiation.

Selection of class I MHC-restricted peptides with the strip-of-helix hydrophobicity algorithm

A strip-of-helix hydrophobicity algorithm to predict class II MHC-restricted peptides, on the basis of their structural similarity to an amphipathic, α -helix in I i, also predicted peptides which were presented to cytotoxic T-cells by class I MHC molecules. This algorithm ranked peptides according to mean Kyte-Doolittle hydrophobicity values of amino acids at positions n, n + 4, n + 7, n + 11, n + 14 and n + 18 in a sequence which when coiled as a putative α-helix, had the indicated residues in an axial strip along one side of the helix. Sequences selected for highly scoring, hydrophobic strips were required to have at least 1 of the 4 adjacent strips scoring more negatively than −1 in the strip-of-helix hydrophobicity index and the entire sequence could contain no prolines. This algorithm predicted the class I MHC-restricted, T-cell-presented peptides in sequences of 4 proteins from which some class I MHC-restricted, T-cell-presented sequences had been experimentally determined. Since both class I and class II MHC-restricted peptides could be identified with this algorithm, one can propose that: (I) foreign peptide-binding sites (desetopes) of the class I and class II MHC molecules are structurally similar; and (2) any one T-cell-presented peptide can be presented by some specific allele of both a class I and a class II MHC antigen.