Amino Acid Hydrophobicity Scales Research Articles

A human infertility-associated KASH5 variant promotes mitochondrial localization

KASH5 is the most recently identified member of the KASH domain family of tail anchored, outer nuclear membrane (ONM) and endoplasmic reticulum (ER) proteins. During meiosis prophase I, KASH5 and SUN1 form a complex that spans the nuclear envelope and which links the telomeres of meiotic chromosomes to cytoplasmic dynein. This connection is essential for homologous chromosome dynamics and pairing. A recent study identified a variant in human KASH5 (L535Q) that correlated with male infertility associated with azoospermia. However, no molecular mechanism was described. Here, we report that this amino acid substitution, within the KASH5 transmembrane domain (TMD) has no predicted effects on secondary structure. However, the overall hydrophobicity of the L535Q TMD, is calculated to be lower than the wild-type KASH5, based on the GES (Goldman–Engelman–Steitz) amino acid hydrophobicity scale. This change in hydrophobicity profoundly affects the subcellular localization of KASH5. Through a series of amino acid substitution studies, we show that the L535Q substitution perturbs KASH5 localization to the ER and ONM and instead results in mistargeting to the mitochondria membrane. We suggest that this mislocalization accounts for the infertility and azoospermia phenotype in patients.

Optimization of a Soft Ensemble Vote Classifier for the Prediction of Chimeric Virus-Like Particle Solubility and Other Biophysical Properties.

Chimeric virus-like particles (cVLPs) are protein-based nanostructures applied as investigational vaccines against infectious diseases, cancer, and immunological disorders. Low solubility of cVLP vaccine candidates is a challenge that can prevent development of these very substances. Solubility of cVLPs is typically assessed empirically, leading to high time and material requirements. Prediction of cVLP solubility in silico can aid in reducing this effort. Protein aggregation by hydrophobic interaction is an important factor driving protein insolubility. In this article, a recently developed soft ensemble vote classifier (sEVC) for the prediction of cVLP solubility was used based on 91 literature amino acid hydrophobicity scales. Optimization algorithms were developed to boost model performance, and the model was redesigned as a regression tool for ammonium sulfate concentration required for cVLP precipitation. The present dataset consists of 568 cVLPs, created by insertion of 71 different peptide sequences using eight different insertion strategies. Two optimization algorithms were developed that (I) modified the sEVC with regard to systematic misclassification based on the different insertion strategies, and (II) modified the amino acid hydrophobicity scale tables to improve classification. The second algorithm was additionally used to synthesize scales from random vectors. Compared to the unmodified model, Matthew’s Correlation Coefficient (MCC), and accuracy of the test set predictions could be elevated from 0.63 and 0.81 to 0.77 and 0.88, respectively, for the best models. This improved performance compared to literature scales was suggested to be due to a decreased correlation between synthesized scales. In these, tryptophan was identified as the most hydrophobic amino acid, i.e., the amino acid most problematic for cVLP solubility, supported by previous literature findings. As a case study, the sEVC was redesigned as a regression tool and applied to determine ammonium sulfate concentrations for the precipitation of cVLPs. This was evaluated with a small dataset of ten cVLPs resulting in an R2 of 0.69. In summary, we propose optimization algorithms that improve sEVC model performance for the prediction of cVLP solubility, allow for the synthesis of amino acid scale tables, and further evaluate the sEVC as regression tool to predict cVLP-precipitating ammonium sulfate concentrations.

Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins

AbstractAs a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article.The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS–DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins.

Structure formation during translocon-unassisted co-translational membrane protein folding

Correctly folded membrane proteins underlie a plethora of cellular processes, but little is known about how they fold. Knowledge of folding mechanisms centres on reversible folding of chemically denatured membrane proteins. However, this cannot replicate the unidirectional elongation of the protein chain during co-translational folding in the cell, where insertion is assisted by translocase apparatus. We show that a lipid membrane (devoid of translocase components) is sufficient for successful co-translational folding of two bacterial α-helical membrane proteins, DsbB and GlpG. Folding is spontaneous, thermodynamically driven, and the yield depends on lipid composition. Time-resolving structure formation during co-translational folding revealed different secondary and tertiary structure folding pathways for GlpG and DsbB that correlated with membrane interfacial and biological transmembrane amino acid hydrophobicity scales. Attempts to refold DsbB and GlpG from chemically denatured states into lipid membranes resulted in extensive aggregation. Co-translational insertion and folding is thus spontaneous and minimises aggregation whilst maximising correct folding.

50years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification.

BackgroundPhysicochemical properties are frequently analyzed to characterize protein-sequences of known and unknown function. Especially the hydrophobicity of amino acids is often used for structural prediction or for the detection of membrane associated or embedded β-sheets and α-helices. For this purpose many scales classifying amino acids according to their physicochemical properties have been defined over the past decades. In parallel, several hydrophobicity parameters have been defined for calculation of peptide properties. We analyzed the performance of separating sequence pools using 98 hydrophobicity scales and five different hydrophobicity parameters, namely the overall hydrophobicity, the hydrophobic moment for detection of the α-helical and β-sheet membrane segments, the alternating hydrophobicity and the exact ß-strand score.ResultsMost of the scales are capable of discriminating between transmembrane α-helices and transmembrane β-sheets, but assignment of peptides to pools of soluble peptides of different secondary structures is not achieved at the same quality. The separation capacity as measure of the discrimination between different structural elements is best by using the five different hydrophobicity parameters, but addition of the alternating hydrophobicity does not provide a large benefit. An in silico evolutionary approach shows that scales have limitation in separation capacity with a maximal threshold of 0.6 in general. We observed that scales derived from the evolutionary approach performed best in separating the different peptide pools when values for arginine and tyrosine were largely distinct from the value of glutamate. Finally, the separation of secondary structure pools via hydrophobicity can be supported by specific detectable patterns of four amino acids.ConclusionIt could be assumed that the quality of separation capacity of a certain scale depends on the spacing of the hydrophobicity value of certain amino acids. Irrespective of the wealth of hydrophobicity scales a scale separating all different kinds of secondary structures or between soluble and transmembrane peptides does not exist reflecting that properties other than hydrophobicity affect secondary structure formation as well. Nevertheless, application of hydrophobicity scales allows distinguishing between peptides with transmembrane α-helices and β-sheets. Furthermore, the overall separation capacity score of 0.6 using different hydrophobicity parameters could be assisted by pattern search on the protein sequence level for specific peptides with a length of four amino acids.Electronic supplementary materialThe online version of this article (doi:10.1186/s40659-016-0092-5) contains supplementary material, which is available to authorized users.

Rapid, Precise, and Reproducible Prediction of Peptide-MHC Binding Affinities from Molecular Dynamics That Correlate Well with Experiment.

The presentation of potentially pathogenic peptides by major histocompatibility complex (MHC) molecules is one of the most important processes in adaptive immune defense. Prediction of peptide-MHC (pMHC) binding affinities is therefore a principal objective of theoretical immunology. Machine learning techniques achieve good results if substantial experimental training data are available. Approaches based on structural information become necessary if sufficiently similar training data are unavailable for a specific MHC allele, although they have often been deemed to lack accuracy. In this study, we use a free energy method to rank the binding affinities of 12 diverse peptides bound by a class I MHC molecule HLA-A*02:01. The method is based on enhanced sampling of molecular dynamics calculations in combination with a continuum solvent approximation and includes estimates of the configurational entropy based on either a one or a three trajectory protocol. It produces precise and reproducible free energy estimates which correlate well with experimental measurements. If the results are combined with an amino acid hydrophobicity scale, then an extremely good ranking of peptide binding affinities emerges. Our approach is rapid, robust, and applicable to a wide range of ligand-receptor interactions without further adjustment.

Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

BackgroundTyrosine sulfation is one of the most important posttranslational modifications. Due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. In order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. A predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. However, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins.ResultsA new approach has been developed for predicting sulfotyrosine sites using the random forest algorithm after a careful evaluation of seven machine learning algorithms. Peptides are formed by consecutive residues symmetrically flanking tyrosine sites. They are then encoded using an amino acid hydrophobicity scale. This new approach has increased the sensitivity by 22%, the specificity by 3%, and the total prediction accuracy by 10% compared with the previous predictor using the same blind data. Meanwhile, both negative and positive predictive powers have been increased by 9%. In addition, the random forest model has an excellent feature for ranking the residues flanking tyrosine sites, hence providing more information for further investigating the tyrosine sulfation mechanism. A web tool has been implemented at for public use.ConclusionThe random forest algorithm is able to deliver a better model compared with the Hidden Markov Model, the support vector machine, artificial neural networks, and others for predicting sulfotyrosine sites. The success shows that the random forest algorithm together with an amino acid hydrophobicity scale encoding can be a good candidate for peptide classification.

MemBrain: Improving the Accuracy of Predicting Transmembrane Helices

Prediction of transmembrane helices (TMH) in α helical membrane proteins provides valuable information about the protein topology when the high resolution structures are not available. Many predictors have been developed based on either amino acid hydrophobicity scale or pure statistical approaches. While these predictors perform reasonably well in identifying the number of TMHs in a protein, they are generally inaccurate in predicting the ends of TMHs, or TMHs of unusual length. To improve the accuracy of TMH detection, we developed a machine-learning based predictor, MemBrain, which integrates a number of modern bioinformatics approaches including sequence representation by multiple sequence alignment matrix, the optimized evidence-theoretic K-nearest neighbor prediction algorithm, fusion of multiple prediction window sizes, and classification by dynamic threshold. MemBrain demonstrates an overall improvement of about 20% in prediction accuracy, particularly, in predicting the ends of TMHs and TMHs that are shorter than 15 residues. It also has the capability to detect N-terminal signal peptides. The MemBrain predictor is a useful sequence-based analysis tool for functional and structural characterization of helical membrane proteins; it is freely available at http://chou.med.harvard.edu/bioinf/MemBrain/.

Distribution of Amino Acids in a Lipid Bilayer from Computer Simulations

We have calculated the distribution in a lipid bilayer of small molecules mimicking 17 natural amino acids in atomistic detail by molecular dynamics simulation. We considered both charged and uncharged forms for Lys, Arg, Glu, and Asp. The results give detailed insight in the molecular basis of the preferred location and orientation of each side chain as well the preferred charge state for ionizable residues. Partitioning of charged and polar side chains is accompanied by water defects connecting the side chains to bulk water. These water defects dominate the energetic of partitioning, rather than simple partitioning between water and a hydrophobic phase. Lys, Glu, and Asp become uncharged well before reaching the center of the membrane, but Arg may be either charged or uncharged at the center of the membrane. Phe has a broad distribution in the membrane but Trp and Tyr localize strongly to the interfacial region. The distributions are useful for the development of coarse-grained and implicit membrane potentials for simulation and structure prediction. We discuss the relationship between the distribution in membranes, bulk partitioning to cyclohexane, and several amino acid hydrophobicity scales.

Determination of solute descriptors of tripeptide derivatives based on high-throughput gradient high-performance liquid chromatography retention data

Recently proposed chromatographic hydrophobicity indices (CHIs) have been calculated from the correlation of fast gradient HPLC retention times with ϕ 0 values from isocratic HPLC measurements for 30 oligopeptide derivatives. Measurements were performed on five HPLC columns; an Inertsil ODS (In), a Prodigy ODS (Pro), an immobilised artificial membrane (IAM), a permethylated β-cylcodextrin (CD) and a cyanopropyl (CN) stationary phase. The ranking of CHI values of the tripeptide derivative of the type Z–Ala–Xaa–Val–OMe for the CD, IAM and CN phases is comparable with other amino acid hydrophobicity scales. The CHI values were used for the determination of three molecular descriptors of the solvation equation established by Abraham; these are the effective hydrogen-bond acidity Σ α 2 H, the effective hydrogen-bond basicity Σ β 2 H and the solute dipolarity/polarisability π 2 H. The comparison of the solute descriptors of the tripeptide derivatives in terms of the change of the sequence and the chirality of the amino acids shows a strong influence on Σ α 2 H, Σ β 2 H and π 2 H.

Physicochemical Basis of Amino Acid Hydrophobicity Scales: Evaluation of Four New Scales of Amino Acid Hydrophobicity Coefficients Derived from RP-HPLC of Peptides

The physicochemical relationships of four new scales of amino acid hydrophobicity coefficients, derived from the reversed-phase high-performance liquid chromatographic (RP-HPLC) retention data of 1738 peptides, have been compared with 12 previously published scales of amino acid hydrobicities. Four different reversed-phase chromatographic systems were used to obtain the experimental data, including three well-characterized hydrophobic adsorbents of different n-alkyl ligand chain length (n-octadecyl (C18), n-octyl (C8), and n-butyl (C4) ligands) and two different aquo-organic eluents, namely a binary water-acetronitrile and a ternary water-acetonitrile-2-propanol elution system, both containing 0.1% trifluoroacetc acid. Significant correlations were observated from the RP-HPLC data of these peptides separated with the C18- and C8-silica absorbents and the water-acetonitrile elution system and the hydrophobicity scales based on the transter free energies of an amino acid entity from a polar to a nonpolar solvent or the statistical degree of exposure to the surrounding solvent or the statistical degree of exposure to the surrrounding solvent of an amino acid side chain within a protein structure. Conversly, lower levels of correlation were observed between the previously calculated hydrophobicity scales for the common α-amino acids and the amino acid hydrophobicity coefficient values derived from this peptide structure-retention data base with the C4 ligand or the C18 ligand in combination with the water-acetonitrile-2-propanol solvent system. The results confirm that the relative ranking and magnitude of the hydrophobicities of amino acid side chains within polypeptides are dependent upon the chemical microenvironment of the interface established between the solute, the solvent and the immunobilized hydrocarbonaceous ligands. The availability of this extensive data base of peptide structure-chromatographic retention behavior has also permitted differences in peptide- nonpolar ligand interactions to be quantified in physicochemical terms, including the propenity of amino acid side chains in environments from aqueous solutions of different hydrogen-bonding of dipolar characteristics

Correlations between Amino Acid Hydrophobicity Scales and Stain Exclusion Capacity of Type I Native Collagen Fibrils

The relationship between the negative staining band pattern of type 1 native collagen fibrils and the amino acid distribution along the fibril axis was studied by comparing averaged microdensitograms with theoretical traces calculated on the basis of different amino acid parameters. As well as the spatial parameter "bulkiness" (volume/length, ratio), various literature-reported scales of "hydrophobicity" were tested. Two "hydrophobicity" sets allowed a better fit with the actual patterns than "bulkiness" values. However, a general improvement in simulations was achieved by associating most "hydrophobicity" sets with the "bulkiness" set. These results suggest that amino acid "hydrophobicity" plays a key role in the appearance of negative staining patterns but a composite mechanism would seem to occur: the accessibility of available intermolecular interstices may be conditioned by molecular hindrance, corresponding to amino acid "bulkiness" as well as by water-repulsion effect, which correlates with amino acid "hydrophobicity." Moreover, a detailed comparison of actual and simulated patterns suggests that a modulation exists in the effectiveness of these two factors along each D-period according to the different molecular packing and concentration of hydrophobic amino acid clusters within overlap regions and gap regions, respectively.

Compartmentalization of amino acids in surfactant aggregates. Partitioning between water and aqueous micellar sodium deodecanoate and between hexane and dodecylammonium propionate trapped water in hexane.

Cationic amino acids, arginine and lysine partition differentially from water into aqueous micellar sodium dodecanoate. Conversely, partitioning of serine, glycine, aspartic acid, glutamic acid, threonine, alanine, proline, valine, leucine, phenylalanine and isoleucine do not vary appreciably. Partitioning from neat hexane into dodecylammonium propionate trapped water in hexane is, however, dependent upon both electrostatic and hydrophobic interactions. These results imply that the interior of dedecylammonium propionate aggregates is negatively charged and is capable of hydrogen bonding in addition to providing a hydrophobic enviroment. The solubilities of amino acids in neat hexane substantiate the previously derived amino acid hydrophobicity scale. Relevance of partitioning in these systems to the postulated selective amino acid compartmentalization is discussed.