Quantitative comparison of the ability of hydropathy scales to recognize surface beta-strands in proteins

Repertoire-scale measures of antigen binding

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers

The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.

Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins

We present improvements to the hydropathy scale (HPS) coarse-grained (CG) model for simulating sequence-specific behavior of intrinsically disordered proteins (IDPs), including their liquid-liquid phase separation (LLPS). The previous model based on an atomistic hydropathy scale by Kapcha and Rossky (KR scale) is not able to capture some well-known LLPS trends such as reduced phase separation propensity upon mutations (R-to-K and Y-to-F). Here, we propose to use the Urry hydropathy scale instead, which was derived from the inverse temperature transitions in a model polypeptide with guest residues X. We introduce two free parameters to shift (Δ) and scale (µ) the overall interaction strengths for the new model (HPS-Urry) and use the experimental radius of gyration for a diverse group of IDPs to find their optimal values. Interestingly, many possible (Δ, µ) combinations can be used for typical IDPs, but the phase behavior of a low-complexity (LC) sequence FUS is only well described by one of these models, which highlights the need for a careful validation strategy based on multiple proteins. The CG HPS-Urry model should enable accurate simulations of protein LLPS and provide a microscopically detailed view of molecular interactions.

Properties of Cavities in Biological Structures-A Survey of the Protein Data Bank

We performed a PDB-wide survey of proteins to assess their cavity content, using the SPACEBALL algorithm to calculate the cavity volumes. In addition, we determined the hydropathy character of the cavities. We demonstrate that the cavities of most proteins are hydrophilic, but smaller proteins tend to have cavities with hydrophobic walls. We propose criteria for distinguishing between cavities and pockets, and single out proteins with the largest cavities.

Proteins at curved fluid-fluid interfaces in a coarse-grained model

We employ an empirical coarse-grained model with a proposed Gaussian-like interfacial potential to describe proteins at curved fluid-fluid interfaces such as occurring in bubbles and droplets. We consider the air-water and oil-water interfaces. We study the mass distributions and the geometry of the aqueous proteins as a function of the radius of curvature for protein G and two lipid transfer proteins. At curved interfaces the distortion of the proteins is different than at flat interfaces. We find that the proteins come closer to the surface of a bubble than to the surface of similarly curved droplet. In addition, the bubbles adsorb more proteins. We identify the pinning residues. We demonstrate the existence of the second layer in the density profile for sufficiently dense solutions.

An Intrinsic Hydrophobicity Scale for Amino Acids and Its Application to Fluorinated Compounds

More than 100 hydrophobicity scales have been introduced, with each being based on a distinct condensed-phase approach. However, a comparison of the hydrophobicity values gained from different techniques, and their relative ranking, is not straightforward, as the interactions between the environment and the amino acid are unique to each method. Here, we overcome this limitation by studying the properties of amino acids in the clean-room environment of the gas phase. In the gas phase, entropic contributions from the hydrophobic effect are by default absent and only the polarity of the side chain dictates the self-assembly. This allows for the derivation of a novel hydrophobicity scale, which is based solely on the interaction between individual amino acid units within the cluster and thus more accurately reflects the intrinsic nature of a side chain. This principle can be further applied to classify non-natural derivatives, as shown here for fluorinated amino acid variants.

Topological transformations in proteins: effects of heating and proximity of an interface

Using a structure-based coarse-grained model of proteins, we study the mechanism of unfolding of knotted proteins through heating. We find that the dominant mechanisms of unfolding depend on the temperature applied and are generally distinct from those identified for folding at its optimal temperature. In particular, for shallowly knotted proteins, folding usually involves formation of two loops whereas unfolding through high-temperature heating is dominated by untying of single loops. Untying the knots is found to generally precede unfolding unless the protein is deeply knotted and the heating temperature exceeds a threshold value. We then use a phenomenological model of the air-water interface to show that such an interface can untie shallow knots, but it can also make knots in proteins that are natively unknotted.

Proteins at air–water and oil–water interfaces in an all-atom model

Proteins with different hydrophobicities are studied at the air–water and oil–water interfaces. The all-atom simulating results are consistent with the coarse-grained interfacial model. Proteins are found to be coupled stronger but diffused slower at the oil–water interface than the air–water one.

Proteins at air-water interfaces: a coarse-grained model

We present a coarse-grained model to describe the adsorption and deformation of proteins at an air-water interface. The interface is introduced empirically in the form of a localized field that couples to a hydropathy scale of amino acids. We consider three kinds of proteins: protein G, egg-white lysozyme, and hydrophobin. We characterize the nature of the deformation and the orientation of the proteins induced by their proximity to and association with the interface. We also study protein diffusion in the layer formed at the interface and show that the diffusion slows with increasing concentration in a manner similar to that for a colloidal suspension approaching the glass transition.

Capturing native/native like structures with a physico-chemical metric (pcSM) in protein folding

Specification of the three dimensional structure of a protein from its amino acid sequence, also called a "Grand Challenge" problem, has eluded a solution for over six decades. A modestly successful strategy has evolved over the last couple of decades based on development of scoring functions (e.g. mimicking free energy) that can capture native or native-like structures from an ensemble of decoys generated as plausible candidates for the native structure. A scoring function must be fast enough in discriminating the native from unfolded/misfolded structures, and requires validation on a large data set(s) to generate sufficient confidence in the score. Here we develop a scoring function called pcSM that detects true native structure in the top 5 with 93% accuracy from an ensemble of candidate structures. If we eliminate the native from ensemble of decoys then pcSM is able to capture near native structure (RMSD<=5Ǻ) in top 10 with 86% accuracy. The parameters considered in pcSM are a C-alpha Euclidean metric, secondary structural propensity, surface areas and an intramolecular energy function. pcSM has been tested on 415 systems consisting 142,698 decoys (public and CASP-largest reported hitherto in literature). The average rank for the native is 2.38, a significant improvement over that existing in literature. In-silico protein structure prediction requires robust scoring technique(s). Therefore, pcSM is easily amenable to integration into a successful protein structure prediction strategy. The tool is freely available at http://www.scfbio-iitd.res.in/software/pcsm.jsp.

Membrane proteins: from bench to bits

Membrane proteins currently receive a lot of attention, in large part thanks to a steady stream of high-resolution X-ray structures. Although the first few structures showed proteins composed of tightly packed bundles of very hydrophobic more or less straight transmembrane α-helices, we now know that helix-bundle membrane proteins can be both highly flexible and contain transmembrane segments that are neither very hydrophobic nor necessarily helical throughout their lengths. This raises questions regarding how membrane proteins are inserted into the membrane and fold in vivo, and also complicates life for bioinformaticians trying to predict membrane protein topology and structure.

Simulaid: a simulation facilitator and analysis program

Simulaid performs a large number of simulation-related tasks: interconversion and modification of structure and trajectory files, optimization of orientation, and a large variety of analysis functions. The program can handle structures in PDB (Berman et al., Nucleic Acids Res 2000, 28, 235), Charmm (Brooks et al., J Comput Chem 4, 187) CRD, Amber (Case et al.), Macromodel (Mohamadi et al., J Comput Chem 1990, 11, 440), Gromos/Gromacs (Hess et al.), InsightII (InsightII. Accelrys Inc.: San Diego, 2005), Grasp (Nicholls et al., Proteins: Struct Funct Genet 1991, 11, 281) .crg, Tripos (Tripos International, S. H. R., St. Louis, MO) .mol2 (input only), and in the MMC (Mezei, M.; MMC: Monte Carlo program for molecular assemblies. Available at: http://inka.mssm.edu/~mezei/mmc) formats; and trajectories in the formats of Charmm, Amber, Macromodel, and MMC. Analysis features include (but are not limited to): (1) simple distance calculations and hydrogen-bond analysis, (2) calculation of 2-D RMSD maps (produced both as text file with the data and as a color-coded matrix) and cross RMSD maps between trajectories, (3) clustering based on RMSD maps, (4) analysis of torsion angles, Ramachandran (Ramachandran and Sasiskharan, Adv Protein Chem 1968, 23, 283) angles, proline kink (Visiers et al., Protein Eng 2000, 13, 603) angles, pseudorotational (Altona and Sundaralingam, J Am Chem Soc 1972, 94, 8205; Cremer and Pople, J Am Chem Soc 1975, 97, 1354) angles, and (5) analysis based on circular variance (Mezei, J Mol Graphics Model 2003, 21, 463). Torsion angle evolutions are presented in dial plots (Ravishanker et al., J Biomol Struct Dyn 1989, 6, 669). Several of these features are unique to Simulaid.

A computational pathway for bracketing native-like structures fo small alpha helical globular proteins

Impressive advances in the applications of bioinformatics for protein structure prediction coupled with growing structural databases on one hand and the insurmountable time-scale problem with ab initio computational methods on the other continue to raise doubts whether a computational solution to the protein folding problem--categorized as an NP-hard problem--is within reach in the near future. Combining some specially designed biophysical filters and vector algebra tools with ab initio methods, we present here a promising computational pathway for bracketing native-like structures of small alpha helical globular proteins departing from secondary structural information. The automated protocol is initiated by generating multiple structures around the loops between secondary structural elements. A set of knowledge-based biophysical filters namely persistence length and radius of gyration, developed and calibrated on approximately 1000 globular proteins, is introduced to screen the trial structures to filter out improbable candidates for the native and reduce the size of the library of probable structures. The ensemble so generated encompasses a few structures with native-like topology. Monte Carlo optimizations of the loop dihedrals are then carried out to remove steric clashes. The resultant structures are energy minimized and ranked according to a scoring function tested previously on a series of decoy sets vis-a-vis their corresponding natives. We find that the 100 lowest energy structures culled from the ensemble of energy optimized trial structures comprise at least a few to within 3-5 angstroms of the native. Thus the formidable "needle in a haystack" problem is narrowed down to finding an optimal solution amongst a computationally tractable number of alternatives. Encouraging results obtained on twelve small alpha helical globular proteins with the above outlined pathway are presented and discussed.

Environmental adaptation of proteins: regression models with simple physicochemical properties

Bio-sequences from ortholog proteins are well suited for statistical inference when the sequences can be divided into ordinal groups based on known environmental features or traits of the host organisms. In this paper two new regression models are described for extracting proteomic trends of extreme environments. The approach is based on physicochemical properties of the amino acids, and may also utilise stratification of the data. We are especially looking for connections of temperature adaptation between the organism and its molecular level. To show the applicability of the methods, we present analyses of genomic data from proteobacteria orders, where we examine the cold adaptation of membrane proteins, intracellular proteins, and the enzyme endonuclease I. Our results confirm earlier findings that redistribution of charge and increase of surface hydrophobicity might be some of the most important signatures for cold adaptation.

Intrinsic amino acid side-chain hydrophilicity/hydrophobicity coefficients determined by reversed-phase high-performance liquid chromatography of model peptides: comparison with other hydrophilicity/hydrophobicity scales

An accurate determination of the intrinsic hydrophilicity/hydrophobicity of amino acid side-chains in peptides and proteins is fundamental in understanding many area of research, including protein folding and stability, peptide and protein function, protein-protein interactions and peptide/protein oligomerization, as well as the design of protocols for purification and characterization of peptides and proteins. Our definition of intrinsic hydrophilicity/hydrophobicity of side-chains is the maximum possible hydrophilicity/hydrophobicity of side-chains in the absence of any nearest-neighbor effects and/or any conformational effects of the polypeptide chain that prevent full expression of side-chain hydrophilicity/hydrophobicity. In this review, we have compared an experimentally derived intrinsic side-chain hydrophilicity/hydrophobicity scale generated from RP-HPLC retention behavior of de novo designed synthetic model peptides at pH 2 and pH 7 with other RP-HPLC-derived scales, as well as scales generated from classic experimental and calculation-based methods of octanol/water partitioning of Nalpha-acetyl-amino-acid amides or free energy of transfer of free amino acids. Generally poor correlation was found with previous RP-HPLC-derived scales, likely due to the random nature of the peptide mixtures in terms of varying peptide size, conformation and frequency of particular amino acids. In addition, generally poor correlation with the classical approaches served to underline the importance of the presence of a polypeptide backbone when generating intrinsic values. We have shown that the intrinsic scale determined here is in full agreement with the structural characteristics of amino acid side-chains.

Impact of residue accessible surface area on the prediction of protein secondary structures

Background

The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.

Results

We studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained.

Conclusion

The success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.

A generalized analysis of hydrophobic and loop clusters within globular protein sequences

Background

Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet.

Results

The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic clusters.

Conclusion

The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction.

Sequence analyses of Type I and Type II chains in human hair and epithelial keratin intermediate filaments: promiscuous obligate heterodimers, Type II template for molecule formation and a rationale for heterodimer formation

Sequence comparisons have been undertaken for all hair and epithelial keratin IF chains from a single species--human. The results lead to several new proposals. First, it is clear that not only is the chain structure of the molecule an obligate heterodimer but promiscuous association of Type I and Type II chains must occur in vivo. Second, the higher predicted content of alpha-helix in Type II chains in solution relative to that expected for Type I chains suggests that it is the Type II chains that precede their Type I counterparts and that they may serve as templates for molecule formation. Third, heterodimer formation leads naturally to greater structural and functional specificity, and this may be required not only because keratin IF have more interacting partners in its cell type than other types of IF have in theirs but also because hair and skin IF have two distinct structures that relate to the "reducing" or "oxidizing" environment in which they can find themselves. The transition between the two forms may require specific head/tail interactions and this, it is proposed, would be more easily accomplished by a heterodimer structure with its greater in-built specificity.

Wiggle-predicting functionally flexible regions from primary sequence

The Wiggle series are support vector machine-based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity.

A protein evolution model with independent sites that reproduces site-specific amino acid distributions from the Protein Data Bank

BACKGROUND

Since thermodynamic stability is a global property of proteins that has to be conserved during evolution, the selective pressure at a given site of a protein sequence depends on the amino acids present at other sites. However, models of molecular evolution that aim at reconstructing the evolutionary history of macromolecules become computationally intractable if such correlations between sites are explicitly taken into account.

RESULTS

We introduce an evolutionary model with sites evolving independently under a global constraint on the conservation of structural stability. This model consists of a selection process, which depends on two hydrophobicity parameters that can be computed from protein sequences without any fit, and a mutation process for which we consider various models. It reproduces quantitatively the results of Structurally Constrained Neutral (SCN) simulations of protein evolution in which the stability of the native state is explicitly computed and conserved. We then compare the predicted site-specific amino acid distributions with those sampled from the Protein Data Bank (PDB). The parameters of the mutation model, whose number varies between zero and five, are fitted from the data. The mean correlation coefficient between predicted and observed site-specific amino acid distributions is larger than <r> = 0.70 for a mutation model with no free parameters and no genetic code. In contrast, considering only the mutation process with no selection yields a mean correlation coefficient of <r> = 0.56 with three fitted parameters. The mutation model that best fits the data takes into account increased mutation rate at CpG dinucleotides, yielding <r> = 0.90 with five parameters.

CONCLUSION

The effective selection process that we propose reproduces well amino acid distributions as observed in the protein sequences in the PDB. Its simplicity makes it very promising for likelihood calculations in phylogenetic studies. Interestingly, in this approach the mutation process influences the effective selection process, i.e. selection and mutation must be entangled in order to obtain effectively independent sites. This interdependence between mutation and selection reflects the deep influence that mutation has on the evolutionary process: The bias in the mutation influences the thermodynamic properties of the evolving proteins, in agreement with comparative studies of bacterial proteomes, and it also influences the rate of accepted mutations.

Hendecad repeat in segment 2A and linker L2 of intermediate filament chains implies the possibility of a right-handed coiled-coil structure

The conformation adopted by intermediate filament chains (IF) has been described in terms of a central rod domain with four, alpha-helical, left-handed coiled-coil segments (1A, 1B, 2A, and 2B) joined by linkers (L1, L12, and L2, respectively). The rod domain is terminated at its N- and C-terminal ends by "globular" head and tail domains, respectively. This analysis, initially undertaken about 20-25 years ago, was based on the recognition of an underlying heptad substructure in the sequence of the rod domain, the presence of which can be directly associated with an alpha-helical coiled-coil structure. In this work, a hendecad sequence motif that is closely related to the heptad repeat but which is nonetheless significantly different from it has been recognized in the primary structure of segments 2A and linker L2. This motif, which is 11 residues long and structurally equivalent to a true heptad plus another heptad with an inclusive stutter, is consistent with the chains adopting a continuous right-handed coiled-coil structure with a long-period pitch length. It is therefore predicted that segment 2 as a whole may have a coiled-coil conformation with both right-handed (2A+L2) and left-handed (2B) regions. The changeover in handedness would be expected to occur at the C-terminal end of linker L2 and N-terminal end of segment 2B.

Understanding the human estrogen receptor-alpha using targeted mutagenesis

The estrogen receptor-alpha is a wonderfully complex protein important in normal biology, breast cancer, and as a target for anti-cancer agents. We are using the available structures of the hERalpha as well as secondary structure predictions to guide site-directed mutagenesis in order to test the importance of specific interactions and regions in the ligand-regulated activity of the protein. In one area of interest, we are investigating the role of the F domain in the ligand-stimulated activity of the hERalpha. Results from our laboratory and others suggest that the F domain modulates the activity of the hERalpha. In order to better understand the role of the F domain in the hERalpha, we have constructed mutants within this region. Mutations within a predicted alpha-helical region alter the response of the ER to estradiol (E2), eliminate or impair the agonist activity of 4-hydroxytamoxifen (4-OHT), and alter the ability of E2 to overcome 4-OHT's antagonist activity. Deleting the F domain increases the affinity of the receptor for E2; by contrast, mutating a residue in the middle of the predicted helix to a proline does not alter the affinity for E2, but does change the binding mechanism from a positive cooperative to a noncooperative interaction. These and other results show the F domain exhibits substantial functional complexity, and support the idea that this domain modulates the activity of the hERalpha. In a second area of interest, we are investigating the role of hydrophobic and hydrogen-bonding interactions at the start of helix 12 in the activity of the hERalpha. Leucine-536 (L536) has been proposed to participate in hydrophobic interactions that form part of a capping motif stabilizing the start of helix 12. When mutated, the resulting receptors exhibit a reduced response, or even an inverted response, to E2 and 4-OHT on both ERE-driven and AP-1-driven promoters. Interestingly, these mutated receptors also exhibit altered interactions with probes that recognize the agonist-bound and 4-OHT-bound conformations of the ERalpha. Thus, L536 couples the binding of ligand with the conformation of the receptor. Overall, these results show that combining structure-based hypotheses with functional tests of the ER's activity can identify regions and interactions that are important in the ligand-stimulated activity of the protein.

Determination of intrinsic hydrophilicity/hydrophobicity of amino acid side chains in peptides in the absence of nearest-neighbor or conformational effects

Understanding the hydrophilicity/hydrophobicity of amino acid side chains in peptides/proteins is one the most important aspects of biology. Though many hydrophilicity/hydrophobicity scales have been generated, an "intrinsic" scale has yet to be achieved. "Intrinsic" implies the maximum possible hydrophilicity/hydrophobicity of side chains in the absence of nearest-neighbor or conformational effects that would decrease the full expression of the side-chain hydrophilicity/hydrophobicity when the side chain is in a polypeptide chain. Such a scale is the fundamental starting point for determining the parameters that affect side-chain hydrophobicity and for quantifying such effects in peptides and proteins. A 10-residue peptide sequence, Ac-X-G-A-K-G-A-G-V-G-L-amide, was designed to enable the determination of the intrinsic values, where position X was substituted by all 20 naturally occurring amino acids and norvaline, norleucine, and ornithine. The coefficients were determined by reversed-phase high-performance liquid chromatography using six different mobile phase conditions involving different pH values (2, 5, and 7), ion-pairing reagents, and the presence and absence of different salts. The results show that the intrinsic hydrophilicity/hydrophobicity of amino acid side chains in peptides (proteins) is independent of pH, buffer conditions, or whether C(8) or C(18) reversed-phase columns were used for 17 side chains (Gly, Ala, Cys, Pro, Val, nVal, Leu, nLeu, Ile, Met, Tyr, Phe, Trp, Ser, Thr, Asn, and Gln) and dependent on pH and buffer conditions, including the type of salt or ion-pairing reagent for potentially charged side chains (Orn, Lys, His, Arg, Asp, and Glu).

Optimality of codon usage in Escherichia coli due to load minimization

The canonical genetic code is known to be highly efficient in minimizing the effects of mistranslational errors and point mutations, an ability which in term is designated "load minimization". One parameter involved in calculating the load minimizing property of the genetic code is codon usage. In most bacteria, synonymous codons are not used with equal frequencies. Different factors have been proposed to contribute to codon usage preference. It has been shown that the codon preference is correlated with the composition of the tRNA pool. Selection for translational efficiency and translational accuracy both result in such a correlation. In this work, it is shown that codon usage bias in Escherichia coli works so as to minimize the consequences of translational errors, i.e. optimized for load minimization.

Looking at structure, stability, and evolution of proteins through the principal eigenvector of contact matrices and hydrophobicity profiles

We review and further develop an analytical model that describes how thermodynamic constraints on the stability of the native state influence protein evolution in a site-specific manner. To this end, we represent both protein sequences and protein structures as vectors: structures are represented by the principal eigenvector (PE) of the protein contact matrix, a quantity that resembles closely the effective connectivity of each site; sequences are represented through the "interactivity" of each amino acid type, using novel parameters that are correlated with hydropathy scales. These interactivity parameters are more strongly correlated than the other hydropathy scales that we examine with: (1) the change upon mutations of the unfolding free energy of proteins with two-states thermodynamics; (2) genomic properties as the genome-size and the genome-wide GC content; (3) the main eigenvectors of the substitution matrices. The evolutionary average of the interactivity vector correlates very strongly with the PE of a protein structure. Using this result, we derive an analytic expression for site-specific distributions of amino acids across protein families in the form of Boltzmann distributions whose "inverse temperature" is a function of the PE component. We show that our predictions are in agreement with site-specific amino acid distributions obtained from the Protein Data Bank, and we determine the mutational model that best fits the observed site-specific amino acid distributions. Interestingly, the optimal model almost minimizes the rate at which deleterious mutations are eliminated by natural selection.

Genomic determinants of protein folding thermodynamics in prokaryotic organisms

Here we investigate how thermodynamic properties of orthologous proteins are influenced by the genomic environment in which they evolve. We performed a comparative computational study of 21 protein families in 73 prokaryotic species and obtained the following main results. (i) Protein stability with respect to the unfolded state and with respect to misfolding are anticorrelated. There appears to be a trade-off between these two properties, which cannot be optimized simultaneously. (ii) Folding thermodynamic parameters are strongly correlated with two genomic features, genome size and G+C composition. In particular, the normalized energy gap, an indicator of folding efficiency in statistical mechanical models of protein folding, is smaller in proteins of organisms with a small genome size and a compositional bias towards A+T. Such genomic features are characteristic for bacteria with an intracellular lifestyle. We interpret these correlations in light of mutation pressure and natural selection. A mutational bias toward A+T at the DNA level translates into a mutational bias toward more hydrophobic (and in general more interactive) proteins, a consequence of the structure of the genetic code. Increased hydrophobicity renders proteins more stable against unfolding but less stable against misfolding. Proteins with high hydrophobicity and low stability against misfolding occur in organisms with reduced genomes, like obligate intracellular bacteria. We argue that they are fixed because these organisms experience weaker purifying selection due to their small effective population sizes. This interpretation is supported by the observation of a high expression level of chaperones in these bacteria. Our results indicate that the mutational spectrum of a genome and the strength of selection significantly influence protein folding thermodynamics.

Hydrophobic peptide tags as tools in bioseparation

Hydrophobic interactions are highly selective, and differences in surface hydrophobicities between proteins can be used as an efficient handle to facilitate protein isolation. Aromatic amino acid residues are of particular importance for molecular recognition because they have a key role in several biological functions. The hydrophobicity of a protein can easily be altered with minor genetic modifications, such as site-directed mutagenesis or fusions of hydrophobic peptide tags. An important advantage of hydrophobic peptide tags over traditional affinity tags is the possibility of exploring simple and inexpensive bioseparation materials. Recent results demonstrate the potential of hydrophobic interaction chromatography and aqueous two-phase systems as tools to study relative hydrophobicities of recombinant proteins with only minor alterations. This review focuses on hydrophobic peptide tags as fusion partners, which can be used as important tools in bioseparation.

Discrimination of single amino acid mutations of the p53 protein by means of deterministic singularities of recurrence quantification analysis

p53 is mutated in roughly 50% of all human tumors, predominantly in the DNA-binding domain codons. Structural, biochemical, and functional studies have reported that the different p53 mutants possess a broad range of behaviors that include the elimination of the tumor-suppression function of wild-type protein, the acquisition of dominant-negative function over the wild-type form, and the establishment of gain-of-function activities. The contribution of each of these types of mutations to tumor progression, grade of malignancy, and response to anticancer treatments has been so far analyzed only for a few "hot-spots." In an attempt to identify new approaches to systematically characterize the complete spectrum of p53 mutations, we applied recurrence quantification analysis (RQA), a non-linear signal analysis technique, to p53 primary structure. Moving from the study of the p53 hydrophobicity pattern, which revealed important similarities with the singular deterministic structuring of prions, we could statistically discriminate, on a pure amino acid sequence basis, between experimentally characterized DNA-contact defective and conformational p53 mutants with a very high percentage of success. This result indicates that RQA is a mathematical tool particularly advantageous for the development of a database of p53 mutations that integrates epidemiological data with structural and functional categorizations.

Evaluation of methods for measuring amino acid hydrophobicities and interactions

The concept of hydrophobicity has been addressed by researchers in all aspects of science, particularly in the fields of biology and chemistry. Over the past several decades, the study of the hydrophobicity of biomolecules, particularly amino acids has resulted in the development of a variety of hydrophobicity scales. In this review, we discuss the various methods of measuring amino acid hydrophobicity and provide explanations for the wide range of rankings that exist among these published scales. A discussion of the literature on amino acid interactions is also presented. Only a surprisingly small number of papers exist in this rather important area of research; measuring pairwise amino acid interactions will aid in understanding structural aspects of proteins.

Stability scale and atomic solvation parameters extracted from 1023 mutation experiments

The stability scale of 20 amino acid residues is derived from a database of 1023 mutation experiments on 35 proteins. The resulting scale of hydrophobic residues has an excellent correlation with the octanol-to-water transfer free energy corrected with an additional Flory-Huggins molar-volume term (correlation coefficient r = 0.95, slope = 1.05, and a near zero intercept). Thus, hydrophobic contribution to folding stability is characterized remarkably well by transfer experiments. However, no corresponding correlation is found for hydrophilic residues. Both the hydrophilic portion and the entire scale, however, correlate strongly with average burial accessible surface (r = 0.76 and 0.97, respectively). Such a strong correlation leads to a near uniform value of the atomic solvation parameters for atoms C, S, O/N, O(-0.5), and N(+0.5,1). All are in the range of 12-28 cal x mol(-1) A(-2), close to the original estimate of hydrophobic contribution of 25-30 cal x mol(-1) A(-2) to folding stability. Without any adjustable parameters, the new stability scale and new atomic solvation parameters yielded an accurate prediction of protein-protein binding free energy for a separate database of 21 protein-protein complexes (r = 0.80 and slope = 1.06, and r = 0.83 and slope = 0.93, respectively).

On filtering false positive transmembrane protein predictions

While helical transmembrane (TM) region prediction tools achieve high (>90%) success rates for real integral membrane proteins, they produce a considerable number of false positive hits in sequences of known nontransmembrane queries. We propose a modification of the dense alignment surface (DAS) method that achieves a substantial decrease in the false positive error rate. Essentially, a sequence that includes possible transmembrane regions is compared in a second step with TM segments in a sequence library of documented transmembrane proteins. If the performance of the query sequence against the library of documented TM segment-containing sequences in this test is lower than an empirical threshold, it is classified as a non-transmembrane protein. The probability of false positive prediction for trusted TM region hits is expressed in terms of E-values. The modified DAS method, the DAS-TMfilter algorithm, has an unchanged high sensitivity for TM segments ( approximately 95% detected in a learning set of 128 documented transmembrane proteins). At the same time, the selectivity measured over a non-redundant set of 526 soluble proteins with known 3D structure is approximately 99%, mainly because a large number of falsely predicted single membrane-pass proteins are eliminated by the DAS-TMfilter algorithm.

C-terminal domain of gyrase A is predicted to have a beta-propeller structure

Two different type II topoisomerases are known in bacteria. DNA gyrase (Gyr) introduces negative supercoils into DNA. Topoisomerase IV (Par) relaxes DNA supercoils. GyrA and ParC subunits of bacterial type II topoisomerases are involved in breakage and reunion of DNA. The spatial structure of the C-terminal fragment in GyrA/ParC is not available. We infer homology between the C-terminal domain of GyrA/ParC and a regulator of chromosome condensation (RCC1), a eukaryotic protein that functions as a guanine-nucleotide-exchange factor for the nuclear G protein Ran. This homology, complemented by detection of 6 sequence repeats with 4 predicted beta-strands each in GyrA/ParC sequences, allows us to predict that the GyrA/ParC C-terminal domain folds into a 6-bladed beta-propeller. The prediction rationalizes available experimental data and sheds light on the spatial properties of the largest topoisomerase domain that lacks structural information.

Sequence comparisons of intermediate filament chains: evidence of a unique functional/structural role for coiled-coil segment 1A and linker L1

A comprehensive analysis of the sequences of all types of intermediate filament chains has been undertaken with a particular emphasis on those of segment 1A and linker L1. This has been done to assess whether structural characteristics can be recognized in the sequences that would be consistent with the role of each region in the recently proposed "swinging head" hypothesis. The analyses show that linker L1 is the most flexible rod domain region, that it is the most elongated structure (on a per residue basis), and that it is the most variable region as regards sequence and length. Segment 1A has one of the two most highly conserved regions of sequence in the rod domain (the other being at the end of segment 2B), with seven particular residues conserved across all chain types. It also contains one of the very few potential interchain ionic interactions that could be conserved across all chain types. However, the aggregation of chains in segment 1A is specified less precisely overall by interchain ionic interactions than are the other coiled-coil segments. The apolar residue contents in positions a and d of the heptad substructure are the highest of any coiled-coil segment in the intermediate filament family. Segment 1A also displays an amino acid composition atypical of not only coiled-coil segments 1B and 2B, but indeed of two-stranded coiled coils in general. Nonetheless, molecular modeling based on the crystal structure of the monomeric 1A fragment from human vimentin shows that coiled-coil formation is plausible. The most extensive regions of apolar/aromatic residues lie at the C-terminal end of segment 2B in the helix termination motif and in segment 1A in and close to the helix initiation motif. The predicted stability of the individual alpha-helices in segment 1A is greater than in those comprising segments 1B and 2B, though potential intrachain ionic interactions are either lacking or are minimal in number. Analysis of the 1A sequence and those regions immediately N- and C-terminal to it has shown that the capping residues are near optimal close to the previously predicted ends, thus adding to the likely stability of the alpha-helical structure. However, a second terminating sequence is predicted in 1A (about 10 residues back from the C-terminus). This allows the possibility of some unwinding of the alpha-helical structure of 1A immediately adjacent to linker L1 when the head domains no longer stabilize the coiled-coil structure. All of these data are consistent with the concept of a flexible hinge at L1 and with the ability of the two alpha-helical coiled-coil strands to separate under appropriate conditions and partly unwind at their C-terminal ends to allow the head domains a greater degree of mobility, thus facilitating function.

Predicting protein conformation by statistical methods

The unique folded structure makes a polypeptide a functional protein. The number of known sequences is about a hundred times larger than the number of known structures and the gap is increasing rapidly. The primary goal of all structure prediction methods is to obtain structure-related information on proteins, whose structures have not been determined experimentally. Besides this goal, the development of accurate prediction methods helps to reveal principles of protein folding. Here we present a brief survey of protein structure predictions based on statistical analyses of known sequence and structure data. We discuss the background of these methods and attempt to elucidate principles, which govern structure formation of soluble and membrane proteins.