Use of secondary structural information and C alpha-C alpha distance restraints to model protein structures with MODELLER

Protein secondary structure predictions and amino acid long range contact map predictions from primary sequence of proteins have been explored to aid in modelling protein tertiary structures. In order to evaluate the usefulness of secondary structure and 3D-residue contact prediction methods to model protein structures we have used the known Q3 (alpha-helix,beta-strands and irregular turns/loops) secondary structure information, along with residue-residue contact information as restraints for MODELLER. We present here results of our modelling studies on 30 best resolved single domain protein structures of varied lengths. The results shows that it is very difficult to obtain useful models even with 100% accurate secondary structure predictions and accurate residue contact predictions for up to 30% of residues in a sequence. The best models that we obtained for proteins of lengths 37, 70, 118, 136 and 193 amino acid residues are of RMSDs 4.17, 5.27, 9.12, 7.89 and 9.69,respectively. The results show that one can obtain better models for the proteins which have high percent of alpha-helix content. This analysis further shows that MODELLER restrain optimization program can be useful only if we have truly homologous structure(s) as a template where it derives numerous restraints, almost identical to the templates used. This analysis also clearly indicates that even if we satisfy several true residue-residue contact distances, up to 30%of their sequence length with fully known secondary structural information, we end up predicting model structures much distant from their corresponding native structures.     

Use of secondary structural information and C<Emphasis Type="Italic">α</Emphasis>-C<Emphasis Type="Italic">α</Emphasis> distance restraints to model protein structures with MODELLER

Protein secondary structure predictions and amino acid long range contact map predictions from primary sequence of proteins have been explored to aid in modelling protein tertiary structures. In order to evaluate the usefulness of secondary structure and 3D-residue contact prediction methods to model protein structures we have used the known Q3 (alpha-helix, beta-strands and irregular turns/loops) secondary structure information, along with residue-residue contact information as restraints for MODELLER. We present here results of our modelling studies on 30 best resolved single domain protein structures of varied lengths. The results shows that it is very difficult to obtain useful models even with 100% accurate secondary structure predictions and accurate residue contact predictions for up to 30% of residues in a sequence. The best models that we obtained for proteins of lengths 37, 70, 118, 136 and 193 amino acid residues are of RMSDs 4.17, 5.27, 9.12, 7.89 and 9.69, respectively. The results show that one can obtain better models for the proteins which have high percent of alpha-helix content. This analysis further shows that MODELLER restrain optimization program can be useful only if we have truly homologous structure(s) as a template where it derives numerous restraints, almost identical to the templates used. This analysis also clearly indicates that even if we satisfy several true residue-residue contact distances, up to 30% of their sequence length with fully known secondary structural information, we end up predicting model structures much distant from their corresponding native structures.     

Characterization of the backbone geometry of protein native state structures

We present a simple method for analyzing the geometry of noncovalent residue-residue interactions stabilizing the protein structure, which takes into account the constraints on the local backbone geometry. We find that the principal geometrical constraints are amino acid aspecific and are associated with hydrogen bond formation in helices and sheets. In contrast, amino acid residues in nonhelical and nonextended conformations, which make noncovalent interactions stabilizing the protein tertiary structure, display greater flexibility. We apply the method to an analysis of the packing of helices in helical bundle proteins requiring an efficient packing of amino acid side-chains of the interacting helices.     

Identification of amino acids involved in protein structural uniqueness: implication for de novo protein design

Structural uniqueness is characteristic of native proteins and is essential to express their biological functions. The major factors that bring about the uniqueness are specific interactions between hydrophobic residues and their unique packing in the protein core. To find the origin of the uniqueness in their amino acid sequences, we analyzed the distribution of the side chain rotational isomers (rotamers) of hydrophobic amino acids in protein tertiary structures and derived deltaS(contact), the conformational-entropy changes of side chains by residue-residue contacts in each secondary structure. The deltaS(contact) values indicate distinct tendencies of the residue pairs to restrict side chain conformation by inter-residue contacts. Of the hydrophobic residues in alpha-helices, aliphatic residues (Leu, Val, Ile) strongly restrict the side chain conformations of each other. In beta-sheets, Met is most strongly restricted by contact with Ile, whereas Leu, Val and Ile are less affected by other residues in contact than those in alpha-helices. In designed and native protein variants, deltaS(contact) was found to correlate with the folding-unfolding cooperativity. Thus, it can be used as a specificity parameter for designing artificial proteins with a unique structure.     

Distribution of amino acid residues and residue-residue contacts in molecular chaperones.

The amino acid distribution and residue-residue contacts in molecular chaperones are different when compared to normal globular proteins. The study of molecular chaperones reveals a different surrounding environment to exist for the residues Cys, Trp, and His which may play an important role in determining the chaperone structures. Unlike globular proteins, it has been observed that a one-to-one correspondence between the amino acid distribution in a sequence and the structures of molecular chaperones. The preference of amino acid residues surrounding all 20 types of residues in secondary structures and their accessible surface areas have been analysed.     

Protein packing: dependence on protein size, secondary structure and amino acid composition

We have used the occluded surface algorithm to estimate the packing of both buried and exposed amino acid residues in protein structures. This method works equally well for buried residues and solvent-exposed residues in contrast to the commonly used Voronoi method that works directly only on buried residues. The atomic packing of individual globular proteins may vary significantly from the average packing of a large data set of globular proteins. Here, we demonstrate that these variations in protein packing are due to a complex combination of protein size, secondary structure composition and amino acid composition. Differences in protein packing are conserved in protein families of similar structure despite significant sequence differences. This conclusion indicates that quality assessments of packing in protein structures should include a consideration of various parameters including the packing of known homologous proteins. Also, modeling of protein structures based on homologous templates should take into account the packing of the template protein structure.     

A study of the preferred environment of amino acid residues in globular proteins

In the native folded conformation of a globular protein, amino acid residues distant along the polypeptide chain come together to form the compact structure. This spatial structure is such that most of the polar residues are on the surface and have contact with the solvent medium and the nonpolar residues buried in the interior which have contact with similar nonpolar side chains. This cooperativity and mutual interaction among the randomly aligned amino acid residues suggest that each type of residue may prefer to have a specific environment. To gain more insight into this aspect of residue-residue cooperativity, a detailed analysis of the preferred environment associated with each of the 20 different amino acid residues in a number of protein crystals has been carried out. The variation of nonpolar nature computed for different sizes of spheres shows that the spatial region between radii of 6 and 8 Å is more favored for hydrophobic interactions and indicates that the influence of each residue over the surrounding medium extends predominantly up to a distance of 8 Å. The analysis of the surrounding amino acid residues associated with each type of residue shows that there is a definite tendency for each type of residue to have association with specific residues. The variation in environment is found even within the polar group as well as in the nonpolar group of residues. The surrounding residues associated with isoleucine, leucine, and valine are purely nonpolar. Proline, a nonpolar residue, is often surrounded by polar residues. The surrounding nonpolar nature of the tryptophan and tyrosine residues implies that even a single polar atom in a nonpolar side chain is sufficient to reduce their hydrophobic environment. There exists a high degree of mutual residue-residue cooperativity between the pairs glutamic acid-lysine, methionine-arginine, asparagine-tryptophan, and glutamine-proline, and the mutual residue-residue noncooperativity is high for the pairs methionine-aspartic acid, cysteine-glutamic acid, histidine-glutamine, and leucine-asparagine. The formation of secondary and tertiary structures is discussed in terms of the preferred environment and mutual cooperativity among various types of amino acid residues.     

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.     

Physicochemical evaluation of protein folds predicted by threading

Protein structure prediction remains an unsolved problem. Since prediction of the native structure seems very difficult, one usually tries to predict the correct fold of a protein. Here the "fold" is defined by the approximate backbone structure of the protein. However, physicochemical factors that determine the correct fold are not well understood. It has recently been reported that molecular mechanics energy functions combined with effective solvent terms can discriminate the native structures from misfolded ones. Using such a physicochemical energy function, we studied the factors necessary for discrimination of correct and incorrect folds. We first selected correct and incorrect folds by a conventional threading method. Then, all-atom models of those folds were constructed by simply minimizing the atomic overlaps. The constructed correct model representing the native fold has almost the same backbone structure as the native structure but differs in side-chain packing. Finally, the energy values of the constructed models were compared with that of the experimentally determined native structure. The correct model as well as the native structure showed lower energy than misfolded models. However, a large energy gap was found between the native structure and the correct model. By decomposing the energy values into their components, it was found that solvent effects such as the hydrophobic interaction or solvent shielding and the Born energy stabilized the correct model rather than the native structure. The large energetic stabilization of the native structure was attained by specific side-chain packing. The stabilization by solvent effects is small compared to that by side-chain packing. Therefore, it is suggested that in order to confidently predict the correct fold of a protein, it is also necessary to predict correct side-chain packing.     

Using physical features of protein core packing to distinguish real proteins from decoys

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. We used two sources to obtain datasets of decoys to compare with real protein structures: submissions to the biennial Critical Assessment of protein Structure Prediction competition, in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence, and also decoys generated by 3DRobot, which have user‐specified global root‐mean‐squared deviations from experimentally determined structures. Our analysis revealed that both sets of decoys possess cores that do not recapitulate the key features that define real protein cores. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a feed‐forward neural network, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoy structures with similar accuracy to that obtained by state‐of‐the‐art methods that incorporate many additional features. The small number of physical features makes our model interpretable, emphasizing the importance of protein packing and hydrophobicity in protein structure prediction.     

The interrelationships of side-chain and main-chain conformations in proteins

The accurate determination of a large number of protein structures by X-ray crystallography makes it possible to conduct a reliable statistical analysis of the distribution of the main-chain and side-chain conformational angles, how these are dependent on residue type, adjacent residue in the sequence, secondary structure, residue-residue interactions and location at the polypeptide chain termini. The interrelationship between the main-chain (phi, psi) and side-chain (chi 1) torsion angles leads to a classification of amino acid residues that simplify the folding alphabet considerably and can be a guide to the design of new proteins or mutational studies. Analyses of residues occurring with disallowed main-chain conformation or with multiple conformations shed some light on why some residues are less favoured in thermophiles.     

SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.     

Structural test of the parameterized-backbone method for protein design

Designing new protein folds requires a method for simultaneously optimizing the conformation of the backbone and the side-chains. One approach to this problem is the use of a parameterized backbone, which allows the systematic exploration of families of structures. We report the crystal structure of RH3, a right-handed, three-helix coiled coil that was designed using a parameterized backbone and detailed modeling of core packing. This crystal structure was determined using another rationally designed feature, a metal-binding site that permitted experimental phasing of the X-ray data. RH3 adopted the intended fold, which has not been observed previously in biological proteins. Unanticipated structural asymmetry in the trimer was a principal source of variation within the RH3 structure. The sequence of RH3 differs from that of a previously characterized right-handed tetramer, RH4, at only one position in each 11 amino acid sequence repeat. This close similarity indicates that the design method is sensitive to the core packing interactions that specify the protein structure. Comparison of the structures of RH3 and RH4 indicates that both steric overlap and cavity formation provide strong driving forces for oligomer specificity.     

Nonatomic solvent-driven Voronoi tessellation of proteins: an open tool to analyze protein folds

A three-dimensional Voronoi tessellation of folded proteins is used to analyze geometrical and topological properties of a set of proteins. To each amino acid is associated a central point surrounded by a Voronoi cell. Voronoi cells describe the packing of the amino acids. Special attention is given to reproduction of the protein surface. Once the Voronoi cells are built, a lot of tools from geometrical analysis can be applied to investigate the protein structure; volume of cells, number of faces per cell, and number of sides per face are the usual signatures of the protein structure. A distinct difference between faces related to primary, secondary, and tertiary structures has been observed. Faces threaded by the main-chain have on average more than six edges, whereas those related to helical packing of the amino acid chain have less than five edges. The faces on the protein surface have on average five edges within 1% error. The average number of faces on the protein surface for a given type of amino acid brings a new point of view in the characterization of the exposition to the solvent and the classification of amino acid as hydrophilic or hydrophobic. It may be a convenient tool for model validation.     

Refolding of Myoglobins of Nonhomologous Sequences by the Island Model

The folding process of sea hare myoglobin was simulated by the island model, which does not rely on sequence homologies or statistical inference from database of known structure. Sea hare myoglobin has low sequence homology (28%), but high structural similarity, with sperm whale myoglobin, which was already simulated by the island model. Their structural similarity is shown physiochemically from the distribution of hydrophobic-residue pairs, that is, the key pairs for packing of the secondary structures. Irrelevant to the sequence homology, the secondary structures can be packed into the tertiary structure through the hydrophobic interactions among the amino acid pairs responsible for the local structure formation. The results on the two species of myoglobins indicate that, in contrast to other prediction methods, the island model is applicable to any type of protein without extra information other than the distribution of hydrophobic-residue pairs and the positions of the secondary structures. Consequently the present results provide another verification of the validity of the island model for elucidating the mechanisms of protein folding and predicting protein structures.     

Inter-residue and solvent-residue interactions in proteins: a statistical study on experimental structures

A large set of protein structures resolved by X-ray or NMR techniques has been extracted from the Protein Data Bank and analyzed using statistical methods. In particular, we investigate the interactions between side chains and the interactions between solvent and side chains, pointing out on the possibility of including the solvent as part of a knowledge-based potential. The solvent-residue contacts are accounted for on the basis of the Voronoi's polyhedron analysis. Our investigation confirms the importance of hydrophobic residues in determining the protein stability. We observe that in general hydrophobic-hydrophobic interactions and, more specifically, aromatic-aromatic contacts tend to be increasingly distally separated in the primary sequence of proteins, thus connecting distinct secondary structure elements. A simple relation expressing the dependence of the protein free energy by the number of residues is proposed. Such a relation includes both the residue-residue and the solvent-residue contributions. The former is dominant for large size proteins, whereas for small sizes (number of residues less than 100) the two terms are comparable. Gapless threading experiments show that the solvent-residue knowledge-based potential yields a significant contribution with respect to discriminating the native structure of proteins. Such contribution is important especially for proteins of small size and is similar to that given by the most favorable residue-residue knowledge-based potential referring to hydrophobic-hydrophobic interactions such as isoleucine-leucine. In general, the inclusion of the solvent-residue interaction produces a relevant increase of the free energy gap between the native structures and decoys.     

Anatomy and evolution of proteins displaying the viral capsid jellyroll topology.

In this paper the anatomy of 25 structures containing a jellyroll motif, consisting of eight antiparallel beta-strands forming a so-called beta-barrel, was investigated. This involved performing a careful structural alignment based on hydrogen bonds for the equivalent regions of the tertiary folds and a subsequent analysis of conserved amino acids, equivalenced residue-residue contacts, and various parameters describing the size, shape and other geometrical characteristics of these regions. It was found that the jellyroll motif is best viewed as a two-sheet wedge structure rather than a barrel. The more conserved parameters are discussed. A model of evolutionary development for the jellyroll fold in the various protein and viral structures is proposed.     

Structure-based function inference using protein family-specific fingerprints

We describe a method to assign a protein structure to a functional family using family-specific fingerprints. Fingerprints represent amino acid packing patterns that occur in most members of a family but are rare in the background, a nonredundant subset of PDB; their information is additional to sequence alignments, sequence patterns, structural superposition, and active-site templates. Fingerprints were derived for 120 families in SCOP using Frequent Subgraph Mining. For a new structure, all occurrences of these family-specific fingerprints may be found by a fast algorithm for subgraph isomorphism; the structure can then be assigned to a family with a confidence value derived from the number of fingerprints found and their distribution in background proteins. In validation experiments, we infer the function of new members added to SCOP families and we discriminate between structurally similar, but functionally divergent TIM barrel families. We then apply our method to predict function for several structural genomics proteins, including orphan structures. Some predictions have been corroborated by other computational methods and some validated by subsequent functional characterization.     

Possibility of biocompatibility of fullerene C60 with oligopeptides using a comparative analysis of their spatial form

It was proposed to elucidate the mechanism of unique biological biocompatibility of carbon materials used for making endoprostheses for medicinal practice. For this purpose, a method of comparing the geometry of individual globular and fibrillar proteins and carbon structures (fullerenes) was advanced, and a comparative analysis of the spatial structure of fullerene C60 and the amino acid sequences of 286 proteins was made. Based on a high degree of similarity in the positions of atoms of the polypeptide chains of proteins and peptides and the corresponding atoms of fullerene and of other structural parameters revealed by the comparison of the spatial structures using mathematical simulation, the phenomenon of biological compatibility was interpreted as an "insertion" of fullerenes into the structure of protein molecules in place of structurally similar amino acid sequences, i.e., as a "prosthetics" at the molecular level. It is proposed that fullerenes can "simulate" structurally similar short peptides in biological processes. It was shown that noncarbon biogenic atoms play a large role in the formation of specific structure of protein molecules.