Number Of Secondary Structure Elements Research Articles

The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement.

We present fast and simple-to-implement measures of the entanglement of protein tertiary structures which are appropriate for highly flexible structure comparison. These are performed using the SKMT algorithm, a novel method of smoothing the Cα backbone to achieve a minimal complexity curve representation of the manner in which the protein's secondary structure elements fold to form its tertiary structure. Its subsequent complexity is characterised using measures based on the writhe and crossing number quantities heavily utilised in DNA topology studies, and which have shown promising results when applied to proteins recently. The SKMT smoothing is used to derive empirical bounds on a protein's entanglement relative to its number of secondary structure elements. We show that large scale helical geometries dominantly account for the maximum growth in entanglement of protein monomers, and further that this large scale helical geometry is present in a large array of proteins, consistent across a number of different protein structure types and sequences. We also show how these bounds can be used to constrain the search space of protein structure prediction from small angle x-ray scattering experiments, a method highly suited to determining the likely structure of proteins in solution where crystal structure or machine learning based predictions often fail to match experimental data. Finally we develop a structural comparison metric based on the SKMT smoothing which is used in one specific case to demonstrate significant structural similarity between Rossmann fold and TIM Barrel proteins, a link which is potentially significant as attempts to engineer the latter have in the past produced the former. We provide the SWRITHE interactive python notebook to calculate these metrics.

Terahertz Spectral Domain Computational Analysis of Hydration Shell of Proteins with Increasingly Complex Tertiary Structure

Solvation dynamics of biomolecules and water in a hydration shell have been studied by different methods; however, a clear picture of this process is not yet established. Terahertz (THz) studies of molecular behavior in binary solutions present unique information on the picosecond motions of molecules. A complete mechanical interpretation of THz absorption spectra associated with solvated biomolecules remains a challenging task. The Gromacs molecular dynamics (MD) simulation package is used here to examine the spectral characteristics of water molecules in close proximity to biomolecules using vibrational density of states (VDOS). Systematic simulation of solvation dynamics of proteins of different size and tertiary structure are presented. The following have been selected for analysis. They range from less to more complex tertiary structure (corresponding to an increased number of secondary structure elements): TRP-cage13-20 peptide, TRP-cage, BPTI, and lysozyme. The initial study predicts that the depth of the hydration shell, determined by VDOS of water, extends to approximately 10 Å and does not depend on protein size. Furthermore the integral perturbation coefficient of the whole solvation layer is found to be increased for larger proteins due to a higher retardation rate of water molecules in their shells. Differences in solvation dynamics among the proteins considered originate primarily from the water molecules buried in the interior of the protein and not from the on-surface molecules.

Automatic structure classification of small proteins using random forest

BackgroundRandom forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.ResultsRandom forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP Class, Fold, Super-family or Family levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.ConclusionsThe utility of random forest in classifying domains from the place-holder classes of SCOP to the true Class, Fold, Super-family or Family levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.

Exploring protein structural dissimilarity to facilitate structure classification.

BackgroundClassification of newly resolved protein structures is important in understanding their architectural, evolutionary and functional relatedness to known protein structures. Among various efforts to improve the database of Structural Classification of Proteins (SCOP), automation has received particular attention. Herein, we predict the deepest SCOP structural level that an unclassified protein shares with classified proteins with an equal number of secondary structure elements (SSEs).ResultsWe compute a coefficient of dissimilarity (Ω) between proteins, based on structural and sequence-based descriptors characterising the respective constituent SSEs. For a set of 1,661 pairs of proteins with sequence identity up to 35%, the performance of Ω in predicting shared Class, Fold and Super-family levels is comparable to that of DaliLite Z score and shows a greater than four-fold increase in the true positive rate (TPR) for proteins sharing the Family level. On a larger set of 600 domains representing 200 families, the performance of Z score improves in predicting a shared Family, but still only achieves about half of the TPR of Ω. The TPR for structures sharing a Super-family is lower than in the first dataset, but Ω performs slightly better than Z score. Overall, the sensitivity of Ω in predicting common Fold level is higher than that of the DaliLite Z score.ConclusionClassification to a deeper level in the hierarchy is specific and difficult. So the efficiency of Ω may be attractive to the curators and the end-users of SCOP. We suggest Ω may be a better measure for structure classification than the DaliLite Z score, with the caveat that currently we are restricted to comparing structures with equal number of SSEs.

Symmetric Connectivity of Secondary Structure Elements Enhances the Diversity of Folding Pathways

The influence of native connectivity of secondary structure elements (SSE) on folding is studied using coarse-grained models of proteins with mixed alpha and beta structure and the analysis of the structural database of wild-type proteins. We found that the distribution of SSE along a sequence determines the diversity of folding pathways. If alpha and beta SSE are localized in different parts of a sequence, the diversity of folding pathways is restricted. An even (symmetric) distribution of alpha and beta SSE with respect to sequence midpoint favors multiple folding routes. Simulations are supplemented by the database analysis of the distribution of SSE in wild-type protein sequences. On an average, two-thirds of wild-type proteins with mixed alpha and beta structure have symmetric distribution of alpha and beta SSE. The propensity for symmetric distribution of SSE is especially evident for large proteins with the number of SSE > or = 10. We suggest that symmetric SSE distribution in protein sequences may arise due to nearly random allocation of alpha and beta structure along wild-type sequences. The tendency of long sequences to misfold is perhaps compensated by the enhanced pathway diversity. In addition, folding pathways are shown to progress via hierarchic assembly of SSE in accordance with their proximity along a sequence. We demonstrate that under mild denaturation conditions folding and unfolding pathways are similar. However, the reversibility of folding/unfolding pathways is shown to depend on the distribution of SSE. If alpha and beta SSE are localized in different parts of a sequence, folding and unfolding pathways are likely to coincide.

Protein Similarity from Knot Theory: Geometric Convolution and Line Weavings

Shape similarity is one of the most elusive and intriguing questions of nature and mathematics. Proteins provide a rich domain in which to test theories of shape similarity. Proteins can match at different scales and in different arrangements. Sometimes the detection of common local structure is sufficient to infer global alignment of two proteins; at other times it provides false information. Proteins with very low sequence identity may share large substructures, or perhaps just a central core. There are even examples of proteins with nearly identical primary sequences in which alpha-helices have become beta-sheets. Shape similarity can be formulated (i) in terms of global metrics, such as RMSD or Hausdorff distance, (ii) in terms of subgraph isomorphisms, such as the detection of shared substructures with similar relative locations, or (iii) purely topologically, in terms of structure preserving transformations. Existing protein structure detection programs are built on the first two types of similarity. The third forms the foundations of knot theory. The thesis of this paper is this: Protein similarity detection leads naturally to algorithms operating at the metric, relational, and isotopic scales. The paper introduces a definition of similarity based on atomic motions that preserve local backbone topology without incurring significant distance errors. Such motions are motivated by the physical requirements for rearranging subsequences of a protein. Similarity detection then seeks rigid body motions able to overlay pairs of substructures, each related by a substructure-preserving motion, without necessarily requiring global structure preservation. This definition is general enough to span a wide range of questions: One can ask for full rearrangement of one protein into another while preserving global topology, as in drug design; or one can ask for rearrangements of sets of smaller substructures, preserving local but not global topology, as in protein evolution. In the appendix, we exhibit an algorithm for answering the general rearrangement question. That algorithm has the complexity of robot motion planning. In the text, we consider a more common case in which one seeks protein similarity by rearrangements of relatively short peptide segments. We exhibit two algorithms, one based on writhing numbers and one based on line weavings. The algorithms have time complexities O(n (4)) and O(s (11)), respectively, where n is the maximum number of residues in the proteins being compared and s is the number of secondary structure elements. In practice, the running times were nearly interactive. We report results obtained with a dozen pairs of proteins, exhibiting a range of typical features.

Analysis of fragments induced by simulated lattice protein folding

The folding process of a set of 42 proteins, representative of the various folds, has been simulated by means of a Monte Carlo method on a discrete lattice, using two different potentials of mean force. Multiple compact fragments of contiguous residues are formed in the simulation, stable in composition, but not in geometry. During time, the number of fragments decreases until one final compact globular state is reached. We focused on the early steps of the folding in order to evidence the maximum number of fragments, provided they are sufficiently stable in sequence. A correlation has been established between these proto fragments and regular secondary-structure elements, whatever their nature, alpha helices or beta strands. Quantitatively, this is revealed by an overall mean one-residue quality factor of nearly 60%, which is better for proteins mainly composed of alpha helices. The correspondence between the number of fragments and the number of secondary-structure elements is of 77% and the regions separating successive fragments are mainly located in loops. Besides, hydrophobic clusters deduced from HCA correspond to fragments with an equivalent accuracy. These results suggest that folding pathways do not contain structurally static intermediate. However, since the beginning of folding, most residues that will later form one given secondary structure are kept close in space by being involved in the same fragment. This aggregation may be a way to accelerate the formation of the native state and enforces the key role played by hydrophobic residues in the formation of the fragments, thus in the folding process itself. To cite this article: J. Chomilier et al., C. R. Biologies 327 (2004).

A coarse-grained, “realistic” model for protein folding

A phenomenological model Hamiltonian to describe the folding of a protein with any given sequence is proposed. The protein is thought of as a collection of pieces of helices; as a consequence its configuration space increases with the number of secondary structure elements rather than with the number of residues. The Hamiltonian presents both local (i.e., single helix, accounting for the stiffness of the chain) and non-local (interactions between hydrophobically–charged helices) terms, and is expected to provide a first tool for studying the folding of real proteins. The partition function for a simplified, but by no means trivial, version of the model is calculated almost completely in an analytical way. The latter simplified model is also applied to the study of a synthetic protein, and some preliminary results are shown.