Protein Supersecondary Structure Research Articles

Crystal structure analysis of helix–turn–helix type motifs in α,γ-hybrid peptides

Design of helix–turn–helix type mimetics using short α,γ-hybrid peptides as helices and (E)-α,β-unsaturated γ-amino acids as conformationally rigid linkers and their conformations in single crystals are reported.

SBILib: a handle for protein modeling and engineering.

The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

Super Secondary Structures of Proteins with Post-Translational Modifications in Colon Cancer.

New advances in protein post-translational modifications (PTMs) have revealed a complex layer of regulatory mechanisms through which PTMs control cell signaling and metabolic pathways, contributing to the diverse metabolic phenotypes found in cancer. Using conformational templates and the three-dimensional (3D) environment investigation of proteins in patients with colorectal cancer, it was demonstrated that most PTMs (phosphorylation, acetylation, and ubiquitination) are localized in the supersecondary structures (helical pairs). We showed that such helical pairs are represented on the outer surface of protein molecules and characterized by a largely accessible area for the surrounding solvent. Most promising and meaningful modifications were observed on the surface of vitamin D-binding protein (VDBP), complement C4-A (CO4A), X-ray repair cross-complementing protein 6 (XRCC6), Plasma protease C1 inhibitor (IC1), and albumin (ALBU), which are related to colorectal cancer developing. Based on the presented data, we propose the impact of the observed modifications in immune response, inflammatory reaction, regulation of cell migration, and promotion of tumor growth. Here, we suggest a computational approach in which high-throughput analysis for identification and characterization of PTM signature, associated with cancer metabolic reprograming, can be improved to prognostic value and bring a new strategy to the targeted therapy.

Clustering Protein Super Secondary Structure with Artificial Neural Network Based Self Organizing Map Machine Learning Approach Using In-Memory Computing Environment

Protein’s super secondary structure knowledge is important to understand protein functions which is vital for all living organisms. In this research, an in-memory computing approach is created to identify the super secondary structure of protein on primary protein sequences. The Artificial Neural Network (ANN) based Self Organizing Map (SOM) algorithm was used to develop a three-way predictive model. The developed clustering model helps to assign protein super secondary structures mainly into â-Turns. Also this model is implemented in an in-memory computing environment—SPARK. The analysis results using ANN-SOM approach in clustering protein super secondary structures provide better high accuracy in the in-memory computing environment.

Sequence Pattern for Supersecondary Structure of Sandwich-Like Proteins.

The goal is to define sequence characteristics of beta-sandwich proteins that are unique for the beta-sandwich supersecondary structure (SSS). Finding of the conserved residues that are critical for protein structure can often be accomplished with homology methods, but these methods are not always adequate as residues with similar structural role do not always occupy the same position as determined by sequence alignment. In this paper, we show how to identify residues that play the same structural role in the different proteins of the same SSS, even when these residue positions cannot be aligned with sequence alignment methods. The SSS characteristics are (a) a set of positions in each strand that are involved in the formation of a hydrophobic core, residue content, and correlations of residues at these key positions, (b) maximum allowable number of "low-frequency residues" for each strand, (c) minimum allowed number of "high-frequency" residues for each loop, and (d) minimum and maximum lengths of each loop. These sequence characteristics are referred to as "sequence pattern" for their respective SSS. The high specificity and sensitivity for a particular SSS are confirmed by applying this pattern to all protein structures in the SCOP data bank. We present here the pattern for one of the most common SSS of beta-sandwich proteins.

Secondary and Supersecondary Structure of Proteins in Light of the Structure of Hydrophobic Cores.

The traditional classification of protein structures (with regard to their supersecondary and tertiary conformation) is based on an assessment of conformational similarities between various polypeptide chains and particularly on the presence of specific secondary structural motifs. Mutual relations between secondary folds determine the overall shape of the protein and may be used to assign proteins to specific families (such as the immunoglobulin-like family). An alternative means of conducting structural assessment focuses on the structure of the protein's hydrophobic core. In this case, the protein is treated as a quasi-micelle, which exposes hydrophilic residues on its surface while internalizing hydrophobic residues. The accordance between the actual distribution of hydrophobicity in a protein and its corresponding theoretical ("idealized") distribution can be determined quantitatively, which, in turn, enables comparative analysis of structures regarded as geometrically similar (as well as geometrically divergent structures which are nevertheless regarded as similar in the sense of the fuzzy oil drop model). In this scope, the protein may be compared to an "intelligent micelle," where local disorder is often intentional and related to biological function-unlike traditional surfactant micelles which remain highly symmetrical throughout and do not carry any encoded information.

Formation of Secondary and Supersecondary Structure of Proteins as a Result of Coupling Between Local and Backbone-Electrostatic Interactions: A View Through Cluster-Cumulant Scope.

The secondary structure of proteins results from both local and long-range interactions, the latter being primarily backbone hydrogen bonding. In this chapter, based on our recent work, we suggest that the striking regularity of secondary structure can be described, in a semi-analytical manner, in terms of Kubo cluster cumulants (corresponding to the expansion of the protein's potential of mean force) that originate from the coupling between the backbone-local and backbone-electrostatic interactions. This finding is illustrated by the analysis of the Protein Data Bank statistics. Examples demonstrating the importance of the coupling terms in coarse-grained treatment of proteins are also presented.

Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility.

The ability to predict how mutations affect protein structure, folding, and flexibility can elucidate the molecular mechanisms leading to disruption of supersecondary structures, the emergence of phenotypes, as well guiding rational protein engineering. The advent of fast and accurate computational tools has enabled us to comprehensively explore the landscape of mutation effects on protein structures, prioritizing mutations for rational experimental validation.Here we describe the use of two complementary web-based in silico methods, DUET and DynaMut, developed to infer the effects of mutations on folding, stability, and flexibility and how they can be used to explore and interpret these effects on protein supersecondary structures.

Information-Theoretic Inference of an Optimal Dictionary of Protein Supersecondary Structures.

We recently developed an unsupervised Bayesian inference methodology to automatically infer a dictionary of protein supersecondary structures (Subramanian et al., IEEE data compression conference proceedings (DCC), 340-349, 2017). Specifically, this methodology uses the information-theoretic framework of minimum message length (MML) criterion for hypothesis selection (Wallace, Statistical and inductive inference by minimum message length, Springer Science & Business Media, New York, 2005). The best dictionary of supersecondary structures is the one that yields the most (lossless) compression on the source collection of folding patterns represented as tableaux (matrix representations that capture the essence of protein folding patterns (Lesk, J Mol Graph. 13:159-164, 1995). This book chapter outlines our MML methodology for inferring the supersecondary structure dictionary. The inferred dictionary is available at http://lcb.infotech.monash.edu.au/proteinConcepts/scop100/dictionary.html .

Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction.

Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.

Formation of toroids by self-assembly of an α-α corner mimetic: supramolecular cyclization.

An α-α corner mimetic self-assembles to form a rod-like supramolecular structure which bends and closes end-to-end like a cyclization reaction to form uniform toroids. Each peptide fragment containing l-leucine, α-aminoisobutyric acid (Aib) and l-tyrosine forms rigid 310 helical structures stabilized by multiple intramolecular N-HO hydrogen bonds. Two 310 helices are connected by the spacer 3-aminomethyl-benzylamine and maintain an angular distance of 120° and therefore mimic the α-α corner motif of a protein super secondary structure. The individual α-α corner subunits are themselves regularly interlinked through multiple water mediated intermolecular hydrogen-bonding interactions to form the rod-like supramolecular structure and toroids. The formation of the supramolecular structure has been proven with X-ray crystallography and other spectroscopic techniques. The cyclization of the supramolecular structure and toroid formation were studied by optical microscope, AFM and FE-SEM experiments. Despite other assignments such as exfoliation of graphene from graphite, the compound exhibits significant memory to finally produce the toroids.

Bloch spin waves and emergent structure in protein folding with HIV envelope glycoprotein as an example.

We inquire how structure emerges during the process of protein folding. For this we scrutinize collective many-atom motions during all-atom molecular dynamics simulations. We introduce, develop, and employ various topological techniques, in combination with analytic tools that we deduce from the concept of integrable models and structure of discrete nonlinear Schrödinger equation. The example we consider is an α-helical subunit of the HIV envelope glycoprotein gp41. The helical structure is stable when the subunit is part of the biological oligomer. But in isolation, the helix becomes unstable, and the monomer starts deforming. We follow the process computationally. We interpret the evolving structure both in terms of a backbone based Heisenberg spin chain and in terms of a side chain based XY spin chain. We find that in both cases the formation of protein supersecondary structure is akin the formation of a topological Bloch domain wall along a spin chain. During the process we identify three individual Bloch walls and we show that each of them can be modelled with a precision of tenths to several angstroms in terms of a soliton solution to a discrete nonlinear Schrödinger equation.

A Novel Prediction Method of Protein Structural Classes Based on Protein Super-Secondary Structure

At present, the feature extraction of protein sequences is the most basic issue to predict protein structural classes and is also the key problem to decide the quality of prediction. In order to predict protein structural classes accurately, we construct a 14-dimensional feature vector based on protein secondary and super-secondary structure information to reflect the content and spatial ordering of the given protein sequences. Among the vector, seven features about -helix bundle, hairpin motifs, Rossman folds, -plaits and other super-secondary structure information are first proposed in our paper. Experiments show that our method improves overall accuracy of lower similarity datasets 1189 and 640 by 0.9% - 3.8% and 0.5% - 4.2% respectively compared with other methods and has a competitive advantage for predicting proteins in and classes.

Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts

The biological function of protein is largely determined by its spatial structure. The research on the relationship between structure and function is the basis of protein structure prediction. However, the prediction of super secondary structure is an important step in the prediction of protein spatial structure. Many algorithms have been proposed for the prediction of protein super secondary structure. However, the parameters used by these methods were primarily based on amino acid sequences. In this paper, we proposed a novel model for predicting five kinds of protein super secondary structures based on the chemical shifts (CSs). Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in five kinds of protein super secondary structures by using the analysis of variance (ANOVA). Secondly, we used chemical shifts of six nuclei as features, and combined with quadratic discriminant analysis (QDA) to predict five kinds of protein super secondary structures. Finally, we achieved the averaged sensitivity, specificity and the overall accuracy of 81.8%, 95.19%, 82.91%, respectively in seven-fold cross-validation. Moreover, we have performed the prediction by combining the five different chemical shifts as features, the maximum overall accuracy up to 89.87% by using the C,Cα,Cβ,N,Hα of Hα chemical shifts, which are clearly superior to that of the quadratic discriminant analysis (QDA) algorithm by using 20 amino acid compositions (AAC) as feature in the seven-fold cross-validation. These results demonstrated that chemical shifts (CSs) are indeed an outstanding parameter for the prediction of five kinds of super secondary structures. In addition, we compared the prediction of the quadratic discriminant analysis (QDA) with that of support vector machine (SVM) by using the same six CSs as features. The result suggested that the quadratic discriminant analysis method by using chemical shifts as features is a good predictor for protein super secondary structures.

Role of Headgroup Dipole Interactions in Phosphatidylcholine and Phosphatidylserine Bilayers

The presence of a dielectric gradient in lipid bilayers is critical for membrane peptide folding in vivo. In addition, the hydrogen bonding ability of lipid head groups is known to affect bilayer properties. To reveal the role of headgroup dielectric properties in phospholipid bilayers, we have developed a polarizable Coarse-Grained (pCG) model, where the dipole moment of polar groups, such as serine and ester groups, can be adjusted through the addition of two extra “dummy” particles inside a CG bead. These dummy particles can mimic the hydrogen bonding network that can exist among head groups. The addition of polarization effects into CG beads has been proven effective in prior work regarding de novo peptide secondary structure folding with no external bias (Ganesan, 2014).In this work, we have explored the role of dipole interactions in monocomponent lipid bilayers using POPC (Palmitoyl Oleoyl Phosphatidyl-Choline) and POPS (Palmitoyl Oleoyl Phosphatidyl-Serine) lipids. An excellent agreement is observed on several structural and dynamical bilayer properties when compared to all-atom simulation data.We will present results on the role of dipole-dipole and dipole-charge interactions in shaping the clustering of PS lipids. Since dipole interactions are influenced by charge screening, the model is sensitive to changes in salt concentration. Our results suggest that headgroup dipole interactions may participate in PS/PC lipid phase separation as observed in experiments.Reference“The role of backbone dipole interactions in the formation of secondary and super-secondary structures of proteins”, Ganesan S. J., Matysiak S., Journal of Chemical Theory and Computation (2014).

The Role of Backbone Dipole Interactions in the Formation of Secondary and Supersecondary Structures

We present a generic solvated coarse-grained protein model that can be used to characterize the driving forces behind protein folding. Each amino acid is coarse-grained with two beads, a backbone and side-chain. While the backbone beads are modeled as polar entities, side-chains are either hydrophobic, polar or charged, thus allowing the exploration of how sequence patterning determines a protein fold. The change in orientation of the atoms of the coarse-grained unit is captured by the addition of two oppositely charged dummy particles inside backbone coarse-grained bead. These two dummy charges represent a dipole which can fluctuate thus introducing structural polarization into the coarse-grained model. Realistic alpha/beta content is achieved de novo without any biases in the force-field toward a particular secondary structure The dipoles created by the dummy particles interact with each other and drive the protein models to fold into unique structures depending on the amino acid patterning and presence of capping residues. We will present the role of dipole-dipole and dipole-charge interactions in shaping secondary and supersecondary structure of proteins. Since dipole interactions are influenced by the dielectric environment, the model is sensitive to the nature of the environment (low or high dielectric). Results on how changes in dielectric can tune the emergence of different folds will be presented.

Role of Interfaces in Peptide Folding and Aggregation

Protein misfolding is an intrinsic property of polypeptides, and misfolded conformations have a propensity to aggregate. In the past decade, the development of various coarse grained models for proteins have provided key insights into the driving forces in folding and aggregation. We recently developed a low resolution polarizable coarse grained model by adding oppositely charged dummy particles inside protein backbone beads. With this model, we were able to achieve significant alpha/beta secondary structure content de novo, without any added bias1. We now extend the model to study peptide aggregation at hydrophobic-hydrophilic interfaces and draw comparisons to aggregation in explicit water solvent. Elastin-like octapeptides (GV)4 are used as a model system for this study. Our results suggest that dipolar interactions play a role in aggregation, and thus more likely to play a role for aggregating, less-hydrophobic peptides.1. Role of Backbone Dipole Interactions in the Formation of Secondary and Supersecondary Structures of Proteins; Sai J. Ganesan and S. Matysiak; Journal of Chemical Theory and Computation 2014 10 (6), 2569-2576

Application of Machine Learning Method in Genomics and Proteomics

With the avalanche of genomic and proteomic data generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively analyzing and predicting the structure, function, and other properties of DNA and protein. The machine learning methods have become an important strategy for the discovery of potential knowledge in genomics and proteomics. Researches in recent years have shown tremendous advances in the properties prediction of DNA fragments and protein sequences by various pattern recognition methods. These techniques provide economical and timesaving solutions for identifying the properties of DNA and protein. This special issue was hosted for the recent development of the application of machine learning methods in genomics and proteomics. In this special issue, five works focused on the protein classification. How to extract key features from a protein was a key step in the discrimination of protein class. B. Liu et al. proposed to use Position-Specific Score Matrix (PSSM) and Accessible Surface Area (ASA) to formulate protein samples. The hidden Markov support vector machine (HM-SVM) was employed to predict protein binding site. Simulation in fivefold cross-validation on a benchmark dataset including 1124 protein chains showed that their method is more accurate for protein binding site prediction than some state-of-the art methods. This method can also be applied in DNA binding site, vitamin binding site, and posttranslational modification of proteins. Based on chemical shift (CS) information derived from nuclear magnetic resonance (NMR), F. Yonge proposed a novel feature to predict protein supersecondary structures. The quadratic discriminant (QD) analysis was selected as the prediction algorithm. Overall accuracy in threefold cross-validation is 77.3% for predicting four types of supersecondary structures. According to the concept of pseudo amino acids, G.-L. Fan et al. proposed the average chemical shifts (ACS) composition and established an online webserver called acACS which was calculated from average chemical shift information and protein secondary structure. By using SVM as the classification algorithm, the acACS was used in the discrimination between acidic and alkaline enzymes and between bioluminescent and nonbioluminescent proteins. Encouraging results were achieved. The protein secondary structure, structure class, and disorder region can be predicted using the AC-based method. L. Nanni et al. proposed to combine different features to improve protein prediction. These features include amino acids composition, PSSM, and substitution matrix representation (SMR). Each feature is used to train a separate SVM. Total of 15 benchmark datasets were used to evaluate the performance of their proposed method. Comparative results show that the PSSM always produces good accuracies. However, no single descriptor is superior to all others across all test datasets. The major contribution in this paper is to propose an ensemble of classifiers for sequence-based protein classification. H. Lin et al. briefly reviewed the development of ion channel prediction using machine learning method. They initially introduced how to construct a valid and objective benchmark dataset to train and test the predictor. Subsequently, the mathematical descriptors were presented to formulate the ion channel sequences. Moreover, two feature selection techniques on how to optimize feature set were described. Finally, the support vector machine was suggested performing classification. The methods introduced in that review can be generalized into other protein prediction fields as well. The paper from P. Feng et al. was the unique work focused on DNA prediction using machine learning method. They proposed a novel descriptor called pseudo K-tuple nucleotide composition (PseKNC) to formulate the DNA sequences. The feature is calculated from K-tuple nucleotide composition and the structural correlation of DNA dinucleotides. Subsequently, the SVM was used to predict DNase I hypersensitive sites. The jackknife cross-validated accuracy is 83%, which is competitive with that of the existing method. This new descriptor can also be widely used in DNA regulatory elements prediction. Hao Lin Wei Chen Ramu Anandakrishnan Dariusz Plewczynski

The Recognition Method for the Supersecondary Structure of DNA-Binding Protein

The structure of DNA binding proteins is identified that has great significance for the study of gene expression regulation mechanism.The new recognition method is proposed to identify the super-secondary structure and structure domain of DNA-binding protein in this paper. The nucleotide transition probability is calculated by the known DNA-binding protein binding locus sequence. Using mouse data which downloaded from the TRANSFAC establish the binding protein super-secondary structure recognition models. The probability score is calculated by the transition probability of the binding site and the background. This method differs from the conventional method, It is neither the amino acid sequence of the protein, nor the use of homologous proteins. In order to verify the validity of the algorithm, 10 DNA-binding proteins of drosophila and yeast are used to do the experiment. The experimental results show that our method has very good recognition result.

Role of Backbone Dipole Interactions in the Formation of Secondary and Supersecondary Structures of Proteins.

We present a generic solvated coarse-grained protein model that can be used to characterize the driving forces behind protein folding. Each amino acid is coarse-grained with two beads, a backbone, and a side chain. Although the backbone beads are modeled as polar entities, side chains are hydrophobic, polar, or charged, thus allowing the exploration of how sequence patterning determines a protein fold. The change in orientation of the atoms of the coarse-grained unit is captured by the addition of two oppositely charged dummy particles inside the backbone coarse-grained bead. These two dummy charges represent a dipole that can fluctuate, thus introducing structural polarization into the coarse-grained model. Realistic α/β content is achieved de novo without any biases in the force field toward a particular secondary structure. The dipoles created by the dummy particles interact with each other and drive the protein models to fold into unique structures depending on the amino acid patterning and presence of capping residues. We have also characterized the role of dipole–dipole and dipole–charge interactions in shaping the secondary and supersecondary structure of proteins. Formation of helix bundles and β-strands are also discussed.