Ranking the factors that contribute to protein β-sheet folding

Mitochondrial genes modulate the phenotypic expression of congenital scoliosis syndrome caused by mutations in the TBXT gene

BACKGROUND

Congenital scoliosis (CS) is a spinal disorder caused by genetic-congenital vertebral malformations and may be associated with other congenital defects or may occur alone. It is genetically heterogeneous and numerous genes contributing to this disease have been identified. In addition, CS has a wide range of phenotypic and genotypic variability, which has been explained by the intervention of genetic factors like modifiers and environment genes. The aim of the present study was to determine the possible cause of CS in a Tunisian patient and to examine the association between mtDNA mutations and mtDNA content and CS.

METHODS

Here we performed Whole-Exome Sequencing (WES) in a patient presenting clinical features suggestive of severe congenital scoliosis syndrome. Direct sequencing of the whole mitochondrial DNA (mtDNA) was also performed in addition to copy number quantification in the blood of the indexed case. In silico prediction tools, 3D modeling and molecular docking approaches were used.

RESULTS

The WES revealed the homozygous missense mutation c.512A > G (p.H171R) in the TBXT gene. Bioinformatic analysis demonstrated that the p.H171R variant was highly deleterious and caused the TBXT structure instability. Molecular docking revealed that the p.H171R mutation disrupted the monomer stability which seemed to be crucial for maintaining the stability of the homodimer and consequently to the destabilization of the homodimer-DNA complex. On the other hand, we hypothesized that mtDNA can be a modifier factor, so, the screening of the whole mtDNA showed a novel heteroplasmic m.10150T > A (p.M31K) variation in the MT-ND3 gene. Further, qPCR analyses of the patient's blood excluded mtDNA depletion. Bioinformatic investigation revealed that the p.M31K mutation in the ND3 protein was highly deleterious and may cause the ND3 protein structure destabilization and could disturb the interaction between complex I subunits.

CONCLUSION

We described the possible role of mtDNA genetics on the pathogenesis of congenital scoliosis by hypothesizing that the presence of the homozygous variant in TBXT accounts for the CS phenotype in our patient and the MT-ND3 gene may act as a modifier gene.

An ensemble method with hybrid features to identify extracellular matrix proteins

The extracellular matrix (ECM) is a dynamic composite of secreted proteins that play important roles in numerous biological processes such as tissue morphogenesis, differentiation and homeostasis. Furthermore, various diseases are caused by the dysfunction of ECM proteins. Therefore, identifying these important ECM proteins may assist in understanding related biological processes and drug development. In view of the serious imbalance in the training dataset, a Random Forest-based ensemble method with hybrid features is developed in this paper to identify ECM proteins. Hybrid features are employed by incorporating sequence composition, physicochemical properties, evolutionary and structural information. The Information Gain Ratio and Incremental Feature Selection (IGR-IFS) methods are adopted to select the optimal features. Finally, the resulting predictor termed IECMP (Identify ECM Proteins) achieves an balanced accuracy of 86.4% using the 10-fold cross-validation on the training dataset, which is much higher than results obtained by other methods (ECMPRED: 71.0%, ECMPP: 77.8%). Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED. These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples. It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets. For public access, we develop a user-friendly web server for ECM protein identification that is freely accessible at http://iecmp.weka.cc.

A method to predict edge strands in beta-sheets from protein sequences

There is a need for rules allowing three-dimensional structure information to be derived from protein sequences. In this work, consideration of an elementary protein folding step allows protein sub-sequences which optimize folding to be derived for any given protein sequence. Classical mechanics applied to this system and the energy conservation law during the elementary folding step yields an equation whose solutions are taken over the field of rational numbers. This formalism is applied to beta-sheets containing two edge strands and at least two central strands. The number of protein sub-sequences optimized for folding per amino acid in beta-strands is shown in particular to predict edge strands from protein sequences. Topological information on beta-strands and loops connecting them is derived for protein sequences with a prediction accuracy of 75%. The statistical significance of the finding is given. Applications in protein structure prediction are envisioned such as for the quality assessment of protein structure models.

Statistical Analysis of Terminal Extensions of Protein β-Strand Pairs

The long-range interactions, required to the accurate predictions of tertiary structures of β-sheet-containing proteins, are still difficult to simulate. To remedy this problem and to facilitate β-sheet structure predictions, many efforts have been made by computational methods. However, known efforts on β-sheets mainly focus on interresidue contacts or amino acid partners. In this study, to go one step further, we studied β-sheets on the strand level, in which a statistical analysis was made on the terminal extensions of paired β-strands. In most cases, the two paired β-strands have different lengths, and terminal extensions exist. The terminal extensions are the extended part of the paired strands besides the common paired part. However, we found that the best pairing required a terminal alignment, and β-strands tend to pair to make bigger common parts. As a result, 96.97%  of β-strand pairs have a ratio of 25% of the paired common part to the whole length. Also 94.26% and 95.98%  of β-strand pairs have a ratio of 40% of the paired common part to the length of the two β-strands, respectively. Interstrand register predictions by searching interacting β-strands from several alternative offsets should comply with this rule to reduce the computational searching space to improve the performances of algorithms.

A survey of machine learning methods for secondary and supersecondary protein structure prediction

In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.

Computational prediction and analysis of protein γ-carboxylation sites based on a random forest method

The glutamate γ-carboxylation plays a pivotal part in a number of important human diseases. However, traditional protein γ-carboxylation site detection by experimental approaches are often laborious and time-consuming. In this study, we initiated an attempt for the computational prediction of protein γ-carboxylation sites. We developed a new method for predicting the γ-carboxylation sites based on a Random Forest method. As a result, 90.44% accuracy and 0.7739 MCC value were obtained for the training dataset, and 89.83% accuracy and 0.7448 MCC value for the testing dataset. Our method considered several features including sequence conservation, residual disorder, secondary structures, solvent accessibility, physicochemical/biochemical properties and amino acid occurrence frequencies. By means of the feature selection algorithm, an optimal set of 327 features were selected; these features were considered as the ones that contributed significantly to the prediction of protein γ-carboxylation sites. Analysis of the optimal feature set indicated several important factors in determining the γ-carboxylation and a possible consensus sequence of the γ-carboxylation recognition site (γ-CRS) was suggested. These may shed some light on the in-depth understanding of the mechanisms of γ-carboxylation, providing guidelines for experimental validation.

BetaSearch: a new method for querying β-residue motifs

BACKGROUND

Searching for structural motifs across known protein structures can be useful for identifying unrelated proteins with similar function and characterising secondary structures such as β-sheets. This is infeasible using conventional sequence alignment because linear protein sequences do not contain spatial information. β-residue motifs are β-sheet substructures that can be represented as graphs and queried using existing graph indexing methods, however, these approaches are designed for general graphs that do not incorporate the inherent structural constraints of β-sheets and require computationally-expensive filtering and verification procedures. 3D substructure search methods, on the other hand, allow β-residue motifs to be queried in a three-dimensional context but at significant computational costs.

FINDINGS

We developed a new method for querying β-residue motifs, called BetaSearch, which leverages the natural planar constraints of β-sheets by indexing them as 2D matrices, thus avoiding much of the computational complexities involved with structural and graph querying. BetaSearch exhibits faster filtering, verification, and overall query time than existing graph indexing approaches whilst producing comparable index sizes. Compared to 3D substructure search methods, BetaSearch achieves 33 and 240 times speedups over index-based and pairwise alignment-based approaches, respectively. Furthermore, we have presented case-studies to demonstrate its capability of motif matching in sequentially dissimilar proteins and described a method for using BetaSearch to predict β-strand pairing.

CONCLUSIONS

We have demonstrated that BetaSearch is a fast method for querying substructure motifs. The improvements in speed over existing approaches make it useful for efficiently performing high-volume exploratory querying of possible protein substructural motifs or conformations. BetaSearch was used to identify a nearly identical β-residue motif between an entirely synthetic (Top7) and a naturally-occurring protein (Charcot-Leyden crystal protein), as well as identifying structural similarities between biotin-binding domains of avidin, streptavidin and the lipocalin gamma subunit of human C8.

Bacterial protein structures reveal phylum dependent divergence

Protein sequence space is vast compared to protein fold space. This raises important questions about how structures adapt to evolutionary changes in protein sequences. A growing trend is to regard protein fold space as a continuum rather than a series of discrete structures. From this perspective, homologous protein structures within the same functional classification should reveal a constant rate of structural drift relative to sequence changes. The clusters of orthologous groups (COG) classification system was used to annotate homologous bacterial protein structures in the Protein Data Bank (PDB). The structures and sequences of proteins within each COG were compared against each other to establish their relatedness. As expected, the analysis demonstrates a sharp structural divergence between the bacterial phyla Firmicutes and Proteobacteria. Additionally, each COG had a distinct sequence/structure relationship, indicating that different evolutionary pressures affect the degree of structural divergence. However, our analysis also shows the relative drift rate between sequence identity and structure divergence remains constant.

Position-specific propensities of amino acids in the β-strand

Background

Despite the importance of β-strands as main building blocks in proteins, the propensity of amino acid in β-strands is not well-understood as it has been more difficult to determine experimentally compared to α-helices. Recent studies have shown that most of the amino acids have significantly high or low propensity towards both ends of β-strands. However, a comprehensive analysis of the sequence dependent amino acid propensities at positions between the ends of the β-strand has not been investigated.

Results

The propensities of the amino acids calculated from a large non-redundant database of proteins are found to be highly position-specific and vary continuously throughout the length of the β-strand. They follow an unexpected characteristic periodic pattern in inner positions with respect to the cap residues in both termini of β-strands; this periodic nature is markedly different from that of the α-helices with respect to the strength and pattern in periodicity. This periodicity is not only different for different amino acids but it also varies considerably for the amino acids belonging to the same physico-chemical group. Average hydrophobicity is also found to be periodic with respect to the positions from both termini of β-strands.

Conclusions

The results contradict the earlier perception of isotropic nature of amino acid propensities in the middle region of β-strands. These position-specific propensities should be of immense help in understanding the factors responsible for β-strand design and efficient prediction of β-strand structure in unknown proteins.

The interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of beta-strands

It is widely considered that it is not appropriate to treat beta-pairs in isolation, since other secondary structural models (such as helices, coils), protein topology and protein tertiary structures would limit beta-strand pairing. However, to understand the underlying mechanisms of beta-sheet formation, studies ought to be performed separately on more concrete aspects. In this study, we focus on the parallel or antiparallel orientation of beta-strands. First, statistical analysis was performed on the relative frequencies of the interstrand amino acid pairs within parallel and antiparallel beta-strands. Consequently, features were extracted by singular value decomposition from the statistical results. By using the support vector machine to distinguish the features extracted from the two types of beta-strands, high accuracy was achieved (up to 99.4%). This suggests that the interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of beta-strands. These results may provide useful information for developing other useful algorithms to examine to the beta-strand folding pathways, and could eventually lead to protein structure predictions.

Molecular dynamics simulations of a lipovitellin-derived amphiphilic beta-sheet homologous to apoB-100 beta-sheets at a hydrophobic decane-water interface

Lipovitellin, an egg-yolk lipoprotein, transports lipids in a pocket surrounded by amphiphilic beta-sheets. Its X-ray structure provides possibilities to study interactions between lipophilic beta-sheets and lipids at the atomic level. Here, we studied a 67-residue-long amphiphilic beta-sheet of lipovitellin previously suggested a suitable working model for studies of the lipid-binding behaviour of amphiphilic beta-sheet regions in apolipoprotein B-100 (apoB-100). We performed four molecular dynamics simulations with different starting configurations to define characteristics of the amphiphilic beta-sheet model at a decane-water interface. In each simulation the model beta-sheet bound keenly to the decane layer via its hydrophobic surface. The structural profiles showed unchanged secondary structure of the beta-sheet during the attachment. Also, aromatic side chains, especially tryptophans and tyrosines, mediated the attachment to the hydrophobic layer and influenced the orientation of the decane molecules that are in contact with the beta-sheet. In conclusion, the present simulations reveal high affinity of a lipovitellin-derived amphiphilic beta-sheet to a hydrophobic decane layer. They lay thereby the basis for further studies of the interaction between amphiphilic beta-sheets and lipids in complex molecular systems, like LDL particles, in which the large apoB-100 is the main protein component.