Analysis of protein contacts into Protein Units

Three-dimensional structures of proteins are the support of their biological functions. Their folds are maintained by inter-residue interactions which are one of the main focuses to understand the mechanisms of protein folding and stability. Furthermore, protein structures can be composed of single or multiple functional domains that can fold and function independently. Hence, dividing a protein into domains is useful for obtaining an accurate structure and function determination.     

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.     

基于遗传算法的蛋白质结构预测方法

遗传算法源于自然界的进化规律,是一种自适应启发式概率性迭代式全局搜索算法。本文主要介绍了GA的基本原理,算法及优点;总结GA在蛋白质结构预测中建立模型和执行策略,以及多种算法相互结合预测蛋白质结构的研究进展。     

Shaping dots and lines: adding modularity into protein interaction networks using structural information

Determining protein interaction networks and generating models to simulate network changes in time and space are crucial for understanding a biological system and for predicting the effect of mutants found in diseases. In this review we discuss the great potential of using structural information together with computational tools towards reaching this goal: the prediction of new protein interactions, the estimation of affinities and kinetic rate constants between protein complexes, and finally the determination of which interactions are compatible with each other and which interactions are exclusive. The latter one will be important to reorganize large scale networks into functional modular networks.     

Interactions between the conserved hydrophobic region of the prion protein and dodecylphosphocholine micelles

The three-dimensional structure of PrP110-136, a peptide encompassing the conserved hydrophobic region of the human prion protein, has been determined at high resolution in dodecylphosphocholine micelles by NMR. The results support the conclusion that the (Ctm)PrP, a transmembrane form of the prion protein, adopts a different conformation than the reported structures of the normal prion protein determined in solution. Paramagnetic relaxation enhancement studies with gadolinium-diethylenetriaminepentaacetic acid indicated that the conserved hydrophobic region peptide is not inserted symmetrically in the micelle, thus suggesting the presence of a guanidium-phosphate ion pair involving the side chain of the terminal arginine and the detergent headgroup. Titration of dodecylphosphocholine into a solution of PrP110-136 revealed the presence of a surface-bound species. In addition, paramagnetic probes located the surface-bound peptide somewhere below the micelle-water interface when using the inserted helix as a positional reference. This localization of the unknown population would allow a similar ion pair interaction.     

Sequence Representation and Prediction of Protein Secondary Structure for Structural Motifs in Twilight Zone Proteins

Characterizing and classifying regularities in protein structure is an important element in uncovering the mechanisms that regulate protein structure, function and evolution. Recent research concentrates on analysis of structural motifs that can be used to describe larger, fold-sized structures based on homologous primary sequences. At the same time, accuracy of secondary protein structure prediction based on multiple sequence alignment drops significantly when low homology (twilight zone) sequences are considered. To this end, this paper addresses a problem of providing an alternative sequences representation that would improve ability to distinguish secondary structures for the twilight zone sequences without using alignment. We consider a novel classification problem, in which, structural motifs, referred to as structural fragments (SFs) are defined as uniform strand, helix and coil fragments. Classification of SFs allows to design novel sequence representations, and to investigate which other factors and prediction algorithms may result in the improved discrimination. Comprehensive experimental results show that statistically significant improvement in classification accuracy can be achieved by: (1) improving sequence representations, and (2) removing possible noise on the terminal residues in the SFs. Combining these two approaches reduces the error rate on average by 15% when compared to classification using standard representation and noisy information on the terminal residues, bringing the classification accuracy to over 70%. Finally, we show that certain prediction algorithms, such as neural networks and boosted decision trees, are superior to other algorithms.This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).     

Amino acid propensities for secondary structures are influenced by the protein structural class

Amino acid propensities for secondary structures were used since the 1970s, when Chou and Fasman evaluated them within datasets of few tens of proteins and developed a method to predict secondary structure of proteins, still in use despite prediction methods having evolved to very different approaches and higher reliability. Propensity for secondary structures represents an intrinsic property of amino acid, and it is used for generating new algorithms and prediction methods, therefore our work has been aimed to investigate what is the best protein dataset to evaluate the amino acid propensities, either larger but not homogeneous or smaller but homogeneous sets, i.e., all-alpha, all-beta, alpha-beta proteins. As a first analysis, we evaluated amino acid propensities for helix, beta-strand, and coil in more than 2000 proteins from the PDBselect dataset. With these propensities, secondary structure predictions performed with a method very similar to that of Chou and Fasman gave us results better than the original one, based on propensities derived from the few tens of X-ray protein structures available in the 1970s. In a refined analysis, we subdivided the PDBselect dataset of proteins in three secondary structural classes, i.e., all-alpha, all-beta, and alpha-beta proteins. For each class, the amino acid propensities for helix, beta-strand, and coil have been calculated and used to predict secondary structure elements for proteins belonging to the same class by using resubstitution and jackknife tests. This second round of predictions further improved the results of the first round. Therefore, amino acid propensities for secondary structures became more reliable depending on the degree of homogeneity of the protein dataset used to evaluate them. Indeed, our results indicate also that all algorithms using propensities for secondary structure can be still improved to obtain better predictive results.     

Solution structure of the phosphotyrosine binding (PTB) domain of human tensin2 protein in complex with deleted in liver cancer 1 (DLC1) peptide reveals a novel peptide binding mode

The protein deleted in liver cancer 1 (DLC1) interacts with the tensin family of focal adhesion proteins to play a role as a tumor suppressor in a wide spectrum of human cancers. This interaction has been proven to be crucial to the oncogenic inhibitory capacity and focal adhesion localization of DLC1. The phosphotyrosine binding (PTB) domain of tensin2 predominantly interacts with a novel site on DLC1, not the canonical NPXY motif. In this study, we characterized this interaction biochemically and determined the complex structure of tensin2 PTB domain with DLC1 peptide by NMR spectroscopy. Our HADDOCK-derived complex structure model elucidates the molecular mechanism by which tensin2 PTB domain recognizes DLC1 peptide and reveals a PTB-peptide binding mode that is unique in that peptide occupies the binding site opposite to the canonical NPXY motif interaction site with the peptide utilizing a non-canonical binding motif to bind in an extended conformation and that the N-terminal helix, which is unique to some Shc- and Dab-like PTB domains, is required for binding. Mutations of crucial residues defined for the PTB-DLC1 interaction affected the co-localization of DLC1 and tensin2 in cells and abolished DLC1-mediated growth suppression of hepatocellular carcinoma cells. This tensin2 PTB-DLC1 peptide complex with a novel binding mode extends the versatile binding repertoire of the PTB domains in mediating diverse cellular signaling pathways as well as provides a molecular and structural basis for better understanding the tumor-suppressive activity of DLC1 and tensin2.     

Single-molecule force spectroscopy reveals the individual mechanical unfolding pathways of a surface layer protein

Surface layers (S-layers) represent an almost universal feature of archaeal cell envelopes and are probably the most abundant bacterial cell proteins. S-layers are monomolecular crystalline structures of single protein or glycoprotein monomers that completely cover the cell surface during all stages of the cell growth cycle, thereby performing their intrinsic function under a constant intra- and intermolecular mechanical stress. In gram-positive bacteria, the individual S-layer proteins are anchored by a specific binding mechanism to polysaccharides (secondary cell wall polymers) that are linked to the underlying peptidoglycan layer. In this work, atomic force microscopy-based single-molecule force spectroscopy and a polyprotein approach are used to study the individual mechanical unfolding pathways of an S-layer protein. We uncover complex unfolding pathways involving the consecutive unfolding of structural intermediates, where a mechanical stability of 87 pN is revealed. Different initial extensibilities allow the hypothesis that S-layer proteins adapt highly stable, mechanically resilient conformations that are not extensible under the presence of a pulling force. Interestingly, a change of the unfolding pathway is observed when individual S-layer proteins interact with secondary cell wall polymers, which is a direct signature of a conformational change induced by the ligand. Moreover, the mechanical stability increases up to 110 pN. This work demonstrates that single-molecule force spectroscopy offers a powerful tool to detect subtle changes in the structure of an individual protein upon binding of a ligand and constitutes the first conformational study of surface layer proteins at the single-molecule level.     

Structural and biochemical studies on the regulation of biotin carboxylase by substrate inhibition and dimerization

Biotin carboxylase (BC) activity is shared among biotin-dependent carboxylases and catalyzes the Mg-ATP-dependent carboxylation of biotin using bicarbonate as the CO₂ donor. BC has been studied extensively over the years by structural, kinetic, and mutagenesis analyses. Here we report three new crystal structures of Escherichia coli BC at up to 1.9 Å resolution, complexed with different ligands. Two structures are wild-type BC in complex with two ADP molecules and two Ca²⁺ ions or two ADP molecules and one Mg²⁺ ion. One ADP molecule is in the position normally taken by the ATP substrate, whereas the other ADP molecule occupies the binding sites of bicarbonate and biotin. One Ca²⁺ ion and the Mg²⁺ ion are associated with the ADP molecule in the active site, and the other Ca²⁺ ion is coordinated by Glu-87, Glu-288, and Asn-290. Our kinetic studies confirm that ATP shows substrate inhibition and that this inhibition is competitive against bicarbonate. The third structure is on the R16E mutant in complex with bicarbonate and Mg-ADP. Arg-16 is located near the dimer interface. The R16E mutant has only a 2-fold loss in catalytic activity compared with the wild-type enzyme. Analytical ultracentrifugation experiments showed that the mutation significantly destabilized the dimer, although the presence of substrates can induce dimer formation. The binding modes of bicarbonate and Mg-ADP are essentially the same as those to the wild-type enzyme. However, the mutation greatly disrupted the dimer interface and caused a large re-organization of the dimer. The structures of these new complexes have implications for the catalysis by BC.     

Folded-unfolded cross-predictions and protein evolution: The case study of coiled-coils

Here we report a thorough analysis of cross-predictions between coiled-coil and disordered protein segments using various prediction algorithms for both sequence classes. Coiled-coils are often predicted to be unstructured, consistent with their obligate multimeric nature, whereas reverse cross-predictions are rare due to the regularity of coiled-coil sequences. We propose the simultaneous use of the programs Coils and IUPred to achieve acceptable prediction accuracy and minimize the extent of cross-predictions. The relevance of observed cross-predictions might be that disordered sequences can adopt coiled-coil conformation relatively easily during protein evolution.     

Structural rearrangements of the central region of the morbillivirus attachment protein stalk domain trigger F protein refolding for membrane fusion

It is unknown how receptor binding by the paramyxovirus attachment proteins (HN, H, or G) triggers the fusion (F) protein to fuse with the plasma membrane for cell entry. H-proteins of the morbillivirus genus consist of a stalk ectodomain supporting a cuboidal head; physiological oligomers consist of non-covalent dimer-of-dimers. We report here the successful engineering of intermolecular disulfide bonds within the central region (residues 91-115) of the morbillivirus H-stalk; a sub-domain that also encompasses the putative F-contacting section (residues 111-118). Remarkably, several intersubunit crosslinks abrogated membrane fusion, but bioactivity was restored under reducing conditions. This phenotype extended equally to H proteins derived from virulent and attenuated morbillivirus strains and was independent of the nature of the contacted receptor. Our data reveal that the morbillivirus H-stalk domain is composed of four tightly-packed subunits. Upon receptor binding, these subunits structurally rearrange, possibly inducing conformational changes within the central region of the stalk, which, in turn, promote fusion. Given that the fundamental architecture appears conserved among paramyxovirus attachment protein stalk domains, we predict that these motions may act as a universal paramyxovirus F-triggering mechanism.     

A novel soluble protein factor with non-opioid dynorphin A-binding activity

A novel soluble non-opioid dynorphin A-binding factor (DABF) was identified and characterized in neuronal cell lines, rat spinal cord, and brain. DABF binds dynorphin A(1-17), dynorphin A(2-17), and the 32 amino acid prodynorphin fragment big dynorphin consisting of dynorphin A and B, but not other opioid and non-opioid peptides, opiates, and benzomorphans. The IC50 for dynorphin A(1-17), dynorphin A(2-17), and big dynorphin is in the 5-10 nM range. Using dynorphin A and big dynorphin fragments a binding epitope was mapped to dynorphin A(6-13). DABF has a molecular mass of about 70 kDa. SH-groups are apparently involved in the binding of dynorphin A since p-hydroxy-mercuribenzoic acid inhibited this process. Upon interaction with DABF dynorphin A was converted into Leu-enkephalin, which remained bound to the protein. These data suggest that DABF functions as an oligopeptidase that forms stable and specific complexes with dynorphin A. The presence of DABF in brain structures and other tissues with low level of prodynorphin expression suggests that DABF as an oligopeptidase may degrade other peptides. Dynorphin A at the sites of its release in the CNS may attenuate this degradation as a competitor when it specifically binds to the enzyme.     

A review on flavones targeting serine/threonine protein kinases for potential anticancer drugs

Protein kinases have been important targets for antitumor targets due to their key roles in regulating multiple cell signaling pathways. Numerous compounds containing flavonoid scaffold as an indispensable anchor have been found to be potent inhibitors of protein kinases. Some of these flavonoids have been in clinical research as protein kinases inhibitors. Thus, the present review mainly focuses on the structural requirement for anticancer potential of flavone derivatives targeting several key serine/threonine protein kinases. This information may provide an opportunity to scientists of medicinal chemistry to design multi-functional flavone derivatives for the treatment of cancer.     

Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins

Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. The networks have been tested and standard error of prediction (SEP) of 4.19% for alpha helix, 3.49% for beta sheet, and 3.15% for turns have been achieved. The results indicate that there is a significant decrease in the SEP for each type of structure parameter compared to previous works.     

A stochastic method for the reconstruction of protein structures from one-dimensional structural profiles

We discuss a computational approach for reconstructing the native structures of proteins from the knowledge of a structural profile - the first eigenvector of the contact map of the native structure itself. The procedure consists in carrying out Monte Carlo simulations of a tube model of the protein structure with an energy bias towards the target structural profile. We present the reconstruction of two small proteins and address problems arising in the reconstruction of larger proteins. Our results indicate that an accurate physico-chemical energy function should be used in conjunction with the structural profile bias in order to achieve accurate reconstructions.