Identification Of Protein Structures Research Articles

Graph identification of proteins in tomograms (GRIP-Tomo).

In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three-dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo-multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single-domain protein native topologies as well as distinct single-domain proteins from multimeric complexes within noisy volumes.

Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.

In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence exploration in protein data banks and complication of protein structures, experimental methods are found to be inadequate for protein structural class prediction. Hence, it is very much advantageous to design a reliable computational method to predict protein structural classes from protein sequences. In the recent few years there has been an elevated interest in using deep learning to assist protein structure prediction as protein structure prediction models can be utilized to screen a large number of novel sequences. In this regard, we propose a model employing Energy Profile for atom pairs in conjunction with the Legion-Class Bayes function called Energy Profile Legion-Class Bayes Protein Structure Identification model. Followed by this, we use a Thompson Optimized convolutional neural network to extract features between amino acids and then the Thompson Optimized SoftMax function is employed to extract associations between protein sequences for predicting secondary protein structure. The proposed Energy Profile Bayes and Thompson Optimized Convolutional Neural Network (EPB-OCNN) method tested distinct unique protein data and was compared to the state-of-the-art methods, the Template-Based Modeling, Protein Design using Deep Graph Neural Networks, a deep learning-based S-glutathionylation sites prediction tool called a Computational Framework, the Deep Learning and a distance-based protein structure prediction using deep learning. The results obtained when applied with the Biopython tool with respect to protein structure prediction time, protein structure prediction accuracy, specificity, recall, F-measure, and precision, respectively, are measured. The proposed EPB-OCNN method outperformed the state-of-the-art methods, thereby corroborating the objective.

Protein Structure Prediction Using Quantile Dragonfly and Structural Class-Based Deep Learning

Predicting three-dimensional structure of a protein in the field of computational molecular biology has received greater attention. Most of the recent research works aimed at exploring search space, however with the increasing nature and size of data, protein structure identification and prediction are still in the preliminary stage. This work is aimed at exploring search space to tackle protein structure prediction with minimum execution time and maximum accuracy by means of quantile regressive dragonfly and structural class homolog-based deep learning (QRD-SCHDL). The proposed QRD-SCHDL method consists of two distinct steps. They are protein structure identification and prediction. In the first step, protein structure identification is performed by means of QRD optimization model to identify protein structure with minimum error. Here the protein structure identification is first performed as the raw database contains sequence information and does not contain structural information. An optimization model is designed to obtain the structural information from the database. However, protein structure gives much more insight than its sequence. Therefore, to perform computational prediction of protein structure from its sequence, actual protein structure prediction is made. The second step involves the actual protein structure prediction via structural class and homolog-based deep learning. For each protein structure prediction, a scoring matrix is obtained by utilizing structural class maximum correlation coefficient. Finally, the proposed method is tested on a set of different unique numbers of protein data and compared to the state-of-the-art methods. The obtained results showed the potentiality of the proposed method in terms of metrics, error rate, protein structure prediction time, protein structure prediction accuracy, precision, specificity, recall, ROC, Kappa coefficient and [Formula: see text]-measure, respectively. It also shows that the proposed QRD-SCHDL method attains comparable results and outperformed in certain cases, thereby signifying the efficiency of the proposed work.

Bingham deep neural and oppositional fish swarm optimized protein structure prediction

It is familiar that essential proteins take part in managing cellular activities in living organisms. Moreover, protein structure prediction from its amino acid sequence is advantageous to the comprehending of cellular functions. Formerly, several essential protein prediction methods have been proposed. However, those existing prediction methods were not satisfactory because to low sensitivity to imbalance characteristics. To address this issue, this paper presents a novel secondary protein structure prediction method, called, Bingham Deep Convolutional-based Oppositional Artificial Fish Optimized (BDC-OAFO). First, a protein structure identification framework, called, Bingham Distributed Deep Convolutional (BDDC) is designed to identify the essential proteins by eliminating the imbalanced learning issue. Next, secondary structure prediction framework, called, Oppositional Artificial Fish Swarm Optimization is proposed that obtain precise prediction results. Then, predicting secondary protein structure by emulating three biological behaviors of artificial fishes, including foraging behavior, following behavior, swarming behavior in which process, proximal count, oppositional function and Gaussian function are utilized. To evaluate the performance of BDC-OAFO method, we conduct experiments on Protein Data Bank dataset the experimental results show that our method BDC-OAFO achieves a better performance for identifying essential proteins and precise prediction in comparison with several other well-known prediction methods, which confirms the significance of BDC-OAFO. Communicated by Ramaswamy H. Sarma

A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods

Background:Proteins play a crucial role in life activities, such as catalyzing metabolic reactions, DNA replication, responding to stimuli, etc. Identification of protein structures and functions are critical for both basic research and applications. Because the traditional experiments for studying the structures and functions of proteins are expensive and time consuming, computational approaches are highly desired. In key for computational methods is how to efficiently extract the features from the protein sequences. During the last decade, many powerful feature extraction algorithms have been proposed, significantly promoting the development of the studies of protein structures and functions.Objective:To help the researchers to catch up the recent developments in this important field, in this study, an updated review is given, focusing on the sequence-based feature extractions of protein sequences.Method:These sequence-based features of proteins were grouped into three categories, including composition-based features, autocorrelation-based features and profile-based features. The detailed information of features in each group was introduced, and their advantages and disadvantages were discussed. Besides, some useful tools for generating these features will also be introduced.Results:Generally, autocorrelation-based features outperform composition-based features, and profile-based features outperform autocorrelation-based features. The reason is that profile-based features consider the evolutionary information, which is useful for identification of protein structures and functions. However, profile-based features are more time consuming, because the multiple sequence alignment process is required.Conclusion:In this study, some recently proposed sequence-based features were introduced and discussed, such as basic k-mers, PseAAC, auto-cross covariance, top-n-gram etc. These features did make great contributions to the developments of protein sequence analysis. Future studies can be focus on exploring the combinations of these features. Besides, techniques from other fields, such as signal processing, natural language process (NLP), image processing etc., would also contribute to this important field, because natural languages (such as English) and protein sequences share some similarities. Therefore, the proteins can be treated as documents, and the features, such as k-mers, top-n-grams, motifs, can be treated as the words in the languages. Techniques from these filed will give some new ideas and strategies for extracting the features from proteins.

Phyre 2 and I-Tasser web portal for Protein modeling, Prediction and Validation of gel Q and gel K genes from gellan gum producing bacterial strain Sphingomonas paucimobilis ATCC 31461

Gellan gum, an anionic, high-molecular-weight, hetero exo-polysaccharide produced by Sphingomonas paucimobilis ATCC 31461 has potential applications in food and pharmaceutical industries, as a gelling agent, a highly-viscous biogum, a stabilizing agent etc. Three dimensional structure of a protein encoded by a gene could be useful to identify the function of the gene. This study investigates about the 3-D structure prediction of gel Q and gel K protein of Sphingomonas paucimobilis ATCC 31461 using two different protein modeling tools, Phyre2 and I-Tasser. Amplified gel Q and gel K genes were sequenced and the template protein structure identification was carried out using BLASTp with PDB. Superpositioning of the model with the template was analyzed with PyMol. Structure Validation servers of RAMPAGE, PROSA, Verify 3D, ERRAT and Qmean endorsed the 3D structure. The Phyre2 and I-Tasser model of gel Q protein showed best probability conformation with 94.3% and 80.7% residue respectively in the core region of Ramachandran plot showing greater accuracy of model prediction compared to gel K protein with 92.3% and 78.6% residue for Phyre2 and ITasser models respectively. Phyre2 server generated a finer prediction, analysis and validation for gel Q and gel K protein structure than the I-Tasser server. The results generated using Expasy tool also suggested that glycosyl transferase protein, encoded by the genes gel Q and gel K in the gel cluster may be directly involved in the fourth sugar addition of the repeating unit and in the incorporation of GlcA from UDP-glucuronic acid, into glucosyl-α-pyrophosphorylpolyprenol intermediate respectively. In order to develop a recombinant Sphingomonas paucimobilis ATCC 31461, gel Q and gel K gene might be a powerful successor for the overexpression of the gene to enhance gellan gum production.

A novel approach to knowledge discovery and representation in biological databases

Extraction of motifs from biological sequences is among the frontier research issues in bioinformatics, with sequential patterns mining becoming one of the most important computational techniques in this area. A number of applications motivate the search for more structured patterns and concurrent protein motif mining is considered here. This paper builds on the concept of structural relation patterns and applies the Concurrent Sequential Patterns (ConSP) mining approach to biological databases. Specifically, an original method is presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Data modelling is pursued to represent the more interesting concurrent patterns visually. Experiments with real-world protein datasets from the UniProt and NCBI databases highlight the applicability of the ConSP methodology in protein data mining and modelling. The results show the potential for knowledge discovery in the field of protein structure identification. A pilot experiment extends the methodology to DNA sequences to indicate a future direction.

A novel approach to knowledge discovery and representation in biological databases

Extraction of motifs from biological sequences is among the frontier research issues in bioinformatics, with sequential patterns mining becoming one of the most important computational techniques in this area. A number of applications motivate the search for more structured patterns and concurrent protein motif mining is considered here. This paper builds on the concept of structural relation patterns and applies the Concurrent Sequential Patterns (ConSP) mining approach to biological databases. Specifically, an original method is presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Data modelling is pursued to represent the more interesting concurrent patterns visually. Experiments with real-world protein datasets from the UniProt and NCBI databases highlight the applicability of the ConSP methodology in protein data mining and modelling. The results show the potential for knowledge discovery in the field of protein structure identification. A pilot experiment extends the methodology to DNA sequences to indicate a future direction.

Identify High-Quality Protein Structural Models by EnhancedK-Means

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means.

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm

Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

SpeeDB: fast structural protein searches.

Interactions between amino acids are important determinants of the structure, stability and function of proteins. Several tools have been developed for the identification and analysis of such interactions in proteins based on the extensive studies carried out on high-resolution structures from Protein Data Bank (PDB). Although these tools allow users to identify and analyze interactions, analysis can only be performed on one structure at a time. This makes it difficult and time consuming to study the significance of these interactions on a large scale. SpeeDB is a web-based tool for the identification of protein structures based on structural properties. SpeeDB queries are executed on all structures in the PDB at once, quickly enough for interactive use. SpeeDB includes standard queries based on published criteria for identifying various structures: disulphide bonds, catalytic triads and aromatic-aromatic, sulphur-aromatic, cation-π and ionic interactions. Users can also construct custom queries in the user interface without any programming. Results can be downloaded in a Comma Separated Value (CSV) format for further analysis with other tools. Case studies presented in this article demonstrate how SpeeDB can be used to answer various biological questions. Analysis of human proteases revealed that disulphide bonds are the predominant type of interaction and are located close to the active site, where they promote substrate specificity. When comparing the two homologous G protein-coupled receptors and the two protein kinase paralogs analyzed, the differences in the types of interactions responsible for stability accounts for the differences in specificity and functionality of the structures. SpeeDB is available at http://www.parallelcomputing.ca as a web service. d@drobilla.net Supplementary data are available at Bioinformatics online.

Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition

Successful protein structure identification enables researchers to estimate the biological functions of proteins, yet it remains a challenging problem. The most common method for determining an unknown protein’s structural class is to perform expensive and time-consuming manual experiments. Because of the availability of amino acid sequences generated in the post-genomic age, it is possible to predict an unknown protein’s structural class using machine learning methods given a protein’s amino-acid sequence and/or its secondary structural elements. Following recent research in this area, we propose a new machine learning system that is based on combining several protein descriptors extracted from different protein representations, such as position specific scoring matrix (PSSM), the amino-acid sequence, and secondary structural sequences. The prediction engine of our system is operated by an ensemble of support vector machines (SVMs), where each SVM is trained on a different descriptor. The results of each SVM are combined by sum rule. Our final ensemble produces a success rate that is substantially better than previously reported results on three well-established datasets. The MATLAB code and datasets used in our experiments are freely available for future comparison at http://www.dei.unipd.it/node/2357.

Translational Bioinformatics and Computational Systems Medicine

This special issue is dedicated to Translational bioinformatics and Computational Systems Medicine. Translational biomedical informatics is a rapidly emerging discipline to integrate data from medical research, biotechnologies, and electronic medical records, and computational systems medicine is to apply computational and systems biology approaches to solve complex problems in medical research, aiming to improve the diagnosis, prognosis, and treatment of complex diseases. It is also well known that their development needs an integration of mathematical models, statistical methods, and computer algorithms. Complex diseases such as cancers are coursed by a combination of genetic, environmental, and lifestyle factors, and thus the research of complex diseases at a system level like gene sets, pathway level, or static/dynamic network is a necessity. T. Holden et al. present an exploratory bioinformatics study of long noncoding RNA in Alzheimer's disease. The authors also discuss the model for drug development based on fractal dimension and entropy correlation in this study consistent with a zebrafish model and a mouse model. M. Ding et al. explore the genome-wide chromatin localization of Estrogen receptor-DNA binding regions by analyzing ChIP-Seq data from MCF-7 breast cancer cell line. It reveals novel Estrogen receptor-regulated genes pathways for further experimental validation. Y.-w. Lv et al. perform gene ontology category and pathways analysis for relationships among statistically significant genes in Kawasaki disease. The importance of compelling immune pathway of NF-AT signal and leukocyte interactions combined with another transcription factor NF-κB in the pathogenesis of KD is investigated by network analysis. J. Chen et al. perform two case studies on colorectal and prostate cancer microarray datasets to proof two hypotheses that (1) the expression signatures of different cancer microarray datasets are more similar at pathway level than at gene level; (2) the comparability of the cancer molecular mechanisms of different individuals is related to their genetic similarities. The concept of entropy suggests that systems naturally progress from order to disorder. Entropy-based methods provide a novel insight into understanding many phenomena in biological systems. V. Oswal et al. present an automated entropy-based thresholding system for segmentation and quantification of cell nuclei from histologically stained images. The contributions to the application of this entropy-based system to detect cancerous cell nuclei and observe overlapping cellular events occurring during wound healing process in the human body are also presented. H.-T. Wu et al. investigate the feasibility and sensitivity of a novel multiscale entropy index in detecting subtle vascular abnormalities in healthy and diabetic subjects. The authors further focus on four groups of subjects to discuss the application of multiscale entropy index. The role of protein structures in understanding diseases becomes more and more important, due to the following two reasons. One is that there are a lot of disease-associated proteins that were discovered, while the other fact is that many diseases are believed to result from misfolded proteins. Moreover, protein structures can be considered as complex systems, and thus network theory can be used to characterize and to analyze protein structures. J. Sun et al. present a contribution which focuses on the effect of edge definition of complex networks on protein structure identification. After the performance on 2847 proteins, the authors argue that the optimal cutoff value for constructing the protein structure networks is 5.0 A. X. Jiao et al. improve the amino acid network for proteins by introducing similar weight and dissimilar weight. This work demonstrates that highly central residues of the amino acid network are highly correlated with the hot spots in disease-associated proteins. Finally, two bioinformatics tools were also involved in this issue. H. Wu et al. contribute a bioinformatics tool called patGPCR, to predict the 3D structures of transmembrane helices of G-protein-coupled receptors. patGPCR, a parallelized multitemplate approach, extends a bundle-packing related energy function to RosettaMem energy, which improves the TM RMSD (root mean square deviation of the transmembrane helices) of the predicted models. N. Deng et al. contribute another platform, called crcTRP, for colorectal cancer. This server provides the translational research of colorectal cancer by providing various types of biomedical information, including clinical data, epidemiology data, individual omics data, and public omics data. Bairong Shen Hong-Bin Shen Tianhai Tian Qiang Lu Guang Hu

The Effect of Edge Definition of Complex Networks on Protein Structure Identification

The main objective of this study is to explore the contribution of complex network together with its different definitions of vertexes and edges to describe the structure of proteins. Protein folds into a specific conformation for its function depending on interactions between residues. Consequently, in many studies, a protein structure was treated as a complex system comprised of individual components residues, and edges were interactions between residues. What is the proper time for representing a protein structure as a network? To confirm the effect of different definitions of vertexes and edges in constructing the amino acid interaction networks, protein domains and the structural unit of proteins were described using this method. The identification performance of 2847 proteins with domain/domains proved that the structure of proteins was described well when R Cα was around 5.0–7.5 Å, and the optimal cutoff value for constructing the protein structure networks was 5.0 Å (Cα-Cα distances) while the ideal community division method was community structure detection based on edge betweenness in this study.

Applications of graph theory in protein structure identification.

There is a growing interest in the identification of proteins on the proteome wide scale. Among different kinds of protein structure identification methods, graph-theoretic methods are very sharp ones. Due to their lower costs, higher effectiveness and many other advantages, they have drawn more and more researchers’ attention nowadays. Specifically, graph-theoretic methods have been widely used in homology identification, side-chain cluster identification, peptide sequencing and so on. This paper reviews several methods in solving protein structure identification problems using graph theory. We mainly introduce classical methods and mathematical models including homology modeling based on clique finding, identification of side-chain clusters in protein structures upon graph spectrum, and de novo peptide sequencing via tandem mass spectrometry using the spectrum graph model. In addition, concluding remarks and future priorities of each method are given.

Hierarchical nanomechanics of collagen microfibrils

AbstractCollagen constitutes one third of the human proteome, providing mechanical stability, elasticity and strength to connective tissues. Collagen is also the dominating material in the extracellular matrix (ECM) and is thus crucial for cell differentiation, growth and pathology. However, fundamental questions remain with respect to the origin of the unique mechanical properties of collagenous tissues, and in particular its stiffness, extensibility and nonlinear mechanical response. By using x-ray diffraction data of a collagen fibril reported by Orgel et al. (Proceedings of the National Academy of Sciences USA, 2006) in combination with protein structure identification methods, here we present an experimentally validated model of the nanomechanics of a collagen microfibril that incorporates the full biochemical details of the amino acid sequence of the constituting molecules. We report the analysis of its mechanical properties under different levels of stress and solvent conditions, using a full-atomistic force field including explicit water solvent. Mechanical testing of hydrated collagen microfibrils yields a Young’s modulus of ≈300 MPa at small and ≈1.2 GPa at larger deformation in excess of 10% strain, in excellent agreement with experimental data. Dehydrated, dry collagen microfibrils show a significantly increased Young’s modulus of ≈1.8 to 2.25 GPa (or ≈6.75 times the modulus in the wet state) owing to a much tighter molecular packing, in good agreement with experimental measurements (where an increase of the modulus by ≈9 times was found). Our model demonstrates that the unique mechanical properties of collagen microfibrils can be explained based on their hierarchical structure, where deformation is mediated through mechanisms that operate at different hierarchical levels. Key mechanisms involve straightening of initially disordered and helically twisted molecules at small strains, followed by axial stretching of molecules, and eventual molecular uncoiling at extreme deformation. These mechanisms explain the striking difference of the modulus of collagen fibrils compared with single molecules, which is found in the range of 4.8±2 GPa or ≈10-20 times greater. These findings corroborate the notion that collagen tissue properties are highly scale dependent and nonlinear elastic, an issue that must be considered in the development of models that describe the interaction of cells with collagen in the extracellular matrix. A key impact the atomistic model of collagen microfibril mechanics reported here is that it enables the bottom-up elucidation of structure-property relationships in the broader class of collagen materials such as tendon or bone, including studies in the context of genetic disease where the incorporation of biochemical, genetic details in material models of connective tissue is essential.

FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately

Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

AutoPSI: a database for automatic structural classification of protein sequences and structures

In protein research, structural classifications of protein domains provided by databases such as SCOP play an important role. However, as such databases have to be curated and prepared carefully, they update only up to a few times per year, and in between newly entered PDB structures cannot be used in cases where a structural classification is required. The Automated Protein Structure Identification (AutoPSI) database delivers predicted SCOP classifications for several thousand yet unclassified PDB entries as well as millions of UniProt sequences in an automated fashion. In order to obtain predictions, we make use of two recently published methods, namely AutoSCOP (sequence-based) and Vorolign (structure-based) and the consensus of both. With our predictions, we bridge the gap between SCOP versions for proteins with known structures in the PDB and additionally make structure predictions for a very large number of UniProt proteins. AutoPSI is freely accessible at http://www.bio.ifi.lmu.de/AutoPSIDB.

Protein Block Expert (PBE): a web-based protein structure analysis server using a structural alphabet

Encoding protein 3D structures into 1D string using short structural prototypes or structural alphabets opens a new front for structure comparison and analysis. Using the well-documented 16 motifs of Protein Blocks (PBs) as structural alphabet, we have developed a methodology to compare protein structures that are encoded as sequences of PBs by aligning them using dynamic programming which uses a substitution matrix for PBs. This methodology is implemented in the applications available in Protein Block Expert (PBE) server. PBE addresses common issues in the field of protein structure analysis such as comparison of proteins structures and identification of protein structures in structural databanks that resemble a given structure. PBE-T provides facility to transform any PDB file into sequences of PBs. PBE-ALIGNc performs comparison of two protein structures based on the alignment of their corresponding PB sequences. PBE-ALIGNm is a facility for mining SCOP database for similar structures based on the alignment of PBs. Besides, PBE provides an interface to a database (PBE-SAdb) of preprocessed PB sequences from SCOP culled at 95% and of all-against-all pairwise PB alignments at family and superfamily levels. PBE server is freely available at .

Searching for aging‐related proteins in human dermal microvascular endothelial cells treated with anti‐aging agents

Endothelial cells constitute an interface between blood and tissue and act as a medium for active interaction between plasma and the intracellular environment for homeostasis. Aging of endothelial cells plays a significant role in the pathophysiology of age-related vascular diseases; however, precise mechanisms for senescence have not been elucidated. Proteomics allows identification of protein structures, functions, and characteristics, and can be applied to the study of aging processes. Using cultured human dermal microvascular endothelial cells and two-dimensional proteomic mapping, we studied the effects of kinetin, epigallocatechin-3-gallate, all-trans-retinoic acid, and selenium on their senescence and searched for the aging-related proteins. The treatments resulted in 68 qualitative changes and 172 quantitative changes, and we were able to identify 46 spots among them. All of the agents indicated above induced changes in the expression of moesin, rho guanosine-5'-diphosphate-dissociation inhibitor, and actin, confirmed by immunoblotting and confocal laser microscopy. As these proteins were associated with cell cycle and cytoskeleton, immunoblotting of the proteins related to cell cycle was performed. Although practical significance remains to be confirmed by in vivo research, this fundamental discovery may provide a basis for understanding the mechanism of aging and age-related diseases.