9] Profile analysis

A methodology to analyze and characterize small- and large-scale shape and structural features in a macromolecule is presented. The procedure involves the construction of a continuum of spherical shape maps. This continuum is built by analyzing the overcrossing pattern of a molecular space curve from every direction in 3-space as seen by a viewer with a reduced vision field. The method derives global shape descriptors (shape maps) that characterize the backbone as a whole (large-scale structure), but also allows one to focus the analysis on details of the backbone's small-scale structure. A continuous function is proposed as a simplified descriptor derived from the shape maps. This function discriminates reasonably well among elements of secondary structure and supersecondary structural motifs in proteins. The procedure is applied to α-helices, β-sheets, and models of α-helical packing.

A specific treatment of recurrent structural motifs that represent the local bias information has been proven to be an important ingredient in de novo protein structure predication. Significant majority of methods for local structure are based on building blocks, which still suffer from its inherent discrete nature. Instead of using building blocks, this work presents a new protocol framework for local structural motifs prediction based on the direct locating along protein sequence and probabilistic sampling in a continuous (φ, ψ) space. The protein sequence was first scanned by an algorithm of sliding window with variable length of 7 to 19 residues, to match local segments to one of 82 motifs patterns in the fragment library. Identified segments were then labeled and modeled as the correlations of backbone torsion angles with mixture of bivariate cosine distributions in continuous (φ, ψ) space. 3D conformations of corresponding segments were finally sampled by using a backtrack algorithm to the hidden Markov model with single output of (φ, ψ). For local motifs in 50 proteins of testing set, about 62% of eight-residue segments located with high confidence value were predicted within 1.5 Å of their native structures by the method. Majority of local structural motifs were identified and sampled, which indicates the proposed protocol may at least serve as the foundation to obtain better protein tertiary structure prediction.

As the number of available three dimensional coordinates of proteins increases, it is now recognized that proteins from different families and topologies are constructed from independent motifs. Detection of specific structural motifs within proteins aids in understanding their role and the mechanism of their operation. To aid in identification and use of these motifs it has become necessary to develop efficient methods for systematic scanning of structural databases. To date, methods of structural protein comparison suffer from at least one of the following limitations: (1) are not fully automated (require human intervention), (2) are limited to relatively similar structures, (3) are constrained to linear alignments of the structures, (4) are sensitive to insertions, deletions or gaps in the sequences or (5) are very time consuming. We present a method to overcome the above limitations. The method discovers and ranks every piece of structural similarity between the structures compared, thus allowing the simultaneous detection of real 3-D motifs in different domains, between domains, in active sites, surfaces etc. The method uses the Geometric Hashing Paradigm which is an efficient technique originally developed for Computer Vision. The algorithm exploits the geometrical constraints of rigid objects, it is especially geared towards recognition of partial structures in rigid objects belonging to large data bases and is straightforwardly parallelizable. Computer Vision techniques are for the first time applied to molecular structure comparison, resulting in an efficient, fully automated tool. The method has been tested in a number of cases, including comparisons of the haemoglobins, immunoglobulins, serine proteinases, calcium binding proteins, DNA binding proteins and others. In all examples our results were equivalent to the published results from previous methods and in some cases additional structural information was obtained by our method.

The number of gamma-turns in a representative protein dataset selected from the current Protein Data Bank has increased almost seven times during the past decade. Eighty percent classic gamma-turns and 57% inverse gamma-turns are associated as multiple turns with either another y-turn or a beta-turn. We refer to these as multiple turns of the (gammabeta)1,2,3 or (betagamma)1,2,3 type, depending upon whether the gamma-turn is before or after the beta-turn along the protein chain, respectively. However, for multiple turns involving only gamma-turns, we follow the nomenclature analogous to that proposed earlier for the multiple (or double) beta-turns. Fifty-eight per cent beta-turns are associated as multiple turns with another beta-turn. We extracted multiple turns from the protein dataset and classified them on the basis of individual gamma- or beta-turn types and the number of overlapping residues. Furthermore, we evaluated the amino acid positional potentials and determined the statistically significant amino acid preferences, hydrogen bond/side-chain interaction preferences in the multiple turns and secondary structure preferences for residues immediately flanking these turns. The results of our analysis would be useful in the modeling, prediction or design of multiple turns in proteins. The amino acid sequence corresponding to the multiple turn, position in the protein chain, PDB Code/chain in which multiple turn is present and the individual turn types constituting the multiple turns are available from our website and this information would also be integrated in our Database of Structural Motifs in Proteins (http://www.cdfd.org.in/dsmp.html).

Prion diseases are characterized by the accumulation of a pathogenic misfolded form of a prion protein (PrP) encoded by the Prnp gene in humans. In the present study in zebrafish, two transcripts and the corresponding genes encoding prion proteins, PrP1 and PrP2, related to human PrP have been characterized with a relatively divergent deduced amino acid sequence, but a well preserved overall organization of structural prion protein motifs. Whole-mount in situ hybridization analysis performed during embryonic and larval development showed a high level of PrP1 mRNA spatially restricted to the anterior floor-plate of the central nervous system and in ganglia. Transcripts of prp2 were detected in embryonic cells from the mid-blastula transition to the end of the segmentation period. From 24 h postfertilization up to larval stages, prp2 transcripts were localized in distinct anatomical structures, including a major expression in the brain, eye, kidney, lateral line neuromasts, liver, heart, pectoral fins and posterior intestine. The observed differential developmental expression patterns of the two long PrP forms, prp1 and prp2, and the short PrP form prp3, a more divergent prion-related gene previously identified in zebrafish, should contribute to understanding of the phylogenetic and functional relationships of duplicated prion gene forms in the fish genome. Together, the complex history of prion-related genes, reflected in the deduced structural features, conserved amino acid sequence and repeat motifs of the corresponding proteins, and the presence of differential developmental expression patterns suggest possible acquisition or loss of prion protein functions during vertebrate evolution.

ObjectiveThe coiled coil is a superhelical structural protein motif that has been thoroughly investigated in recent years and coiled-coil domain-containing proteins have exhibited a large diversity of function in biological systems (e.g., gene regulation, cell division, membrane fusion, drug extrusion). The aim of this study was to investigate the critical role of coiled-coil domain-containing protein 34 (CCDC34) in bladder carcinogenesis, which has never been reported to date.MethodsImmunohistochemical staining and western blot were used to detect CCDC34 expression in bladder cancers specimens and cell lines. Lentivirus-mediated small interfering RNA (siRNA) against CCDC34 was designed and the silencing effect was examined by quantitative RT-PCR in bladder cancer cell lines T24 and 5637. The biological functions of CCDC34 knockdown on bladder cancer cells were investigated by examining cell proliferation using a high content screening (HCS) assay, BrdU incorporation assay and colony formation assay, as well as cell migration by in vitro wound healing assay. Cell cycle distribution and apoptosis were detected by flow cytometric analysis. The expressions of c-Raf and c-Jun as well as the phosphorylation of MEK, ERK1/2, JNK, P38 and AKT were also measured using Western blot. We further investigated the effect of therapeutic siRNA targeting CCDC34 on T24 xenograft tumor growth in nude mice.ResultsCCDC34 level was significantly increased in human bladder cancer tissues and cell lines. CCDC34 knockdown significantly suppressed bladder cancer cells proliferation and migration, and induced cell cycle arrest at G2/M phase and increased apoptosis in vitro. In addition, intratumor delivery of therapeutic siRNA targeting CCDC34 elicited delayed tumor growth of bladder cancer xenograft in nude mice. Moreover, CCDC34 knockdown decreased the phosphorylation of MEK, ERK1/2, JNK, p38 and Akt, and the expressions of c-Raf and c-Jun, indicating MAPK and AKT pathways (ERK/MAPK, p38/MAPK, JNK/MAPK and PI3K/Akt) might be involved in CCDC34 modulation of bladder cancer cell proliferation and migration.ConclusionsOur findings revealed for the first time a potential oncogenic role for CCDC34 in bladder carcinoma pathogenesis and it may serve as a biomarker or even a therapeutic target for bladder cancer.

α-Helices are the most abundant secondary structural motif in proteins (Kendrew et al., 1958, Pauling, Corey, & Branson, 1951) and an ideal linear helix can be completely described by a single valu...

Chapter 111 - Nonlinear spectroscopy study of vibrational self-trapping in hydrogen bonded crystals

Protein structure motif detection is one of the fundamental problems in Structural Bioinformatics. Compared with sequence motifs, structural motifs are more sensitive in detecting the evolutionary relationships among proteins. A variety of algorithms have been proposed to attack this problem. However, they are either heuristic without theoretical performance guarantee, or inefficient for employing an exhaustive search strategy. Here, we study a reasonably restricted version of this problem: the compact structural motif problem. In this paper, we prove that this restricted version is still NP-hard, and we present a polynomial-time approximation scheme to solve it. To the best of our knowledge, this is the first approximation algorithm with a guarantee ratio for the protein structural motif problem.

Novel high-throughput experimental techniques produce a large amount of data on the 3D structure of proteins and their structural motifs. These motifs can be used as patterns in drug discovery [1], can help to understand the relationship between a protein’s structure and its function [2] and to classify proteins [3]. In order to extract as much information as possible from this data, new techniques and tools are necessary, and among them fast approaches to perform the multiple superimposition of large sets of protein structural motifs. We report here on the development of such a tool. We have implemented our newly developed multiple superimposition methodology in the web application SiteBinder, which is able to process hundreds of protein structural motifs in a very short time and provides an intuitive and user-friendly interface. We also demonstrate the applicability of SiteBinder using three case studies, focused on biologically important protein motifs. In the first case study, we compared the structures of 67 PA-IIL sugar binding sites containing 9 different sugars and we found that the sugar binding sites of PA-IIL and its mutants have a conserved structure despite their binding different sugars. The second case study focused on more than 300 zinc finger central motifs and revealed that the molecular structure in the vicinity of the Zn atom in Cys2His2 zinc fingers is highly conserved. In the last case study, we superimposed 12 BH3 domains from pro-apoptotic proteins, and found that there is a structural basis for the functional segregation of these proteins into activators and enablers.

Applications of artificial neural networks in the field of genome research will be reviewed and some more recent developments in neural network research relevant for future applications will be surveyed. The basic definitions for artificial neural networks and neural learning algorithms will be introduced. The applications range from the recognition of translation initiation sites in nucleic acid sequences, the recognition of splice junctions and exons/ introns in mRNA, the detection of uncommon sequences in cDNA, to the prediction of secondary and tertiary structures of proteins from the amino acid sequence, the detection of structural motifs in protein sequences and the classification of protein sequences into functional families. Most applications employ multilayer feedforward networks trained supervised with the backpropagation learning algorithm or self-organising Kohonen maps adapted unsupervised for feature extraction. The most promising developments in neural network research usable in all mentioned applications are new modular network architectures with more problem-tailored connection topologies such as linked receptive fields and recurrent networks with short-term memory capable of modelling any dynamical system using only inductive learning.

Reentrant loops are an important structural motif in alpha-helical transmembrane proteins. A reentrant loop is a structural motif that goes only halfway through the membrane and then turns back to the side from which it originates. The question of what causes the reentrant loops to form such a unique topology is still unanswered. In this study, we try to answer this question by analyzing the hydrophobicity distribution on the amino acid sequences of the reentrant loops. Our results show that reentrant loops have very low hydrophobicity around the deepest point buried in the membrane and relative high hydrophobicity close to the membrane surfaces. We speculate that this hydrophobicity distribution is a major force that stabilizes the unique reentrant loop structure. Our results also show that this hydrophobicity distribution results in special patterns on protein sequences, which can be captured using profile hidden Markov models (HMMs). The resulting profile HMMs can detect reentrant loops on protein sequences with high sensitivity and perfect specificity.

Camelina sativa stands out among oilseed crops due to its remarkable resilience to challenging environmental conditions and its significant potential for biodiesel production. The MADS-box transcription factors play a pivotal role in numerous biological processes within plants, encompassing growth, development, and responses to environmental stressors. In this research, by employing the BLAST, we have successfully identified 325 MADS-box genes within Camelina sativa genome. These genes were systematically categorized into two principal groups: type I (comprising Mα, Mβ, and Mγ) and type II (including MI-KCC and MIKC*) predicated upon their phylogenetic relationships, structural protein motifs, and exon-intron configurations. Our findings reveal that type II MADS-box genes have, in general, experienced a more profound expansion relative to type I genes. Specifically, the TM3 subgroup within type II MADS-box genes exhibited the highest degree of gene expansion, comprising 21 TM3 genes. The amino acid sequences encoded by these genes exhibited a length variation ranging from 150 to 820 aa. The predicted molecular weights (MW) of the CsMADS-box proteins displayed a range from 17.01 to 94.06 kDa, while the isoelectric points (pI) were observed to span from 4.13 to 10.09. Evolutionary analysis predicated on the Ka/Ks ratios indicates that the evolutionary pathway of MADS-box genes in Camelina sativa has been predominantly driven by the mechanisms of purifying selection. Moreover, an investigation of cis-acting elements has elucidated the participation of MADS-box genes in the adaptive responses to abiotic stressors. The expression profiles of six Type I and three MIKCC genes across diverse organs and under varying drought treatment conditions demonstrated that these genes are expressed in both reproductive and vegetative structures, displaying uniform expression patterns throughout several developmental phases of flowering. The expression levels of CsMADS035, CsMADS115, CsMADS131, and CsMADS181 were notably modified in reaction to drought stress conditions. The detailed annotation and comprehensive transcriptome profiling provided in this research yield essential insights into the functional roles that MADS-box genes perform in stress resistance, as well as their contributions to growth and developmental processes. This acquired understanding establishes a foundational framework for the functional characterization and potential genetic engineering initiatives pertaining to Camelina sativa, thereby augmenting the prospective application of these candidate genes.

This article presents a new approach to detect coiled coil and leucine zipper (L-Zip) motifs in protein sequences. The approach is based on protein scale calculation and sequence analysis. For this purpose, the wavelet-based local extrema extraction is employed, and window-based variations of local extrema afterward. This, in turn, provided a way to distinguish coiled coil subsequences and potential L-Zip motifs. The approach is validated on carefully chosen protein sequences that return inconclusive results within known frameworks for L-Zip detection, for example, 2ZIP. The results show that this new approach represents an improvement over previously presented approaches.

Data Mining is the process to extract hidden predictive information from large databases. In Bioinformatics, data mining enables researchers to meet the challenge of mining large amount of biomolecular data to discover real knowledge. Major research efforts done in the area of bioinformatics involves sequence analysis, protein structure prediction and gene finding. Proteins are said to be prominent molecules in our cells. They involve virtually in all cell functions. The activities and functions of proteins can be determined by protein sequence motifs. These protein motifs are identified from the segments of protein sequences. All segments may not be important to produce good motif patterns. The generated sequence segments do not have classes or labels. Hence, unsupervised segment selection technique is adopted to select significant segments. Therefore Singular Value Decomposition (SVD) entropy method is adopted to select significant sequence segments. In this proposed work, weighted K-Means and Adaptive Fuzzy C-Means have been applied to the selected segments to generate granules, since large amount of segments cannot be grouped or clustered as such. Each granules generated by weighted K-Means algorithm are further clustered by using the K-Means algorithm and granules generated by Adaptive Fuzzy C-Means algorithm are clustered by using Weighted K-Means. The two proposed models are compared with K-Means granular computing model. The experimental results show that Adaptive Fuzzy C-Means with Weighted K-Means technique produces better results than K-Means and weighted K-Means granular computing methods.