Finding Compact Structural Motifs

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.

9] Profile analysis

Motifs are the biologically significant fragments of nucleotide or peptide sequences in a specific pattern. Motifs are categorized as structural motifs and sequence motifs. These are discovered by phylogenetic studies of similar genes across species. Structural motifs are formed by three dimensional arrangements of amino acids consisting of two or more α helices or β strands whereas sequence motifs are formed by the nucleotide fragments appearing in the exons of a gene. The arrangement of residues in structural motifs may not be continuous while it is continuous in sequence motifs. Sequence motifs may encode to the structural motifs. The algorithms used for motif discovery are important part of the bio-computational studies. The purpose of motif discovery is to identify patterns in biopolymer (nucleotide or protein) sequences to understand the structure and function of the molecules and their evolutionary aspects. The main aim of this paper is to provide systematic compilation of a review on different approaches, databases and tools used in motif discovery.

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.

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.

Minimum-latency broadcast scheduling problem has been a long-studied problem on the basis of conflict avoidance. However, no algorithms with theoretical performance guarantees have been proposed for this problem in 3D mobile ad hoc networks (MANETs), due to the significant hardness brought by various node mobilities in 3D space. As the first attempt in the literature to study this problem in 3D MANETs, we provide a localized approximation algorithm (LBS) with both theoretical and experimental guarantees with respect to time latency and message complexity.

Fragment-based drug design relies heavily on structural information for the elaboration and optimisation of hits. The ability to identify neighbouring binding hot spots, energetically favourable interactions and conserved binding motifs in protein structures through X-ray crystallography can inform the evolution of fragments into lead-like compounds through structure-based design. The composition of fragment libraries can be designed and curated to fit this purpose and herein, we describe and compare screening libraries containing compounds comprising between 2 and 18 heavy atoms. We evaluate the properties of the compounds in these libraries and assess their ability to probe protein surfaces for binding hot spots.

The long list of examples in this book underscores the roles that RNA (regulatory) motifs play in controlling the genetic repertoire of cells and developing organisms. As explained in Chapter 3, identification of an RNA-processing signal, a ribozyme, an RNA localization signal or a translational or mRNA stability control element (examples for each are summarized in Chap. 2, Table 2.2) allows the search for other RNA sequences that bear similar regulatory signals (consensus search). Whilst DNA regulatory elements can often be described by a consensus sequence, RNA signals are frequently composed of combined sequence and structure motifs. A sequence motif is, for example, the hexameric polyadenylation signal AAUAAA. Other RNA motifs are often characterized by complex secondary and tertiary structures that can constitute the major part of the signal, e.g. the clover-leaf pattern typical for the secondary structure of tRNAs or stemloop structures involved as a second part of the signal in delineating the polyadenylation site in several retroviridae. The search for such motifs and signals is an exercise in understanding RNA structures and sequences as a type of language. As in a human language, both single characters (nucleotides) and higher-order structures (stem-loop structures and more complex tertiary interactions such as pseudoknots) are important. This can also be summarized as interfering contexts of regulatory sequence elements (Trifonov 1996). This chapter first illustrates how searches for new RNA motifs are conducted by computer-aided database screening of genomic sequences and databases for such motifs composed of different sequences and structures. After this we will examine more general (template-free) types of searches.

The interaction between RNA and RNA-binding proteins (RBPs) has a key role in the regulation of gene expression, in RNA stability, and in many other biological processes. RBPs accomplish these functions by binding target RNA molecules through specific sequence and structure motifs. The identification of these binding motifs is therefore fundamental to improve our knowledge of the cellular processes and how they are regulated. Here, we present BRIO (BEAM RNA Interaction mOtifs), a new web server designed for the identification of sequence and structure RNA-binding motifs in one or more RNA molecules of interest. BRIO enables the user to scan over 2508 sequence motifs and 2296 secondary structure motifs identified in Homo sapiens and Mus musculus, in three different types of experiments (PAR-CLIP, eCLIP, HITS). The motifs are associated with the binding of 186 RBPs and 69 protein domains. The web server is freely available at http://brio.bio.uniroma2.it.

The emerging network function virtualization is migrating traditional middleboxes, e.g., firewalls, load balancers, proxies, from dedicated hardware to virtual network functions (VNFs) running on commercial servers defined as network points of presence (N-PoPs). VNFs further chain up for more complex network services called service function chains (SFCs). SFCs introduce new flexibility and scalability which greatly reduce expenses and rolling out time of network services. However, chasing the lowest cost may lead to congestion on popular N-PoPs and links, thus resulting in performance degradation or violation of service-level agreements. To address this problem, we propose a novel scheme that reduces the operating cost and controls network congestion at the same time. It does so by placing VNFs and routing flows among them jointly. Given the problem is NP-hard, we design an approximation algorithm named candidate path selection (CPS) with a theoretical performance guarantee. We then consider cases when SFC demands fluctuate frequently. We propose an online candidate path selection (OCPS) algorithm to handle such cases considering the VNF migration cost. OCPS is designed to preserve good performance under various migration costs and prediction errors. Extensive simulation results highlight that CPS and OCPS algorithms perform better than baselines and comparably to the optimal solution.

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.

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.

We describe a novel procedure for generating and optimizing pattern descriptors that can be used to find structural motifs in DNA or RNA sequences. This combines a pattern-description language (based primarily on secondary structure alignment and conservation of some key nucleotides) with a scoring function that relies heavily on estimated folding free energies for the secondary structure of interest. For the cloverleaf secondary structure characteristic of tRNA, we show that a fairly simple pattern descriptor can find almost all known tRNA genes in both bacterial and eukaryotic genomes, and that false positives (sequences that match the pattern but that are probably not tRNAs) can be recognized by their high estimated folding free energies. A general procedure for optimizing descriptors (and hence for finding new structural motifs) is also described. For six bacterial, four eukaryotic, and four archaea genome sequences, our results compare favorably with those of the more complex and specialized tRNAscan-SE algorithm. Prospects for using this general approach to find other RNA structural motifs are discussed.

Adenosine 5′‐triphosphate (ATP) binds to a great number of proteins to elicit a wide variety of effects, including energy production and molecular signalling. Proteins have evolved different strategies to specifically recognize ATP, utilizing different ways of binding the phosphoryl moieties as well as the adenine base. The most common, conserved sequence and structural motif for binding ATP is the Walker‐A motif, or P‐loop, found in many different protein structural families. Greater variation in the sequence of the P‐loop is being recognized, as more ATP‐binding proteins are being structurally and functionally characterized. In contrast to the P‐loop, recognition of the adenine base often makes use of conserved structural motifs of main‐chain atoms via hydrogen‐bonding interactions, or side‐chains in stacking interactions, without a definitive amino acid sequence pattern. Key concepts: A major class of ATP‐binding proteins are those that contain a P‐loop or Walker‐A motif. P‐loops or glycine‐rich loops function by binding the phosphoryl groups of ATP. Several sequence variations on the Walker‐A motif are now known and have been functionally characterized. The Walker‐B motif contains a conserved acidic residue (Glu/Asp) that functions to bind directly or indirectly a metal ion important in catalysis. Adenine‐binding does not occur through specific sequence motifs, but rather uses a conserved pattern of polar and nonpolar interactions within a structural motif. Both main‐chain hydrogen bonding and aromatic residue stacking contribute to adenine‐binding by proteins.