Identification Of Structural Motifs Research Articles

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies.

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering, and structural motif identification and discovery of protein kinases, hydrolases, and SARS-CoV-2’s protein via the application of amino acid grouping

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.

Accurate and Efficient Determination of Unknown Metabolites in Metabolomics by NMR-Based Molecular Motif Identification.

Knowledge of the chemical identity of metabolite molecules is critical for the understanding of the complex biological systems to which they belong. Since metabolite identities and their concentrations are often directly linked to the phenotype, such information can be used to map biochemical pathways and understand their role in health and disease. A very large number of metabolites however are still unknown; i.e., their spectroscopic signatures do not match those in existing databases, suggesting unknown molecule identification is both imperative and challenging. Although metabolites are structurally highly diverse, the majority shares a rather limited number of structural motifs, which are defined by sets of 1H and 13C chemical shifts of the same spin system. This allows one to characterize unknown metabolites by a divide-and-conquer strategy that identifies their structural motifs first. Here, we present the structural motif-based approach "SUMMIT Motif" for the de novo identification of unknown molecular structures in complex mixtures, without the need for extensive purification, using NMR in tandem with two newly curated NMR molecular structural motif metabolomics databases (MSMMDBs). For the identification of structural motif(s), first, the 1H and 13C chemical shifts of all the individual spin systems are extracted from 2D and 3D NMR spectra of the complex mixture. Next, the molecular structural motifs are identified by querying these chemical shifts against the new MSMMDBs. One database, COLMAR MSMMDB, was derived from experimental NMR chemical shifts of known metabolites taken from the COLMAR metabolomics database, while the other MSMMDB, pNMR MSMMDB, is based on predicted chemical shifts of metabolites of several existing large metabolomics databases. For molecules consisting of multiple spin systems, spin systems are connected via long-range scalar J-couplings. When this motif-based identification method was applied to the hydrophilic extract of mouse bile fluid, two unknown metabolites could be successfully identified. This approach is both accurate and efficient for the identification of unknown metabolites and hence enables the discovery of new biochemical processes and potential biomarkers.

Unsupervised Discovery of Geometrically Common Structural Motifs and Long-Range Contacts in Protein 3D Structures.

The essential role of small evolutionarily conserved structural units in proteins has been extensively researched and validated. A popular example are serine proteases, where the peptide cleavage reaction is realized by a configuration of only three residues. Brought to spatial proximity during the protein folding process, such structural motifs are often long-range contacts and usually hard to detect at sequence level. Due to the constantly increasing resource of protein 3D structure data, the computational identification of structural motifs can contribute significantly to the understanding of protein fold and function. Thus, we propose a method to discover structural motifs of high geometrical similarity and desired sequence separation in protein 3D structure data. By utilizing methods originated from data mining, no a priori knowledge is required. The applicability of the method is demonstrated by the identification of the catalytic unit of serine proteases and the ion-coordination center of cupredoxins. Furthermore, large-scale analysis of the entire Protein Data Bank points towards the presence of ubiquitous structural motifs, independent of any specific fold or function. We envision that our method is suitable to uncover functional mechanisms and to derive fingerprint libraries of structural motifs, which could be used to assess protein family association.

Bioinformatics Study of Structural Patterns in Plant MicroRNA Precursors.

According to the RNA world theory, RNAs which stored genetic information and catalyzed chemical reactions had their contribution in the formation of current living organisms. In recent years, researchers studied this molecule diversity, i.a. focusing on small non-coding regulatory RNAs. Among them, of particular interest is evolutionarily ancient, 19–24 nt molecule of microRNA (miRNA). It has been already recognized as a regulator of gene expression in eukaryotes. In plants, miRNA plays a key role in the response to stress conditions and it participates in the process of growth and development. MicroRNAs originate from primary transcripts (pri-miRNA) encoded in the nuclear genome. They are processed from single-stranded stem-loop RNA precursors containing hairpin structures. While the mechanism of mature miRNA production in animals is better understood, its biogenesis in plants remains less clear. Herein, we present the results of bioinformatics analysis aimed at discovering how plant microRNAs are recognized within their precursors (pre-miRNAs). The study has been focused on sequential and structural motif identification in the neighbourhood of microRNA.

RNAMotifScanX: a graph alignment approach for RNA structural motif identification.

RNA structural motifs are recurrent three-dimensional (3D) components found in the RNA architecture. These RNA structural motifs play important structural or functional roles and usually exhibit highly conserved 3D geometries and base-interaction patterns. Analysis of the RNA 3D structures and elucidation of their molecular functions heavily rely on efficient and accurate identification of these motifs. However, efficient RNA structural motif search tools are lacking due to the high complexity of these motifs. In this work, we present RNAMotifScanX, a motif search tool based on a base-interaction graph alignment algorithm. This novel algorithm enables automatic identification of both partially and fully matched motif instances. RNAMotifScanX considers noncanonical base-pairing interactions, base-stacking interactions, and sequence conservation of the motifs, which leads to significantly improved sensitivity and specificity as compared with other state-of-the-art search tools. RNAMotifScanX also adopts a carefully designed branch-and-bound technique, which enables ultra-fast search of large kink-turn motifs against a 23S rRNA. The software package RNAMotifScanX is implemented using GNU C++, and is freely available from http://genome.ucf.edu/RNAMotifScanX.

Identification of Structural Motifs in P-Glycoprotein Responsible for the Drug-Mediated Inhibition of ATP Hydrolysis

P-glycoprotein (P-gp, ABCB1) is the most studied member of the ATP-Binding Cassette (ABC) transporter superfamily. This transporter utilizes energy from ATP hydrolysis for the efflux of a great variety of compounds, including anticancer drugs. P-gp is expressed at the apical surface of epithelial cells in the intestines, kidney, liver, adrenal gland, blood-brain barrier, and placenta, and therefore affects the pharmacokinetics of many drugs. The expression of P-gp at the surface of tumor cells is also one of the mechanisms of multidrug resistance, which occurs in many cancers. The development of an effective inhibitor requires first the understanding of the mechanism of action of P-gp. In our recent work, mutagenesis and molecular modeling studies led to the identification of a pair of phenylalanine-tyrosine structural motifs that mediate the inhibition of ATP hydrolysis by drugs such as zosuquidar, tariquidar and elacridar. Upon mutation of any of these residues, drugs that inhibit the ATPase activity of P-gp switch to stimulation of the activity. Molecular modeling revealed that the phenylalanine residues interact with the tyrosines in an edge-to-face conformation, helping the tyrosines to adopt the proper orientation to effectively establish hydrogen-bond contact with the inhibitor. Biochemical investigations along with transport studies in intact cells showed that the inhibitors bind at a high affinity site to produce inhibition of ATP hydrolysis and transport function. Upon mutation of tyrosine or phenylalanine residues that disrupts the structural motifs, they bind at lower affinity sites, leading to stimulation of ATP hydrolysis and poor inhibition of transport.

Homology Modeling and Docking Studies Identify Subtype-Specific Characteristics of Melanocortin Receptor Activation

Melanocortin receptors (MCRs) belong to the family of G protein-coupled receptors (GPCRs), and the five subtypes of MCRs (MC1R, MC2R, MC3R, MC4R, and MC5R) are implicated in diverse physiological processes such as pigmentation, food intake, and energy homeostasis. Because of their physiological importance, MCRs are natural targets for drug development. It is known that synthetic cyclic analogues of natural MCR hormones have the potential for pharmaceutical applications. However, 1) no current structures exist for MCRs and 2) the synthetic cyclic peptides possess little specificity for MCR subtypes, making identification of structural motifs in receptor-ligand binding extremely difficult. Our hypothesis is that MCR activity is determined by specific intermolecular interactions of ligand side chains with amino acid residues of the melanocortin receptor, while the selectivity of the ligands towards different subtypes of MCRs is controlled by conformational restraints of ligand backbones. Identification of general characteristics of MCR-ligand interactions will facilitate intelligent design of modified ligand structures that lead to MCR subtype specificity. To test this hypothesis, we developed homology models of the different MCR subtypes and conducted docking studies of a known agonist (Melanotan-II) and antagonist (SHU-9119) on each MCR model, in order to characterize the respective binding affinities of each receptor-ligand complex. Our results show that agonists and antagonists have a common set of binding characteristics along with interactions that are unique to each ligand that can be directly related to their function. The ability to accurately characterize unique structural interactions between known MCR agonists and antagonists is the first step in developing the ability to design effective, subtype-specific therapeutic agents of melanocortin receptors.

Identification of structural motifs as tunneling two-level systems in amorphous alumina at low temperatures

One of the most accepted models that describe the anomalous thermal behavior of amorphous materials at temperatures below 1 K relies on the quantum mechanical tunneling of atoms between two nearly equivalent potential energy wells forming a two-level system (TLS). Indirect evidence for TLSs is widely available. However, the atomistic structure of these TLSs remains an unsolved topic in the physics of amorphous materials. Here, using classical molecular dynamics, we found several hitherto unknown bistable structural motifs that may be key to understanding the anomalous thermal properties of amorphous alumina at low temperatures. We show through free energy profiles that the complex potential energy surface can be reduced to canonical TLSs. The tunnel splitting predicted from instanton theory, the number density, dipole moment, and coupling to external strain of the discovered motifs are consistent with experiments.

IncMD: Incremental trie-based structural motif discovery algorithm

The discovery of common RNA secondary structure motifs is an important problem in bioinformatics. The presence of such motifs is usually associated with key biological functions. However, the identification of structural motifs is far from easy. Unlike motifs in sequences, which have conserved bases, structural motifs have common structure arrangements even if the underlying sequences are different. Over the past few years, hundreds of algorithms have been published for the discovery of sequential motifs, while less work has been done for the structural motifs case. Current structural motif discovery algorithms are limited in terms of accuracy and scalability. In this paper, we present an incremental and scalable algorithm for discovering RNA secondary structure motifs, namely IncMD. We consider the structural motif discovery as a frequent pattern mining problem and tackle it using a modified a priori algorithm. IncMD uses data structures, trie-based linked lists of prefixes (LLP), to accelerate the search and retrieval of patterns, support counting, and candidate generation. We modify the candidate generation step in order to adapt it to the RNA secondary structure representation. IncMD constructs the frequent patterns incrementally from RNA secondary structure basic elements, using nesting and joining operations. The notion of a motif group is introduced in order to simulate an alignment of motifs that only differ in the number of unpaired bases. In addition, we use a cluster beam approach to select motifs that will survive to the next iterations of the search. Results indicate that IncMD can perform better than some of the available structural motif discovery algorithms in terms of sensitivity (Sn), positive predictive value (PPV), and specificity (Sp). The empirical results also show that the algorithm is scalable and runs faster than all of the compared algorithms.

Abstract 783: Identification of structural determinants of human proton-coupled folate transporter oligomerization

Abstract The human proton-coupled folate transporter (hPCFT) is expressed in human solid tumors and is active at pHs characterizing the human microenvironment. hPCFT has 459 amino acids with 12 transmembrane domains and cytosolic N- and C-termini. Recent attention has focused on exploiting hPCFT for targeting solid tumors with novel cytotoxic antifolates (Kugel Desmoulin et al., Cancer Biol Ther 13: 1355-1373, 2012). hPCFT forms homo-oligomers with functional significance (Hou et al., J Biol Chem 287: 4982-4995, 2012). The hPCFT primary sequence includes GXXXG motifs in transmembrane domain (TMD) 2 (G93XXXG97) and 4 (G155XXXG159) that are analogous to dimerization motifs in other amphipathic proteins. To investigate the role of these GXXXG motifs in hPCFT oligomerization, we mutated Gly to Leu to obtain single (G93L, G97L, G155L, G159L) or multiple (G93L/G97L, G155L/G159L, G93L/G97L/G155L/G159L) mutants. Only with the G159L and G93L mutants, was [3H]methotrexate transport at pH 5.5 substantially preserved. Transport activity correlated closely with surface hPCFT levels by sulfo-NHS-SS-biotin labeling and Western blots. When hemagglutin (HA)-tagged G-to-L hPCFT mutants were co-expressed with FLAG-His10-tagged wild-type (WT) hPCFT in hPCFT-null HeLa cells, both mutant and WT proteins were retained on nickel affinity columns, suggesting that the hPCFT GXXXG motifs are not involved in protein oligomerization. This was substantiated by fluorescent resonance energy transfer (FRET) with N-terminal YFP-tagged and C-terminal CFP-tagged hPCFT constructs ectopically expressed in hPCFT-null HeLa cells. The GXXXG motifs in hPCFT are important for proper hPCFT folding and intracellular trafficking. A functional Cys-less (CL) hPCFT construct was generated in which the 7 Cys residues were replaced by Ser. Individual Cys insertion mutants were generated in which Cys was inserted from positions 115-137 in TMD3, and from positions 213-236 in TMD6. Constructs were transfected into hPCFT-null HeLa cells, followed by cross-linking with MTS-6-MTS (TMD3) or MTS-1-MTS (TMD6). Cross-links were detected for V118, A128, V132, Q136 and L137 in TMD 3, and for W213C, A227C, F228C, L234C, K235C, and E236C in TMD6, establishing proximities between vicinal TMDs in separate hPCFT monomers. These results strongly imply that TMD3 and TMD6 provide critical polypeptide interfaces for the formation of hPCFT oligomers. Identification of structural motifs or domains involved in hPCFT oligomerization may lead to novel approaches for therapeutically “rescuing” functionally impaired hPCFT mutants, or enhancing surface expression of hPCFT in tumors treated with hPCFT-selective antifolates. Key words: proton-coupled folate transporter, folate, oligomerization, cross-linking, transporter. Citation Format: Zhanjun Hou, M. Roy Wilson, Lucas Wilson, Sita Kugel Desmoulin, Jenny Huang, Larry H. Matherly. Identification of structural determinants of human proton-coupled folate transporter oligomerization. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 783. doi:10.1158/1538-7445.AM2014-783

Identification of autoantigens recognized by the 2F5 and 4E10 broadly neutralizing HIV-1 antibodies

Many human monoclonal antibodies that neutralize multiple clades of HIV-1 are polyreactive and bind avidly to mammalian autoantigens. Indeed, the generation of neutralizing antibodies to the 2F5 and 4E10 epitopes of HIV-1 gp41 in man may be proscribed by immune tolerance because mice expressing the V(H) and V(L) regions of 2F5 have a block in B cell development that is characteristic of central tolerance. This developmental blockade implies the presence of tolerizing autoantigens that are mimicked by the membrane-proximal external region of HIV-1 gp41. We identify human kynureninase (KYNU) and splicing factor 3b subunit 3 (SF3B3) as the primary conserved, vertebrate self-antigens recognized by the 2F5 and 4E10 antibodies, respectively. 2F5 binds the H4 domain of KYNU which contains the complete 2F5 linear epitope (ELDKWA). 4E10 recognizes an epitope of SF3B3 that is strongly dependent on hydrophobic interactions. Opossums carry a rare KYNU H4 domain that abolishes 2F5 binding, but they retain the SF3B3 4E10 epitope. Immunization of opossums with HIV-1 gp140 induced extraordinary titers of serum antibody to the 2F5 ELDKWA epitope but little or nothing to the 4E10 determinant. Identification of structural motifs shared by vertebrates and HIV-1 provides direct evidence that immunological tolerance can impair humoral responses to HIV-1.

Identification of structural motifs in the E2 glycoprotein of Chikungunya involved in virus–host interaction

Chikungunya fever is one of the reemerging vector-borne diseases. It has become a major global health problem especially in the developing countries. There are no vaccines or specific antiviral drugs available to date. This study reports small molecule inhibitors of envelope glycoprotein 2 (E2 glycoprotein) which are predicted based on Chikungunya virus–host interactions. E2 glycoprotein of Chikungunya virus interacts at 216 residue of the host receptor protein which plays a vital role in initiating infection. Understanding the structural aspects of E2 glycoprotein is crucial to develop specific inhibitors to prevent the virus binding from host receptors. In silico method was adopted to predict the sequence motifs of envelope protein, as the method like yeast two hybrid system is laborious, time consuming, and costly. The E2 glycoprotein structure of the Indian isolate was modeled using two templates (2XFC and 3JOC) and then validated. The class III PDZ domain binding motif was found to be identified at 213–216 amino acids. The corresponding peptide structures which recognize the PDZ domain binding motif were identified by the literature search and were used for generating five point pharmacophore model (ADDDR) containing acceptor, donor and aromatic ring features. Databases such as Asinex, TosLab and Maybridge were searched for the matches for the predicted pharmacophore model. Two compounds were identified as lead molecules as their glide score is > 5 kcal/mol. Since the pharmacophore model is developed based on Chikungunya virus–host interaction, it can be used for designing promising antiviral lead compounds for the treatment of Chikungunya fever.An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:21

Identification of Structural Motif of RNAs Interacting with the G-rich Sequence RNA

The G-quadruplexes play a biological role in DNA telomere ends,the purine-rich DNA strands of the oncogenic promoterelements such as c-myc and c-kit, and RNA 5'-untranslatedregion (UTR) in close to translational start sites. Telomereslocated at the ends of eukaryotic chromosomes are com-posed of the tandem DNA repeats of guanine-rich sequencesand essential for chromosome stability. They appear to playa critical role in cellular aging and cancer.

Clustering RNA structural motifs in ribosomal RNAs using secondary structural alignment

RNA structural motifs are the building blocks of the complex RNA architecture. Identification of non-coding RNA structural motifs is a critical step towards understanding of their structures and functionalities. In this article, we present a clustering approach for de novo RNA structural motif identification. We applied our approach on a data set containing 5S, 16S and 23S rRNAs and rediscovered many known motifs including GNRA tetraloop, kink-turn, C-loop, sarcin–ricin, reverse kink-turn, hook-turn, E-loop and tandem-sheared motifs, with higher accuracy than the state-of-the-art clustering method. We also identified a number of potential novel instances of GNRA tetraloop, kink-turn, sarcin–ricin and tandem-sheared motifs. More importantly, several novel structural motif families have been revealed by our clustering analysis. We identified a highly asymmetric bulge loop motif that resembles the rope sling. We also found an internal loop motif that can significantly increase the twist of the helix. Finally, we discovered a subfamily of hexaloop motif, which has significantly different geometry comparing to the currently known hexaloop motif. Our discoveries presented in this article have largely increased current knowledge of RNA structural motifs.

From High‐Throughput Cell Culture Screening to Mouse Model: Identification of New Inhibitor Classes against Prion Disease

Transmissible spongiform encephalopathies (TSE) or prion diseases belong to a category of fatal and so far untreatable neurodegenerative conditions. All prion diseases are characterized by both degeneration in the central nervous system (CNS) in humans and animals and the deposition and accumulation of Proteinase K-resistant prion protein (PrP(res)). Until now, no pharmaceutical product has been available to cure these diseases or to alleviate their associated symptoms. Here, a cell-culture screening system is described that allows for the large-scale analysis of the PrP(res) inhibitory potential of a library of compounds and the identification of structural motifs leading potent compounds able to cause PrP(res) clearance at the cellular level. Based on different scrapie-infected cell lines, 10,000 substances were tested, out of which 530 potential inhibitors were identified. After re-screening and validation using a series of dilutions, 14 compounds were identified as the most effective. These 14 compounds were then used for therapeutic studies in a mouse bioassay to test and verify their in vivo potency. Two compounds exhibited therapeutic potential in the mouse model by significantly extending the survival time of intracerebrally infected mice, when treated 90 days after infection with scrapie.

SMpred: A Support Vector Machine Approach to Identify Structural Motifs in Protein Structure Without Using Evolutionary Information

Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information. Unfortunately, there are many structures in the protein structure databases have no homologous structures or sequences. In this work, we report an SVM method, SMpred, to identify structural motifs from single protein structure without using sequence and structural homologs. SMpred method was trained and tested using 132 proteins domains containing 581 motifs. SMpred method achieved 78.79% accuracy with 79.06% sensitivity and 78.53% specificity. The performance of SMpred was evaluated with MegaMotifBase using 188 proteins containing 1161 motifs. Out of 1161 motifs, SMpred correctly identified 1503 structural motifs reported in MegaMotifBase. Further, we showed that SMpred is useful approach for the length deviant superfamilies and single member superfamilies. This result suggests the usefulness of our approach for facilitating the identification of structural motifs in protein structure in the absence of sequence and structural homologs. The dataset and executable for the SMpred algorithm is available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/SMpred.htm.

RNAMotifScan: automatic identification of RNA structural motifs using secondary structural alignment

Recent studies have shown that RNA structural motifs play essential roles in RNA folding and interaction with other molecules. Computational identification and analysis of RNA structural motifs remains a challenging task. Existing motif identification methods based on 3D structure may not properly compare motifs with high structural variations. Other structural motif identification methods consider only nested canonical base-pairing structures and cannot be used to identify complex RNA structural motifs that often consist of various non-canonical base pairs due to uncommon hydrogen bond interactions. In this article, we present a novel RNA structural alignment method for RNA structural motif identification, RNAMotifScan, which takes into consideration the isosteric (both canonical and non-canonical) base pairs and multi-pairings in RNA structural motifs. The utility and accuracy of RNAMotifScan is demonstrated by searching for kink-turn, C-loop, sarcin-ricin, reverse kink-turn and E-loop motifs against a 23S rRNA (PDBid: 1S72), which is well characterized for the occurrences of these motifs. Finally, we search these motifs against the RNA structures in the entire Protein Data Bank and the abundances of them are estimated. RNAMotifScan is freely available at our supplementary website (http://genome.ucf.edu/RNAMotifScan).

RASMOT-3D PRO: a 3D motif search webserver

Detection of structural motif of residues in protein structures allows identification of structural or functional similarity between proteins. In the field of protein engineering, structural motif identification is essential to select protein scaffolds on which a motif of residues can be transferred to design a new protein with a given function. We describe here the RASMOT-3D PRO webserver (http://biodev.extra.cea.fr/rasmot3d/) that performs a systematic search in 3D structures of protein for a set of residues exhibiting a particular topology. Comparison is based on Cα and Cβ atoms in two steps: inter-atomic distances and RMSD. RASMOT-3D PRO takes in input a PDB file containing the 3D coordinates of the searched motif and provides an interactive list of identified protein structures exhibiting residues of similar topology as the motif searched. Each solution can be graphically examined on the website. The topological search can be conducted in structures described in PDB files uploaded by the user or in those deposited in the PDB. This characteristic as well as the possibility to reject scaffolds sterically incompatible with the target, makes RASMOT-3D PRO a unique webtool in the field of protein engineering.

Motif Identification in Materials Simulations

AbstractWe present an algorithm for the identification of structural motifs on large scale atomistic simulation systems of solid materials. Given an arbitrary motif (model), the geometric hashing paradigm is used to find corresponding matches in a large structure (target). To account for real materials, the algorithm tolerates motif deformation and different orientations. Application to solid state science is illustrated by the examples of crystal growth simulations and grain analysis in polycrystalline structures.