An efficient automated computer vision based technique for detection of three dimensional structural motifs in proteins.

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.

9] Profile analysis

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.

There has been much research on computer vision in last three decades. Computer vision methods have been developed for different situations. One example is a detection of human face. For computers face detection is hard. Faces look different from different viewing directions. Facial expressions affect to the look of the face. Each individual person has a unique face. The lightning conditions can vary and so on.However, face detection is currently possible in limited conditions. In addition, there are some methods that can be used for gender recognition [3], face recognition [5] and facial expression recognition [2].Nonetheless, there has been very little research on how to combine these methods. There has also been quite little research on how to apply these methods in human-computer interaction (HCI).Finding sets of techniques that complement each other in a useful way is one research challenge. There are some applications that take advantage of one or two computer vision techniques. For example, Christian and Avery [1] have developed an information kiosk that uses computer vision to detect potential users from a distance. A similar kiosk has been developed by us in the University of Tampere [4]. There are also some games that use simple computer vision techniques for the interaction. However, there are very few applications that use several computer vision techniques together such as face detection, facial expression recognition and gender recognition. Overall, there has been very little effort in combining different techniques.In my research I develop computer vision methods and combine them, so that the combined method can detect face, recognize gender and facial expressions. After successfully combining the methods, it is easier to develop HCI applications that take advantage of computer vision. Applications that can be used by small group of people are my specific interest. These applications allow me to build adaptive user interfaces and analyze the use of computer vision techniques in improving human-computer interaction.

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

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).

A procedure to detect similar local structures of proteins from C alpha coordinates is presented. First, the conformations of seven-residue peptide segments are approximated by a limited number of representatives, each of which is assigned a symbol. Thus, the overall conformation of a protein is represented by a symbol string. The comparison of these symbol strings using a sequence alignment technique then gives pairs of similar local structures. These pairs are considered candidates of structural motifs. The application of the procedure to the analysis of 93 proteins gave 858 pairs of similar local structures, which included several well-known structural motifs such as the nucleotide-binding beta alpha beta-unit and the calcium-binding EF hand. The characterization of amino acid patterns of similar local structures given by the procedure should be useful for the development of protein structure prediction based on the acquisition of empirical rules from a large-scale database.

Evaluation of Surface Finish Quality Using Computer Vision Techniques

Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators

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.

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

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.

Proteins with insignificant sequence and overall structure similarity may still share locally conserved contiguous structural segments; i.e. structural/3D motifs. Most methods for finding 3D motifs require a known motif to search for other similar structures or functionally/structurally crucial residues. Here, without requiring a query motif or essential residues, a fully automated method for discovering 3D motifs of various sizes across protein families with different folds based on a 16-letter structural alphabet is presented. It was applied to structurally non-redundant proteins bound to DNA, RNA, obligate/non-obligate proteins as well as free DNA-binding proteins (DBPs) and proteins with known structures but unknown function. Its usefulness was illustrated by analyzing the 3D motifs found in DBPs. A non-specific motif was found with a ‘corner’ architecture that confers a stable scaffold and enables diverse interactions, making it suitable for binding not only DNA but also RNA and proteins. Furthermore, DNA-specific motifs present ‘only’ in DBPs were discovered. The motifs found can provide useful guidelines in detecting binding sites and computational protein redesign.