MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, Cα only models, Alternative alignments, and Non-sequential alignments

BACKGROUND: Protein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed. RESULTS: We have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, C(α) only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here. CONCLUSIONS: MICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool for automatically detecting non-trivial structural relationships of proteins, such as circular permutations and segment-swapping, many of which have been identified manually by human experts so far. The source code of MICAN is freely download-able at http://www.tbp.cse.nagoya-u.ac.jp/MICAN

A case report of acute cardiac tamponade creation in a macaque

Although acute cardiac tamponade is one of the major problems in clinical practice, a suitable animal model is still lacking. We tried to create acute cardiac tamponade in macaques by echo-guided catheter manipulation. A 13-year-old male macaque was anesthetized, and a long sheath was inserted into the left ventricle via the left carotid artery under the guidance of transthoracic echocardiography. The sheath was then inserted into the orifice of the left coronary artery to perforate the proximal site of the left anterior descending branch. A cardiac tamponade was successfully created. Injection of diluted contrast agent into the pericardial space via a catheter made it possible to clearly distinguish between the hemopericardium and the surrounding tissues on postmortem computed tomography. This procedure did not need an X-ray imaging system during catheterization. Our present model would help us examine the intrathoracic organs in the presence of acute cardiac tamponade

Comparison of 2D-and 3D-culture models as drug-testing platforms in breast cancer

markdownabstract__Abstract__ Multinomial choices of individuals are likely to be correlated. Nonetheless, econometric models for this phenomenon are scarce. A problem of multivariate multinomial choice models is that the number of potential outcomes can become very large which makes parameter interpretation and inference difficult. We propose a novel Multivariate Multinomial Logit specification, where (i) the number of parameters stays limited; (ii) there is a clear interpretation of the parameters in terms of odds ratios; (iii) zero restrictions on parameters result in independence between the multinomial choices and; (iv) parameter inference is feasible using a composite likelihood approach even if the multivariate dimension is large. Finally, these nice properties are also valid in a fixed-effects panel version of the model

A Young Adult Patient with Nonalcoholic Steatohepatitis Developed Severe Gastroesophageal Varices Associated with Severe Obesity and Diabetes Mellitus

Obesity is a major contributor to insulin resistance and nonalcoholic fatty liver disease, which is the most common cause of chronic liver diseases. Nonalcoholic steatohepatitis (NASH) can progress to liver cirrhosis and end-stage liver diseases. Some cases already show severe liver fibrosis at the time of diagnosis. We present the case of a 44-year-old male with overt obesity who was admitted with hematemesis due to the rupture of gastric varices. We diagnosed him with NASH with severe liver fibrosis. This case shows that we should be concerned about the progression of liver fibrosis due to NASH associated with severe obesity even in young patients

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified

Characteristics of interactions at protein segments without non-local intramolecular contacts in the Protein Data Bank.

The principle of three-dimensional protein structure formation is a long-standing conundrum in structural biology. A globular domain of a soluble protein is formed by a network of atomic contacts among amino acid residues, but regions without intramolecular non-local contacts are often observed in the protein structure, especially in loop, linker, and peripheral segments with secondary structures. Although these regions can play key roles for protein function as interfaces for intermolecular interactions, their nature remains unclear. Here, we termed protein segments without non-local contacts as floating segments and sought them in tens of thousands of entries in the Protein Data Bank. As a result, we found that 0.72% of residues are in floating segments. Regarding secondary structural elements, coil structures are enriched in floating segments, especially for long segments. Interactions with polypeptides and polynucleotides, but not chemical compounds, are enriched in floating segments. The amino acid preferences of floating segments are similar to those of surface residues, with exceptions; the small side chain amino acids, Gly and Ala, are preferred, and some charged side chains, Arg and His, are disfavored for floating segments compared to surface residues. Our comprehensive characterization of floating segments may provide insights into understanding protein sequence-structure-function relationships

How a Spatial Arrangement of Secondary Structure Elements Is Dispersed in the Universe of Protein Folds

<div><p>It has been known that topologically different proteins of the same class sometimes share the same spatial arrangement of secondary structure elements (SSEs). However, the frequency by which topologically different structures share the same spatial arrangement of SSEs is unclear. It is important to estimate this frequency because it provides both a deeper understanding of the geometry of protein folds and a valuable suggestion for predicting protein structures with novel folds. Here we clarified the frequency with which protein folds share the same SSE packing arrangement with other folds, the types of spatial arrangement of SSEs that are frequently observed across different folds, and the diversity of protein folds that share the same spatial arrangement of SSEs with a given fold, using a protein structure alignment program MICAN, which we have been developing. By performing comprehensive structural comparison of SCOP fold representatives, we found that approximately 80% of protein folds share the same spatial arrangement of SSEs with other folds. We also observed that many protein pairs that share the same spatial arrangement of SSEs belong to the different classes, often with an opposing N- to C-terminal direction of the polypeptide chain. The most frequently observed spatial arrangement of SSEs was the 2-layer <i>α</i>/<i>β</i> packing arrangement and it was dispersed among as many as 27% of SCOP fold representatives. These results suggest that the same spatial arrangements of SSEs are adopted by a wide variety of different folds and that the spatial arrangement of SSEs is highly robust against the N- to C-terminal direction of the polypeptide chain.</p></div

Structures of the target d2ffga1 and its structural neighbors.

<p>The cartoon representation of the protein structure possessing the most frequently observed spatial arrangement of SSEs (d2ffga1) and five examples of its structural neighbors (d1go4a_, d1aopa3, dlrlha_, d2jfga2, and d2p12a1) are presented. This spatial arrangement of SSEs consists of four strands and two helices, which are highlighted by colors in each structure. In the structure of d2ffga1, the strands and helices are highlighted in blue and red, respectively. In the other structures, the colors of the strands and helices with the same chain direction as those in d2ffga1 are identical to those in d2ffga1. The helices and reverse strands with opposing directions are colored in salmon and cyan, respectively. The connectivity diagrams are also shown near the cartoon representations. The color scheme is the same as those for the cartoon representations. The TM-score(d2ffga1 example) calculated by the SQ, RW, and RR schemes is also shown.</p