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arXiv 2018年

作者： Warrell, Jonathan Mohsen, Hussein Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University Molecular Biophysics and Biochemistry Yale University Department of Computer Science Yale University

A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be interested in picking out a prioritized collection of subsets of the inputs across a range of scales according to their importance for an output node, and not simply a prioritized ranking across the inputs as singletons. Such a situation may arise in biological applications, for instance, where we are interested in epistatic effects between groups of genes in determining a trait of interest. Here, we outline a flexible framework which may be used to generate multiscale network interpretations, using any previously defined scoring function. We demonstrate the ability of our method to pick out biologically important genes and gene sets in the domains of cancer and psychiatric genomics. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Review: Precision medicine and driver mutations: computational methods, functional assays and conformational principles for interpreting cancer drivers (vol 15, e1006658, 2019)

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PLOS computational biology 2019年第6期15卷 e1006658-e1006658页

作者： Nussinov, Ruth Jang, Hyunbum Tsai, Chung-Jung Cheng, Feixiong Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute Frederick Maryland United States of America Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv Israel Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland Ohio United States of America Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio United States of America

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

关键词： INDIVIDUALIZED medicine CANCER genetics MUTATION (biology) ONCOLOGY CANCER treatment EXOMES PHARMACOLOGY TUMOR genetics

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An analysis of conformational changes upon RNA-Protein binding 14

An analysis of conformational changes upon RNA-Protein bindi...

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5th ACM Conference on Bioinformatics, computational biology, and Health Informatics, ACM BCB 2014

作者： Sankar, Kannan Jernigan, Robert L. Walia, Rasna R. Honavar, Vasant G. Mann, Carla M. Dobbs, Drena Bioinformatics and Computational Biology Program Dept. of Biochem. Biophysics and Molecular Biology Iowa State University United States Dept. of Biochem. L.H. Baker Center for Bioinformatics and Biological Statistics Iowa State University United States Bioinformatics and Computational Biology Program Dept. of Computer Science Iowa State University United States Bioinformatics and Genomics Program College of Information Sciences and Technology Pennsylvania State University United States Dept. of Genetics Development and Cell Biology L.H. Baker Center for Bioinformatics Iowa State University United States

ISBN: (纸本)9781450328944

RNA-binding proteins (RBPs) have myriad functions in transcription, translation, and post-transcriptional gene regulation, with central roles in normal development as well as in both genetic and infectious diseases. When a protein binds RNA, a conformational change often occurs. For RNA-protein complexes that have been characterized, conformational changes have been observed in the protein, the RNA, or both. These conformational changes have not been sufficiently characterized, however, in part due to the small number of structures of bound and unbound complexes of RNA-binding proteins available until recently. Here we systematically analyze a new dataset of 90 pairs of bound and unbound proteins to evaluate the conformational changes that occur upon RNA binding. Most of the conformational changes were observed in noninterfacial regions of the RNA-binding proteins. Detailed analyses of the modes of RNA binding and any associated conformational changes in proteins are critical for fully understanding the mechanisms of RNA-protein recognition, for developing better RNA-protein docking methods and methods for predicting interfacial residues, and for RNA-based drug design. Copyright © 2014 ACM.

关键词： RNA

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An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

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BMC Research Notes 2009年第1期2卷 1-7页

作者： Sasidharan, Rajkumar Agarwal, Ashish Rozowsky, Joel Gerstein, Mark Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 United States Department of Plant Biology Carnegie Institution for Science Stanford CA 94305 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

Background. There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. Findings. This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. Conclusion. Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure. © 2009 Sasidharan et al;licensee BioMed Central Ltd.

关键词： Tiling Array Transcript Mapping Tile Path Tiling Microarray Intergenic Transcript

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Modeling Protein Structure in Macromolecular Assemblies at Near Atomic Resolutions

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Microscopy and Microanalysis 2015年第S3期21卷 541-542页

作者： Matthew L. Baker Muyuan Chen Philip R. Baldwin Steven J. Ludtke Wah Chiu National Center for Macromolecular Imaging Verna and Marrs McLean Department of Biochemistry and Molecular Biology Baylor College of Medicine Houston TXUSA Program in Structural and Computational Biology Baylor College of Medicine Houston TXUSA

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molecular imaging with optical coherence tomography and its biomedical applications

Molecular imaging with optical coherence tomography and its ...

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Optical Tomography and Spectroscopy, OTS 2018

作者： de la Zerda, Adam Departments of Structural Biology and Electrical Engineering Stanford University StanfordCA94305 United States Molecular Imaging Program at Stanford StanfordCA94305 United States The Bio-X Program StanfordCA94305 United States Biophysics Program at Stanford StanfordCA94305 United States The Chan Zuckerberg Biohub San FranciscoCA94158 United States

We developed a way to turn OCT into a molecular imaging modality through the development of novel nanoparticles. We explored its application in neurosurgery and in visualizing the role of the immune system in tumors. ... 详细信息

ISBN: (纸本)9781943580415

关键词： molecular imaging

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structural insights into TDP-43, a new player in neurodegenerative diseases

Structural insights into TDP-43, a new player in neurodegene...

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亚洲晶体学联合大会

作者： Pan-Hsien Kuo Lyudmila G.Doudeva Che-Kun James Shen Hanna S.Yuan Institute of Bioinformatics and Structural Biology National Tsing Hua University Institute of Molecular Biology Academia Sinica Taiwan International Graduate Program Chemical Biology and Molecular BiophysicsAcademia Sinica Graduate Institute of Biochemistry and Molecular Biology National Taiwan University

TDP-43 is a pathogenic protein: its normal function in binding to UG-rich RNA is related to cystic fibrosis, and inclusion of its C-terminal fragments in brain cells is directly linked to frontotemporal lobar degenera... 详细信息

关键词： TDP RNA domain structural insights into TDP-43 a new player in neurodegenerative diseases

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Identifying components in 3D density maps of protein nanomachines by multi-scale segmentation

Identifying components in 3D density maps of protein nanomac...

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IEEE/NLM Life Science Systems and Applications Workshop

作者： Grigore Pintilie Junjie Zhang Wah Chiu David Gossard Electrical Engineering and Computer Science MIT USA Structural & Computational Biology and Molecular Biophysics Baylor University College of Medicine USA Department of Mechanical Engineering MIT USA

Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a multi-scale segmentation method for accomplishing this task that requires very little user interaction. The method uses the concept of scale space, which is created by convolution of the input density map with a Gaussian filter. The latter process smoothes the density map. The standard deviation of the Gaussian filter is varied, with smaller values corresponding to finer scales and larger values to coarser scales. Each of the maps at different scales is segmented using the watershed method, which is very efficient, completely automatic, and does not require the specification of seed points. Some detail is lost in the smoothing process. A sharpening process reintroduces detail into the segmentation at the coarsest scale by using the segmentations at the finer scales. We apply the method to simulated density maps, where the exact segmentation (or ground truth) is known, and rigorously evaluate the accuracy of the resulting segmentations.

关键词： Proteins Image segmentation Nanobioscience Filters Object detection computational biology molecular biophysics Educational institutions Microscopy Convolution

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Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

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BMC Research Notes 2010年第1期3卷 1-18页

作者： Guo, Beibei Villagran, Alejandro Vannucci, Marina Wang, Jian Davis, Caleb Man, Tsz-Kwong Lau, Ching Guerra, Rudy Department of Statistics Rice University Houston TX 77005-1827 6100 Main United States Department of Statistics University of Connecticut Storrs CT 06269-4120 United States Department of Pediatrics Baylor College of Medicine Texas Children's Hospital Houston TX 77030 6621 Fannin St. United States Structural and Computational Biology and Molecular Biophysics Program Baylor College of Medicine Houston TX 77030 One Baylor Plaza United States

Background. The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference. Results. The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection. Conclusions. We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples. © 2010 Guerra et al;licensee BioMed Central Ltd.

关键词： Comparative Genomic Hybridization Misclassification Rate Copy Number Aberration Normal Copy Number Reversible Jump Markov Chain Monte Carlo

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A complex structure of arrestin-2 bound to a G protein-coupled receptor

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细胞研究（英文版） 2019年第12期29卷 971-983页

作者： Wanchao Yin Zhihai Li Mingliang Jin Yu-Ling Yin Parker W.de Waal Kuntal Pal Yanting Yin Xiang Gao Yuanzheng He Jing Gao Xiaoxi Wang Yan Zhang Hu Zhou Karsten Melcher Yi Jiang Yao Cong X.Edward Zhou Xuekui Yu H.Eric Xu The CAS Key Laboratory of Receptor Research Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai 201203 China The CAS Key Laboratory of Receptor Research Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai 201203 China Cryo-Electron Microscopy Research Center Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai 201203 China National Center for Protein Science Shanghai State Key Laboratory of Molecular Biology CAS Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China The CAS Key Laboratory of Receptor Research Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai 201203 China University of Chinese Academy of Sciences Beijing 100049 China Center for Cancer and Cell Biology Program for Structural Biology Van Andel Research Institute Grand Rapids MI 49503 USA Center for Cancer and Cell Biology Program for Structural Biology Van Andel Research Institute Grand Rapids MI 49503 USA Department of Biotechnology School of Life Science and Biotechnology Adamas University Kolkata India The CAS Key Laboratory of Receptor Research Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai 201203 China Center for Cancer and Cell Biology Program for Structural Biology Van Andel Research Institute Grand Rapids MI 49503 USA Center for Cancer and Cell Biology Program for Structural Biology Van Andel Research Institute Grand Rapids MI 49503 USA Laboratory of Receptor Structure and Signaling HIT Center for Life Science Harbin Institute of Technology Harbin 150001 China Department of Pathology of Sir Run Run Shaw Hospital and Department of Biophysics Zhejiang University School of Medicine Hangzhou 310058 China

Arrestins comprise a family of signal regulators of G-protein-coupled receptors (GPCRs),which include arrestins 1 to *** arrestins 1 and 4 are visual arrestins dedicated to rhodopsin,arrestins 2 and 3 (Arr2 and Arr3) are β-arrestins known to regulate many nonvisual *** dynamic and promiscuous coupling of Arr2 to nonvisual GPCRs has posed technical challenges to tackle the basis of arrestin binding to *** we report the structure of Art2 in complex with neurotensin receptor 1 (NTSR1),which reveals an overall assembly that is strikingly different from the visual arrestin-rhodopsin complex by a 90° rotation of Arr2 relative to the *** this new configuration,intracellular loop 3 (ICL3) and transmembrane helix 6 (TM6) of the receptor are oriented toward the N-terminal domain of the arrestin,making it possible for GPCRs that lack the C-terminal tail to couple Arr2 through their *** dynamics simulation and crosslinking data further support the assembly of the Arr2-NTSR1 *** analysis and homology modeling suggest that the Arr2-NTSR1 complex structure may provide an alternative template for modeling arrestin-GPCR interactions.

关键词： dynamics simulation homology modeling complex structure

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