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Calibrated Langevin-dynamics simulations of intrinsically disordered proteins

Physical Review E 2014年第4期90卷 042709-042709页

作者： W. Wendell Smith Po-Yi Ho Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520-8120 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520-8114 USA Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520-8286 USA Department of Applied Physics Yale University New Haven Connecticut 06520-8267 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform extensive coarse-grained (CG) Langevin dynamics simulations of intrinsically disordered proteins (IDPs), which possess fluctuating conformational statistics between that for excluded volume random walks and collapsed globules. Our CG model includes repulsive steric, attractive hydrophobic, and electrostatic interactions between residues and is calibrated to a large collection of single-molecule fluorescence resonance energy transfer data on the interresidue separations for 36 pairs of residues in five IDPs: α−, β−, and γ-synuclein, the microtubule-associated protein τ, and prothymosin α. We find that our CG model is able to recapitulate the average interresidue separations regardless of the choice of the hydrophobicity scale, which shows that our calibrated model can robustly capture the conformational dynamics of IDPs. We then employ our model to study the scaling of the radius of gyration with chemical distance in 11 known IDPs. We identify a strong correlation between the distance to the dividing line between folded proteins and IDPs in the mean charge and hydrophobicity space and the scaling exponent of the radius of gyration with chemical distance along the protein.

关键词： LANGEVIN equations SIMULATION methods & models CONFORMATIONAL analysis MICROTUBULE-associated proteins HYDROPHOBIC compounds

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Diffusion curvature for estimating local curvature in high dimensional data 22

Diffusion curvature for estimating local curvature in high d...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Dhananjay Bhaskar Kincaid MacDonald Oluwadamilola Fasina Dawson Thomas Bastian Rieck Ian Adelstein Smita Krishnaswamy Department of Genetics Yale University Department of Mathematics Yale University Applied Mathematics Program Yale University Department of Mathematics Department of Physics Yale University Institute of AI for Health Helmholtz Pioneer Campus Helmholtz Munich Department of Computer Science Department of Genetics Applied Mathematics Program Program for Computational Biology and Bioinformatics Yale University

ISBN: (纸本)9781713871088

We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusion operator, to structure point cloud data and define local curvature based on the laziness of a random walk starting at a point or region of the data. We show that this laziness directly relates to volume comparison results from Riemannian geometry. We then extend this scalar curvature notion to an entire quadratic form using neural network estimations based on the diffusion map of point-cloud data. We show applications of both estimations on toy data, single-cell data and on estimating local Hessian matrices of neural network loss landscapes.

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Directed Scattering for Knowledge Graph-Based Cellular Signaling Analysis

Directed Scattering for Knowledge Graph-Based Cellular Signa...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Aarthi Venkat Joyce Chew Ferran Cardoso Rodriguez Christopher J. Tape Michael Perlmutter Smita Krishnaswamy Program for Computational Biology and Bioinformatics Yale University Department of Mathematics University of California Los Angeles Department of Oncology University College London Cancer Institute Department of Mathematics Boise State University Departments of Genetics and Computer Science Yale University

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.

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Construction and validation of a mouse model for studying severe human adenovirus infections

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Virologica Sinica 2024年第6期39卷 963-973页

作者： Dingbin Chen Yuqian Yan Ting Mei Peipei Yang Siqi Deng Yiqiang Li Tie Zhao Ning Xin Biyan Duan Weifeng Liang Yuemei Yang Wei Zhao Donald Seto Junxian Ou Qiwei Zhang Institute of Medical Microbiology Department of Immunology and MicrobiologyCollege of Life Science and TechnologyJinan UniversityGuangzhou510632China BSL-3 Laboratory(Guangdong) Guangdong Provincial Key Laboratory of Tropical Disease ResearchSchool of Public HealthSouthern Medical UniversityGuangzhou510515China Department of Clinical Laboratory Guangzhou Women and Children's Medical CenterGuangzhou Medical UniversityGuangzhou510620China South Yunnan Central Hospital(Honghe First People's Hospital) MengziYunnan661100China Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason UniversityManassasVA20110USA The Affiliated Guangdong Second Provincial General Hospital of Jinan University Guangzhou510310China Key Laboratory of Viral Pathogenesis&Infection Prevention and Control(Jinan University) Ministry of EducationGuangzhou510632China

Human adenoviruses(HAdVs)are highly contagious pathogens with various genotypes implicated in acute respiratory disease(ARD)and linked to fatality,especially in immunosuppressed patients,young children,and military ***,no vaccines or specific drugs are approved for clinical *** hosts of adenoviruses are strictly species-specific,which strongly limits the development of vaccines and drugs against *** this study,immunocompetent BALB/c mice were challenged with different doses of human adenovirus type 5(HAdV-5)via tail intravenous injection(i.v.).All mice challenged with a high dose of HAdV-5(3.21010 TCID50/kg)died within 3–5 days,while those receiving a low dose of HAdV-5(8109 or 4109 TCID50/kg)***,among the mice receiving a medium dose of HAdV-5(1.61010 TCID50/kg),60%(n¼3/5)of male mice died,while all female mice *** suggests that male mice may be more susceptible to HAdV-5 infection than female mice,consistent with clinical findings in ***-5 DNA was mainly distributed in the liver,followed by the spleen and *** changes were observed in the lung,liver,and spleen,with severity increasing in correlation with the virus challenge *** and qPCR analyses of the liver indicated that the down-regulated expression of the H2-Aa,H2-Ea-ps,CD74,and H2-Eb1 genes in male mice,as well as the AHR gene in female mice,may contribute to the observed higher mortality rates in male ***,this effective,feasible,and costefficient mouse model could serve as a candidate for evaluating HAdV vaccines and anti-adenovirus therapeutics.

关键词： Human adenovirus type 5(HAdV-5) Animal model BALB/c mice Severe human adenovirus infections

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Erratum to: An approach to compare genome tiling microarray and MPSS sequencing data for transcript mapping

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BMC Research Notes 2009年第1期2卷 1-2页

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

We are correcting the abstract of our published article ([1]). The sentence that starts "We observe that 4.5% of MPSS tags...." was not scientifically complete in the original abstract, having only two of the four numbers required to describe a comparison of two technologies in two different organisms. The abstract below more accurately describes our findings, as documented in Figure 1 of the manuscript.

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Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts

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Briefings in bioinformatics 2023年第2期24卷 bbac633-bbac633页

作者： Tu, Yueh-Hua Juan, Hsueh-Fen Huang, Hsuan-Cheng The Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taiwan Department of Life Science Center for Computational and Systems Biology National Taiwan University Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan

Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level. © The Author(s) 2023.

关键词： Gaussian mixture model gene regulatory network single-cell RNA sequencing trajectory analysis

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Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Hematopoietic Stem Cells in JAK2-Mutant Myeloproliferative Neoplasms

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BLOOD 2020年第Sup1期136卷 7-8页

作者： Van Egeren, Debra Escabi, Javier Nguyen, Maximilian Liu, Shichen Reilly, Christopher R. Patel, Sachin Kamaz, Baransel Kalyva, Maria DeAngelo, Daniel J. Galinsky, Ilene Wadleigh, Martha Winer, Eric S. Luskin, Marlise R. Stone, Richard M. Garcia, Jacqueline S. Hobbs, Gabriela S. Camargo, Fernando D. Michor, Franziska Mullally, Ann Cortes-Ciriano, Isidro Hormoz, Sahand Department of Systems Biology Harvard Medical School Boston Princeton University Princeton Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Boston Department of Medical Oncology Dana-Farber Cancer Institute Boston Hematology/Oncology Department and Stem Cell Program Boston Children's Hospital Boston Division of Hematology Brigham and Women's Hospital Boston European Bioinformatics Institute Cambridge United Kingdom Department of Medical Oncology Dana-Farber Cancer Inst. Boston MA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Leukemia Center Massachusetts General Hospital Boston MA

*equal contribution, # co-corresponding authors Although the JAK2V617F mutation is the most common MPN phenotypic driver mutation, the precise consequences of the mutation on the behavior of individual human hematopoietic stem cells (HSCs) in vivo remains unknown. We used whole genome sequencing and single-cell profiling of hematopoietic stem and progenitor cells (HSPCs) to quantify the impact of JAK2V617F on the proliferation dynamics of HSCs and the differentiation trajectories of their progenies in individual newly diagnosed MPN patients. We reconstructed the lineage history of individual HSCs obtained from patients with newly diagnosed essential thrombocythemia (ET), using the pattern of spontaneous somatic mutations accrued in their genomes over decades (Figure 1). Intriguingly, our analysis indicates that the JAK2V617F mutation occurred in a single HSC many years before MPN diagnosis - at age 9±2 years in a 34 year-old patient, and at age 19±3 years in a 63 year-old patient. In each patient, we inferred the number of mutated HSCs over the years and computed their fitness. After escaping stochastic extinction, the population of mutated HSCs grew exponentially by 63±15% and 44±13% every year in the two patients respectively. To contrast the differentiation trajectories of the JAK2 -mutant HSCs with those of healthy HSCs, we simultaneously measured the full transcriptome and somatic mutations in single HSPCs in the two ET patients in whom we had performed whole genome sequencing and in one additional ET patient (N=3 total) and also in patients with polycythemia vera (PV) (N=3). We observed, at the time of MPN diagnosis, a consistent lineage bias of JAK2 -mutant HSPCs toward megakaryocyte-erythrocyte fate, across ET and PV patients. Exploiting our ability to discriminate JAK2 -mutant cells from JAK2 wild-type cells within individual MPN patients, we identified genes involved in antigen presentation and inflammation as differentially up-regulated in JAK2 -mutant HS

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A software framework integrating gene expression patterns, binding site analysis and gene ontology to hypothesize gene regulation relationships

A software framework integrating gene expression patterns, b...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Pujan Joshi Baikang Pei Seung-Hyun Hong Ivo Kalajzic Dong-Ju Shin David Rowe Dong-Guk Shin Computer Science and Engineering University of Connecticut Storrs CT Computational Biology and Bioinformatics Yale University New Haven CT Genetics & Dev. Biology University of Connecticut Health Center Farmington CT Metabolic Signaling and Disease Program Sanford-Burnham Medical Research Institute Orlando FL

One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system that narrows down biological hypotheses by integrating gene expression patterns, transcription factor (TF) binding site analysis outcomes, and Gene Ontology (GO) enrichment analysis outcomes. This system identifies regulated genes from microarray experiments through statistical processes, categorizes similarly behaving groups of genes and then carries out binding site analysis and gene function enrichment analysis based on some significant clusters. The output is an ordered set of "putative" pair-wise relationships between TFs and their potential target genes. The relationships are ranked based on their closeness to the experimental context. We demonstrate the effectiveness of our framework using two independent microarray data sets.

关键词： Gene expression Context Sociology Statistics bioinformatics Bones

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Domain Adaptation via Rebalanced Sub-domain Alignment

arXiv

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arXiv 2023年

作者： Liu, Yiling Dong, Juncheng Jiang, Ziyang Aloui, Ahmed Li, Keyu Klein, Hunter Tarokh, Vahid Carlson, David Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Unsupervised domain adaptation (UDA) is a technique used to transfer knowledge from a labeled source domain to a different but related unlabeled target domain. While many UDA methods have shown success in the past, they often assume that the source and target domains must have identical class label distributions, which can limit their effectiveness in real-world scenarios. To address this limitation, we propose a novel generalization bound that reweights source classification error by aligning source and target sub-domains. We prove that our proposed generalization bound is at least as strong as existing bounds under realistic assumptions, and we empirically show that it is much stronger on real-world data. We then propose an algorithm to minimize this novel generalization bound. We demonstrate by numerical experiments that this approach improves performance in shifted class distribution scenarios compared to state-of-the-art methods. © 2023, CC BY.

关键词： Numerical methods

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Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel

arXiv

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arXiv 2022年

作者： Jiang, Ziyang Zheng, Tongshu Liu, Yiling Carlson, David Department of Civil and Environmental Engineering Duke University United States Division of Natural and Applied Science Duke Kunshan University China Program in Computational Biology and Bioinformatics Duke University School of Medicine United States Department of Civil and Environmental Engineering Department of Biostatistics and Bioinformatics Department of Computer Science Duke University United States

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional neural networks (CNNs) are frequently used in remote sensing, which is subject to strong seasonal effects. We propose to blend the strengths of NNs and the clear modeling capabilities of GPs by using a composite kernel that combines a kernel implicitly defined by a neural network with a second kernel function chosen to model known properties (e.g., seasonality). We implement this idea by combining a deep network and an efficient mapping function based on either Nyström approximation or random Fourier features, which we call Implicit Composite Kernel (ICK). We then adopt a sample-then-optimize approach to approximate the full GP posterior distribution. We demonstrate that ICK has superior performance and flexibility on both synthetic and real-world datasets including a remote sensing dataset. The ICK framework can be used to include prior information into neural networks in many applications. © 2022, CC BY.

关键词： Remote sensing

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