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arXiv 2019年

作者： Mei, Zhe Treado, John D. Grigas, Alex T. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures|in the root-mean-square deviations of the Cα atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography. Copyright © 2019, The Authors. All rights reserved.

关键词： X ray crystallography

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A Multi-Site Break through Cancer Trial: Phase II Study Investigating Dual Inhibition of BCL2 and Menin in AML MRD Using the Combination of Venetoclax and Revumenib (Trial In Progress)

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Blood 2024年 144卷 4265.2-4265.2页

作者： Ghayas C. Issa Alexander J. Ambinder Wenbin Xiao Scott Manalis Alex K Shalek Lukasz P. Gondek Alex Kentsis Nicholas J. Short Naval Daver Jing Ning Lianchun Xiao Michael Hemann Ken Chen Keyur P. Patel Pavan Bachireddy Andrew A. Lane Marina Konopleva Omar Abdel-Wahab Jeffrey J. Molldrem Ross L Levine Jacqueline S. Garcia Department of Leukemia The University of Texas MD Anderson Cancer Center Houston TX Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Baltimore MD Department of Pathology and Laboratory Medicine Hematopathology Service Memorial Sloan Kettering Cancer Center New York NY Massachusetts Institute of Technology Cambridge MA Institute for Medical Engineering & Science and Department of Chemistry Massachusetts Institute of Technology Cambridge MA Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD Tow Center for Developmental Oncology Department of Pediatrics Memorial Sloan Kettering Cancer Center New York NY MD Anderson Cancer Center Houston TX The University of Texas M.D. Anderson Cancer Center Houston TX Department of Biostatistics University of Texas MD Anderson Cancer Center Houston David H. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX Department of Hematopathology The University of Texas MD Anderson Cancer Center Houston TX Department of Hematopoietic Biology & Malignancy The University of Texas M.D. Anderson Cancer Center Houston TX Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Albert Einstein College of Medicine Bronx NY Leukemia Service Department of Medicine Memorial Sloan Kettering Cancer Center New York NY Departments of Hematopoietic Biology and Malignancy The University of Texas MD Anderson Cancer Center Houston TX Human Oncology and Pathogenesis Program Memorial Sloan Kettering Cancer Center New York NY

Background and Significance: Measurable residual disease (MRD) represents the fundamental driver of relapse and mortality in acute myeloid leukemia (AML). However, there are currently no established approaches to address this unmet need. Our central hypothesis is that targeting vulnerabilities associated with specific leukemia genotypes will eradicate MRD and prevent disease relapse. Break Through Cancer is a collaboration between our centers aimed at making progress in the deadliest cancers by stimulating radical clinical and laboratory research collaboration. The menin-KMT2A interaction is a critical dependency in acute leukemias caused by rearrangement of the Lysine methyl transferase ( KMT2Ar ) or Nucleoporin 98 ( NUP98r ) genes, or mutation of the Nucleophosmin 1 gene ( NPM1mt ). Revumenib (previously SNDX-5613), is a potent, oral, selective inhibitor of this interaction with an established safety and efficacy in refractory leukemias with these genotypes (Issa GC, Nature 2023). Additionally, KMT2Ar or NPM1mt leukemias can undergo BCL2-dependent apoptosis, and dual Bcl-2 and menin inhibition led to synergistic activity in KMT2Ar or NPM1mt leukemia models (Carter BZ, Blood 2021; Fiskus W, BCJ 2022). Therefore, we designed this study investigating the combination of revumenib and venetoclax to eradicate MRD in these AML subtypes (NCT06284486). Study Design and Methods: This is a single arm, open label, multicenter, phase I/II investigator-initiated study. Patients (pts) age ≥ 12 years with weight ≥ 45Kg, and known history of NPM1mt , or KMT2Ar , or NUP98r AML with MRD ≥ 0.1% identified by multiparameter flow cytometry (MFC) using central testing would be eligible; no morphologic evidence of AML (blasts <5%) in first remission following high intensity therapy or at least 2 cycles of low intensity therapy, or in second remission following any therapy. Up to 12 pts will be enrolled on the phase I, using 3+3, with escalating doses of revumenib and a target dose of 163

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Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

npj metabolic health and disease

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npj metabolic health and disease 2024年第1期2卷 15页

作者： Annalise Schweickart Richa Batra Bryan J Neth Cameron Martino Liat Shenhav Anru R Zhang Pixu Shi Naama Karu Kevin Huynh Peter J Meikle Leyla Schimmel Amanda Hazel Dilmore Kaj Blennow Henrik Zetterberg Colette Blach Pieter C Dorrestein Rob Knight Suzanne Craft Rima Kaddurah-Daouk Jan Krumsiek Tri-Institutional Program in Computational Biology & Medicine Weill Cornell Medicine New York NY USA. Department of Physiology and Biophysics Weill Cornell Medicine Institute for Computational Biomedicine Englander Institute for Precision Medicine New York NY USA. Department of Neurology Mayo Clinic Rochester MN USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Department of Microbiology New York University Grossman School of Medicine New York NY USA. Department of Biostatistics and Bioinformatics Duke University Durham NC USA. Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS) Hobart TAS Australia. Baker Heart and Diabetes Institute 75 Commercial Road Melbourne VIC Australia. Baker Department of Cardiovascular Research Translation and Implementation La Trobe University Bundoora VIC Australia. Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA. Department of Psychiatry and Neurochemistry University of Gothenburg Gothenburg Sweden. Duke Molecular Physiology Institute Duke University Durham NC USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Departments of Pediatrics Computer Science and Engineering Bioengineering University of California San Diego La Jolla CA USA. Department of Gerontology and Geriatric Medicine Wake Forest University School of Medicine Winston Salem NC USA. Duke Institute of Brain Sciences Duke University Durham NC USA. Department of Medicine Duke University Durham NC USA.

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

关键词： Diseases Metabolomics

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The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic

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四川生理科学杂志 2022年第7期44卷 1246-1246页

作者： Michael Worobey Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics University of Utah School of Medicine Salt Lake City UT 84112 USA Vaccine and Infectious Disease Organization University of Saskatchewan Saskatoon SK S7N 5E3 Canada Center for Global Health Science and Security Georgetown University Washington DC 20057 USA Department of Zoology University of Oxford Oxford OX1 3SZ UK Wildlife Conservation Research Unit Department of Zoology The Recanati-Kaplan Centre University of Oxford Oxford OX13 5QL UK Pandemic and Disaster Preparedness Centre Erasmus University Medical Center 3015 CE Rotterdam Netherlands Department of Viroscience Erasmus University Medical Center 3015 CE Rotterdam Netherlands Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA 90095 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology Immunology and Transplantation Rega Institute for Medical Research KU Leuven 3000 Leuven Belgium Global Virus Network (GVN) Baltimore MD 21201 USA MRC-University of Glasgow Center for Virus Research Glasgow G61 1QH UK Tulane University School of Medicine Department of Microbiology and Immu

Understanding how severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)emerged in 2019 is critical to preventing zoonotic outbreaks before they become the next *** Huanan Seafood Wholesale Market in Wuhan,China,was identified as a likely source of cases in early reports but later this conclusion became *** show the earliest known COVID-19 cases from December 2019,including those without reported direct links,were geographically centered on this *** report that live SARS-CoV-2 susceptible mammals were sold at the market in late 2019 and,within the market,SARS-CoV-2-positive environmental samples were spatially associated with vendors selling live mammals.

关键词： Market likely Wuhan

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Neural networks versus Logistic regression for 30 days all-cause readmission prediction

arXiv

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arXiv 2018年

作者： Allam, Ahmed Nagy, Mate Thoma, George Krauthammer, Michael Department of Quantitative Biomedicine University of Zurich Biomedical Informatics Group University Hospital of Zurich Program in Computational Biology and Bioinformatics Yale University Department of Pathology Yale School of Medicine Lister Hill National Center for Biomedical Communications National Library of Medicine

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1) explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2) to examine the additive value of patients’ hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95% CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Rapid Sampling of Hydrogen Bond Networks for computational Protein Design (vol 14, pg 2751, 2018)

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JOURNAL OF CHEMICAL THEORY AND COMPUTATION 2018年第10期14卷 5434-5434页

作者： Maguire, Jack B. Boyken, Scott E. Baker, David Kuhlman, Brian [†] Program in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 United States [‡] Department of Biochemistry University of Washington Seattle Washington 98195 United States [§] Institute for Protein Design University of Washington Seattle Washington 98195 United States [∥] Howard Hughes Medical Institute University of Washington Seattle Washington 98195 United States [⊥] Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 United States [#] Lineberger Comprehensive Cancer Center University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 United States

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

关键词： Interfaces Peptides and proteins Monomers Noncovalent interactions Computer simulations

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Author Correction: A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing

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Nature communications 2022年第1期13卷 2828页

作者： Sanju Sinha Karina Barbosa Kuoyuan Cheng Mark D M Leiserson Prashant Jain Anagha Deshpande David M Wilson Bríd M Ryan Ji Luo Ze'ev A Ronai Joo Sang Lee Aniruddha J Deshpande Eytan Ruppin Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. Laboratory of Human Carcinogenesis Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20850 USA. Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 USA. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. Laboratory of Molecular Gerontology National Institute on Aging Intramural Research Program National Institutes of Health Baltimore MD 21224 USA. Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institute of Health Bethesda MD 20850 USA. Samsung Medical Center Sungkyunkwan University School of Medicine Suwon 16419 Republic of Korea. Department of Precision Medicine School of Medicine and Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 Republic of Korea. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. adeshpande@***. Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. eytan.ruppin@nih.gov.

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Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function (vol 571, pg 403, 2019)

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NATURE 2019年第7773期573卷 E2-E2页

作者： Bailis, Will Shyer, Justin A. Zhao, Jun Canaveras, Juan Carlos Garcia Al Khazal, Fatimah J. Qu, Rihao Steach, Holly R. Bielecki, Piotr Khan, Omair Jackson, Ruaidhri Kluger, Yuval Maher, Louis J., III Rabinowitz, Joshua Craft, Joe Flavell, Richard A. Department of Immunobiology Yale School of Medicine New Haven USA Department of Pathology Children’s Hospital of Philadelphia Philadelphia USA Department of Pathology Yale School of Medicine New Haven USA Program of Computational Biology and Bioinformatics Yale University New Haven USA Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton USA Department of Chemistry Princeton University Princeton USA Diabetes Research Center University of Pennsylvania Philadelphia USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and Science Rochester USA Applied Mathematics Program Yale University New Haven USA Department of Internal Medicine (Rheumatology) Yale School of Medicine New Haven USA Howard Hughes Medical Institute Chevy Chase USA

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Elastic restricted Boltzmann machines for cancer data analysis

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Frontiers of Electrical and Electronic Engineering in China 2017年第2期5卷 159-172页

作者： Sai Zhang Muxuan Liang Zhongjun Zhou Chen Zhang Ning Chen Ting Chen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Department of Statistics University of Wisconsin-Madison Madison WI 53706-1685 USA Bioinformatics Division TNLIST Department of Computer Science and Technology Tsinghua University Beijing 100084 China Program in Computational Biology and Bioinformatics Univesity of Southern California Los Angeles CA 90089 USA

Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.

关键词： RBMs regularization cancer data analysis survival time prediction

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Correction to: CD200 Immune-Checkpoint Peptide Elicits an Anti-glioma Response Through the DAP10 Signaling Pathway

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Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 2021年第3期18卷 2131页

作者： Elisabet Ampudia-Mesias Francisco Puerta-Martinez Miurel Bridges David Zellmer Andrew Janeiro Matt Strokes Yuk Y Sham Ayman Taher Maria G Castro Christopher L Moertel G Elizabeth Pluhar Michael R Olin Department of Pediatrics University of Minnesota Minneapolis MN 55455 USA. Department of Molecular and Computational Biology University of Southern California Los Angeles CA 90089 USA. Bioinformatics and Computational Biology Program University of Minnesota Minneapolis MN 55455 USA. Masonic Cancer Center University of Minnesota Minneapolis MN 55455 USA. Cell Signaling Technology Inc Danvers MA 09123 USA. Department of Integrative Biology and Physiology University of Minnesota Minneapolis MN 55455 USA. Department of Neurosurgery and Department of Cell and Developmental Biology University of Michigan Medical School Ann Arbor MI 48109 USA. Department of Veterinary Clinical Sciences College of Veterinary Medicine University of Minnesota Minneapolis MN 55455 USA. Department of Pediatrics University of Minnesota Minneapolis MN 55455 USA. olin0012@umn.edu. Masonic Cancer Center University of Minnesota Minneapolis MN 55455 USA. olin0012@umn.edu. University of Minnesota 2-167 Moos Tower 515 Delaware St SE Minneapolis MN 55455 USA. olin0012@umn.edu.

A correction to this paper has been published: https://***/10.1007/s13311-021-01062-1

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