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Alberto Cisneros, Amanda M. Duran, Jessica A. Finn, Darwin
This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. Current Topic pubs.acs.org/biochemistry Protocols for Molecular Modeling with Rosetta3 and RosettaScripts † ‡ ‡ § ‡ ∥ ‡ ⊥ ‡ ∥ Brian J. Bender, , Alberto Cisneros, III, , Amanda M. Duran, , Jessica A. Finn, , Darwin Fu, , ‡ # ‡ ∥ ‡ ∥ ‡ § Alyssa D. Lokits, , Benjamin K. Mueller, , Amandeep K. Sangha, , Marion F. Sauer, , ‡ § ‡ ∥ ‡ † ‡ § ∥ ⊥ # Alexander M. Sevy, , Gregory Sliwoski, , Jonathan H. Sheehan, Frank DiMaio,@ Jens Meiler, , , , , , ‡ ∥ and Rocco Moretti*, , † Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232-6600, United States ‡ Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States § Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee 37232-0301, United States ∥ Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States ⊥ Department of Pathology, Microbiology and Immunology, Vanderbilt University, Nashville, Tennessee 37232-2561, United States # Neuroscience Program, Vanderbilt University, Nashville, Tennessee 37235, United States @Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States *S Supporting Information ABSTRACT: Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987−2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. -
Jonathan Sheehan
1 Jonathan H. Sheehan, Ph.D. Director, Program in Personalized Structural Biology / Precision Medicine Research Associate Professor Phone (W): 615-936-2516 Center for Structural Biology and Dept. of Biochemistry Phone (H): 615-673-9457 Vanderbilt University PMB 407917 Fax: 615-936-2211 5137 BIOSCI/MRB III [email protected] 465 21st Ave. South http://structbio.vanderbilt.edu/~sheehajh/ Nashville, TN 37232-8725 DOB: 1/29/1966, New Haven, CT Education 1984 - 1988 Harvard College (Cambridge, MA), A.B. with honors, Classics Summer 1987 Aegean Institute (Galatas, Greece) with honors in Greek and classical drama 2000 - 2006 Ph.D. Biochemistry, Vanderbilt University. Mentor: Walter Chazin Dissertation Title: Probing functional diversity of EF-hand calcium-binding proteins through mutant design and structural analysis 2006 - 2008 Postdoctoral Training: Vanderbilt University. Mentor: Jens Meiler Academic Appointments 1990 - 1995 Research Asst. in Jim Forman©s lab, Immunology at UTSW Med. Ctr., Dallas, TX 1995 - 1996 Research Asst. in the Embryonic Stem Cell Core Lab at VUMC 1996 - 1997 Research Asst. in Mark Magnuson©s lab, Molecular Physiol. and Biophys., VUMC 1997 - 2000 Manager of Dave Piston©s Cell Imaging Core Lab at VUMC 2009 - 2011 Research Instructor, Dept. of Biochemistry and V.U. Center for Structural Biology 2011 - 2016 Research Assistant Professor, Dept. of Biochemistry and Ctr. for Structural Biology 2016 - present Research Associate Professor, Dept. of Biochemistry and Ctr. for Structural Biology 2016 - present Program Director in Personalized Structural Biology / Precision Medicine Employment (other than academic appointments) 1989 - 1990 Peace Corps Volunteer, building potable water systems in Cañar, Ecuador Teaching June 2007 Vanderbilt University Center for Structural Biology Workshop: Molecular Visualization Tools and Analysis (with Eric Dawson) Aug. -
Bringing a Trait‐Based Approach to Plant‐Associated Fungi
Biol. Rev. (2020), 95, pp. 409–433. 409 doi: 10.1111/brv.12570 Fungal functional ecology: bringing a trait-based approach to plant-associated fungi Amy E. Zanne1,∗ , Kessy Abarenkov2, Michelle E. Afkhami3, Carlos A. Aguilar-Trigueros4, Scott Bates5, Jennifer M. Bhatnagar6, Posy E. Busby7, Natalie Christian8,9, William K. Cornwell10, Thomas W. Crowther11, Habacuc Flores-Moreno12, Dimitrios Floudas13, Romina Gazis14, David Hibbett15, Peter Kennedy16, Daniel L. Lindner17, Daniel S. Maynard11, Amy M. Milo1, Rolf Henrik Nilsson18, Jeff Powell19, Mark Schildhauer20, Jonathan Schilling16 and Kathleen K. Treseder21 1Department of Biological Sciences, George Washington University, Washington, DC 20052, U.S.A. 2Natural History Museum, University of Tartu, Vanemuise 46, Tartu 51014, Estonia 3Department of Biology, University of Miami, Coral Gables, FL 33146, U.S.A. 4Freie Universit¨at-Berlin, Berlin-Brandenburg Institute of Advanced Biodiversity Research, 14195 Berlin, Germany 5Department of Biological Sciences, Purdue University Northwest, Westville, IN 46391, U.S.A. 6Department of Biology, Boston University, Boston, MA 02215, U.S.A. 7Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97330, U.S.A. 8Department of Plant Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, U.S.A. 9Department of Biology, University of Louisville, Louisville, KY 40208, U.S.A. 10Evolution & Ecology Research Centre, School of Biological Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia 11Department of Environmental Systems Science, Institute of Integrative Biology, ETH Z¨urich, 8092, Z¨urich, Switzerland 12Department of Ecology, Evolution, and Behavior, and Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, U.S.A. -
Cheminfo - Qualitative Analysis of Machine Learning Models for Activation of HSD Involved in Alzheimer’S Disease
Bcl::ChemInfo - Qualitative Analysis Of Machine Learning Models For Activation Of HSD Involved In Alzheimer’s Disease Mariusz Butkiewicz, Edward W. Lowe, Jr., Jens Meiler Abstract— In this case study, a ligand-based virtual high type 10 (HSD) has been found in elevated concentrations in throughput screening suite, bcl::ChemInfo, was applied to the hippocampi of Alzheimer’s disease patients. HSD may screen for activation of the protein target 17-beta play a role in the degradation of neuroprotective agents. The hydroxysteroid dehydrogenase type 10 (HSD) involved in inhibition of HSD has been indicated as a possible mean’s of Alzheimer’s Disease. bcl::ChemInfo implements a diverse set treating Alzheimer's disease. Dysfunctions in human 17 beta- of machine learning techniques such as artificial neural hydroxysteroid dehydrogenases result in disorders of biology networks (ANN), support vector machines (SVM) with the of reproduction and neuronal diseases, the enzymes are also extension for regression, kappa nearest neighbor (KNN), and involved in the pathogenesis of various cancers. HSD has a decision trees (DT). Molecular structures were converted into high affinity for amyloid proteins. Thus, it has been proposed a distinct collection of descriptor groups involving 2D- and 3D- that HSD may contribute to the amyloid plaques found in autocorrelation, and radial distribution functions. A confirmatory high-throughput screening data set contained Alzheimer's patients [2]. Furthermore, HSD degrades over 72,000 experimentally validated compounds, available neuroprotective agents like allopregnanolone which may lead through PubChem. Here, the systematical model development to memory loss. Therefore, it has been postulated that was achieved through optimization of feature sets and inhibition of HSD may help lessen the symptoms associated algorithmic parameters resulting in a theoretical enrichment of with Alzheimer's. -
Purified Bioactive Compounds from Mentha Spp. Oils As a Source of Candidosis Treatment
Incentivo governamental para Arranjos Produtivos Locais de Plantas Medicinais e Fitoterápicos no âmbito do SUS REVISÃO FARMACOLOGIA Purified bioactive compounds from Mentha spp. oils as a source of Candidosis treatment. A brief review Compostos bioativos purificados de óleos de Mentha spp. como fonte de tratamento de candidose. Uma breve revisão DOI 10.5935/2446-4775.20170009 1BONI, Giovana C.*; 1FEIRIA, Simone N. B. de; 1HÖFLING, José F. 1University of Campinas - UNICAMP, Piracicaba Dental School, Piracicaba, SP, Brazil. *Correspondência: [email protected] Abstract Medicinal plants have been the subject of many studies in an attempt to discovery alternative drugs, since they are sources of potentially bioactive compounds that may act in the maintenance of human health. The discovery of new antimicrobial substances or biocomponents derived from natural products has been important in the control of microorganisms, especially due to the increase of cases of resistance to conventional antimicrobials. In parallel, yeasts of the genus Candida are becoming a public health problem in the last decades due to the increase of infections denominated candidosis. Candida spp. has mechanisms of virulence, such as polymorphism and biofilm formation, that facilitate the development of the infection and difficult the treatment. In this sense, studies found in the literature with bioactive compounds from Mentha spp. essential oil, describe their antifungal action, especially from the isolated compounds as carvone, mentone, menthofuran and pulegone. In this sense, this review describes studies about antimicrobial activity of these compounds especially against yeasts of Candida species and some particularities of this genus such as virulence mechanisms once these themes are crucial for the development of new alternative drugs and/or antifungal agents that may act as adjuncts to conventional treatments against these microorganisms. -
Efforts to Establish a Federally Supported Rosetta Center
Efforts to establish a federally supported Rosetta center Jens Meiler Associate Professor Vanderbilt University Departments of Chemistry, Pharmacology, and Biomedical Informatics Center for Structural Biology, Institute of Chemical Biology NIH Biomedical Technology Research Center (BTRC) . 12/02/2008 – 1st pre-proposal submission . 20 pages of scientific and organizational description . Scored well but not yet invited for full application . 10/08/2009 – 2nd pre-proposal submission . 23 pages of scientific and organizational description . Scored excellent and was invited for full application . 09/28/2010 – 1st full proposal submission . 176 pages of 357 pages of scientific and organizational description . 03/22/2011 – NIH site visit . Scored mediocre and was not yet funded . … more to come … 18 August 2011 RosettaBTRC Review 2 RosettaCommons Consortium of 16 Laboratories Maintains Rosetta Code . Maintains core code functionality of Rosetta . Releases software semi-annually . Basis the BTRC will build upon core Andrew Leaver- Fay UNC RosettaCon 2009, Leavenworth, WA, USA 8:20 am 8:40 am Andrew Leaver‐Fay: Rosetta software development 18 August 2011 RosettaBTRC Review 3 TR&D1: Develop, Integrate, and Test Rosetta Scoring and Sampling . Feature Database and Testing System . Comparative analysis of Sampling for Energy Function Improvement Efficiency core Phil Bradley, David Baker, Brian Kuhlman, Ora Furman, FHCRC UW UNC HUJ 8:40 am 9:10 am Phil Bradley: TR&D1 ‐Rosetta scoring and sampling 18 August 2011 RosettaBTRC Review 4 TR&D2: Integrate Novel Methods for Design of Biological Function . Robotics-Inspired Conformational . Problem-Targeted Refinement of Designs Sampling with High-Order Energy Functions c o r e . Scaffolding Epitopes core and Binding Sites Tanja Kortem- Bill Schief, Jim Havranek, Jeff Gray, me, UCSF SCRIPPS Wash U JHU 9:10 am 9:30 am Jim Havranek: TR&D2 ‐Design of Function 18 August 2011 RosettaBTRC Review 5 DBP1: HIV Host-Pathogen Interactions, Nevan Krogan, UCSF . -
I've Gut a Feeling: Microbiota Impacting the Conceptual And
International Journal of Molecular Sciences Review I’ve Gut A Feeling: Microbiota Impacting the Conceptual and Experimental Perspectives of Personalized Medicine Amedeo Amedei 1,2,* and Federico Boem 1 1 Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla, 03 50134, Firenze, Italy; [email protected] 2 Department of Biomedicine, Azienda Ospedaliera Universitaria Careggi (AOUC), Largo Brambilla, 03 50134, Firenze, Italy * Correspondence: amedeo.amedei@unifi.it Received: 21 September 2018; Accepted: 16 November 2018; Published: 27 November 2018 Abstract: In recent years, the human microbiota has gained increasing relevance both in research and clinical fields. Increasing studies seem to suggest the centrality of the microbiota and its composition both in the development and maintenance of what we call “health” and in generating and/or favoring (those cases in which the microbiota’s complex relational architecture is dysregulated) the onset of pathological conditions. The complex relationships between the microbiota and human beings, which invest core notions of biomedicine such as “health” and “individual,” do concern not only problems of an empirical nature but seem to require the need to adopt new concepts and new perspectives in order to be properly analysed and utilized, especially for their therapeutic implementation. In this contribution we report and discuss some of the theoretical proposals and innovations (from the ecological component to the notion of polygenomic organism) aimed at producing this change of perspective. In conclusion, we summarily analyze what impact and what new challenges these new approaches might have on personalized/person centred/precision medicine. Keywords: microbiome; health; precision medicine; genomics 1. -
Jens Meiler Improving Quantitative Structure-Activity Relationship
J Comput Aided Mol Des DOI 10.1007/s10822-016-9895-2 Improving quantitative structure–activity relationship models using Artificial Neural Networks trained with dropout 1 1 Jeffrey Mendenhall • Jens Meiler Received: 15 September 2015 / Accepted: 15 January 2016 Ó Springer International Publishing Switzerland 2016 Abstract Dropout is an Artificial Neural Network (ANN) Background and significance training technique that has been shown to improve ANN performance across canonical machine learning (ML) data- Quantitative Structure Activity Relationship (QSAR) sets. Quantitative Structure Activity Relationship (QSAR) models are an established means of Ligand-Based Com- datasets used to relate chemical structure to biological activity puter-Aided Drug Discovery (LB-CADD), i.e. finding in Ligand-Based Computer-Aided Drug Discovery pose novel compounds that bind to a particular protein target, unique challenges for ML techniques, such as heavily biased given a dataset of known binders and non-binders [1]. dataset composition, and relatively large number of descrip- Physicochemical properties are encoded using spatial and tors relative to the number of actives. To test the hypothesis topological representations (descriptors) of the local atomic that dropout also improves QSAR ANNs, we conduct a environments within the molecule. To model the non-linear benchmark on nine large QSAR datasets. Use of dropout relation between chemical structure and biological activity improved both enrichment false positive rate and log-scaled for a particular protein target, a machine learning method, area under the receiver-operating characteristic curve such as an ANN, is trained to predict binding or activity at (logAUC) by 22–46 % over conventional ANN implemen- a particular protein target. -
By Submitted in Partial Satisfaction of the Requirements for Degree of in In
New computational protein design methods for de novo small molecule binding sites by James Edward Lucas DISSERTATION Submitted in partial satisfaction of the requirements for degree of DOCTOR OF PHILOSOPHY in Bioengineering in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, SAN FRANCISCO AND UNIVERSITY OF CALIFORNIA, BERKELEY Approved: ______________________________________________________________________________Tanja Kortemme Chair ______________________________________________________________________________John Dueber ______________________________________________________________________________Michael J Keiser ______________________________________________________________________________Andrej Sali ______________________________________________________________________________ Committee Members Copyright 2020 James Edward Lucas ii Acknowledgements Upon reflection, I am incredibly grateful to have had the opportunity to complete a PhD in such an exciting field of study. This would not have been possible without the support of my friends, family, and mentors, each of whom contributed to the success of my PhD in their own way. I would like to thank my advisor, Tanja Kortemme, for taking me into her lab and allowing me to pursue ambitious research projects. I also want to thank the members of the Kortemme lab for sharing their expertise with me. Special thanks to Kale, Kyle, Xingjie, and Shane for answering my incessant questions; my dissertation ended up being purely computational and my research was only possible because of your help. I’m delighted that Kyle and I are still able to catch up over lunch every month and that Kale and I still talk science even now that he has moved across the country. I also want to thank Anum for providing me with much needed perspective and support during our ice cream and boba walks. A special thanks to John for investing so much of his time into me. -
Science Citation Indexed Journal List
SCIENCE CITATION INDEXED JOURNAL LIST Sr.No. Journal Title ISSN E-ISSN Publisher 1 2D MATERIALS 2053-1583 2053-1583 IOP PUBLISHING LTD 2 3 BIOTECH 2190-572X 2190-5738 SPRINGER HEIDELBERG 3 3D PRINTING AND ADDITIVE MANUFACTURING 2329-7662 2329-7670 MARY ANN LIEBERT, INC 4 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 1619-4500 1614-2411 SPRINGER HEIDELBERG 5 AAPG BULLETIN 0149-1423 1558-9153 AMER ASSOC PETROLEUM GEOLOGIST 6 AAPS JOURNAL 1550-7416 1550-7416 SPRINGER 7 AAPS PHARMSCITECH 1530-9932 1530-9932 SPRINGER 8 AATCC JOURNAL OF RESEARCH 2330-5517 2330-5517 AMER ASSOC TEXTILE CHEMISTS COLORISTS-AATCC 9 AATCC REVIEW 1532-8813 1532-8813 AMER ASSOC TEXTILE CHEMISTS COLORISTS-AATCC 10 ABDOMINAL RADIOLOGY 2366-004X 2366-0058 SPRINGER ABHANDLUNGEN AUS DEM MATHEMATISCHEN SEMINAR DER 11 0025-5858 1865-8784 SPRINGER HEIDELBERG UNIVERSITAT HAMBURG 12 ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 0065-7727 AMER CHEMICAL SOC 13 ACADEMIC EMERGENCY MEDICINE 1069-6563 1553-2712 WILEY 14 ACADEMIC MEDICINE 1040-2446 1938-808X LIPPINCOTT WILLIAMS & WILKINS 15 ACADEMIC PEDIATRICS 1876-2859 1876-2867 ELSEVIER SCIENCE INC 16 ACADEMIC RADIOLOGY 1076-6332 1878-4046 ELSEVIER SCIENCE INC 17 ACAROLOGIA 0044-586X 2107-7207 ACAROLOGIA-UNIVERSITE PAUL VALERY 18 ACCOUNTABILITY IN RESEARCH-POLICIES AND QUALITY ASSURANCE 0898-9621 1545-5815 TAYLOR & FRANCIS INC 19 ACCOUNTS OF CHEMICAL RESEARCH 0001-4842 1520-4898 AMER CHEMICAL SOC 20 ACCREDITATION AND QUALITY ASSURANCE 0949-1775 1432-0517 SPRINGER 21 ACI MATERIALS JOURNAL 0889-325X 1944-737X AMER CONCRETE -
Starr Thesis
Disentangling the rhizosphere community through stable isotope informed genome-resolved metagenomics and assembled metatranscriptomes By Evan P. Starr A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Microbiology in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Jillian F. Banfield, Co-Chair Professor Mary K. Firestone, Co-Chair Professor Britt Koskella Summer 2019 Abstract Disentangling the holistic rhizosphere community through stable isotope informed genome- resolved metagenomics and assembled metatranscriptomes By Evan P Starr Doctor of Philosophy in Microbiology University of California, Berkeley Professor Jillian Banfield, Co-Chair Professor Mary Firestone, Co-Chair The functioning, health, and productivity of soil is intimately tied to the complex network of interactions in the rhizosphere. Because of this, the rhizosphere has been rigorously studied for over a century, but due to technical limitations many aspects of soil biology have been overlooked. In order to better understand rhizosphere functioning, my work has focused on the less explored organisms and interactions in microbial communities, this includes unculturable bacteria along with viruses and eukaryotes. Only by considering soil biology more holistically can we better understand the functioning of this enigmatic yet critical ecosystem. Knowledge about these interactions could direct how we think about plant-microbe relationships, soil carbon stabilization and the roles of understudied organisms in biogeochemical cycling. The transformation of plant photosynthate into soil organic carbon and its recycling to CO2 by soil microorganisms is one of the central components of the terrestrial carbon cycle. There are currently large knowledge gaps related to which soil-associated microorganisms take up plant carbon in the rhizosphere and the fate of that carbon. -
The Physiology of Mycobacterium Tuberculosis in The
DOI: 10.5772/intechopen.69594 Provisional chapter Chapter 7 The Physiology of Mycobacterium tuberculosis in Thethe ContextPhysiology of Drug of Mycobacterium Resistance: A Systemtuberculosis Biology in the ContextPerspective of Drug Resistance: A System Biology Perspective Luisa Maria Nieto, Carolina Mehaffy and LuisaKaren Maria M. Dobos Nieto, Carolina Mehaffy and KarenAdditional M. information Dobos is available at the end of the chapter Additional information is available at the end of the chapter http://dx.doi.org/10.5772/intechopen.69594 Abstract Tuberculosis (TB), a disease caused by Mycobacterium tuberculosis (Mtb), is the main cause of death due to an infectious disease. After more than 100 years of the discovery of Mtb, clinicians still face difficulties finding an effective treatment for the increasing number of drug-resistant cases. The difficulties in the clinical setting can be related to the slow pace at which the understanding of the physiology of this bacterium has occurred. Mtb is distinct from other microorganisms not only due to its slow growth and difficulties to study in the laboratory, but also due to its inherent physiology such as its complex cell envelope and its metabolic pathways. Understanding the physiology of drug susceptible and resistant Mtb strains is crucial for the design of an effective chemotherapy against TB. This chapter will review the mycobacterial cell envelope and major physiological pathways together with recent discoveries in Mtb drug resistance through different “omics” disciplines. Keywords: drug resistance, physiology, systems biology, proteomics, genomics, lipidomics 1. Introduction The history of tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb), has a remarkable involvement in human history; particularly in the evolution of human society and in the development of many scientific disciplines.