检索结果-内蒙古大学图书馆

Size, distribution, and vulnerability of the global soil inorganic carbon

SCIENCE 2024年第6692期384卷 233-239页

作者： Huang, Yuanyuan Song, Xiaodong Wang, Ying-Ping Canadell, Josep G. Luo, Yiqi Ciais, Philippe Chen, Anping Hong, Songbai Wang, Yugang Tao, Feng Li, Wei Xu, Yiming Mirzaeitalarposhti, Reza Elbasiouny, Heba Savin, Igor Shchepashchenko, Dmitry Rossel, Raphael A. Viscarra Goll, Daniel S. Chang, Jinfeng Houlton, Benjamin Z. Wu, Huayong Yang, Fei Feng, Xiaoming Chen, Yongzhe Liu, Yu Niu, Shuli Zhang, Gan-Lin Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Ecosyst Network Observat & Modeling Beijing 100101 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Chinese Acad Sci Inst Soil Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Peoples R China CSIRO Environm Private Bag 10 Clayton Vic 3169 Australia CSIRO Environm Black Mt ACT 2601 Australia Cornell Univ Sch Integrat Plant Sci Soil & Crop Sci Sect Ithaca NY 14853 USA Univ Paris Saclay Lab Sci Climat & Environm CEA CNRSUVSQ F-91990 Gif sur yvette France Colorado State Univ Dept Biol Ft Collins CO 80523 USA Colorado State Univ Grad Degree Program Ecol Ft Collins CO 80523 USA Peking Univ Sch Urban Planning & Design Shenzhen Grad Sch Shenzhen 518055 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Urumqi 830011 Xinjiang Peoples R China Cornell Univ Dept Ecol & Evolutionary Biol Ithaca NY 14853 USA Cornell Univ Dept Global Dev Ithaca NY 14853 USA Tsinghua Univ Inst Global Change Studies Key Lab Earth Syst Modelling Dept Earth Syst SciMinist Educ Beijing 100084 Peoples R China Beijing Forestry Univ Sch Grassland Sci Beijing 100083 Peoples R China Univ Hohenheim Inst Crop Sci 340i Fruwirthstr 20 D-70599 Stuttgart Germany Al Azhar Univ Agr Fac Girls Cairo 11651 Egypt VV Dokuchaev Soil Sci Inst Moscow 119017 Russia RUDN Univ Inst Environm Engn Moscow 117198 Russia Int Inst Appl Syst Anal IIASA Schlossplatz 1 A-2361 Laxenburg Austria Russian Acad Sci Ctr Forest Ecol & Prod Moscow 117997 Russia Siberian Fed Univ Inst Ecol & Geog 79 Svobodny Prospect Krasnoyarsk 660041 Russia Curtin Univ Fac Sci & Engn Soil & Landscape Sci Sch Mol & Life Sci GPO Box U1987 Perth WA 6845 Australia Zhejiang Univ Coll Environm & Resource Sci Hangzhou 310058 Peoples R China Chinese Acad Sci Res Ctr Ecoenvironm Sci Beijing 100085 Peoples R China Univ Ho

Global estimates of the size, distribution, and vulnerability of soil inorganic carbon (SIC) remain largely unquantified. By compiling 223,593 field-based measurements and developing machine-learning models, we report that global soils store 2305 +/- 636 (+/- 1 SD) billion tonnes of carbon as SIC over the top 2-meter depth. Under future scenarios, soil acidification associated with nitrogen additions to terrestrial ecosystems will reduce global SIC (0.3 meters) up to 23 billion tonnes of carbon over the next 30 years, with India and China being the most affected. Our synthesis of present-day land-water carbon inventories and inland-water carbonate chemistry reveals that at least 1.13 +/- 0.33 billion tonnes of inorganic carbon is lost to inland-waters through soils annually, resulting in large but overlooked impacts on atmospheric and hydrospheric carbon dynamics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

引用

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2023年第12期91卷 1658-1683页

作者： Lensink, Marc F. Brysbaert, Guillaume Raouraoua, Nessim Bates, Paul A. Giulini, Marco Honorato, Rodrigo V. van Noort, Charlotte Teixeira, Joao M. C. Bonvin, Alexandre M. J. J. Kong, Ren Shi, Hang Lu, Xufeng Chang, Shan Liu, Jian Guo, Zhiye Chen, Xiao Morehead, Alex Roy, Raj S. Wu, Tianqi Giri, Nabin Quadir, Farhan Chen, Chen Cheng, Jianlin Del Carpio, Carlos A. Ichiishi, Eichiro Rodriguez-Lumbreras, Luis A. Fernandez-Recio, Juan Harmalkar, Ameya Chu, Lee-Shin Canner, Sam Smanta, Rituparna Gray, Jeffrey J. Li, Hao Lin, Peicong He, Jiahua Tao, Huanyu Huang, Sheng-You Roel-Touris, Jorge Jimenez-Garcia, Brian Christoffer, Charles W. Jain, Anika J. Kagaya, Yuki Kannan, Harini Nakamura, Tsukasa Terashi, Genki Verburgt, Jacob C. Zhang, Yuanyuan Zhang, Zicong Fujuta, Hayato Sekijima, Masakazu Kihara, Daisuke Khan, Omeir Kotelnikov, Sergei Ghani, Usman Padhorny, Dzmitry Beglov, Dmitri Vajda, Sandor Kozakov, Dima Negi, Surendra S. Ricciardelli, Tiziana Barradas-Bautista, Didier Cao, Zhen Chawla, Mohit Cavallo, Luigi Oliva, Romina Yin, Rui Cheung, Melyssa Guest, Johnathan D. Lee, Jessica Pierce, Brian G. Shor, Ben Cohen, Tomer Halfon, Matan Schneidman-Duhovny, Dina Zhu, Shaowen Yin, Rujie Sun, Yuanfei Shen, Yang Maszota-Zieleniak, Martyna Bojarski, Krzysztof K. Lubecka, Emilia A. Marcisz, Mateusz Danielsson, Annemarie Dziadek, Lukasz Gaardlos, Margrethe Gieldon, Artur Liwo, Adam Samsonov, Sergey A. Slusarz, Rafal Zieba, Karolina Sieradzan, Adam K. Czaplewski, Cezary Kobayashi, Shinpei Miyakawa, Yuta Kiyota, Yasuomi Takeda-Shitaka, Mayuko Olechnovic, Kliment Valancauskas, Lukas Dapkunas, Justas Venclovas, Ceslovas Wallner, Bjorn Yang, Lin Hou, Chengyu He, Xiaodong Guo, Shuai Jiang, Shenda Ma, Xiaoliang Duan, Rui Qui, Liming Xu, Xianjin Zou, Xiaoqin Velankar, Sameer Wodak, Shoshana J. Univ Lille CNRS UMR8576 UGSF Unite Glycobiol Struct & Fonct F-59000 Lille France Francis Crick Inst Biomol Modeling Lab London England Univ Utrecht Fac Sci Chem Bijvoet Ctr Biomol Res Utrecht Netherlands Jiangsu Univ Technol Inst Bioinformat & Med Engn Sch Elect & Informat Engn Changzhou Jiangsu Peoples R China Univ Missouri Dept Elect Engn & Comp Sci Columbia MO USA Fukushimuta Hosp Choju Med Inst Toyohashi Aichi Japan Int Univ Hlth & Welf IUHV Hosp Nasushiobara Japan CSIC Univ Rioja Gobierno La Rioja Inst Ciencias Vida & Vino ICVV Logrono Spain Barcelona Supercomp Ctr BSC Barcelona Spain Johns Hopkins Univ Dept Chem & Biomol Engn Baltimore MD USA Johns Hopkins Univ Program Mol Biophys Baltimore MD USA Huazhong Univ Sci & Technol Sch Phys Wuhan Peoples R China Mol Biol Inst Barcelona IBMB CSIC Dept Biol Struct Prot Design & Modeling Lab Barcelona Spain Zymvol Biomodeling Barcelona Spain Purdue Univ Dept Comp Sci W Lafayette IN USA Purdue Univ Dept Biol Sci W Lafayette IN USA Indian Inst Technol Madras Bhupat & Jyoti Mehta Sch Biosci Dept Biotechnol Chennai Tamil Nadu India Tokyo Inst Technol Dept Comp Sci Yokohama Kanagawa Japan Boston Univ Boston MA USA SUNY Stony Brook New York NY USA Univ Texas Med Branch Sealy Ctr Struct Biol & Mol Biophys Galveston TX USA King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia Univ Salerno Dept Chem & Biol Fisciano Italy Univ Naples Parthenope Naples Italy Univ Maryland Inst Biosci & Biotechnol Res Rockville MD USA Univ Maryland Dept Cell Biol & Mol Genet College Pk MD USA Univ Maryland Dept Chem & Biochem College Pk MD USA Hebrew Univ Jerusalem Sch Comp Sci & Engn Jerusalem Israel Texas M Univ Dept Elect & Comp Engn College Stn TX USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX USA Texas A&M Univ Inst Biosci & Technol Houston TX USA Texas A&M Univ Dept Translat Med Sci Houston TX USA Univ Gdansk Gdansk Poland Techn Univ Gdansk Gdansk Poland Kitasato Univ Sch Ph

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average similar to 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

关键词： AlphaFold blind prediction CAPRI CASP deep learning protein assemblies protein complexes protein-protein interaction

来源：评论

学校读者我要写书评

暂无评论

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

引用

PLOS COMPUTATIONAL BIOLOGY 2021年第9期17卷 e1009302-e1009302页

作者： Xiong, Zhaoping Jeon, Minji Allaway, Robert J. Kang, Jaewoo Park, Donghyeon Lee, Jinhyuk Jeon, Hwisang Ko, Miyoung Jiang, Hualiang Zheng, Mingyue K. Tan, Aik Choon Guo, Xindi Dang, Kristen K. K. Tropsha, Alex A. Hecht, Chana Das, Tirtha K. Carlson, Heather A. Abagyan, Ruben Guinney, Justin Schlessinger, Avner Cagan, Ross ShanghaiTech Univ Shanghai Inst Adv Immunochem Studies Shanghai Peoples R China Korea Univ Dept Comp Sci & Engn Seoul South Korea Sage Bionetworks Seattle WA USA Korea Univ Interdisciplinary Grad Program Bioinformat Seoul South Korea Chinese Acad Sci Shanghai Inst Mat Med State Key Lab Drug Res Drug Discovery & Design Ctr Shanghai Peoples R China H Lee Moffitt Canc Ctr & Res Inst Dept Biostat & Bioinformat Tampa FL USA Univ N Carolina UNC Eshelman Sch Pharm Lab Mol Modeling Div Chem Biol & Med Chem Chapel Hill NC 27515 USA Icahn Sch Med Mt Sinai Dept Cell Dev & Regenerat Biol New York NY 10029 USA Univ Michigan Dept Med Chem Ann Arbor MI 48109 USA Univ Calif San Diego Skaggs Sch Pharm & Pharmaceut Sci San Diego CA 92103 USA Icahn Sch Med Mt Sinai Dept Pharmacol Sci New York NY 10029 USA Univ Glasgow Inst Canc Sci Glasgow Lanark Scotland

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Author summary Many modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, 'dirty;' they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop 'cleaner' therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules

关键词： Drug research and development Drosophila melanogaster Cancers and neoplasms Machine learning Genetic screens Neural networks Drug screening Genetic suppression

来源：评论

学校读者我要写书评

暂无评论

Adaptive Cyber-Physical-Human Systems: Exploiting Cognitive modeling and Machine Learning in the Control Loop

引用

Insight 2018年第3期21卷 87-93页

作者： Madni, Azad M. Sievers, Michael Madni, Carla Conaway Program in the University of Southern California's Viterbi School of Engineering United States University of Southern California United States research in modeling and simulation humancomputer interface design embedded training United States

Cyber-physical-human (CPH) systems are purposeful arrangements of sensors, computers, communication devices, and humans to perform tasks that achieve specific mission objectives. These systems typically allow other systems, devices, and data streams to connect/disconnect as needed during mission execution. The roles of humans in CPH systems are quite varied. In adaptive CPH systems, humans collaborate with the cyber-physical elements to jointly accomplish tasks, and adapt to changing contexts to accomplish mission goals. Mutual adaptation based on prior knowledge, cognitive modeling, and online machine learning are key characteristics of adaptive CPH systems. This paper presents key challenges in realizing adaptive CPH systems. It discusses learning and adaptation, as well as human and CPS roles in adaptive CPH systems. It offers a functional (reference) architecture to inform and guide the development of adaptive CPH systems. It concludes with a discussion of research needed to advance the state-of-the-art of adaptive CPH systems. © 2018 by Azad M. Madni. Published and used by INCOSE with permission.

关键词： Man machine systems

来源：评论

学校读者我要写书评

暂无评论

Geometric Targets for UAS Lidar

引用

REMOTE SENSING 2019年第24期11卷 3019-3019页

作者： Wilkinson, Benjamin Lassiter, H. Andrew Abd-Elrahman, Amr Carthy, Raymond R. Ifju, Peter Broadbent, Eben Grimes, Nathan Univ Florida Sch Forest Resources & Conservat Geomat Program Gainesville FL 32611 USA Univ Florida Sch Forest Resources & Conservat Geospatial Modeling & Applicat Lab Gainesville FL 32611 USA US Geol Survey Florida Cooperat Fish & Wildlife Res Unit Gainesville FL 32611 USA Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Univ Florida Sch Forest Resources & Conservat Spatial Ecol & Conservat SPEC Lab Gainesville FL 32601 USA Univ Illinois Sch Art & Design Champaign IL 61802 USA

Lidar from small unoccupied aerial systems (UAS) is a viable method for collecting geospatial data associated with a wide variety of applications. Point clouds from UAS lidar require a means for accuracy assessment, calibration, and adjustment. In order to carry out these procedures, specific locations within the point cloud must be precisely found. To do this, artificial targets may be used for rural settings, or anywhere there is a lack of identifiable and measurable features in the scene. This paper presents the design of lidar targets for precise location based on geometric structure. The targets and associated mensuration algorithm were tested in two scenarios to investigate their performance under different point densities, and different levels of algorithmic rigor. The results show that the targets can be accurately located within point clouds from typical scanning parameters to < 2 cm sigma, and that including observation weights in the algorithm based on propagated point position uncertainty leads to more accurate results.

关键词： UAS drones lidar targets accuracy calibration adjustment

来源：评论

学校读者我要写书评

暂无评论

Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals

引用

JOURNAL OF CHEMICAL INFORMATION AND modeling 2017年第5期57卷 1013-1017页

作者： Braga, Rodolpho C. Alves, Vinicius M. Muratov, Eugene N. Strickland, Judy Kleinstreuer, Nicole Trospsha, Alexander Andrade, Carolina Horta Univ Fed Goias Lab Mol Modeling & Drug Design Fac Pharm BR-74605170 Goiania Go Brazil Univ N Carolina UNC Eshelman Sch Pharm Div Chem Biol & Med Chem Lab Mol Modeling Chapel Hill NC 27599 USA Odessa Natl Polytech Univ Dept Chem Technol UA-65000 Odessa Ukraine Integrated Lab Syst Inc POB 13501 Res Triangle Pk NC 27709 USA Natl Inst Environm Hlth Sci Interagency Ctr Evaluat Alternat Toxicol Methods Natl Toxicol Program Res Triangle Pk NC 27709 USA

Chemically induced skin sensitization is a complex immunological disease with a profound impact on quality of life and working ability. Despite some progress in developing alternative methods for assessing the skin sensitization potential of chemical substances, there is no in vitro test that correlates well with human data. Computational QSAR models provide a rapid screening approach and contribute valuable information for the assessment of chemical toxicity. We describe the development of a freely accessible web-based and mobile application for the identification of potential skin sensitizers. The application is based on previously developed binary QSAR models of skin sensitization potential from human (109 compounds) and murine local lymph node assay (LLNA, 515 compounds) data with good external correct classification rate (0.70-0.81 and 0.72-0.84, respectively). We also included a multiclass skin sensitization potency model based on LLNA data (accuracy ranging between 0.73 and 0.76). When a user evaluates a compound in the web app, the outputs are (i) binary predictions of human and murine skin sensitization potential;(ii) multiclass prediction of murine skin sensitization;and (iii) probability maps illustrating the predicted contribution of chemical fragments. The app is the first tool available that incorporates quantitative structure activity relationship (QSAR) models based on human data as well as multiclass models for LLNA. The Pred-Skin web app version 1.0 is freely available for the web, iOS, and Android (in development) at the LabMol web portal (http://***/predskin/), in the Apple Store, and on Google Play, respectively. We will continuously update the app as new skin sensitization data and respective models become available.

关键词： human data multi-class chemical compounds web applications Androids skin sensitization local lymph node assay alternative methods Methyltestosterone Quantitative Structure-Activity Relationship Mobile Applications

来源：评论

学校读者我要写书评

暂无评论

Pandemic gardening: A narrative review, vignettes and implications for future research

引用

URBAN FORESTRY & URBAN GREENING 2023年 87卷

作者： Kingsley, Jonathan Donati, Kelly Litt, Jill Shimpo, Naomi Blythe, Chris Vavra, Jan Caputo, Silvio Milbourne, Paul Diekmann, Lucy O. Rose, Nick Fox-Kaemper, Runrid van den Berg, Agnes Metson, Genevieve S. Ossola, Alessandro Feng, Xiaoqi Astell-Burt, Thomas Baker, Amy Lin, Brenda B. Egerer, Monika Marsh, Pauline Pettitt, Philip Scott, Theresa L. Alaimo, Katherine Neale, Kate Glover, Troy Byrne, Jason Swinburne Univ Technol Sch Hlth Sci 12 Wakefield StSwinburne Pl West Hawthorn Vic 3122 Australia Swinburne Univ Technol Ctr Urban Transit Level 1 EW Bldg Hawthorn Vic 3122 Australia William Angliss Inst Fac Higher Educ 555 La Trobe St Melbourne Vic 3000 Australia Univ Colorado Boulder Environm Studies Program 4001 Discovery Dr Boulder CO 80309 USA Barcelona Inst Global Hlth ISGlobal C Doctor Aiguader 88 Barcelona 08003 Spain Univ Pompeu Fabra UPF Barcelona Spain CIBER Epidemiol & Salud Publ CIBERESP Madrid Spain Univ Hyogo Grad Sch Landscape Design & Management 954-2 Nojimatokiwa Awaji Hyogo 6561726 Japan Oxford Brookes Univ Fac Hlth & Life Sci Dept Psychol Hlth & Profess Dev Oxford England Czech Acad Sci Inst Sociol Jilska 1 Prague 1 Czech Republic Univ Kent Kent Sch Architecture & Planning Marlowe Bldg Canterbury CT2 7NR Kent England Cardiff Univ Sch Geog & Planning King Edward VII Ave Cardiff CF10 3WA Wales Univ Calif Cooperat Extens 1553 Berger Dr San Jose CA 95112 USA ILS Res Inst Reg & Urban Dev Bruderweg 22-24 D-44135 Dortmund Germany Univ Twente Fac Behav Management & Social Sci De Zul 10 NL-7522 NJ Enschede Netherlands Linkoping Univ Ecol & Environm Modeling Div IFM Campus Valla SE-58183 Linkoping Sweden Univ Western Ontario Dept Geog & Environm Social Sci Ctr Rm 2322 London ON N6A 5C2 Canada Univ Calif Davis Dept Plant Sci 387 N Quad Ave Davis CA 95616 USA Populat Wellbeing & Environm Res Lab Power Sydney NSW 2000 Australia Univ New South Wales Fac Med & Hlth Sch Populat Hlth Sydney NSW 2052 Australia George Inst Global Hlth Sydney NSW 2042 Australia Univ Wollongong Fac Arts Social Sci & Humanities Sch Hlth & Soc Wollongong 2522 Australia Univ South Australia Allied Hlth & Human Performance Bonython Jubilee BldgCity East Campus Adelaide SA 5001 Australia CSIRO Land & Water GPO Box 2583 Brisbane Qld 4001 Australia Tech Univ Munich Sch Life Sci Urban Prod Ecosyst Hans Carl von Carlowitz Pl 2 D-85354 Frei

There is a significant amount of evidence highlighting the health, wellbeing and social benefits of gardening during previous periods of crises. These benefits were also evident during the COVID-19 pandemic. This paper presents a narrative review exploring gardening during the early stages of the COVID-19 pandemic to understand the different forms of gardening that took place during this crisis and key elements of this activity. Research about gardening during the pandemic focused on food (in)security and disrupted food systems, the health and wellbeing benefits of gardening, and the social dimensions of gardening. We offer three vignettes of our own research to highlight key insights from local, national and international perspectives of gardening during the pandemic. The paper's conclusion outlines how researchers, policy makers and public health practitioners can harness what has been learned from gardening during the pandemic to ensure these benefits are more widely available and do not exacerbate already entrenched health inequalities in society.

关键词： Garden COVID-19 Urban agriculture Food security Public health

来源：评论

学校读者我要写书评

暂无评论

QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

引用

EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 2017年 137卷 126-138页

作者： Gomes, Marcelo N. Braga, Rodolpho C. Grzelak, Edyta M. Neves, Bruno J. Muratov, Eugene Ma, Rui Klein, Larry L. Cho, Sanghyun Oliveira, Guilherme R. Franzblau, Scott G. Andrade, Carolina Horta Univ Fed Goias Fac Farm LabMol Lab Mol Modeling & Drug Design Rua 240Qd87 BR-74605510 Goiania Go Brazil Univ Illinois Inst TB Res 833 South Wood St Chicago IL 60612 USA Univ Ctr Anapolis UniEVANGELICA Postgrad Program Soc Technol & Environm BR-75083515 Anapolis Go Brazil Univ N Carolina Eshelman Sch Pharm Lab Mol Modeling Chapel Hill NC 27955 USA Odessa Natl Polytech Univ Dept Chem Technol UA-65000 Odessa Ukraine Univ Fed Goias Chem Inst Goiania Go Brazil Univ Fed Goias Goiania Go Brazil

New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (ERMP and rINH) (<1 mu M and <10 mu M, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents. (C) 2017 Elsevier Masson SAS. All rights reserved.

关键词： Tuberculosis In silico design QSAR Nitroaromatic compounds Chalcone Anti-TB agents

来源：评论

学校读者我要写书评

暂无评论

Computer-aided discovery of two novel chalcone-like compounds active and selective against Leishmania infantum

引用

BIOORGANIC & MEDICINAL CHEMISTRY LETTERS 2017年第11期27卷 2459-2464页

作者： Gomes, Marcelo N. Alcantara, Laura M. Neves, Bruno J. Melo-Filho, Cleber C. Freitas-Junior, Lucio H. Moraes, Carolina B. Ma, Rui Franzblau, Scott G. Muratov, Eugene Andrade, Carolina Horta Univ Fed Goias Fac Farm LabMol Lab Mol Modeling & Drug Design Rua 240Qd 87Setor Leste Univ BR-74605510 Goiania Go Brazil Brazilian Ctr Res Energy & Mat CNPEM Brazilian Biosci Natl Lab LNBio BR-13083970 Campinas SP Brazil Inst Butantan Sao Paulo BR-05503900 Sao Paulo Brazil Univ Ctr Anapolis UniEVANGELICA Postgrad Program Soc Technol & Enviroment BR-75083515 Anapolis Go Brazil Univ Illinois Inst TB Res 833 South Wood St Chicago IL 60612 USA Univ N Carolina Eshelman Sch Pharm Lab Mol Modeling Chapel Hill NC 27599 USA Odessa Natl Polytech Univ Dept Chem Technol UA-65000 Odessa Ukraine Univ Fed Goias Goiania Go Brazil

Leishmaniasis are infectious diseases caused by parasites of genus Leishmania that affect affects 12 million people in 98 countries mainly in Africa, Asia, and Latin America. Effective treatments for this disease are urgently needed. In this study, we present a computer-aided approach to investigate a set of 32 recently synthesized chalcone and chalcone-like compounds to act as antileishmanial agents. As a result, nine most promising compounds and three potentially inactive compounds were experimentally evaluated against Leishmania infantum amastigotes and mammalian cells. Four compounds exhibited EC50 in the range of 6.2-10.98 mu M. In addition, two compounds, LabMol-65 and LabMol-73, exhibited cytotoxicity in macrophages > 50 mu M that resulted in better selectivity compared to standard drug amphotericin B. These two compounds also demonstrated low cytotoxicity and high selectivity towards Vero cells. The results of target fishing followed by homology modeling and docking studies suggest that these chalcone compounds could act in Leishmania because of their interaction with cysteine proteases, such as procathepsin L. Finally, we have provided structural recommendations for designing new antileishmanial chalcones. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Antileishmanial agents Nitroheterocycle chalcones Selectivity Molecular modeling Target fishing

来源：评论

学校读者我要写书评

暂无评论

Neolignans from leaves of Nectandra leucantha (Lauraceae) display in vitro antitrypanosomal activity via plasma membrane and mitochondrial damages

引用

CHEMICO-BIOLOGICAL INTERACTIONS 2017年 277卷 55-61页

作者： Grecco, Simone S. Costa-Silva, Thais A. Jerz, Gerold de Sousa, Fernanda S. Londero, Vinicius S. Galuppo, Mariana K. Lima, Marta L. Neves, Bruno J. Andrade, Carolina H. Tempone, Andre G. Lago, Joao Henrique G. Fed Univ ABC Ctr Nat Sci & Humanities BR-09210180 Santo Andre SP Brazil Tech Univ Carolo Wilhelmina Braunschweig Inst Food Chem D-38106 Braunschweig Germany Anhanguera Univ Sao Paulo Biotechnol & Innovat Hlth Program BR-05145200 Sao Paulo Brazil Univ Fed Sao Paulo Inst Environm Chem & Pharmaceut Sci BR-09972270 Diadema SP Brazil Adolfo Lutz Inst Ctr Parasitol & Mycol BR-01246902 Sao Paulo Brazil Univ Sao Paulo Inst Med Trop Sao Paulo BR-05403000 Sao Paulo Brazil Univ Fed Goias Fac Pharm Lab Mol Modeling & Drug Design LabMol BR-74605170 Goiania Go Brazil Unievangel Univ Ctr Postgrad Program Soc Technol & Environm BR-75083515 Anapolis Go Brazil

Chagas disease is a neglected tropical disease, caused by the protozoan parasite Trypanosoma cruzi, which affects more than eight million people in Tropical and Subtropical countries especially in Latin America. Current treatment is limited to nifurtimox and benznidazole, both with reduced effectiveness and high toxicity. In this work, the n-hexane extract from leaves of Nectandra leucantha (Lauraceae) displayed in vitro antitrypanosomal activity against T. cruzi. Using several chromatographic steps, four related neolignans were isolated and chemically characterized as dehydrodieugenol B (1), 1-(8-propenyl)-3-[3'-methoxy-1'-(8-propenyl)-phenoxy]-4,5dimethoxybenzene (2), 1-[(7S)-hydroxy-8-propenyl]-3-[3'-methoxy-1'-(8'-propenyl)-phenoxy]-4hydroxy-5-methoxybenzene (3), and 1-[(7S)-hydroxy-8-propenyl]-3-[3'-methoxy-1'-(8'-propenyl)-phenoxy]-4,5-dimethoxybenzene (4). These compounds were tested against intracellular amastigotes and extracellular trypomastigotes of T. cruzi and for mammalian cytotoxicity. Neolignan 4 showed the higher selectivity index (SI) against trypomastigotes (>5) and amastigotes (>13) of T. cruzi. The investigation of the mechanism of action demonstrated that neolignan 4 caused substantial alteration of the plasma membrane permeability, together with mitochondrial dysfunctions in trypomastigote forms. In silico studies of pharmacokinetics and toxicity (ADMET) properties predicted that all compounds were non-mutagenic, non-carcinogenic, non-genotoxic, weak hERG blockers, with acceptable volume of distribution (1.66-3.32 L/kg), and low rodent oral toxicity (LD50 810-e2200 mg/kg). Considering some clinical events of cerebral Chagas disease, the compounds also demonstrated favorable properties, such as blood-brain barrier penetration. Unfavorable properties were also predicted as high promiscuity for P450 isoforms, high plasma protein binding affinity (>91%), and moderate-to-low oral bioavailability. Finally, none of the isolated neolignans was predict

关键词： Nectandra leucantha Neolignans Trypanosoma cruzi Plasma membrane permeability Mitochondrial dysfunctions ADMET

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：