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Andrej Sali

Bio: Andrej Sali is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Protein structure & Protein structure prediction. The author has an hindex of 119, co-authored 430 publications receiving 74984 citations. Previous affiliations of Andrej Sali include University of California, Berkeley & California Institute for Quantitative Biosciences.


Papers
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Journal ArticleDOI
TL;DR: A comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures, which is automated and illustrated by the modelling of trypsin from two other serine proteinases.

12,386 citations

Journal ArticleDOI
TL;DR: This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications.
Abstract: Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

3,495 citations

Journal ArticleDOI
David E. Gordon, Gwendolyn M. Jang, Mehdi Bouhaddou, Jiewei Xu, Kirsten Obernier, Kris M. White1, Matthew J. O’Meara2, Veronica V. Rezelj3, Jeffrey Z. Guo, Danielle L. Swaney, Tia A. Tummino4, Ruth Hüttenhain, Robyn M. Kaake, Alicia L. Richards, Beril Tutuncuoglu, Helene Foussard, Jyoti Batra, Kelsey M. Haas, Maya Modak, Minkyu Kim, Paige Haas, Benjamin J. Polacco, Hannes Braberg, Jacqueline M. Fabius, Manon Eckhardt, Margaret Soucheray, Melanie J. Bennett, Merve Cakir, Michael McGregor, Qiongyu Li, Bjoern Meyer3, Ferdinand Roesch3, Thomas Vallet3, Alice Mac Kain3, Lisa Miorin1, Elena Moreno1, Zun Zar Chi Naing, Yuan Zhou, Shiming Peng4, Ying Shi, Ziyang Zhang, Wenqi Shen, Ilsa T Kirby, James E. Melnyk, John S. Chorba, Kevin Lou, Shizhong Dai, Inigo Barrio-Hernandez5, Danish Memon5, Claudia Hernandez-Armenta5, Jiankun Lyu4, Christopher J.P. Mathy, Tina Perica4, Kala Bharath Pilla4, Sai J. Ganesan4, Daniel J. Saltzberg4, Rakesh Ramachandran4, Xi Liu4, Sara Brin Rosenthal6, Lorenzo Calviello4, Srivats Venkataramanan4, Jose Liboy-Lugo4, Yizhu Lin4, Xi Ping Huang7, Yongfeng Liu7, Stephanie A. Wankowicz, Markus Bohn4, Maliheh Safari4, Fatima S. Ugur, Cassandra Koh3, Nastaran Sadat Savar3, Quang Dinh Tran3, Djoshkun Shengjuler3, Sabrina J. Fletcher3, Michael C. O’Neal, Yiming Cai, Jason C.J. Chang, David J. Broadhurst, Saker Klippsten, Phillip P. Sharp4, Nicole A. Wenzell4, Duygu Kuzuoğlu-Öztürk4, Hao-Yuan Wang4, Raphael Trenker4, Janet M. Young8, Devin A. Cavero4, Devin A. Cavero9, Joseph Hiatt9, Joseph Hiatt4, Theodore L. Roth, Ujjwal Rathore9, Ujjwal Rathore4, Advait Subramanian4, Julia Noack4, Mathieu Hubert3, Robert M. Stroud4, Alan D. Frankel4, Oren S. Rosenberg, Kliment A. Verba4, David A. Agard4, Melanie Ott, Michael Emerman8, Natalia Jura, Mark von Zastrow, Eric Verdin4, Eric Verdin10, Alan Ashworth4, Olivier Schwartz3, Christophe d'Enfert3, Shaeri Mukherjee4, Matthew P. Jacobson4, Harmit S. Malik8, Danica Galonić Fujimori, Trey Ideker6, Charles S. Craik, Stephen N. Floor4, James S. Fraser4, John D. Gross4, Andrej Sali, Bryan L. Roth7, Davide Ruggero, Jack Taunton4, Tanja Kortemme, Pedro Beltrao5, Marco Vignuzzi3, Adolfo García-Sastre, Kevan M. Shokat, Brian K. Shoichet4, Nevan J. Krogan 
30 Apr 2020-Nature
TL;DR: A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.
Abstract: A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19. A human–SARS-CoV-2 protein interaction map highlights cellular processes that are hijacked by the virus and that can be targeted by existing drugs, including inhibitors of mRNA translation and predicted regulators of the sigma receptors.

3,319 citations

Journal ArticleDOI
TL;DR: There is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes and to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.
Abstract: ■ Abstract Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more pro- teins of known structure (templates). The prediction process consists of fold assign- ment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are in- creasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are nec- essary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

3,085 citations

Journal ArticleDOI
TL;DR: This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications.
Abstract: Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

3,006 citations


Cited by
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Journal ArticleDOI
TL;DR: Two unusual extensions are presented: Multiscale, which adds the ability to visualize large‐scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales.
Abstract: The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.

35,698 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: A description is given of Phaser-2.1: software for phasing macromolecular crystal structures by molecular replacement and single-wavelength anomalous dispersion phasing.
Abstract: Phaser is a program for phasing macromolecular crystal structures by both molecular replacement and experimental phasing methods. The novel phasing algorithms implemented in Phaser have been developed using maximum likelihood and multivariate statistics. For molecular replacement, the new algorithms have proved to be significantly better than traditional methods in discriminating correct solutions from noise, and for single-wavelength anomalous dispersion experimental phasing, the new algorithms, which account for correlations between F+ and F−, give better phases (lower mean phase error with respect to the phases given by the refined structure) than those that use mean F and anomalous differences ΔF. One of the design concepts of Phaser was that it be capable of a high degree of automation. To this end, Phaser (written in C++) can be called directly from Python, although it can also be called using traditional CCP4 keyword-style input. Phaser is a platform for future development of improved phasing methods and their release, including source code, to the crystallographic community.

17,755 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal Article
Fumio Tajima1
30 Oct 1989-Genomics
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.

11,521 citations