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Single-sequence protein structure prediction using a language model and deep learning (Oct, 10.1038/s41587-022-01432-w, 2022)

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NATURE bioTECHNOLOGY 2022年第11期40卷 1692-1692页

作者： Chowdhury, Ratul Bouatta, Nazim Biswas, Surojit Floristean, Christina Kharkar, Anant Roy, Koushik Rochereau, Charlotte Ahdritz, Gustaf Zhang, Joanna Church, George M. Sorger, Peter K. AlQuraishi, Mohammed Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Nabla Bio Inc. Boston MA USA Department of Computer Science Columbia University New York NY USA Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New York NY USA Department of Systems Biology Columbia University New York NY USA Department of Systems Biology Harvard Medical School Boston MA USA

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

关键词： Computational biophysics Machine learning Protein structure predictions

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Single-sequence protein structure prediction using language models from deep learning

Single-sequence protein structure prediction using language ...

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2021 AIChE Annual Meeting

作者： Chowdhury, Ratul Bouatta, Nazim Biswas, Surojit Rochereau, Charlotte Church, George M. Sorger, Peter K. Alquraishi, Mohammed Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Nabla Bio. Inc. United States Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New YorkNY United States Department of Systems Biology Columbia University New YorkNY United States

ISBN: (纸本)9781713852834

AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, three challenges remain: (i) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated, (ii) rapid exploration of designed structures, and (iii) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) able to predict protein structure from single protein sequences without use of MSAs. This deep learning system has two novel elements: a protein language model (AminoBERT) that uses a Transformer to learn latent structural information from millions of unaligned proteins and a geometric module that compactly represents Cabackbone geometry. RGN2 outperforms AlphaFold2 and RoseTTAFold (as well as trRosetta) on orphan proteins and is competitive with designed sequences, while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction. © 2021 American Institute of Chemical Engineers. All rights reserved.

关键词： Proteins

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Synthetic biology in Indonesia: Potential and projection in a country with mega biodiversity

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biotechnology Notes 2023年第1期4卷 41-48页

作者： Sanka, Immanuel Kusuma, Ali Budhi Martha, Faustina Hendrawan, Andre Pramanda, Ihsan Tria Wicaksono, Adhityo Jati, Afif Pranaya Mazaya, Maulida Dwijayanti, Ari Izzati, Nurul Maulana, Muhammad Farhan Widyaningrum, Aulia Reski Synthetic Biology Indonesia (Synbio.id) Jl. Raya Lintas Sumbawa-Bima Block AA No. 1 Boak Village Subdistrict Unter Iwes Sumbawa 84316 Indonesia Department of Chemistry and Biotechnology Tallinn University of Technology Akadeemia tee 15 Tallinn 12618 Estonia Indonesian Centre for Extremophile Bioresources and Biotechnology (ICEBB) Faculty of Life Sciences and Technology Sumbawa University of Technology Jl. Raya Olat Maras Sumbawa 84371 Indonesia Science Communication Steering Committee iGEM Foundation 45 Prospect St Cambridge 02139 MA United States Department of Bio Technology Indonesia International Institute for Life Sciences (i3L) Jl. Pulomas Barat Kav. 88 Pulomas Jakarta 13210 Indonesia Division of Biotechnology Genbinesia Foundation Jalan Swadaya Barat no. 4 Gresik 61171 Indonesia Infection Program Monash Biomedicine Discovery Institute and Department of Microbiology Monash University Melbourne VIC Australia Research Center for Computing Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Cibinong Science Center Jl. Raya Jakarta-Bogor KM 46 West Java Cibinong 16911 Indonesia

Synthetic biology has gained many interest around the globe in the last two decades, not only due to its rapid development but also the potential to provide addressable solutions using standardized design of biological systems. Considering its huge population, biodiversity, and natural resources, Indonesia could play an important role in shaping the future of synthetic biology towards a sustainable bio-circular economy. Here, we provide an overview of synthetic biology development in Indonesia, especially on exploring the potential of our biodiversity. We also discuss some potentials of synthetic biology in solving national issues. Furthermore, we also provide the projection and future landscape of synthetic biology development in Indonesia. In addition, we briefly explain the potential challenges that may arise during the development. © 2023 The Authors

关键词： Indonesia Mega biodiversity Synbio development Synthetic biology

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Structure-function relationship in leishmanial globin coupled soluble adenylate cyclase

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Proceedings of the Indian National Science Academy 2020年 86卷

作者： Santara, Sumit Sen Adak, Subrata Program in Cellular and Molecular Medicine Boston Children's Hospital Department of Pediatrics Harvard Medical School BostonMA02115 United States Structural Biology and Bio-Informatics Division CSIR-Indian Institute of Chemical Biology 4 Raja S. C. Mullick Road Kolkata700032 India

Leishmania promastigotes inhabit the midgut of sandfly, where they are densely packed together and the environment of these promastigotes is then likely to become hypoxic or even anoxic. Interestingly, Leishmania can survive in low oxygen concentrations, which is an exception from Trypanosoma brucei. Although some transcriptional regulators in mammals help to respond in adaptive responses during hypoxia, Leishmania lacks hypoxic inducible factor (HIF) as well as transcriptional regulation. Thus, the underlying mechanism still remains unclear. Recently, our laboratory has discovered a globin coupled heme containing adenylate cyclase from L. major called HemAC-Lm, which is likely to function in cellular adaptability under various O2 limiting conditions. HemAC-Lm proteins usually consist of an N-terminal heme-containing O2 sensor domain and a C-terminal adenylate cyclase domain. The N-terminal heme-containing O2 sensor domain has two globin domains (globin-A and globin-B). biochemical studies of wild type and mutant proteins further suggest that the oxygenated form of heme is only present in globin-A domain, leading to maximum cAMP production. Furthermore, the data from HemAC-Lm knock down, as well as overexpressed cells, suggest that cAMP generation shows stimulatory, as well as inhibitory role in cell proliferation during normoxia. In addition, O2-dependent cAMP signaling via protein kinase A plays a fundamental role in cell survival through suppression of oxidative stress under hypoxia. © 2020 Indian National Science Academy. All rights reserved.

关键词： Mammals

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Publisher Correction: Single-sequence protein structure prediction using a language model and deep learning

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Nature biotechnology 2022年第11期40卷 1692页

作者： Ratul Chowdhury Nazim Bouatta Surojit Biswas Christina Floristean Anant Kharkar Koushik Roy Charlotte Rochereau Gustaf Ahdritz Joanna Zhang George M Church Peter K Sorger Mohammed AlQuraishi Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School Boston MA USA. Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School Boston MA USA. nazim_bouatta@hms.harvard.edu. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Nabla Bio Inc. Boston MA USA. Department of Computer Science Columbia University New York NY USA. Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New York NY USA. Department of Systems Biology Columbia University New York NY USA. Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School Boston MA USA. peter_sorger@hms.harvard.edu. Department of Systems Biology Harvard Medical School Boston MA USA. peter_sorger@hms.harvard.edu. Department of Computer Science Columbia University New York NY USA. ma4129@cumc.columbia.edu. Department of Systems Biology Columbia University New York NY USA. ma4129@cumc.columbia.edu.

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Development of genomic based diagnostics in various application domains 19

Development of genomic based diagnostics in various applicat...

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19th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2017

作者： Szallasi, Zoltan Department of Bio and Health Informatics Technical University of Denmark Kemitorvet 208 Lyngby2800 Denmark Computational Health Informatics Program Boston Children's Hospital United States Harvard Medical School Boston United States

We will review the revolution brought about by low cost next generation sequencing in a wide array of diagnostic and industrial applications with a special emphasis on computational requirements and big data challenges.

关键词： Big data

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Data-augmented modeling of intracranial pressure

arXiv

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arXiv 2018年

作者： Wang, Jian-Xun Hu, Xiao Shadden, Shawn C. Mechanical Engineering University of California BerkeleyCA United States Aerospace and Mechanical Engineering Center of Informatics and Computational Science University of Notre Dame Notre DameIN United States Department of Physiological Nursing Department of Neurological surgery Institute of Computational Health Sciences UCSF Joint Bio-Engineering Graduate Program University of California San FranciscoCA United States

Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Diagnosis

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Expression quantitative trait locus fine mapping of the 17q12–21 asthma locus in African American children: a genetic association and gene expression study

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The Lancet Respiratory Medicine 2020年第5期8卷 482-492页

作者： Ober, Carole McKennan, Chris G Magnaye, Kevin M Altman, Matthew C Washington, Charles Stanhope, Catherine Naughton, Katherine A Rosasco, Mario G Bacharier, Leonard B Billheimer, Dean Gold, Diane R Gress, Lisa Hartert, Tina Havstad, Suzanne Khurana Hershey, Gurjit K Hallmark, Brian Hogarth, D Kyle Jackson, Daniel J Johnson, Christine C Kattan, Meyer Lemanske, Robert F Lynch, Susan V Mendonca, Eneida A Miller, Rachel L Naureckas, Edward T Seroogy, Christine M Wegienka, Ganesa White, Steven R Wood, Robert A Wright, Anne L Zoratti, Edward M Martinez, Fernando D Ownby, Dennis Nicolae, Dan L Levin, Albert M Gern, James E Achten, Niek Ainsworth, John Akkerman, Nonna Anderson, Elizabeth Anderson, Larry J. Andrews, Howard Armagost, Elizabeth Aubuchon, Mary Ann Bach, Julia Barnes, Kathrine L. Barone, Charles Bauer, Irma Beamer, Paloma Becker, Patrice Bednarek, Alyssa Bellemore, Stacey Bendixsen, Casper G. Biagini Myers, Jocelyn M. Billstrand, Christine Birg, Geraldine Blocki, Shirley Bloomberg, Gordon Bobbitt, Kevin Bochkov, Yury Bourgeois, Karen Boushey, Homer Brockman-Schneider, Rebecca Brunwasser, Steven M. Budrevich, Richard Burkle, Jeffrey W. Busse, William Calatroni, Agustin Campbell, Janice Carlson-Dakes, Kirsten Cassidy-Bushrow, Andrea Chappell, James D. Chasman, Deborah Chipps, Teresa M. Chirkova, Tatiana Cole, Deanna Connolly, Alexandra Cootauco, Michelle Costello, Kaitlin Couch, Philip Coull, Brent Craven, Mark Crisafi, Gina Cruikshank, William Curtsinger, Kristi Custovic, Adnan Das, Suman R. DaSilva, Douglas Datta, Soma Davidson, Brent De La Ossa, Lydia DeVries, Mark Di, Qian Dixon, Samara Donnerbauer, Erin Dorst, Marian Doyle, Susan Dresen, Amy Dupont, William D. Durrange, Janet Erickson, Heidi Evans, Michael D. Ezell, Jerel Farnham, Leanna Filardo-Collins, Roxanne Finazzo, Salvatore Flege, Zachary Fleurat, Conner Floerke, Heather Floerke, Dorothy Foss, Terry Freie, Angela Frome, Wayne Fye, Samantha Gagalis, Lisa Gammell, Rebecca Gangnon, Ronald E. Ge, James E. Gebretsadik, Tebeb Gergen, Peter Gibson, Heike Gj Department of Human Genetics University of Chicago Chicago IL United States Section of Pulmonary and Critical Care Medicine Department of Medicine University of Chicago Chicago IL United States Department of Statistics University of Pittsburgh Pittsburgh PA United States Division of Allergy and Infectious Diseases Department of Medicine University of Washington Seattle WA United States Systems Immunology Program Benaroya Research Institute Seattle WA United States Division of Pediatric Allergy Immunology and Pulmonary Medicine Department of Pediatrics Washington University and St Louis Children's Hospital St Louis MO United States College of Public Health University of Arizona Tucson AZ United States Asthma and Airway Disease Research Center University of Arizona Tucson AZ United States BIO5 Institute University of Arizona Tucson AZ United States The Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States Department of Environmental Health Harvard T H Chan School of Public Health Boston MA United States Department of Pediatrics University of Wisconsin School of Medicine and Public Health Madison WI United States Department of Medicine Vanderbilt University School of Medicine Nashville TN United States Department of Public Health Sciences Henry Ford Health System Detroit MI United States Division of Allergy and Clinical Immunology Henry Ford Health System Detroit MI United States Division of Asthma Research Cincinnati Children's Hospital and Department of Pediatrics University of Cincinnati Cincinnati OH United States Department of Pediatrics Columbia University Medical Center New York NY United States Division of Gastroenterology Department of Medicine University of California San Francisco San Francisco CA United States Department of Biostatistics and Medical Informatics University of Wisconsin–Madison Madison WI United States Department of Pediatri

Background: African ancestry is associated with a higher prevalence and greater severity of asthma than European ancestries, yet genetic studies of the most common locus associated with childhood-onset asthma, 17q12–21, in African Americans have been inconclusive. The aim of this study was to leverage both the phenotyping of the Children's Respiratory and Environmental Workgroup (CREW) birth cohort consortium, and the reduced linkage disequilibrium in African Americans, to fine map the 17q12–21 locus. Methods: We first did a genetic association study and meta-analysis using 17q12–21 tag single-nucleotide polymorphisms (SNPs) for childhood-onset asthma in 1613 European American and 870 African American children from the CREW consortium. Nine tag SNPs were selected based on linkage disequilibrium patterns at 17q12–21 and their association with asthma, considering the effect allele under an additive model (0, 1, or 2 effect alleles). Results were meta-analysed with publicly available summary data from the EVE consortium (on 4303 European American and 3034 African American individuals) for seven of the nine SNPs of interest. Subsequently, we tested for expression quantitative trait loci (eQTLs) among the SNPs associated with childhood-onset asthma and the expression of 17q12–21 genes in resting peripheral blood mononuclear cells (PBMCs) from 85 African American CREW children and in upper airway epithelial cells from 246 African American CREW children;and in lower airway epithelial cells from 44 European American and 72 African American adults from a case-control study of asthma genetic risk in Chicago (IL, USA). Findings: 17q12–21 SNPs were broadly associated with asthma in European Americans. Only two SNPs (rs2305480 in gasdermin-B [GSDMB] and rs8076131 in ORMDL sphingolipid biosynthesis regulator 3 [ORMDL3]) were associated with asthma in African Americans, at a Bonferroni-corrected threshold of p<0·0055 (for rs2305480_G, odds ratio [OR] 1·36 [95% CI 1·12–1·65], p=0·0

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Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions

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BMC bioinformatics 2020年第1期21卷 495页

作者： Samir Rachid Zaim Colleen Kenost Joanne Berghout Wesley Chiu Liam Wilson Hao Helen Zhang Yves A Lussier Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Department of Mathematics College of Sciences The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. Lussier.Y@***. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. Lussier.Y@***. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The Center for Applied Genetic and Genomic Medicine 1295 N. Martin Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona Cancer Center 3838 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona BIO5 Institute 1657 E. Helen Street Tucson AZ 85721 USA. Lussier.Y@***.

An amendment to this paper has been published and can be accessed via the original article.

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Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.

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