Highly Accurate Protein Structure Prediction for the Human Proteome
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Bioinformatic Analysis of Structure and Function of LIM Domains of Human Zyxin Family Proteins
International Journal of Molecular Sciences Article Bioinformatic Analysis of Structure and Function of LIM Domains of Human Zyxin Family Proteins M. Quadir Siddiqui 1,† , Maulik D. Badmalia 1,† and Trushar R. Patel 1,2,3,* 1 Alberta RNA Research and Training Institute, Department of Chemistry and Biochemistry, University of Lethbridge, 4401 University Drive, Lethbridge, AB T1K 3M4, Canada; [email protected] (M.Q.S.); [email protected] (M.D.B.) 2 Department of Microbiology, Immunology and Infectious Disease, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive, Calgary, AB T2N 4N1, Canada 3 Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB T6G 2E1, Canada * Correspondence: [email protected] † These authors contributed equally to the work. Abstract: Members of the human Zyxin family are LIM domain-containing proteins that perform critical cellular functions and are indispensable for cellular integrity. Despite their importance, not much is known about their structure, functions, interactions and dynamics. To provide insights into these, we used a set of in-silico tools and databases and analyzed their amino acid sequence, phylogeny, post-translational modifications, structure-dynamics, molecular interactions, and func- tions. Our analysis revealed that zyxin members are ohnologs. Presence of a conserved nuclear export signal composed of LxxLxL/LxxxLxL consensus sequence, as well as a possible nuclear localization signal, suggesting that Zyxin family members may have nuclear and cytoplasmic roles. The molecular modeling and structural analysis indicated that Zyxin family LIM domains share Citation: Siddiqui, M.Q.; Badmalia, similarities with transcriptional regulators and have positively charged electrostatic patches, which M.D.; Patel, T.R. -
Phylogenomic Analysis of the Chlamydomonas Genome Unmasks Proteins Potentially Involved in Photosynthetic Function and Regulation
Photosynth Res DOI 10.1007/s11120-010-9555-7 REVIEW Phylogenomic analysis of the Chlamydomonas genome unmasks proteins potentially involved in photosynthetic function and regulation Arthur R. Grossman • Steven J. Karpowicz • Mark Heinnickel • David Dewez • Blaise Hamel • Rachel Dent • Krishna K. Niyogi • Xenie Johnson • Jean Alric • Francis-Andre´ Wollman • Huiying Li • Sabeeha S. Merchant Received: 11 February 2010 / Accepted: 16 April 2010 Ó The Author(s) 2010. This article is published with open access at Springerlink.com Abstract Chlamydomonas reinhardtii, a unicellular green performed to identify proteins encoded on the Chlamydo- alga, has been exploited as a reference organism for iden- monas genome which were likely involved in chloroplast tifying proteins and activities associated with the photo- functions (or specifically associated with the green algal synthetic apparatus and the functioning of chloroplasts. lineage); this set of proteins has been designated the Recently, the full genome sequence of Chlamydomonas GreenCut. Further analyses of those GreenCut proteins with was generated and a set of gene models, representing all uncharacterized functions and the generation of mutant genes on the genome, was developed. Using these gene strains aberrant for these proteins are beginning to unmask models, and gene models developed for the genomes of new layers of functionality/regulation that are integrated other organisms, a phylogenomic, comparative analysis was into the workings of the photosynthetic apparatus. Keywords Chlamydomonas Á GreenCut Á Chloroplast Á Phylogenomics Á Regulation A. R. Grossman (&) Á M. Heinnickel Á D. Dewez Á B. Hamel Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA Introduction e-mail: [email protected] Chlamydomonas reinhardtii as a reference organism S. -
An Emerging Field for the Structural Analysis of Proteins on the Proteomic Scale † ‡ ‡ ‡ § ‡ ∥ Upneet Kaur, He Meng, Fang Lui, Renze Ma, Ryenne N
Perspective Cite This: J. Proteome Res. 2018, 17, 3614−3627 pubs.acs.org/jpr Proteome-Wide Structural Biology: An Emerging Field for the Structural Analysis of Proteins on the Proteomic Scale † ‡ ‡ ‡ § ‡ ∥ Upneet Kaur, He Meng, Fang Lui, Renze Ma, Ryenne N. Ogburn, , Julia H. R. Johnson, , ‡ † Michael C. Fitzgerald,*, and Lisa M. Jones*, ‡ Department of Chemistry, Duke University, Durham, North Carolina 27708-0346, United States † Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland 21201, United States ABSTRACT: Over the past decade, a suite of new mass- spectrometry-based proteomics methods has been developed that now enables the conformational properties of proteins and protein− ligand complexes to be studied in complex biological mixtures, from cell lysates to intact cells. Highlighted here are seven of the techniques in this new toolbox. These techniques include chemical cross-linking (XL−MS), hydroxyl radical footprinting (HRF), Drug Affinity Responsive Target Stability (DARTS), Limited Proteolysis (LiP), Pulse Proteolysis (PP), Stability of Proteins from Rates of Oxidation (SPROX), and Thermal Proteome Profiling (TPP). The above techniques all rely on conventional bottom-up proteomics strategies for peptide sequencing and protein identification. However, they have required the development of unconventional proteomic data analysis strategies. Discussed here are the current technical challenges associated with these different data analysis strategies as well as the relative analytical capabilities of the different techniques. The new biophysical capabilities that the above techniques bring to bear on proteomic research are also highlighted in the context of several different application areas in which these techniques have been used, including the study of protein ligand binding interactions (e.g., protein target discovery studies and protein interaction network analyses) and the characterization of biological states. -
Static Retention of the Lumenal Monotopic Membrane Protein Torsina in the Endoplasmic Reticulum
The EMBO Journal (2011) 30, 3217–3231 | & 2011 European Molecular Biology Organization | All Rights Reserved 0261-4189/11 www.embojournal.org TTHEH E EEMBOMBO JJOURNALOURN AL Static retention of the lumenal monotopic membrane protein torsinA in the endoplasmic reticulum Abigail B Vander Heyden1, despite the fact that it has been a decade since the protein was Teresa V Naismith1, Erik L Snapp2 and first described and linked to dystonia (Breakefield et al, Phyllis I Hanson1,* 2008). Based on its membership in the AAA þ family of ATPases (Ozelius et al, 1997; Hanson and Whiteheart, 2005), 1Department of Cell Biology and Physiology, Washington University School of Medicine, St Louis, MO, USA and 2Department of Anatomy it is likely that torsinA disassembles or changes the confor- and Structural Biology, Albert Einstein College of Medicine, Bronx, mation of a protein or protein complex in the ER or NE. The NY, USA DE mutation is thought to compromise this function (Dang et al, 2005; Goodchild et al, 2005). TorsinA is a membrane-associated enzyme in the endo- TorsinA is targeted to the ER lumen by an N-terminal plasmic reticulum (ER) lumen that is mutated in DYT1 signal peptide. Analyses of torsinA’s subcellular localization, dystonia. How it remains in the ER has been unclear. We processing, and glycosylation show that the signal peptide is report that a hydrophobic N-terminal domain (NTD) di- cleaved and the mature protein resides in the lumen of the ER rects static retention of torsinA within the ER by excluding (Kustedjo et al, 2000; Hewett et al, 2003; Liu et al, 2003), it from ER exit sites, as has been previously reported for where it is a stable protein (Gordon and Gonzalez-Alegre, short transmembrane domains (TMDs). -
CASP)-Round V
PROTEINS: Structure, Function, and Genetics 53:334–339 (2003) Critical Assessment of Methods of Protein Structure Prediction (CASP)-Round V John Moult,1 Krzysztof Fidelis,2 Adam Zemla,2 and Tim Hubbard3 1Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, Rockville, Maryland 2Biology and Biotechnology Research Program, Lawrence Livermore National Laboratory, Livermore, California 3Sanger Institute, Wellcome Trust Genome Campus, Cambridgeshire, United Kingdom ABSTRACT This article provides an introduc- The role and importance of automated servers in the tion to the special issue of the journal Proteins structure prediction field continue to grow. Another main dedicated to the fifth CASP experiment to assess the section of the issue deals with this topic. The first of these state of the art in protein structure prediction. The articles describes the CAFASP3 experiment. The goal of article describes the conduct, the categories of pre- CAFASP is to assess the state of the art in automatic diction, and the evaluation and assessment proce- methods of structure prediction.16 Whereas CASP allows dures of the experiment. A brief summary of progress any combination of computational and human methods, over the five CASP experiments is provided. Related CAFASP captures predictions directly from fully auto- developments in the field are also described. Proteins matic servers. CAFASP makes use of the CASP target 2003;53:334–339. © 2003 Wiley-Liss, Inc. distribution and prediction collection infrastructure, but is otherwise independent. The results of the CAFASP3 experi- Key words: protein structure prediction; communi- ment were also evaluated by the CASP assessors, provid- tywide experiment; CASP ing a comparison of fully automatic and hybrid methods. -
PROTEOMICS the Human Proteome Takes the Spotlight
RESEARCH HIGHLIGHTS PROTEOMICS The human proteome takes the spotlight Two papers report mass spectrometry– big data. “We then thought, include some surpris- based draft maps of the human proteome ‘What is a potentially good ing findings. For example, and provide broadly accessible resources. illustration for the utility Kuster’s team found protein For years, members of the proteomics of such a database?’” says evidence for 430 long inter- community have been trying to garner sup- Kuster. “We very quickly genic noncoding RNAs, port for a large-scale project to exhaustively got to the idea, ‘Why don’t which have been thought map the normal human proteome, including we try to put together the not to be translated into pro- identifying all post-translational modifica- human proteome?’” tein. Pandey’s team refined tions and protein-protein interactions and The two groups took the annotations of 808 genes providing targeted mass spectrometry assays slightly different strategies and also found evidence and antibodies for all human proteins. But a towards this common goal. for the translation of many Nik Spencer/Nature Publishing Group Publishing Nik Spencer/Nature lack of consensus on how to exactly define Pandey’s lab examined 30 noncoding RNAs and pseu- Two groups provide mass the proteome, how to carry out such a mis- normal tissues, including spectrometry evidence for dogenes. sion and whether the technology is ready has adult and fetal tissues, as ~90% of the human proteome. Obtaining evidence for not so far convinced any funding agencies to well as primary hematopoi- the last roughly 10% of pro- fund on such an ambitious project. -
AI for Health Examples from Industry, Government, and Academia Table of Contents
guidehouse.com AI for Health Examples from industry, government, and academia Table of Contents Abstract 3 Introduction to artificial intelligence 4 Introduction to health data 7 Molecules: AI for understanding biology and developing therapies 10 Fundamental research 10 Drug development 11 Patients: AI for predicting disease and improving care 13 Diagnostics and outcome risks 13 Personalized medicine 14 Groups: AI for optimizing clinical trials and safeguarding public health 15 Clinical trials 15 Public health 17 Conclusion and outlook 18 2 Guidehouse Abstract This paper is directed toward a health-informed reader who is curious about the developments and potential of artificial intelligence (AI) in the health space, but could equally be read by AI practitioners curious about how their knowledge and methods are being used to advance human health. We present a brief, equation-free introduction to AI and its major subfields in order to provide a framework for understanding the technical context of the examples that follow. We discuss the various data sources available for questions of health and life sciences, as well as the unique challenges inherent to these fields. We then consider recent (past five years) applications of AI that have already had tangible, measurable impact to the advancement of biomedical knowledge and the development of new and improved treatments. These examples are organized by scale, ranging from the molecule (fundamental research and drug development) to the patient (diagnostics, risk-scoring, and personalized medicine) to the group (clinical trials and public health). Finally, we conclude with a brief summary and our outlook for the future of AI for health. -
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2017.DOI Pre-training of Deep Bidirectional Protein Sequence Representations with Structural Information SEONWOO MIN1,2, SEUNGHYUN PARK3, SIWON KIM1, HYUN-SOO CHOI4, BYUNGHAN LEE5 (Member, IEEE), and SUNGROH YOON1,6 (Senior Member, IEEE) 1Department of Electrical and Computer engineering, Seoul National University, Seoul 08826, South Korea 2LG AI Research, Seoul 07796, South Korea 3Clova AI Research, NAVER Corp., Seongnam 13561, South Korea 4Department of Computer Science and Engineering, Kangwon National University, Chuncheon 24341, South Korea 5Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul 01811, South Korea 6Interdisciplinary Program in Artificial Intelligence, ASRI, INMC, and Institute of Engineering Research, Seoul National University, Seoul 08826, South Korea Corresponding author: Byunghan Lee ([email protected]) or Sungroh Yoon ([email protected]) This research was supported by the National Research Foundation (NRF) of Korea grants funded by the Ministry of Science and ICT (2018R1A2B3001628 (S.Y.), 2014M3C9A3063541 (S.Y.), 2019R1G1A1003253 (B.L.)), the Ministry of Agriculture, Food and Rural Affairs (918013-4 (S.Y.)), and the Brain Korea 21 Plus Project in 2021 (S.Y.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ABSTRACT Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to various downstream tasks. -
Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences
bioRxiv preprint doi: https://doi.org/10.1101/622803; this version posted May 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. BIOLOGICAL STRUCTURE AND FUNCTION EMERGE FROM SCALING UNSUPERVISED LEARNING TO 250 MILLION PROTEIN SEQUENCES Alexander Rives ∗y z Siddharth Goyal ∗x Joshua Meier ∗x Demi Guo ∗x Myle Ott x C. Lawrence Zitnick x Jerry Ma y x Rob Fergus y z x ABSTRACT In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In biology, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Learning the natural distribution of evolutionary protein sequence variation is a logical step toward predictive and generative modeling for biology. To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million sequences spanning evolutionary diversity. The resulting model maps raw sequences to representations of biological properties without labels or prior domain knowledge. The learned representation space organizes sequences at multiple levels of biological granularity from the biochemical to proteomic levels. Unsupervised learning recovers information about protein structure: secondary structure and residue-residue contacts can be identified by linear projections from the learned representations. Training language models on full sequence diversity rather than individual protein families increases recoverable information about secondary structure. -
Benchmarking the POEM@HOME Network for Protein Structure
3rd International Workshop on Science Gateways for Life Sciences (IWSG 2011), 8-10 JUNE 2011 Benchmarking the POEM@HOME Network for Protein Structure Prediction Timo Strunk1, Priya Anand1, Martin Brieg2, Moritz Wolf1, Konstantin Klenin2, Irene Meliciani1, Frank Tristram1, Ivan Kondov2 and Wolfgang Wenzel1,* 1Institute of Nanotechnology, Karlsruhe Institute of Technology, PO Box 3640, 76021 Karlsruhe, Germany. 2Steinbuch Centre for Computing, Karlsruhe Institute of Technology, PO Box 3640, 76021 Karlsruhe, Germany ABSTRACT forcefields (Fitzgerald, et al., 2007). Knowledge-based potentials, in contrast, perform very well in differentiating native from non- Motivation: Structure based methods for drug design offer great native protein structures (Wang, et al., 2004; Zhou, et al., 2007; potential for in-silico discovery of novel drugs but require accurate Zhou, et al., 2006) and have recently made inroads into the area of models of the target protein. Because many proteins, in particular protein folding. Physics-based models retain the appeal of high transmembrane proteins, are difficult to characterize experimentally, transferability, but the present lack of truly transferable potentials methods of protein structure prediction are required to close the gap calls for the development of novel forcefields for protein structure prediction and modeling (Schug, et al., 2006; Verma, et al., 2007; between sequence and structure information. Established methods Verma and Wenzel, 2009). for protein structure prediction work well only for targets of high We have earlier reported the rational development of transferable homology to known proteins, while biophysics based simulation free energy forcefields PFF01/02 (Schug, et al., 2005; Verma and methods are restricted to small systems and require enormous Wenzel, 2009) that correctly predict the native conformation of computational resources. -
Methods for the Refinement of Protein Structure 3D Models
International Journal of Molecular Sciences Review Methods for the Refinement of Protein Structure 3D Models Recep Adiyaman and Liam James McGuffin * School of Biological Sciences, University of Reading, Reading RG6 6AS, UK; [email protected] * Correspondence: l.j.mcguffi[email protected]; Tel.: +44-0-118-378-6332 Received: 2 April 2019; Accepted: 7 May 2019; Published: 1 May 2019 Abstract: The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge. -
Advances in Rosetta Protein Structure Prediction on Massively Parallel Systems
UC San Diego UC San Diego Previously Published Works Title Advances in Rosetta protein structure prediction on massively parallel systems Permalink https://escholarship.org/uc/item/87g6q6bw Journal IBM Journal of Research and Development, 52(1) ISSN 0018-8646 Authors Raman, S. Baker, D. Qian, B. et al. Publication Date 2008 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Advances in Rosetta protein S. Raman B. Qian structure prediction on D. Baker massively parallel systems R. C. Walker One of the key challenges in computational biology is prediction of three-dimensional protein structures from amino-acid sequences. For most proteins, the ‘‘native state’’ lies at the bottom of a free- energy landscape. Protein structure prediction involves varying the degrees of freedom of the protein in a constrained manner until it approaches its native state. In the Rosetta protein structure prediction protocols, a large number of independent folding trajectories are simulated, and several lowest-energy results are likely to be close to the native state. The availability of hundred-teraflop, and shortly, petaflop, computing resources is revolutionizing the approaches available for protein structure prediction. Here, we discuss issues involved in utilizing such machines efficiently with the Rosetta code, including an overview of recent results of the Critical Assessment of Techniques for Protein Structure Prediction 7 (CASP7) in which the computationally demanding structure-refinement process was run on 16 racks of the IBM Blue Gene/Le system at the IBM T. J. Watson Research Center. We highlight recent advances in high-performance computing and discuss future development paths that make use of the next-generation petascale (.1012 floating-point operations per second) machines.