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Van Norden M, Falls Z, Mandloi S, Segal BH, Baysal BE, Samudrala R, Elkin PL. The implications of APOBEC3-mediated C-to-U RNA editing for human disease. Commun Biol 2024; 7:529. [PMID: 38704509 DOI: 10.1038/s42003-024-06239-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 04/24/2024] [Indexed: 05/06/2024] Open
Abstract
Intra-organism biodiversity is thought to arise from epigenetic modification of constituent genes and post-translational modifications of translated proteins. Here, we show that post-transcriptional modifications, like RNA editing, may also contribute. RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosine to uracil. RNAsee (RNA site editing evaluation) is a computational tool developed to predict the cytosines edited by these enzymes. We find that 4.5% of non-synonymous DNA single nucleotide polymorphisms that result in cytosine to uracil changes in RNA are probable sites for APOBEC3A/G RNA editing; the variant proteins created by such polymorphisms may also result from transient RNA editing. These polymorphisms are associated with over 20% of Medical Subject Headings across ten categories of disease, including nutritional and metabolic, neoplastic, cardiovascular, and nervous system diseases. Because RNA editing is transient and not organism-wide, future work is necessary to confirm the extent and effects of such editing in humans.
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Affiliation(s)
- Melissa Van Norden
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Sapan Mandloi
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Brahm H Segal
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Bora E Baysal
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Peter L Elkin
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
- Department of Veterans Affairs, VA Western New York Healthcare System, Buffalo, NY, USA.
- Faculty of Engineering, University of Southern Denmark, Odense, Denmark.
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2
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Van Norden M, Falls Z, Mandloi S, Segal B, Baysal B, Samudrala R, Elkin PL. The Role of C-to-U RNA Editing in Human Biodiversity. bioRxiv 2023:2023.07.31.550344. [PMID: 37577456 PMCID: PMC10418052 DOI: 10.1101/2023.07.31.550344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Intra-organism biodiversity is thought to arise from epigenetic modification of our constituent genes and post-translational modifications after mRNA is translated into proteins. We have found that post-transcriptional modification, also known as RNA editing, is also responsible for a significant amount of our biodiversity, substantively expanding this story. The APOBEC (apolipoprotein B mRNA editing catalytic polypeptide-like) family RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosines to uracils (C>U) in specific stem-loop structures.1,2 We used RNAsee (RNA site editing evaluation), a tool developed to predict the locations of APOBEC3A/G RNA editing sites, to determine whether known single nucleotide polymorphisms (SNPs) in DNA could be replicated in RNA via RNA editing. About 4.5% of non-synonymous SNPs which result in C>U changes in RNA, and about 5.4% of such SNPs labelled as pathogenic, were identified as probable sites for APOBEC3A/G editing. This suggests that the variant proteins created by these DNA mutations may also be created by transient RNA editing, with the potential to affect human health. Those SNPs identified as potential APOBEC3A/G-mediated RNA editing sites were disproportionately associated with cardiovascular diseases, digestive system diseases, and musculoskeletal diseases. Future work should focus on common sites of RNA editing, any variant proteins created by these RNA editing sites, and the effects of these variants on protein diversity and human health. Classically, our biodiversity is thought to come from our constitutive genetics, epigenetic phenomenon, transcriptional differences, and post-translational modification of proteins. Here, we have shown evidence that RNA editing, often stimulated by environmental factors, could account for a significant degree of the protein biodiversity leading to human disease. In an era where worries about our changing environment are ever increasing, from the warming of our climate to the emergence of new diseases to the infiltration of microplastics and pollutants into our bodies, understanding how environmentally sensitive mechanisms like RNA editing affect our own cells is essential.
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Affiliation(s)
- Melissa Van Norden
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
| | - Sapan Mandloi
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
| | - Brahm Segal
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA
- Roswell Park Cancer Center
| | | | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
| | - Peter L Elkin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
- Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA
- Department of Veterans Affairs, VA Western New York Healthcare System, Buffalo, NY, USA
- Faculty of Engineering, University of Southern Denmark
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3
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Mangione W, Falls Z, Samudrala R. Effective holistic characterization of small molecule effects using heterogeneous biological networks. Front Pharmacol 2023; 14:1113007. [PMID: 37180722 PMCID: PMC10169664 DOI: 10.3389/fphar.2023.1113007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.
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Affiliation(s)
| | | | - Ram Samudrala
- Jacobs School of Medicine and Biomedical Sciences, Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States
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4
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Kumari R, Sharma SD, Kumar A, Ende Z, Mishina M, Wang Y, Falls Z, Samudrala R, Pohl J, Knight PR, Sambhara S. Antiviral Approaches against Influenza Virus. Clin Microbiol Rev 2023; 36:e0004022. [PMID: 36645300 PMCID: PMC10035319 DOI: 10.1128/cmr.00040-22] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Preventing and controlling influenza virus infection remains a global public health challenge, as it causes seasonal epidemics to unexpected pandemics. These infections are responsible for high morbidity, mortality, and substantial economic impact. Vaccines are the prophylaxis mainstay in the fight against influenza. However, vaccination fails to confer complete protection due to inadequate vaccination coverages, vaccine shortages, and mismatches with circulating strains. Antivirals represent an important prophylactic and therapeutic measure to reduce influenza-associated morbidity and mortality, particularly in high-risk populations. Here, we review current FDA-approved influenza antivirals with their mechanisms of action, and different viral- and host-directed influenza antiviral approaches, including immunomodulatory interventions in clinical development. Furthermore, we also illustrate the potential utility of machine learning in developing next-generation antivirals against influenza.
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Affiliation(s)
- Rashmi Kumari
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
- Department of Anesthesiology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
| | - Suresh D. Sharma
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Amrita Kumar
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Zachary Ende
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
- Oak Ridge Institute for Science and Education (ORISE), CDC Fellowship Program, Oak Ridge, Tennessee, USA
| | - Margarita Mishina
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Yuanyuan Wang
- Biotechnology Core Facility Branch, Division of Scientific Resources, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
- Association of Public Health Laboratories, Silver Spring, Maryland, USA
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Jan Pohl
- Biotechnology Core Facility Branch, Division of Scientific Resources, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Paul R. Knight
- Department of Anesthesiology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
| | - Suryaprakash Sambhara
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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5
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Handelmann CR, Tsompana M, Samudrala R, Buck M. The impact of nucleosome structure on CRISPR/Cas9 fidelity. Nucleic Acids Res 2023; 51:2333-2344. [PMID: 36727449 PMCID: PMC10018339 DOI: 10.1093/nar/gkad021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 01/05/2023] [Accepted: 01/31/2023] [Indexed: 02/03/2023] Open
Abstract
The clustered regularly interspaced short palindromic repeats (CRISPR) Cas system is a powerful tool that has the potential to become a therapeutic gene editor in the near future. Cas9 is the best studied CRISPR system and has been shown to have problems that restrict its use in therapeutic applications. Chromatin structure is a known impactor of Cas9 targeting and there is a gap in knowledge on Cas9's efficacy when targeting such locations. To quantify at a single base pair resolution how chromatin inhibits on-target gene editing relative to off-target editing of exposed mismatching targets, we developed the gene editor mismatch nucleosome inhibition assay (GEMiNI-seq). GEMiNI-seq utilizes a library of nucleosome sequences to examine all target locations throughout nucleosomes in a single assay. The results from GEMiNI-seq revealed that the location of the protospacer-adjacent motif (PAM) sequence on the nucleosome edge drives the ability for Cas9 to access its target sequence. In addition, Cas9 had a higher affinity for exposed mismatched targets than on-target sequences within a nucleosome. Overall, our results show how chromatin structure impacts the fidelity of Cas9 to potential targets and highlight how targeting sequences with exposed PAMs could limit off-target gene editing, with such considerations improving Cas9 efficacy and resolving current limitations.
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Affiliation(s)
- Christopher R Handelmann
- Department of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
| | - Maria Tsompana
- Department of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
| | - Michael J Buck
- Department of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY 14203, USA
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6
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Bruggemann L, Falls Z, Mangione W, Schwartz SA, Battaglia S, Aalinkeel R, Mahajan SD, Samudrala R. Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell Lung Cancer. Int J Mol Sci 2023; 24:ijms24020997. [PMID: 36674513 PMCID: PMC9867122 DOI: 10.3390/ijms24020997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/04/2022] [Accepted: 11/06/2022] [Indexed: 01/06/2023] Open
Abstract
Pharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions using large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK expression is strongly correlated with decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. These results indicate that our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales.
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Affiliation(s)
- Liana Bruggemann
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | - William Mangione
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
| | | | | | | | - Supriya D. Mahajan
- Department of Medicine, University at Buffalo, Buffalo, NY 14260, USA
- Correspondence: (S.D.M.); (R.S.)
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
- Correspondence: (S.D.M.); (R.S.)
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7
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Manish M, Mishra S, Pahuja M, Anand A, Subbarao N, Samudrala R. Computational Grafting of Epitopes. Methods Mol Biol 2023; 2673:111-122. [PMID: 37258909 DOI: 10.1007/978-1-0716-3239-0_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Epitopes are the cornerstones for the development of rational vaccine design strategies. Conventionally, epitopes are used by chemical conjugation with the carrier protein. This chapter describes our computational epitope grafting methodology to identify the preferential grafting site in a carrier protein/scaffold. We have used the mota epitope as an example, as it was already experimentally validated by an independent group. In this chapter, we have provided sufficient details to enable the wet experimentalist to employ this computational methodology in their research objective. Scripts/programs are extensively described in this chapter and freely accessible through the provided link.
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Affiliation(s)
- Manish Manish
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Smriti Mishra
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
| | - Monika Pahuja
- Indian Council of Medical Research, New Delhi, India
| | - Ayush Anand
- BP Koirala Institute of Health Sciences, Dharan, Nepal
| | - Naidu Subbarao
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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8
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Mammen MJ, Tu C, Morris MC, Richman S, Mangione W, Falls Z, Qu J, Broderick G, Sethi S, Samudrala R. Proteomic Network Analysis of Bronchoalveolar Lavage Fluid in Ex-Smokers to Discover Implicated Protein Targets and Novel Drug Treatments for Chronic Obstructive Pulmonary Disease. Pharmaceuticals (Basel) 2022; 15:566. [PMID: 35631392 PMCID: PMC9147475 DOI: 10.3390/ph15050566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 12/23/2022] Open
Abstract
Bronchoalveolar lavage of the epithelial lining fluid (BALF) can sample the profound changes in the airway lumen milieu prevalent in chronic obstructive pulmonary disease (COPD). We compared the BALF proteome of ex-smokers with moderate COPD who are not in exacerbation status to non-smoking healthy control subjects and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins for the treatment of COPD. Proteomic profiles of BALF were obtained from (1) never-smoker control subjects with normal lung function (n = 10) or (2) individuals with stable moderate (GOLD stage 2, FEV1 50−80% predicted, FEV1/FVC < 0.70) COPD who were ex-smokers for at least 1 year (n = 10). After identifying potential crucial hub proteins, drug−proteome interaction signatures were ranked by the computational analysis of novel drug opportunities (CANDO) platform for multiscale therapeutic discovery to identify potentially repurposable drugs. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that most likely ameliorate inflammatory processes. Proteomic network analysis demonstrated that 233 of the >1800 proteins identified in the BALF were significantly differentially expressed in COPD versus control. Functional annotation of the differentially expressed proteins was used to detail canonical pathways containing the differential expressed proteins. Topological network analysis demonstrated that four putative proteins act as central node proteins in COPD. The drugs with the most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The drugs identified using CANDO were subsequently analyzed using a knowledge-based technique to determine an optimal two-drug combination that had the most appropriate effect on the central node proteins. Network analysis of the BALF proteome identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach.
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Affiliation(s)
- Manoj J. Mammen
- Department of Medicine, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
- Department of Biomedical Informatics, Jacobs School of Medicine and Biological Sciences, State University of New York, Buffalo, NY 14214, USA; (W.M.); (Z.F.)
| | - Chengjian Tu
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA; (C.T.); (J.Q.)
- New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, NY 14203, USA
| | - Matthew C. Morris
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY 14621, USA; (M.C.M.); (S.R.); (G.B.)
| | - Spencer Richman
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY 14621, USA; (M.C.M.); (S.R.); (G.B.)
| | - William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biological Sciences, State University of New York, Buffalo, NY 14214, USA; (W.M.); (Z.F.)
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biological Sciences, State University of New York, Buffalo, NY 14214, USA; (W.M.); (Z.F.)
| | - Jun Qu
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA; (C.T.); (J.Q.)
- New York State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott Street, Buffalo, NY 14203, USA
| | - Gordon Broderick
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY 14621, USA; (M.C.M.); (S.R.); (G.B.)
| | - Sanjay Sethi
- WNY VA Healthcare System, Buffalo, NY 14215, USA;
- Department of Medicine, Jacobs School of Medicine and Biological Sciences, State University of New York at Buffalo, Buffalo, NY 14214, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biological Sciences, State University of New York, Buffalo, NY 14214, USA; (W.M.); (Z.F.)
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9
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Mangione W, Falls Z, Samudrala R. Optimal COVID-19 therapeutic candidate discovery using the CANDO platform. Front Pharmacol 2022; 13:970494. [PMID: 36091793 PMCID: PMC9452636 DOI: 10.3389/fphar.2022.970494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/07/2022] [Indexed: 01/22/2023] Open
Abstract
The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.
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Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
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10
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Schuler J, Falls Z, Mangione W, Hudson ML, Bruggemann L, Samudrala R. Evaluating the performance of drug-repurposing technologies. Drug Discov Today 2022; 27:49-64. [PMID: 34400352 PMCID: PMC10014214 DOI: 10.1016/j.drudis.2021.08.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/20/2021] [Accepted: 08/08/2021] [Indexed: 01/22/2023]
Abstract
Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.
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Affiliation(s)
- James Schuler
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Matthew L Hudson
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Liana Bruggemann
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
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11
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Overhoff B, Falls Z, Mangione W, Samudrala R. A Deep-Learning Proteomic-Scale Approach for Drug Design. Pharmaceuticals (Basel) 2021; 14:1277. [PMID: 34959678 PMCID: PMC8709297 DOI: 10.3390/ph14121277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/27/2021] [Accepted: 11/29/2021] [Indexed: 12/26/2022] Open
Abstract
Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.
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Affiliation(s)
| | | | | | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA; (B.O.); (Z.F.); (W.M.)
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12
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Palanikumar L, Karpauskaite L, Al-Sayegh M, Chehade I, Alam M, Hassan S, Maity D, Ali L, Kalmouni M, Hunashal Y, Ahmed J, Houhou T, Karapetyan S, Falls Z, Samudrala R, Pasricha R, Esposito G, Afzal AJ, Hamilton AD, Kumar S, Magzoub M. Protein mimetic amyloid inhibitor potently abrogates cancer-associated mutant p53 aggregation and restores tumor suppressor function. Nat Commun 2021; 12:3962. [PMID: 34172723 PMCID: PMC8233319 DOI: 10.1038/s41467-021-23985-1] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 05/26/2021] [Indexed: 02/05/2023] Open
Abstract
Missense mutations in p53 are severely deleterious and occur in over 50% of all human cancers. The majority of these mutations are located in the inherently unstable DNA-binding domain (DBD), many of which destabilize the domain further and expose its aggregation-prone hydrophobic core, prompting self-assembly of mutant p53 into inactive cytosolic amyloid-like aggregates. Screening an oligopyridylamide library, previously shown to inhibit amyloid formation associated with Alzheimer's disease and type II diabetes, identified a tripyridylamide, ADH-6, that abrogates self-assembly of the aggregation-nucleating subdomain of mutant p53 DBD. Moreover, ADH-6 targets and dissociates mutant p53 aggregates in human cancer cells, which restores p53's transcriptional activity, leading to cell cycle arrest and apoptosis. Notably, ADH-6 treatment effectively shrinks xenografts harboring mutant p53, while exhibiting no toxicity to healthy tissue, thereby substantially prolonging survival. This study demonstrates the successful application of a bona fide small-molecule amyloid inhibitor as a potent anticancer agent.
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Affiliation(s)
- L Palanikumar
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Laura Karpauskaite
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Mohamed Al-Sayegh
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Ibrahim Chehade
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Maheen Alam
- Department of Biology, SBA School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
| | - Sarah Hassan
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Debabrata Maity
- Department of Chemistry, New York University, New York, NY, USA
| | - Liaqat Ali
- Core Technology Platforms, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Mona Kalmouni
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Yamanappa Hunashal
- Chemistry Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates.,DAME, Università di Udine, Udine, Italy
| | - Jemil Ahmed
- Department of Chemistry and Biochemistry and Knoebel Institute for Healthy Aging, The University of Denver, Denver, CO, USA
| | - Tatiana Houhou
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Shake Karapetyan
- Physics Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Zackary Falls
- Department of Biomedical Informatics, School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo, NY, USA
| | - Renu Pasricha
- Core Technology Platforms, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | - Gennaro Esposito
- Chemistry Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates.,INBB, Rome, Italy
| | - Ahmed J Afzal
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates
| | | | - Sunil Kumar
- Department of Chemistry and Biochemistry and Knoebel Institute for Healthy Aging, The University of Denver, Denver, CO, USA.
| | - Mazin Magzoub
- Biology Program, Division of Science, New York University Abu Dhabi, Saadiyat Island Campus, Abu Dhabi, United Arab Emirates.
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13
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Hudson ML, Samudrala R. Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform. Molecules 2021; 26:2581. [PMID: 33925237 PMCID: PMC8125683 DOI: 10.3390/molecules26092581] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 12/02/2022] Open
Abstract
Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.
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Affiliation(s)
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA;
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14
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Falls Z, Fine J, Chopra G, Samudrala R. Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK. Front Chem 2021; 9:775513. [PMID: 35111726 PMCID: PMC8801943 DOI: 10.3389/fchem.2021.775513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/25/2021] [Indexed: 12/27/2022] Open
Abstract
The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.
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Affiliation(s)
- Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Jonathan Fine
- Department of Chemistry, Purdue University, West Lafayette, IN, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue University, West Lafayette, IN, United States.,Purdue Institute for Drug Discovery, West Lafayette, IN, United States.,Purdue Center for Cancer Research, West Lafayette, IN, United States.,Purdue Institute for Inflammation, Immunology and Infectious Disease, West Lafayette, IN, United States.,Purdue Institute for Integrative Neuroscience, West Lafayette, IN, United States
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
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15
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Abstract
Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.
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Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, University at Buffalo, Buffalo, New York 14120, United States
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, New York 14120, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue Institute for Drug Discovery, Integrated Data Science Institute, Purdue University, West Lafayette, Indiana 47907, United States
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, New York 14120, United States
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16
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Mangione W, Falls Z, Melendy T, Chopra G, Samudrala R. Shotgun drug repurposing biotechnology to tackle epidemics and pandemics. Drug Discov Today 2020; 25:1126-1128. [PMID: 32405249 PMCID: PMC7217781 DOI: 10.1016/j.drudis.2020.05.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 05/03/2020] [Accepted: 05/05/2020] [Indexed: 12/14/2022]
Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States
| | - Thomas Melendy
- Department of Microbiology and Immunology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue Institute for Drug Discovery, Integrated Data Science Institute, Purdue University, West Lafayette, IN, 47907, United States.
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 14203, United States.
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17
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Bruggemann L, Hawthorne C, Samudrala R, Lopez-Campos GH. Linking Genome and Exposome: Computational Analysis of Human Variation in Chemical-Target Interactions. Stud Health Technol Inform 2020; 270:1331-1332. [PMID: 32570644 DOI: 10.3233/shti200427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The growing amount of available public data repositories containing a plethora of rich chemical and biomedical information is enabling new in silico research avenues. In this project we aim to link human genome variations and the exposome applying in silico biomedical informatics approaches to analyse the potential effects of those variants in the interactions with different chemicals.
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Affiliation(s)
- Liana Bruggemann
- Department of Biomedical Informatics, State University of New York (SUNY), Buffalo, NY, USA
| | - Christopher Hawthorne
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Ram Samudrala
- Department of Biomedical Informatics, State University of New York (SUNY), Buffalo, NY, USA
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18
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Mandloi S, Falls Z, Deng R, Samudrala R, Elkin PL. Association of C>U RNA Editing with Human Disease Variants. Stud Health Technol Inform 2020; 270:1205-1206. [PMID: 32570581 DOI: 10.3233/shti200364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
RNA-editing is an important post-transcriptional RNA sequence modification performed by two catalytic enzymes, "ADAR"(A>I) and "APOBEC"(C>U). Although APOBEC-mediated C>U editing has been associated with a number of human cancers, the extent of C>U editing in human disease remains unclear. Here, we performed an association study and found that at least 1293 human disease variants occur at sites predicted by sequence motif analysis (RNASee protocol) to undergo APOBEC3A/G C>U editing. These variants were associated with a wide array of human disease conditions ranging from cancer, metabolic disorders, retinopathies, cardiomyopathies, neurodegenerative disorders and immunodeficiencies. These results indicate that APOBEC mediated C>U RNA editing may have widespread and previously unreported contributions to human disease conditions.
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Affiliation(s)
- Sapan Mandloi
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
| | - Rong Deng
- Jacobs School Of Medicine And Biomedical Sciences, Buffalo, NY, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA
| | - Peter L Elkin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA.,Department of Veterans Affairs, VA Western New York Healthcare System, Buffalo, NY, USA
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19
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Mangione W, Falls Z, Melendy T, Chopra G, Samudrala R. Shotgun drug repurposing biotechnology to tackle epidemics and pandemics. ChemRxiv 2020:10.26434/chemrxiv.12045318.v2. [PMID: 32511286 PMCID: PMC7252447 DOI: 10.26434/chemrxiv.12045318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this manuscript we highlight consensus between the list of drugs currently in clinical trials to treat COVID-19, the worldwide pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), and the list of predictions made using our shotgun drug discovery, repurposing, and design platform known as CANDO (Computational Analysis of Novel Drug Opportunities). We make the argument that increased funding and development for drug repurposing biotechnology like ours will help combat the inevitable pathogenic outbreaks of the future. <br>
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Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, 14120, United States
| | - Zackary Falls
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, 14120, United States
| | - Thomas Melendy
- Department of Microbiology and Immunology, University at Buffalo, Buffalo, NY, 14120, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue Institute for Drug Discovery, Integrated Data Science Institute, Purdue University, West Lafayette, IN, 47907, United States
| | - Ram Samudrala
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, 14120, United States
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20
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Bondoc JMG, Gutka HJ, Almutairi MM, Patwell R, Rutter MW, Wolf NM, Samudrala R, Mehboob S, Dementiev A, Abad-Zapatero C, Movahedzadeh F. Rv0100, a proposed acyl carrier protein in Mycobacterium tuberculosis: expression, purification and crystallization. Corrigendum. Acta Crystallogr F Struct Biol Commun 2020; 76:192-193. [PMID: 32254053 PMCID: PMC7137385 DOI: 10.1107/s2053230x2000271x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 02/26/2020] [Indexed: 11/10/2022] Open
Abstract
The true identity of the protein found in the crystals reported by Bondoc et al. [(2019), Acta Cryst. F75, 646-651] is given.
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Affiliation(s)
- Jasper Marc G Bondoc
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Hiten J Gutka
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Mashal M Almutairi
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Ryan Patwell
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Maxwell W Rutter
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Nina M Wolf
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), University at Buffalo, 77 Goodell Street, Buffalo, NY 14203, USA
| | - Shahila Mehboob
- NovaScan Inc., 950 N. 12th Street, Milwaukee,, WI 53233, USA
| | - Alexey Dementiev
- Structural Biology Center, Advanced Photon Source, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL 60439, USA
| | - Cele Abad-Zapatero
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Farahnaz Movahedzadeh
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
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21
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Fine J, Konc J, Samudrala R, Chopra G. CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials. J Chem Inf Model 2020; 60:1509-1527. [PMID: 32069042 DOI: 10.1021/acs.jcim.9b00686] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.
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Affiliation(s)
- Jonathan Fine
- Department of Chemistry, Purdue University, 720 Clinic Drive, West Lafayette, Indiana 47906, United States
| | - Janez Konc
- National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia
| | - Ram Samudrala
- Department of Biomedical Informatics, SUNY, Buffalo, New York 14260, United States
| | - Gaurav Chopra
- Department of Chemistry, Purdue University, 720 Clinic Drive, West Lafayette, Indiana 47906, United States.,Purdue Institute for Drug Discovery, West Lafayette, Indiana 47907, United States.,Purdue Center for Cancer Research, West Lafayette, Indiana 47907, United States.,Purdue Institute for Inflammation, Immunology and Infectious Disease, West Lafayette, Indiana 47907, United States.,Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana 47907, United States.,Integrative Data Science Initiative, West Lafayette, Indiana 47907, United States
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22
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Schuler J, Samudrala R. Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing. ACS Omega 2019; 4:17393-17403. [PMID: 31656912 PMCID: PMC6812124 DOI: 10.1021/acsomega.9b02160] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 08/30/2019] [Indexed: 05/08/2023]
Abstract
We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing by including ligand-based, data fusion, and decision tree pipelines. The goal of shotgun drug repurposing is to screen and rank every existing human use drug or compound for every disease/indication. The first version of CANDO implemented a structure-based pipeline that modeled interactions between compounds and proteins on a large scale, generating compound-proteome interaction signatures used to infer the similarity of drug behavior; the new pipelines accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We obtain improved benchmarking performance with the new pipelines across all three evaluation metrics used: average indication accuracy, pairwise accuracy, and coverage. The best performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, thereby enhancing our ability to generate putative therapeutic repurposing candidates, and increasing drug discovery efficiency.
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Affiliation(s)
- James Schuler
- Department of Biomedical
Informatics, Jacobs School of Medicine and
Biomedical Sciences at the University at Buffalo, Buffalo, New York 14203, United States
| | - Ram Samudrala
- Department of Biomedical
Informatics, Jacobs School of Medicine and
Biomedical Sciences at the University at Buffalo, Buffalo, New York 14203, United States
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23
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Bondoc JMG, Gutka HJ, Almutairi MM, Patwell R, Rutter MW, Wolf NM, Samudrala R, Mehboob S, Movahedzadeh F. Rv0100, a proposed acyl carrier protein in Mycobacterium tuberculosis: expression, purification and crystallization. Acta Crystallogr F Struct Biol Commun 2019; 75:646-651. [PMID: 31584013 PMCID: PMC6777135 DOI: 10.1107/s2053230x19012652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 09/11/2019] [Indexed: 11/10/2022] Open
Abstract
Acyl carrier proteins (ACPs) are important components in fatty-acid biosynthesis in prokaryotes. Rv0100 is predicted to be an essential ACP in Mycobacterium tuberculosis, the pathogen that is the causative agent of tuberculosis, and therefore has the potential to be a novel antituberculosis drug target. Here, the successful cloning and purification of Rv0100 using Mycobacterium smegmatis as a host is reported. Crystals of the purified protein were obtained that diffracted to a resolution of 1.9 Å. Overall, this work lays the foundation for the future pursuit of drug discovery and development against this potentially novel drug target.
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Affiliation(s)
- Jasper Marc G. Bondoc
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Hiten J. Gutka
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
- Oncobiologics Inc., 7 Clarke Drive, Cranbury, NJ 08512, USA
| | - Mashal M. Almutairi
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 12371, Saudi Arabia
- Vaccines and Biologics Research Unit, College of Pharmacy, King Saud University, Riyadh 12371, Saudi Arabia
- Department of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Ryan Patwell
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
- Department of Psychiatry, University of Illinois at Chicago, 1601 West Taylor Street, Room 425, Chicago, IL 60612, USA
| | - Maxwell W. Rutter
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
- Hollingbery and Son Hops Inc., 302 North First Avenue, Yakima, WA 98907, USA
| | - Nina M. Wolf
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), University at Buffalo, 77 Goodell Street, Buffalo, NY 14203, USA
| | - Shahila Mehboob
- Neugenica LLC, 2242 West Harrison Street #201, Chicago, IL 60612, USA
| | - Farahnaz Movahedzadeh
- Institute for Tuberculosis Research, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, USA
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Fine J, Lackner R, Samudrala R, Chopra G. Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications. Sci Rep 2019; 9:13155. [PMID: 31511563 PMCID: PMC6739337 DOI: 10.1038/s41598-019-49515-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 07/31/2019] [Indexed: 12/17/2022] Open
Abstract
We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.
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Affiliation(s)
- Jonathan Fine
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Rachel Lackner
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, SUNY, Buffalo, NY, USA.
| | - Gaurav Chopra
- Department of Chemistry, Purdue University, West Lafayette, IN, USA.
- Purdue Institute for Drug Discovery, Purdue Institute for Integrative Neuroscience, Purdue Institute for Integrative Neuroscience, Purdue Institute for Immunology, Inflammation and Infectious Disease, Integrative Data Science Initiative, Purdue Center for Cancer Research, West Lafayette, IN, USA.
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25
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Kielkopf CL, Feeney CF, Chatrikhi R, MacRae A, Alachouzos G, Falls Z, Laird KM, Jenkins JL, Samudrala R, Frontier AJ, Jurica MS. A synthetic molecule stalls pre-mRNA splicing by enhancing cancer-relevant U2AF2–RNA complexes. Acta Crystallogr A Found Adv 2019. [DOI: 10.1107/s0108767319098969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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Abstract
OBJECTIVE Ascertain the optimal interaction scoring criteria for the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing to improve benchmarking performance, thereby enabling more accurate prediction of novel therapeutic drug-indication pairs. RESULTS We have investigated and enhanced the interaction scoring criteria in the bioinformatic docking protocol in the newest version of our platform (v1.5), with the best performing interaction scoring criterion yielding increased benchmarking accuracies from 11.7% in v1 to 12.8% in v1.5 at the top10 cutoff (the most stringent one) and correspondingly from 24.9 to 31.2% at the top100 cutoff.
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Affiliation(s)
- Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 77 Goodell St., Suite 540, Buffalo, NY, 14203, USA
| | - William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 77 Goodell St., Suite 540, Buffalo, NY, 14203, USA
| | - James Schuler
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 77 Goodell St., Suite 540, Buffalo, NY, 14203, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 77 Goodell St., Suite 540, Buffalo, NY, 14203, USA.
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27
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Mangione W, Samudrala R. Identifying Protein Features Responsible for Improved Drug Repurposing Accuracies Using the CANDO Platform: Implications for Drug Design. Molecules 2019; 24:molecules24010167. [PMID: 30621144 PMCID: PMC6337359 DOI: 10.3390/molecules24010167] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 12/21/2018] [Accepted: 12/29/2018] [Indexed: 01/17/2023] Open
Abstract
Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 2030 indications/diseases using 3733 drugs/compounds to predict interactions with 46,784 proteins and relating them via proteomic interaction signatures. The accuracy is calculated by comparing interaction similarities of drugs approved for the same indications. We performed a unique subset analysis by breaking down the full protein library into smaller subsets and then recombining the best performing subsets into larger supersets. Up to 14% improvement in accuracy is seen upon benchmarking the supersets, representing a 100⁻1000-fold reduction in the number of proteins considered relative to the full library. Further analysis revealed that libraries comprised of proteins with more equitably diverse ligand interactions are important for describing compound behavior. Using one of these libraries to generate putative drug candidates against malaria, tuberculosis, and large cell carcinoma results in more drugs that could be validated in the biomedical literature compared to using those suggested by the full protein library. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in drug repurposing, with implications for drug design and machine learning approaches to improve the CANDO platform.
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Affiliation(s)
- William Mangione
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA.
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY 14203, USA.
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28
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Chopra G, Samudrala R. Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform. Curr Pharm Des 2017; 22:3109-23. [PMID: 27013226 DOI: 10.2174/1381612822666160325121943] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 03/01/2015] [Indexed: 01/05/2023]
Abstract
BACKGROUND Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery. METHODS We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner. RESULTS We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform. CONCLUSION These results have implications for future drug development and repurposing in the context of polypharmacology.
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Affiliation(s)
- Gaurav Chopra
- Department of Chemistry, Purdue University, West Lafayette, IN, USA.
| | - Ram Samudrala
- Department of Biomedical Informatics, SUNY, Buffalo, NY, USA.
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29
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Schuler J, Hudson ML, Schwartz D, Samudrala R. A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment. Molecules 2017; 22:E1777. [PMID: 29053626 PMCID: PMC6151658 DOI: 10.3390/molecules22101777] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 09/16/2017] [Accepted: 09/19/2017] [Indexed: 12/30/2022] Open
Abstract
Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.
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Affiliation(s)
- James Schuler
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14203, USA.
| | - Matthew L Hudson
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14203, USA.
| | - Diane Schwartz
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14203, USA.
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14203, USA.
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30
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Craig JK, Risler JK, Loesch KA, Dong W, Baker D, Barrett LK, Subramanian S, Samudrala R, Van Voorhis WC. Mycobacterium Cytidylate Kinase Appears to Be an Undruggable Target. J Biomol Screen 2016; 21:695-700. [PMID: 27146385 PMCID: PMC8565994 DOI: 10.1177/1087057116646702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 04/05/2016] [Indexed: 11/17/2022]
Abstract
New and improved drugs against tuberculosis are urgently needed as multi-drug-resistant forms of the disease become more prevalent. Mycobacterium tuberculosis cytidylate kinase is an attractive target for screening due to its essentiality and different substrate specificity to the human orthologue. However, we selected the Mycobacterium smegmatis cytidylate kinase for screening because of the availability of high-resolution X-ray crystallographic data defining its structure and the high likelihood of active site structural similarity to the M. tuberculosis orthologue. We report the development and implementation of a high-throughput luciferase-based activity assay and screening of 19,920 compounds derived from small-molecule libraries and an in silico screen predicting likely inhibitors of the cytidylate kinase enzyme. Hit validation included a counterscreen for luciferase inhibitors that would result in false positives in the initial screen. Results of this counterscreen ruled out all of the putative cytidylate kinase inhibitors identified in the initial screening, leaving no compounds as candidates for drug development. Although a negative result, this study indicates that this important drug target may in fact be undruggable and serve as a warning for future investigations.
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Affiliation(s)
- Justin K Craig
- Department of Medicine, Division of Allergy and Infectious Diseases, Center for Emerging and Reemerging Infectious Disease (CERID), University of Washington, Seattle, WA, USA
| | - Jenni K Risler
- Fred Hutchinson Cancer Research Center (Fred Hutch), Genomics Shared Resource, High-Throughput Screening Facility, Seattle, WA, USA
| | - Kimberly A Loesch
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX, USA
| | - Wen Dong
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX, USA
| | - Dwight Baker
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX, USA
| | - Lynn K Barrett
- Department of Medicine, Division of Allergy and Infectious Diseases, Center for Emerging and Reemerging Infectious Disease (CERID), University of Washington, Seattle, WA, USA
| | | | - Ram Samudrala
- Department of Biomedical Informatics, University of Buffalo, State University of New York, Buffalo, NY, USA
| | - Wesley C Van Voorhis
- Department of Medicine, Division of Allergy and Infectious Diseases, Center for Emerging and Reemerging Infectious Disease (CERID), University of Washington, Seattle, WA, USA Departments of Global Health and Microbiology, University of Washington, Seattle, WA, USA
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31
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Sethi G, Chopra G, Samudrala R. Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO platform. Mini Rev Med Chem 2016; 15:705-17. [PMID: 25694071 DOI: 10.2174/1389557515666150219145148] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 10/30/2014] [Accepted: 11/25/2014] [Indexed: 01/27/2023]
Abstract
We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery.
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Affiliation(s)
| | | | - Ram Samudrala
- Department of Biomedical Informatics, School of Medicine and Biomedical Sciences, State University of New York (SUNY), 923 Main Street, Buffalo, NY 14203, USA.
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32
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Hartmann K, Samudrala R, Hofmann T, Schieberle P, Hitzmann B, Hinrichs J. Thermo-physical parameters applied for instrumental profiling and statistical evaluation of German Emmental cheese. Int Dairy J 2015. [DOI: 10.1016/j.idairyj.2015.05.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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33
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Minie M, Chopra G, Sethi G, Horst J, White G, Roy A, Hatti K, Samudrala R. CANDO and the infinite drug discovery frontier. Drug Discov Today 2014; 19:1353-63. [PMID: 24980786 DOI: 10.1016/j.drudis.2014.06.018] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 06/18/2014] [Accepted: 06/19/2014] [Indexed: 12/21/2022]
Abstract
The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 'high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.
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Affiliation(s)
- Mark Minie
- University of Washington, Department of Bioengineering, Seattle, WA 98109, United States
| | - Gaurav Chopra
- University of Washington, Department of Microbiology, Seattle, WA 98109, United States; University of California, San Francisco, Diabetes Center, San Francisco, CA 94143, United States
| | - Geetika Sethi
- University of Washington, Department of Microbiology, Seattle, WA 98109, United States
| | - Jeremy Horst
- University of California, School of Medicine, San Francisco, CA 94143, United States
| | - George White
- University of Washington, Department of Microbiology, Seattle, WA 98109, United States
| | - Ambrish Roy
- Georgia Institute of Technology, Center for the Study of Systems Biology, Atlanta, GA 30318, United States
| | - Kaushik Hatti
- Molecular Biophysics Unit, Indian Institute of Science Bangalore, 560012, India
| | - Ram Samudrala
- University of Washington, Department of Microbiology, Seattle, WA 98109, United States.
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Abstract
MOTIVATION fast_protein_cluster is a fast, parallel and memory efficient package used to cluster 60 000 sets of protein models (with up to 550 000 models per set) generated by the Nutritious Rice for the World project. RESULTS fast_protein_cluster is an optimized and extensible toolkit that supports Root Mean Square Deviation after optimal superposition (RMSD) and Template Modeling score (TM-score) as metrics. RMSD calculations using a laptop CPU are 60× faster than qcprot and 3× faster than current graphics processing unit (GPU) implementations. New GPU code further increases the speed of RMSD and TM-score calculations. fast_protein_cluster provides novel k-means and hierarchical clustering methods that are up to 250× and 2000× faster, respectively, than Clusco, and identify significantly more accurate models than Spicker and Clusco. AVAILABILITY AND IMPLEMENTATION fast_protein_cluster is written in C++ using OpenMP for multi-threading support. Custom streaming Single Instruction Multiple Data (SIMD) extensions and advanced vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvidia GPUs. fast_protein_cluster is available under the M.I.T. license. (http://software.compbio.washington.edu/fast_protein_cluster)
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Affiliation(s)
- Ling-Hong Hung
- Department of Microbiology, University of Washington, Seattle, WA 98109, USA
| | - Ram Samudrala
- Department of Microbiology, University of Washington, Seattle, WA 98109, USA
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35
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Matasci N, Hung LH, Yan Z, Carpenter EJ, Wickett NJ, Mirarab S, Nguyen N, Warnow T, Ayyampalayam S, Barker M, Burleigh JG, Gitzendanner MA, Wafula E, Der JP, dePamphilis CW, Roure B, Philippe H, Ruhfel BR, Miles NW, Graham SW, Mathews S, Surek B, Melkonian M, Soltis DE, Soltis PS, Rothfels C, Pokorny L, Shaw JA, DeGironimo L, Stevenson DW, Villarreal JC, Chen T, Kutchan TM, Rolf M, Baucom RS, Deyholos MK, Samudrala R, Tian Z, Wu X, Sun X, Zhang Y, Wang J, Leebens-Mack J, Wong GKS. Data access for the 1,000 Plants (1KP) project. Gigascience 2014; 3:17. [PMID: 25625010 PMCID: PMC4306014 DOI: 10.1186/2047-217x-3-17] [Citation(s) in RCA: 395] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2014] [Accepted: 10/02/2014] [Indexed: 01/06/2023] Open
Abstract
The 1,000 plants (1KP) project is an international multi-disciplinary consortium that has generated transcriptome data from over 1,000 plant species, with exemplars for all of the major lineages across the Viridiplantae (green plants) clade. Here, we describe how to access the data used in a phylogenomics analysis of the first 85 species, and how to visualize our gene and species trees. Users can develop computational pipelines to analyse these data, in conjunction with data of their own that they can upload. Computationally estimated protein-protein interactions and biochemical pathways can be visualized at another site. Finally, we comment on our future plans and how they fit within this scalable system for the dissemination, visualization, and analysis of large multi-species data sets.
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Affiliation(s)
- Naim Matasci
- iPlant Collaborative, Tucson 85721, AZ, USA ; Department of Ecology and Evolutionary Biology, University of Arizona, Tucson 85721, AZ, USA
| | - Ling-Hong Hung
- Department of Microbiology, University of Washington, Seattle 98109, WA, USA
| | - Zhixiang Yan
- BGI-Shenzhen, Bei Shan Industrial Zone, Shenzhen, China
| | - Eric J Carpenter
- Department of Biological Sciences, University of Alberta, Edmonton T6G 2E9, AB, Canada
| | - Norman J Wickett
- Chicago Botanic Garden, Glencoe 60022, IL, USA ; Program in Biological Sciences, Northwestern University, Evanston 60208, IL, USA
| | - Siavash Mirarab
- Department of Computer Science, University of Texas, Austin, TX, 78712, USA
| | - Nam Nguyen
- Department of Computer Science, University of Texas, Austin, TX, 78712, USA
| | - Tandy Warnow
- Department of Computer Science, University of Texas, Austin, TX, 78712, USA
| | | | - Michael Barker
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson 85721, AZ, USA
| | - J Gordon Burleigh
- Department of Biology, University of Florida, Gainesville, FL 32611, USA
| | | | - Eric Wafula
- Department of Biology, Penn State University, University Park, Pennsylvania, PA, 16801, USA
| | - Joshua P Der
- Department of Biology, Penn State University, University Park, Pennsylvania, PA, 16801, USA
| | - Claude W dePamphilis
- Department of Biology, Penn State University, University Park, Pennsylvania, PA, 16801, USA
| | - Béatrice Roure
- Département de Biochimie, Centre Robert-Cedergren, Université de Montréal, Succursale Centre-Ville, Montréal, Québec H3C3J7, Canada
| | - Hervé Philippe
- Département de Biochimie, Centre Robert-Cedergren, Université de Montréal, Succursale Centre-Ville, Montréal, Québec H3C3J7, Canada ; CNRS, USR 2936, Station d' Ecologie Expérimentale du CNRS, Moulis 09200, France
| | - Brad R Ruhfel
- Department of Biology, University of Florida, Gainesville, FL 32611, USA ; Department of Biological Sciences, Eastern Kentucky University, Richmond, KY, 40475, USA
| | | | - Sean W Graham
- Department of Botany, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Sarah Mathews
- Arnold Arboretum of Harvard University, Cambridge, MA, 02138, USA
| | - Barbara Surek
- Botanical Institute, Universität zu Köln, Köln D-50674, Germany
| | | | - Douglas E Soltis
- Department of Biology, University of Florida, Gainesville, FL 32611, USA ; Florida Museum of Natural History, Gainesville, FL, 32611, USA ; Genetics Institute, University of Florida, Gainesville, FL, 32611, USA
| | - Pamela S Soltis
- Department of Biology, University of Florida, Gainesville, FL 32611, USA ; Florida Museum of Natural History, Gainesville, FL, 32611, USA ; Genetics Institute, University of Florida, Gainesville, FL, 32611, USA
| | - Carl Rothfels
- Department of Biology, Duke University, Durham, NC 27708, USA ; Department of Zoology, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Lisa Pokorny
- Department of Biology, Duke University, Durham, NC 27708, USA ; Department of Biodiversity and Conservation, Real Jardín Botánico (RJB-CSIC), 28014 Madrid, Spain
| | - Jonathan A Shaw
- Department of Biology, Duke University, Durham, NC 27708, USA
| | | | | | - Juan Carlos Villarreal
- Systematic Botany and Mycology, University of Munich (LMU), Menzinger Str. 67, 80638 Munich, Germany
| | - Tao Chen
- Shenzhen Fairy Lake Botanical Garden, The Chinese Academy of Sciences, Shenzhen, Guangdong, 518004, China
| | - Toni M Kutchan
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA
| | - Megan Rolf
- Donald Danforth Plant Science Center, St. Louis, MO, 63132, USA
| | - Regina S Baucom
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
| | - Michael K Deyholos
- Department of Biological Sciences, University of Alberta, Edmonton T6G 2E9, AB, Canada
| | - Ram Samudrala
- Department of Microbiology, University of Washington, Seattle 98109, WA, USA
| | - Zhijian Tian
- BGI-Shenzhen, Bei Shan Industrial Zone, Shenzhen, China
| | - Xiaolei Wu
- BGI-Shenzhen, Bei Shan Industrial Zone, Shenzhen, China
| | - Xiao Sun
- BGI-Shenzhen, Bei Shan Industrial Zone, Shenzhen, China
| | - Yong Zhang
- BGI-Shenzhen, Bei Shan Industrial Zone, Shenzhen, China
| | - Jun Wang
- BGI-Shenzhen, Bei Shan Industrial Zone, Shenzhen, China
| | - Jim Leebens-Mack
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
| | - Gane Ka-Shu Wong
- BGI-Shenzhen, Bei Shan Industrial Zone, Shenzhen, China ; Department of Biological Sciences, University of Alberta, Edmonton T6G 2E9, AB, Canada ; Department of Medicine, University of Alberta, Edmonton, AB, T6G 2E1, Canada
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Horst JA, Pieper U, Sali A, Zhan L, Chopra G, Samudrala R, Featherstone JDB. Strategic protein target analysis for developing drugs to stop dental caries. Adv Dent Res 2013; 24:86-93. [PMID: 22899687 DOI: 10.1177/0022034512449837] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Dental caries is the most common disease to cause irreversible damage in humans. Several therapeutic agents are available to treat or prevent dental caries, but none besides fluoride has significantly influenced the disease burden globally. Etiologic mechanisms of the mutans group streptococci and specific Lactobacillus species have been characterized to various degrees of detail, from identification of physiologic processes to specific proteins. Here, we analyze the entire Streptococcus mutans proteome for potential drug targets by investigating their uniqueness with respect to non-cariogenic dental plaque bacteria, quality of protein structure models, and the likelihood of finding a drug for the active site. Our results suggest specific targets for rational drug discovery, including 15 known virulence factors, 16 proteins for which crystallographic structures are available, and 84 previously uncharacterized proteins, with various levels of similarity to homologs in dental plaque bacteria. This analysis provides a map to streamline the process of clinical development of effective multispecies pharmacologic interventions for dental caries.
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Affiliation(s)
- J A Horst
- Division of Pediatric Dentistry, Department of Orofacial Sciences, University of California, San Francisco, CA, USA.
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Afzal AJ, Srour A, Goil A, Vasudaven S, Liu T, Samudrala R, Dogra N, Kohli P, Malakar A, Lightfoot DA. Homo-dimerization and ligand binding by the leucine-rich repeat domain at RHG1/RFS2 underlying resistance to two soybean pathogens. BMC Plant Biol 2013; 13:43. [PMID: 23497186 PMCID: PMC3626623 DOI: 10.1186/1471-2229-13-43] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2012] [Accepted: 02/05/2013] [Indexed: 05/26/2023]
Abstract
BACKGROUND The protein encoded by GmRLK18-1 (Glyma_18_02680 on chromosome 18) was a receptor like kinase (RLK) encoded within the soybean (Glycine max L. Merr.) Rhg1/Rfs2 locus. The locus underlies resistance to the soybean cyst nematode (SCN) Heterodera glycines (I.) and causal agent of sudden death syndrome (SDS) Fusarium virguliforme (Aoki). Previously the leucine rich repeat (LRR) domain was expressed in Escherichia coli. RESULTS The aims here were to evaluate the LRRs ability to; homo-dimerize; bind larger proteins; and bind to small peptides. Western analysis suggested homo-dimers could form after protein extraction from roots. The purified LRR domain, from residue 131-485, was seen to form a mixture of monomers and homo-dimers in vitro. Cross-linking experiments in vitro showed the H274N region was close (<11.1 A) to the highly conserved cysteine residue C196 on the second homo-dimer subunit. Binding constants of 20-142 nM for peptides found in plant and nematode secretions were found. Effects on plant phenotypes including wilting, stem bending and resistance to infection by SCN were observed when roots were treated with 50 pM of the peptides. Far-Western analyses followed by MS showed methionine synthase and cyclophilin bound strongly to the LRR domain. A second LRR from GmRLK08-1 (Glyma_08_g11350) did not show these strong interactions. CONCLUSIONS The LRR domain of the GmRLK18-1 protein formed both a monomer and a homo-dimer. The LRR domain bound avidly to 4 different CLE peptides, a cyclophilin and a methionine synthase. The CLE peptides GmTGIF, GmCLE34, GmCLE3 and HgCLE were previously reported to be involved in root growth inhibition but here GmTGIF and HgCLE were shown to alter stem morphology and resistance to SCN. One of several models from homology and ab-initio modeling was partially validated by cross-linking. The effect of the 3 amino acid replacements present among RLK allotypes, A87V, Q115K and H274N were predicted to alter domain stability and function. Therefore, the LRR domain of GmRLK18-1 might underlie both root development and disease resistance in soybean and provide an avenue to develop new variants and ligands that might promote reduced losses to SCN.
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Affiliation(s)
- Ahmed J Afzal
- Department of Molecular Biology, Microbiology and Biochemistry and Center for Excellence the Illinois Soybean Center, Southern Illinois University at Carbondale, IL 62901, USA.
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38
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Affiliation(s)
- Mark E Minie
- Bioengineering Department, University of Washington, USA
| | - Ram Samudrala
- Microbiology Department, University of Washington, USA
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Lertkiatmongkol P, Assawamakin A, White G, Chopra G, Rongnoparut P, Samudrala R, Tongsima S. Distal effect of amino acid substitutions in CYP2C9 polymorphic variants causes differences in interatomic interactions against (S)-warfarin. PLoS One 2013; 8:e74053. [PMID: 24023924 PMCID: PMC3759441 DOI: 10.1371/journal.pone.0074053] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Accepted: 07/25/2013] [Indexed: 11/18/2022] Open
Abstract
Cytochrome P450 2C9 (CYP2C9) is crucial in excretion of commonly prescribed drugs. However, changes in metabolic activity caused by CYP2C9 polymorphisms inevitably result in adverse drug effects. CYP2C9*2 and *3 are prevalent in Caucasian populations whereas CYP2C9*13 is remarkable in Asian populations. Single amino acid substitutions caused by these mutations are located outside catalytic cavity but affect kinetic activities of mutants compared to wild-type enzyme. To relate distal effects of these mutations and defective drug metabolisms, simulations of CYP2C9 binding to anti-coagulant (S)-warfarin were performed as a system model. Representative (S)-warfarin-bound forms of wild-type and mutants were sorted and assessed through knowledge-based scoring function. Interatomic interactions towards (S)-warfarin were predicted to be less favorable in mutant structures in correlation with larger distance between hydroxylation site of (S)-warfarin and reactive oxyferryl heme than wild-type structure. Using computational approach could delineate complication of CYP polymorphism in management of drug therapy.
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Affiliation(s)
- Panida Lertkiatmongkol
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand
- Genomics Institute, National Center for Genetic Engineering and Biotechnology, Pathumtani, Thailand
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Anunchai Assawamakin
- Department of Pharmacology, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand
| | - George White
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Gaurav Chopra
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
- Diabetes Center, University of California San Francisco, San Francisco, California, United States of America
| | - Pornpimol Rongnoparut
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Ram Samudrala
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Sissades Tongsima
- Genomics Institute, National Center for Genetic Engineering and Biotechnology, Pathumtani, Thailand
- * E-mail:
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Martinez-Avila O, Wu S, Kim SJ, Cheng Y, Khan F, Samudrala R, Sali A, Horst JA, Habelitz S. Self-assembly of filamentous amelogenin requires calcium and phosphate: from dimers via nanoribbons to fibrils. Biomacromolecules 2012; 13:3494-502. [PMID: 22974364 PMCID: PMC3496023 DOI: 10.1021/bm300942c] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Enamel matrix self-assembly has long been suggested as the driving force behind aligned nanofibrous hydroxyapatite formation. We tested if amelogenin, the main enamel matrix protein, can self-assemble into ribbon-like structures in physiologic solutions. Ribbons 17 nm wide were observed to grow several micrometers in length, requiring calcium, phosphate, and pH 4.0-6.0. The pH range suggests that the formation of ion bridges through protonated histidine residues is essential to self-assembly, supported by a statistical analysis of 212 phosphate-binding proteins predicting 12 phosphate-binding histidines. Thermophoretic analysis verified the importance of calcium and phosphate in self-assembly. X-ray scattering characterized amelogenin dimers with dimensions fitting the cross-section of the amelogenin ribbon, leading to the hypothesis that antiparallel dimers are the building blocks of the ribbons. Over 5-7 days, ribbons self-organized into bundles composed of aligned ribbons mimicking the structure of enamel crystallites in enamel rods. These observations confirm reports of filamentous organic components in developing enamel and provide a new model for matrix-templated enamel mineralization.
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Affiliation(s)
- Olga Martinez-Avila
- Department of Preventative and Restorative Dental Sciences, 707 Parnassus Ave., San Francisco, CA 94143, University of California
| | - Shenping Wu
- Department of Biochemistry & Biophysics, 600 16th Street, Room S312B, San Francisco, CA 94158 University of California
| | - Seung Joong Kim
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall Room 503B, 1700 4th Street, San Francisco, CA 94158 University of California
| | - Yifan Cheng
- Department of Biochemistry & Biophysics, 600 16th Street, Room S312B, San Francisco, CA 94158 University of California
| | - Feroz Khan
- Department of Preventative and Restorative Dental Sciences, 707 Parnassus Ave., San Francisco, CA 94143, University of California
| | - Ram Samudrala
- Department of Microbiology, 208 Rosen Building, Box 357735 · Seattle WA 98195 University of Washington
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, Byers Hall Room 503B, 1700 4th Street, San Francisco, CA 94158 University of California
| | - Jeremy A. Horst
- Department of Orofacial Sciences, 513 Parnassus Ave. San Francisco, CA 94143 University of California
| | - Stefan Habelitz
- Department of Preventative and Restorative Dental Sciences, 707 Parnassus Ave., San Francisco, CA 94143, University of California
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Abstract
We introduce the concept of metaconsensus and employ it to make high confidence predictions of early enzyme functions and the metabolic properties that they may have produced. Several independent studies have used comparative bioinformatics methods to identify taxonomically broad features of genomic sequence data, protein structure data, and metabolic pathway data in order to predict physiological features that were present in early, ancestral life forms. But all such methods carry with them some level of technical bias. Here, we cross-reference the results of these previous studies to determine enzyme functions predicted to be ancient by multiple methods. We survey modern metabolic pathways to identify those that maintain the highest frequency of metaconsensus enzymes. Using the full set of modern reactions catalyzed by these metaconsensus enzyme functions, we reconstruct a representative metabolic network that may reflect the core metabolism of early life forms. Our results show that ten enzyme functions, four hydrolases, three transferases, one oxidoreductase, one lyase, and one ligase, are determined by metaconsensus to be present at least as late as the last universal common ancestor. Subnetworks within central metabolic processes related to sugar and starch metabolism, amino acid biosynthesis, phospholipid metabolism, and CoA biosynthesis, have high frequencies of these enzyme functions. We demonstrate that a large metabolic network can be generated from this small number of enzyme functions.
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Affiliation(s)
- Aaron David Goldman
- Department of Ecology and Evolutionary Biology, Princeton, New Jersey, United States of America.
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Abstract
Motivation: Accurate comparisons of different protein structures play important roles in structural biology, structure prediction and functional annotation. The root-mean-square-deviation (RMSD) after optimal superposition is the predominant measure of similarity due to the ease and speed of computation. However, global RMSD is dependent on the length of the protein and can be dominated by divergent loops that can obscure local regions of similarity. A more sophisticated measure of structure similarity, Template Modeling (TM)-score, avoids these problems, and it is one of the measures used by the community-wide experiments of critical assessment of protein structure prediction to compare predicted models with experimental structures. TM-score calculations are, however, much slower than RMSD calculations. We have therefore implemented a very fast version of TM-score for Graphical Processing Units (TM-score-GPU), using a new and novel hybrid Kabsch/quaternion method for calculating the optimal superposition and RMSD that is designed for parallel applications. This acceleration in speed allows TM-score to be used efficiently in computationally intensive applications such as for clustering of protein models and genome-wide comparisons of structure. Results: TM-score-GPU was applied to six sets of models from Nutritious Rice for the World for a total of 3 million comparisons. TM-score-GPU is 68 times faster on an ATI 5870 GPU, on average, than the original CPU single-threaded implementation on an AMD Phenom II 810 quad-core processor. Availability and implementation: The complete source, including the GPU code and the hybrid RMSD subroutine, can be downloaded and used without restriction at http://software.compbio.washington.edu/misc/downloads/tmscore/. The implementation is in C++/OpenCL. Contact:ram@compbio.washington.edu Supplementary Information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ling-Hong Hung
- Department of Microbiology, University of Washington, Seattle, WA 98195-7735, USA
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43
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Gungormus M, Oren EE, Horst JA, Fong H, Hnilova M, Somerman MJ, Snead ML, Samudrala R, Tamerler C, Sarikaya M. Cementomimetics-constructing a cementum-like biomineralized microlayer via amelogenin-derived peptides. Int J Oral Sci 2012; 4:69-77. [PMID: 22743342 PMCID: PMC3412665 DOI: 10.1038/ijos.2012.40] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2012] [Accepted: 05/03/2012] [Indexed: 01/09/2023] Open
Abstract
Cementum is the outer-, mineralized-tissue covering the tooth root and an essential part of the system of periodontal tissue that anchors the tooth to the bone. Periodontal disease results from the destructive behavior of the host elicited by an infectious biofilm adhering to the tooth root and left untreated, may lead to tooth loss. We describe a novel protocol for identifying peptide sequences from native proteins with the potential to repair damaged dental tissues by controlling hydroxyapatite biomineralization. Using amelogenin as a case study and a bioinformatics scoring matrix, we identified regions within amelogenin that are shared with a set of hydroxyapatite-binding peptides (HABPs) previously selected by phage display. One 22-amino acid long peptide regions referred to as amelogenin-derived peptide 5 (ADP5) was shown to facilitate cell-free formation of a cementum-like hydroxyapatite mineral layer on demineralized human root dentin that, in turn, supported attachment of periodontal ligament cells in vitro. Our findings have several implications in peptide-assisted mineral formation that mimic biomineralization. By further elaborating the mechanism for protein control over the biomineral formed, we afford new insights into the evolution of protein-mineral interactions. By exploiting small peptide domains of native proteins, our understanding of structure-function relationships of biomineralizing proteins can be extended and these peptides can be utilized to engineer mineral formation. Finally, the cementomimetic layer formed by ADP5 has the potential clinical application to repair diseased root surfaces so as to promote the regeneration of periodontal tissues and thereby reduce the morbidity associated with tooth loss.
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Affiliation(s)
- Mustafa Gungormus
- Department of Materials Science and Engineering, University of Washington, Seattle, WA, USA
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44
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Moughon SE, Samudrala R. LoCo: a novel main chain scoring function for protein structure prediction based on local coordinates. BMC Bioinformatics 2011; 12:368. [PMID: 21920038 PMCID: PMC3184297 DOI: 10.1186/1471-2105-12-368] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2011] [Accepted: 09/15/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Successful protein structure prediction requires accurate low-resolution scoring functions so that protein main chain conformations that are close to the native can be identified. Once that is accomplished, a more detailed and time-consuming treatment to produce all-atom models can be undertaken. The earliest low-resolution scoring used simple distance-based "contact potentials," but more recently, the relative orientations of interacting amino acids have been taken into account to improve performance. RESULTS We developed a new knowledge-based scoring function, LoCo, that locates the interaction partners of each individual residue within a local coordinate system based only on the position of its main chain N, Cα and C atoms. LoCo was trained on a large set of experimentally determined structures and optimized using standard sets of modeled structures, or "decoys." No structure used to train or optimize the function was included among those used to test it. When tested against 29 other published main chain functions on a group of 77 commonly used decoy sets, our function outperformed all others in Cα RMSD rank of the best-scoring decoy, with statistically significant p-values < 0.05 for 26 out of the 29 other functions considered. LoCo is fast, requiring on average less than 6 microseconds per residue for interaction and scoring on commonly-used computer hardware. CONCLUSIONS Our function demonstrates an unmatched combination of accuracy, speed, and simplicity and shows excellent promise for protein structure prediction. Broader applications may include protein-protein interactions and protein design.
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Affiliation(s)
- Stewart E Moughon
- Department of Microbiology, University of Washington, Box 357735, Seattle, Washington 98195-7242, USA.
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Cunningham ML, Horst JA, Rieder MJ, Hing AV, Stanaway IB, Park SS, Samudrala R, Speltz ML. IGF1R variants associated with isolated single suture craniosynostosis. Am J Med Genet A 2011; 155A:91-7. [PMID: 21204214 DOI: 10.1002/ajmg.a.33781] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The genetic contribution to the pathogenesis of isolated single suture craniosynostosis is poorly understood. The role of mutations in genes known to be associated with syndromic synostosis appears to be limited. We present our findings of a candidate gene resequencing approach to identify rare variants associated with the most common forms of isolated craniosynostosis. Resequencing of the coding regions, splice junction sites, and 5' and 3' untranslated regions of 27 candidate genes in 186 cases of isolated non-syndromic single suture synostosis revealed three novel and two rare sequence variants (R406H, R595H, N857S, P190S, M446V) in insulin-like growth factor I receptor (IGF1R) that are enriched relative to control samples. Mapping the resultant amino acid changes to the modeled homodimer protein structure suggests a structural basis for segregation between these and other disease-associated mutations found in IGF1R. These data suggest that IGF1R mutations may contribute to the risk and in some cases cause single suture craniosynostosis.
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Affiliation(s)
- Michael L Cunningham
- Seattle Children's Hospital Craniofacial Center, University of Washington, 98195, USA.
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46
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Horst JA, Wang K, Horst OV, Cunningham ML, Samudrala R. Disease risk of missense mutations using structural inference from predicted function. Curr Protein Pept Sci 2011; 11:573-88. [PMID: 20887259 DOI: 10.2174/138920310794109139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2010] [Accepted: 07/27/2010] [Indexed: 12/17/2022]
Abstract
Advancements in sequencing techniques place personalized genomic medicine upon the horizon, bringing along the responsibility of clinicians to understand the likelihood for a mutation to cause disease, and of scientists to separate etiology from nonpathologic variability. Pathogenicity is discernable from patterns of interactions between a missense mutation, the surrounding protein structure, and intermolecular interactions. Physicochemical stability calculations are not accessible without structures, as is the case for the vast majority of human proteins, so diagnostic accuracy remains in infancy. To model the effects of missense mutations on functional stability without structure, we combine novel protein sequence analysis algorithms to discern spatial distributions of sequence, evolutionary, and physicochemical conservation, through a new approach to optimize component selection. Novel components include a combinatory substitution matrix and two heuristic algorithms that detect positions which confer structural support to interaction interfaces. The method reaches 0.91 AUC in ten-fold cross-validation to predict alteration of function for 6,392 in vitro mutations. For clinical utility we trained the method on 7,022 disease associated missense mutations within the Online Mendelian inheritance in man amongst a larger randomized set. In a blinded prospective test to delineate mutations unique to 186 patients with craniosynostosis from those in the 95 highly variant Coriell controls and 1000 age matched controls, we achieved roughly 1/3 sensitivity and perfect specificity. The component algorithms retained during machine learning constitute novel protein sequence analysis techniques to describe environments supporting neutrality or pathology of mutations. This approach to pathogenetics enables new insight into the mechanistic relationship of missense mutations to disease phenotypes in our patients.
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Affiliation(s)
- Jeremy A Horst
- Department of Microbiology School of Medicine, University of Washington, 1959 NE Pacific St 357132, Seattle, WA 98195, USA
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47
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Hung LH, Guerquin M, Samudrala R. GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition. BMC Res Notes 2011; 4:97. [PMID: 21453553 PMCID: PMC3087690 DOI: 10.1186/1756-0500-4-97] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2010] [Accepted: 04/01/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Calculation of the root mean square deviation (RMSD) between the atomic coordinates of two optimally superposed structures is a basic component of structural comparison techniques. We describe a quaternion based method, GPU-Q-J, that is stable with single precision calculations and suitable for graphics processor units (GPUs). The application was implemented on an ATI 4770 graphics card in C/C++ and Brook+ in Linux where it was 260 to 760 times faster than existing unoptimized CPU methods. Source code is available from the Compbio website http://software.compbio.washington.edu/misc/downloads/st_gpu_fit/ or from the author LHH. FINDINGS The Nutritious Rice for the World Project (NRW) on World Community Grid predicted de novo, the structures of over 62,000 small proteins and protein domains returning a total of 10 billion candidate structures. Clustering ensembles of structures on this scale requires calculation of large similarity matrices consisting of RMSDs between each pair of structures in the set. As a real-world test, we calculated the matrices for 6 different ensembles from NRW. The GPU method was 260 times faster that the fastest existing CPU based method and over 500 times faster than the method that had been previously used. CONCLUSIONS GPU-Q-J is a significant advance over previous CPU methods. It relieves a major bottleneck in the clustering of large numbers of structures for NRW. It also has applications in structure comparison methods that involve multiple superposition and RMSD determination steps, particularly when such methods are applied on a proteome and genome wide scale.
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Affiliation(s)
- Ling-Hong Hung
- Department of Microbiology, University of Washington, Seattle WA USA.
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48
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Horst OV, Horst JA, Samudrala R, Dale BA. Caries induced cytokine network in the odontoblast layer of human teeth. BMC Immunol 2011; 12:9. [PMID: 21261944 PMCID: PMC3036664 DOI: 10.1186/1471-2172-12-9] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2010] [Accepted: 01/24/2011] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Immunologic responses of the tooth to caries begin with odontoblasts recognizing carious bacteria. Inflammatory propagation eventually leads to tooth pulp necrosis and danger to health. The present study aims to determine cytokine gene expression profiles generated within human teeth in response to dental caries in vivo and to build a mechanistic model of these responses and the downstream signaling network. RESULTS We demonstrate profound differential up-regulation of inflammatory genes in the odontoblast layer (ODL) in human teeth with caries in vivo, while the pulp remains largely unchanged. Interleukins, chemokines, and all tested receptors thereof were differentially up-regulated in ODL of carious teeth, well over one hundred-fold for 35 of 84 genes. By interrogating reconstructed protein interaction networks corresponding to the differentially up-regulated genes, we develop the hypothesis that pro-inflammatory cytokines highly expressed in ODL of carious teeth, IL-1β, IL-1α, and TNF-α, carry the converged inflammatory signal. We show that IL1β amplifies antimicrobial peptide production in odontoblasts in vitro 100-fold more than lipopolysaccharide, in a manner matching subsequent in vivo measurements. CONCLUSIONS Our data suggest that ODL amplifies bacterial signals dramatically by self-feedback cytokine-chemokine signal-receptor cycling, and signal convergence through IL1R1 and possibly others, to increase defensive capacity including antimicrobial peptide production to protect the tooth and contain the battle against carious bacteria within the dentin.
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Affiliation(s)
- Orapin V Horst
- Department of Orofacial Sciences, School of Dentistry, University of California, San Francisco, 513 Parnassus Street, San Francisco, CA 94143, Box 0422, USA.
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Nicholson CO, Costin JM, Rowe DK, Lin L, Jenwitheesuk E, Samudrala R, Isern S, Michael SF. Viral entry inhibitors block dengue antibody-dependent enhancement in vitro. Antiviral Res 2011; 89:71-4. [PMID: 21093488 DOI: 10.1016/j.antiviral.2010.11.008] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2010] [Revised: 11/02/2010] [Accepted: 11/10/2010] [Indexed: 02/01/2023]
Abstract
Severe dengue virus (DENV) disease symptoms, including dengue hemorrhagic fever and dengue shock syndrome, have been correlated with the presence of pre-existing antibodies that enhance rather than neutralize infections in Fc receptor bearing cells. These antibodies can originate from previous infection with a different serotype of dengue, or from waning antibody titers that occur in infants and young children as they are weaned from breast milk that contains protective dengue-specific antibodies. Despite the apparent importance of this antibody dependent enhancement (ADE) effect, there has been no description of any specific inhibitors of this process. We explored DENV entry inhibitors as a potential strategy to block ADE. Two different peptide entry inhibitors were tested for the ability to block antibody-mediated DENV-2 infection of human, FcRII bearing K562 cells in vitro. Both peptides were able to inhibit ADE, showing that entry inhibitors are possible candidates for the development of specific treatment for severe DENV infection.
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Affiliation(s)
- Cindo O Nicholson
- Department of Biological Sciences, Florida Gulf Coast University, Fort Myers, FL 33965, USA.
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Notman R, Oren EE, Tamerler C, Sarikaya M, Samudrala R, Walsh TR. Solution Study of Engineered Quartz Binding Peptides Using Replica Exchange Molecular Dynamics. Biomacromolecules 2010; 11:3266-74. [DOI: 10.1021/bm100646z] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Rebecca Notman
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, United Kingdom, Genetically Engineered Materials Science and Engineering Center, and Departments of Materials Science and Engineering and Microbiology, University of Washington, Seattle, Washington, United States
| | - E. Emre Oren
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, United Kingdom, Genetically Engineered Materials Science and Engineering Center, and Departments of Materials Science and Engineering and Microbiology, University of Washington, Seattle, Washington, United States
| | - Candan Tamerler
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, United Kingdom, Genetically Engineered Materials Science and Engineering Center, and Departments of Materials Science and Engineering and Microbiology, University of Washington, Seattle, Washington, United States
| | - Mehmet Sarikaya
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, United Kingdom, Genetically Engineered Materials Science and Engineering Center, and Departments of Materials Science and Engineering and Microbiology, University of Washington, Seattle, Washington, United States
| | - Ram Samudrala
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, United Kingdom, Genetically Engineered Materials Science and Engineering Center, and Departments of Materials Science and Engineering and Microbiology, University of Washington, Seattle, Washington, United States
| | - Tiffany R. Walsh
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, United Kingdom, Genetically Engineered Materials Science and Engineering Center, and Departments of Materials Science and Engineering and Microbiology, University of Washington, Seattle, Washington, United States
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