1
|
Abstract
Fungal infections are a serious problem affecting many people worldwide, creating critical economic and medical consequences. Fungi are ubiquitous and can cause invasive diseases in individuals mostly living in developing countries or with weakened immune systems, and antifungal drugs currently available have important limitations in tolerability and efficacy. In an effort to counteract the high morbidity and mortality rates associated with invasive fungal infections, various approaches are being utilized to discover and develop new antifungal agents. This review discusses the challenges posed by fungal infections, outlines different methods for developing antifungal drugs and reports on the status of drugs currently in clinical trials, which offer hope for combating this serious global problem.
Collapse
Affiliation(s)
- Melissa E Munzen
- Department of Oral Biology, University of Florida College of Dentistry, Gainesville, FL 32610, USA
| | | | - Luis R Martinez
- Department of Oral Biology, University of Florida College of Dentistry, Gainesville, FL 32610, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA
- Center for Immunology and Transplantation, University of Florida, Gainesville, FL 32610, USA
- Center for Translational Research in Neurodegenerative Disease, University of Florida, Gainesville, FL 32610, USA
| |
Collapse
|
2
|
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.
Collapse
Affiliation(s)
| | | | - Ram Samudrala
- Jacobs School of Medicine and Biomedical Sciences, Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
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.
Collapse
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.)
| |
Collapse
|
5
|
Moukheiber L, Mangione W, Moukheiber M, Maleki S, Falls Z, Gao M, Samudrala R. Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology. Molecules 2022; 27. [PMID: 35566372 DOI: 10.3390/molecules27093021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 02/01/2023] Open
Abstract
Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.
Collapse
|
6
|
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.
Collapse
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.)
| |
Collapse
|
7
|
Mullins JGL. Drug repurposing in silico screening platforms. Biochem Soc Trans 2022:BST20200967. [PMID: 35285479 DOI: 10.1042/BST20200967] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
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.
Collapse
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.)
| |
Collapse
|
11
|
Olías-Molero AI, de la Fuente C, Cuquerella M, Torrado JJ, Alunda JM. Antileishmanial Drug Discovery and Development: Time to Reset the Model? Microorganisms 2021; 9:2500. [PMID: 34946102 PMCID: PMC8703564 DOI: 10.3390/microorganisms9122500] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [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: 11/17/2021] [Revised: 11/26/2021] [Accepted: 12/01/2021] [Indexed: 01/27/2023] Open
Abstract
Leishmaniasis is a vector-borne parasitic disease caused by Leishmania species. The disease affects humans and animals, particularly dogs, provoking cutaneous, mucocutaneous, or visceral processes depending on the Leishmania sp. and the host immune response. No vaccine for humans is available, and the control relies mainly on chemotherapy. However, currently used drugs are old, some are toxic, and the safer presentations are largely unaffordable by the most severely affected human populations. Moreover, its efficacy has shortcomings, and it has been challenged by the growing reports of resistance and therapeutic failure. This manuscript presents an overview of the currently used drugs, the prevailing model to develop new antileishmanial drugs and its low efficiency, and the impact of deconstruction of the drug pipeline on the high failure rate of potential drugs. To improve the predictive value of preclinical research in the chemotherapy of leishmaniasis, several proposals are presented to circumvent critical hurdles-namely, lack of common goals of collaborative research, particularly in public-private partnership; fragmented efforts; use of inadequate surrogate models, especially for in vivo trials; shortcomings of target product profile (TPP) guides.
Collapse
Affiliation(s)
- Ana Isabel Olías-Molero
- Department of Animal Health, Faculty of Veterinary Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain; (A.I.O.-M.); (C.d.l.F.); (M.C.)
| | - Concepción de la Fuente
- Department of Animal Health, Faculty of Veterinary Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain; (A.I.O.-M.); (C.d.l.F.); (M.C.)
| | - Montserrat Cuquerella
- Department of Animal Health, Faculty of Veterinary Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain; (A.I.O.-M.); (C.d.l.F.); (M.C.)
| | - Juan J. Torrado
- Department of Pharmaceutics and Food Technology, Faculty of Pharmacy, Universidad Complutense de Madrid, 28040 Madrid, Spain;
| | - José M. Alunda
- Department of Animal Health, Faculty of Veterinary Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain; (A.I.O.-M.); (C.d.l.F.); (M.C.)
| |
Collapse
|
12
|
Ahmed M, Hasani HJ, Kalyaanamoorthy S, Barakat K. GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds. Sci Rep 2021; 11:9510. [PMID: 33947911 DOI: 10.1038/s41598-021-88939-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 04/12/2021] [Indexed: 02/02/2023] Open
Abstract
The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews' correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.
Collapse
|
13
|
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.
Collapse
Affiliation(s)
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA;
| |
Collapse
|
14
|
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/.
Collapse
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
| |
Collapse
|
15
|
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.
| |
Collapse
|
16
|
Pal A, Pawar A, Goswami K, Sharma P, Prasad R. Hydroxychloroquine and Covid-19: A Cellular and Molecular Biology Based Update. Indian J Clin Biochem 2020; 35:274-84. [PMID: 32641874 DOI: 10.1007/s12291-020-00900-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/03/2020] [Indexed: 12/15/2022]
Abstract
As the time for finding a definitive and safe cure as a vaccine for novel Corona Virus Disease 2019 (Covid-19) is still far, there is need to study in depth about the other potential drugs, which can save millions of lives due to Covid-19 pandemic. Right at the center of the debate is the use of drug "Hydroxychloroquine" as a prophylaxis as well as a treatment strategy against Covid-19 in conjunction with azithromycin. In this review, we will study the cellular and molecular aspects of hydroxychloroquine, which had driven its use in Covid-19 patients, as well as its chemistry and pharmacokinetics along with clinical trials going on worldwide using hydroxychloroquine against Covid-19.
Collapse
|
17
|
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>
Collapse
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
| |
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
Schrezenmeier E, Dörner T. Mechanisms of action of hydroxychloroquine and chloroquine: implications for rheumatology. Nat Rev Rheumatol 2020; 16:155-66. [PMID: 32034323 DOI: 10.1038/s41584-020-0372-x] [Citation(s) in RCA: 776] [Impact Index Per Article: 194.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/09/2020] [Indexed: 12/15/2022]
Abstract
Despite widespread clinical use of antimalarial drugs such as hydroxychloroquine and chloroquine in the treatment of rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and other inflammatory rheumatic diseases, insights into the mechanism of action of these drugs are still emerging. Hydroxychloroquine and chloroquine are weak bases and have a characteristic 'deep' volume of distribution and a half-life of around 50 days. These drugs interfere with lysosomal activity and autophagy, interact with membrane stability and alter signalling pathways and transcriptional activity, which can result in inhibition of cytokine production and modulation of certain co-stimulatory molecules. These modes of action, together with the drug's chemical properties, might explain the clinical efficacy and well-known adverse effects (such as retinopathy) of these drugs. The unknown dose-response relationships of these drugs and the lack of definitions of the minimum dose needed for clinical efficacy and what doses are toxic pose challenges to clinical practice. Further challenges include patient non-adherence and possible context-dependent variations in blood drug levels. Available mechanistic data give insights into the immunomodulatory potency of hydroxychloroquine and provide the rationale to search for more potent and/or selective inhibitors.
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
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.
Collapse
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.
| |
Collapse
|
22
|
Pinzi L, Rastelli G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci 2019; 20:E4331. [PMID: 31487867 DOI: 10.3390/ijms20184331] [Citation(s) in RCA: 702] [Impact Index Per Article: 140.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 09/02/2019] [Accepted: 09/02/2019] [Indexed: 12/11/2022] Open
Abstract
Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.
Collapse
|
23
|
Pulley JM, Rhoads JP, Jerome RN, Challa AP, Erreger KB, Joly MM, Lavieri RR, Perry KE, Zaleski NM, Shirey-Rice JK, Aronoff DM. Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data. Annu Rev Pharmacol Toxicol 2019; 60:333-352. [PMID: 31337270 DOI: 10.1146/annurev-pharmtox-010919-023537] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [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: 11/09/2022]
Abstract
The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.
Collapse
Affiliation(s)
- Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jillian P Rhoads
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Rebecca N Jerome
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Anup P Challa
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kevin B Erreger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Meghan M Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Robert R Lavieri
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kelly E Perry
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Nicole M Zaleski
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jana K Shirey-Rice
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - David M Aronoff
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.,Departments of Obstetrics and Gynecology, and Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
| |
Collapse
|
24
|
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.
Collapse
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.
| |
Collapse
|
25
|
Schneider-Futschik EK, Hoyer D, Khromykh AA, Baell JB, Marsh GA, Baker MA, Li J, Velkov T. Contemporary Anti-Ebola Drug Discovery Approaches and Platforms. ACS Infect Dis 2019; 5:35-48. [PMID: 30516045 DOI: 10.1021/acsinfecdis.8b00285] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [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: 12/13/2022]
Abstract
The Ebola virus has a grave potential to destabilize civil society as we know it. The past few deadly Ebola outbreaks were unprecedented in size: The 2014-15 Ebola West Africa outbreak saw the virus spread from the epicenter through to Guinea, Sierra Leone, Nigeria, Congo, and Liberia. The 2014-15 Ebola West Africa outbreak was associated with almost 30,000 suspected or confirmed cases and over 11,000 documented deaths. The more recent 2018 outbreak in the Democratic Republic of Congo has so far resulted in 216 suspected or confirmed cases and 139 deaths. There is a general acceptance within the World Health Organization (WHO) and the Ebola outbreak response community that future outbreaks will become increasingly more frequent and more likely to involve intercontinental transmission. The magnitude of the recent outbreaks demonstrated in dramatic fashion the shortcomings of our mass casualty disease response capabilities and lack of therapeutic modalities for supporting Ebola outbreak prevention and control. Currently, there are no approved drugs although vaccines for human Ebola virus infection are in the trial phases and some potential treatments have been field tested most recently in the Congo Ebola outbreak. Treatment is limited to pain management and supportive care to counter dehydration and lack of oxygen. This underscores the critical need for effective antiviral drugs that specifically target this deadly disease. This review examines the current approaches for the discovery of anti-Ebola small molecule or biological therapeutics, their viral targets, mode of action, and contemporary platforms, which collectively form the backbone of the anti-Ebola drug discovery pipeline.
Collapse
Affiliation(s)
- Elena K. Schneider-Futschik
- Department of Pharmacology and Therapeutics, School of Biomedical Sciences, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Daniel Hoyer
- Department of Pharmacology and Therapeutics, School of Biomedical Sciences, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, Victoria 3052, Australia
- Department of Molecular Medicine, The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, California 92037, United States
| | - Alexander A. Khromykh
- Australian Infectious Diseases Research Centre, School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland 4072, Australia
| | - Jonathan B. Baell
- School of Pharmaceutical Sciences, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing, Jiangsu 211816, People’s Republic of China
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
| | - Glenn A. Marsh
- CSIRO Livestock Industries, Australian Animal Health Laboratory, Geelong, Victoria 3220, Australia
| | - Mark A. Baker
- Priority Research Centre in Reproductive Science, School of Environmental and Life Sciences, University of Newcastle, Callaghan, New South Wales 2308, Australia
| | - Jian Li
- Monash Biomedicine Discovery Institute, Department of Microbiology, Monash University, Clayton, Victoria 3800, Australia
| | - Tony Velkov
- Department of Pharmacology and Therapeutics, School of Biomedical Sciences, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia
| |
Collapse
|
26
|
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.
Collapse
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.
| |
Collapse
|
27
|
An J, Woodward JJ, Lai W, Minie M, Sun X, Tanaka L, Snyder JM, Sasaki T, Elkon KB. Inhibition of Cyclic GMP-AMP Synthase Using a Novel Antimalarial Drug Derivative in Trex1-Deficient Mice. Arthritis Rheumatol 2018; 70:1807-1819. [PMID: 29781188 DOI: 10.1002/art.40559] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 05/10/2018] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Type I interferon (IFN) is strongly implicated in the pathogenesis of systemic lupus erythematosus (SLE) as well as rare monogenic interferonopathies such as Aicardi-Goutières syndrome (AGS), a disease attributed to mutations in the DNA exonuclease TREX1. The DNA-activated type I IFN pathway cyclic GMP-AMP (cGAMP) synthase (cGAS) is linked to subsets of AGS and lupus. This study was undertaken to identify inhibitors of the DNA-cGAS interaction, and to test the lead candidate drug, X6, in a mouse model of AGS. METHODS Trex1-/- mice were treated orally from birth with either X6 or hydroxychloroquine (HCQ) for 8 weeks. Expression of IFN-stimulated genes (ISGs) was quantified by quantitative polymerase chain reaction. Multiple reaction monitoring by ultra-performance liquid chromatography coupled with tandem mass spectrometry was used to quantify the production of cGAMP and X6 drug concentrations in the serum and heart tissue of Trex1-/- mice. RESULTS On the basis of the efficacy-to-toxicity ratio established in vitro, drug X6 was selected as the lead candidate for treatment of Trex1-/- mice. X6 was significantly more effective than HCQ in attenuating ISG expression in mouse spleens (P < 0.01 for Isg15 and Isg20) and hearts (P < 0.05 for Isg15, Mx1, and Ifnb, and P < 0.01 for Cxcl10), and in reducing the production of cGAMP in mouse heart tissue (P < 0.05), thus demonstrating target engagement by the X6 compound. Of note, X6 was also more effective than HCQ in reducing ISG expression in vitro (P < 0.05 for IFI27 and MX1, and P < 0.01 for IFI44L and PKR) in human peripheral blood mononuclear cells from patients with SLE. CONCLUSION This study demonstrates that X6 is superior to HCQ for the treatment of an experimental autoimmune myocarditis mediated in vivo by the cGAS/stimulator of IFN genes (cGAS/STING) pathway. The findings suggest that drug X6 could be developed as a novel treatment for AGS and/or lupus to inhibit activation of the cGAS/STING pathway.
Collapse
Affiliation(s)
- Jie An
- University of Washington, Seattle
| | | | - Weinan Lai
- University of Washington, Seattle, and Nanfang Hospital, Southern Medical University, Guangzhou, China
| | | | | | | | | | | | | |
Collapse
|
28
|
|
29
|
Somody JC, MacKinnon SS, Windemuth A. Structural coverage of the proteome for pharmaceutical applications. Drug Discov Today 2017; 22:1792-1799. [DOI: 10.1016/j.drudis.2017.08.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 08/16/2017] [Accepted: 08/17/2017] [Indexed: 01/09/2023]
|
30
|
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.
Collapse
Affiliation(s)
- Gaurav Chopra
- Department of Chemistry, Purdue University, West Lafayette, IN, USA.
| | - Ram Samudrala
- Department of Biomedical Informatics, SUNY, Buffalo, NY, USA.
| |
Collapse
|
31
|
Hernandez-Perez M, Chopra G, Fine J, Conteh AM, Anderson RM, Linnemann AK, Benjamin C, Nelson JB, Benninger KS, Nadler JL, Maloney DJ, Tersey SA, Mirmira RG. Inhibition of 12/15-Lipoxygenase Protects Against β-Cell Oxidative Stress and Glycemic Deterioration in Mouse Models of Type 1 Diabetes. Diabetes 2017; 66:2875-2887. [PMID: 28842399 PMCID: PMC5652601 DOI: 10.2337/db17-0215] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 08/15/2017] [Indexed: 12/11/2022]
Abstract
Islet β-cell dysfunction and aggressive macrophage activity are early features in the pathogenesis of type 1 diabetes (T1D). 12/15-Lipoxygenase (12/15-LOX) is induced in β-cells and macrophages during T1D and produces proinflammatory lipids and lipid peroxides that exacerbate β-cell dysfunction and macrophage activity. Inhibition of 12/15-LOX provides a potential therapeutic approach to prevent glycemic deterioration in T1D. Two inhibitors recently identified by our groups through screening efforts, ML127 and ML351, have been shown to selectively target 12/15-LOX with high potency. Only ML351 exhibited no apparent toxicity across a range of concentrations in mouse islets, and molecular modeling has suggested reduced promiscuity of ML351 compared with ML127. In mouse islets, incubation with ML351 improved glucose-stimulated insulin secretion in the presence of proinflammatory cytokines and triggered gene expression pathways responsive to oxidative stress and cell death. Consistent with a role for 12/15-LOX in promoting oxidative stress, its chemical inhibition reduced production of reactive oxygen species in both mouse and human islets in vitro. In a streptozotocin-induced model of T1D in mice, ML351 prevented the development of diabetes, with coincident enhancement of nuclear Nrf2 in islet cells, reduced β-cell oxidative stress, and preservation of β-cell mass. In the nonobese diabetic mouse model of T1D, administration of ML351 during the prediabetic phase prevented dysglycemia, reduced β-cell oxidative stress, and increased the proportion of anti-inflammatory macrophages in insulitis. The data provide the first evidence to date that small molecules that target 12/15-LOX can prevent progression of β-cell dysfunction and glycemic deterioration in models of T1D.
Collapse
Affiliation(s)
- Marimar Hernandez-Perez
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
| | - Gaurav Chopra
- Department of Chemistry, Purdue Institute for Drug Discovery; Purdue Center for Cancer Research; Purdue Institute for Inflammation, Immunology and Infectious Disease; and Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN
| | - Jonathan Fine
- Department of Chemistry, Purdue Institute for Drug Discovery; Purdue Center for Cancer Research; Purdue Institute for Inflammation, Immunology and Infectious Disease; and Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN
| | - Abass M. Conteh
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
| | - Ryan M. Anderson
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN
| | - Amelia K. Linnemann
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN
| | - Chanelle Benjamin
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
| | - Jennifer B. Nelson
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
| | - Kara S. Benninger
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
| | - Jerry L. Nadler
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA
| | - David J. Maloney
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD
| | - Sarah A. Tersey
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
| | - Raghavendra G. Mirmira
- Department of Pediatrics and the Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
- Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| |
Collapse
|
32
|
Imanishi S, Takahashi R, Katagiri S, Kobayashi C, Umezu T, Ohyashiki K, Ohyashiki JH. Teriflunomide restores 5-azacytidine sensitivity via activation of pyrimidine salvage in 5-azacytidine-resistant leukemia cells. Oncotarget 2017; 8:69906-69915. [PMID: 29050250 PMCID: PMC5642525 DOI: 10.18632/oncotarget.19436] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [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: 11/09/2016] [Accepted: 06/19/2017] [Indexed: 12/21/2022] Open
Abstract
Previous studies showed that downregulation of pyrimidine salvage underlies resistance against 5-azacytidine (AZA), indicating an important role for de novo pyrimidine synthesis in AZA resistance. Because de novo pyrimidine synthesis is inhibited by the immunomodulator teriflunomide and its pro-drug leflunomide, we examined the effect of combined treatment with AZA and teriflunomide on AZA resistance to develop a novel strategy to cancel and prevent AZA resistance. Teriflunomide markedly inhibited the growth of AZA-resistant human leukemia cell lines (R-U937 and R-HL-60) in comparison with their AZA-sensitive counterparts (U937 and HL-60). In the presence of a non-toxic concentration of teriflunomide (1 μM), AZA induced apoptosis in AZA-resistant cells and leukemia cells from AZA-resistant patients. AZA acted as a DNA methyltransferase 3A inhibitor in AZA-resistant cells in the presence of 1 μM teriflunomide. Although AZA-sensitive cells acquired AZA resistance after continuous treatment with AZA for 42 days, the growth of AZA-sensitive cells continuously treated with the combination of AZA and teriflunomide was significantly inhibited in the presence of AZA, demonstrating that the combined treatment prevented AZA resistance. These results suggest that combined treatment with AZA and teriflunomide can be a novel strategy to overcome AZA resistance.
Collapse
Affiliation(s)
- Satoshi Imanishi
- Department of Molecular Oncology, Institute for Medical Science, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan
| | - Ryoko Takahashi
- Department of Molecular Oncology, Institute for Medical Science, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan
| | - Seiichiro Katagiri
- Department of Hematology, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan
| | - Chiaki Kobayashi
- Department of Molecular Oncology, Institute for Medical Science, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan.,Department of Hematology, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan
| | - Tomohiro Umezu
- Department of Molecular Oncology, Institute for Medical Science, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan.,Department of Hematology, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan
| | - Kazuma Ohyashiki
- Department of Hematology, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan
| | - Junko H Ohyashiki
- Department of Molecular Oncology, Institute for Medical Science, Tokyo Medical University, Nishi-Shinjuku, Shinjuku, Tokyo, Japan
| |
Collapse
|
33
|
Duran-Frigola M, Siragusa L, Ruppin E, Barril X, Cruciani G, Aloy P. Detecting similar binding pockets to enable systems polypharmacology. PLoS Comput Biol 2017; 13:e1005522. [PMID: 28662117 PMCID: PMC5490940 DOI: 10.1371/journal.pcbi.1005522] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [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: 10/11/2016] [Accepted: 04/15/2017] [Indexed: 01/19/2023] Open
Abstract
In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia. Traditionally, the fact that most drugs are promiscuous binders has been a major concern in pharmacology, due to the occurrence of undesired off-target clinical events. In the recent years, however, the realization that many diseases are the result of complex biological processes has encouraged rethinking of drug promiscuity as a promising feature, since it is sometimes necessary to interfere with multiple receptors in order to overcome the robustness of disease-related networks. One way to identify groups of proteins that could be targeted simultaneously is to look for similar binding sites. We have massively done so for all human proteins with a known high-resolution three-dimensional structure, unveiling a vast space of ‘polypharmacology’ opportunities. Of these, we know, a great majority is not of therapeutic interest. To pinpoint promising multi-target combinations, we advocate for the use of computational tools that are able to rapidly simulate the effect of drug-target interactions on biological networks.
Collapse
Affiliation(s)
- Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | | | - Eytan Ruppin
- Department of Computer Science & Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
- School of Computer Sciences, Tel Aviv University, Tel Aviv, Israel
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Xavier Barril
- Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Gabriele Cruciani
- Molecular Discovery Limited, London, United Kingdom
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
- * E-mail:
| |
Collapse
|
34
|
Chopra G, Kaushik S, Elkin PL, Samudrala R. Combating Ebola with Repurposed Therapeutics Using the CANDO Platform. Molecules 2016; 21:E1537. [PMID: 27898018 DOI: 10.3390/molecules21121537] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 10/23/2016] [Accepted: 10/28/2016] [Indexed: 12/20/2022] Open
Abstract
Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.
Collapse
|
35
|
Abstract
The best known of the naturally occurring antimalarial compounds are quinine, extracted from cinchona bark, and artemisinin (qinghao), extracted from Artemisia annua in China. These and other derivatives are now chemically synthesized and remain the mainstay of therapy to treat malaria. The beneficial effects of several of the antimalarial drugs (AMDs) on clinical features of autoimmune disorders were discovered by chance during World War II. In this review, we discuss the chemistry of AMDs and their mechanisms of action, emphasizing how they may impact multiple pathways of innate immunity. These pathways include Toll-like receptors and the recently described cGAS-STING pathway. Finally, we discuss the current and future impact of AMDs on systemic lupus erythematosus, rheumatoid arthritis, and devastating monogenic disorders (interferonopathies) characterized by expression of type I interferon in the brain.
Collapse
Affiliation(s)
- Jie An
- Division of Rheumatology, Department of Medicine, University of Washington, Seattle, Washington 98195; email
| | | | | | | | - Keith B Elkon
- Division of Rheumatology, Department of Medicine, University of Washington, Seattle, Washington 98195; email .,Department of Immunology, University of Washington, Seattle, Washington 98195; , , ,
| |
Collapse
|
36
|
Grammer AC, Ryals MM, Heuer SE, Robl RD, Madamanchi S, Davis LS, Lauwerys B, Catalina MD, Lipsky PE. Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis. Lupus 2016; 25:1150-70. [DOI: 10.1177/0961203316657437] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [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
Lupus patients are in need of modern drugs to treat specific manifestations of their disease effectively and safely. In the past half century, only one new treatment has been approved by the US Food and Drug Administration (FDA) for systemic lupus erythematosus (SLE). In 2014–2015, the FDA approved 71 new drugs, only one of which targeted a rheumatic disease and none of which was approved for use in SLE. Repositioning/repurposing drugs approved for other diseases using multiple approaches is one possible means to find new treatment options for lupus patients. “Big Data” analysis approaches this challenge from an unbiased standpoint whereas literature mining and crowd sourcing for candidates assessed by the CoLTs (Combined Lupus Treatment Scoring) system provide a hypothesis-based approach to rank potential therapeutic candidates for possible clinical application. Both approaches mitigate risk since the candidates assessed have largely been extensively tested in clinical trials for other indications. The usefulness of a multi-pronged approach to drug repositioning in lupus is highlighted by orthogonal confirmation of hypothesis-based drug repositioning predictions by “Big Data” analysis of differentially expressed genes from lupus patient samples. The goal is to identify novel therapies that have the potential to affect disease processes specifically. Involvement of SLE patients and the scientists that study this disease in thinking about new drugs that may be effective in lupus though crowd-sourcing sites such as LRxL-STAT ( www.linkedin.com/in/lrxlstat ) is important in stimulating the momentum needed to test these novel drug targets for efficacy in lupus rapidly in small, proof-of-concept trials conducted by LuCIN, the Lupus Clinical Investigators Network ( www.linkedin.com/in/lucinstat ).
Collapse
Affiliation(s)
- A C Grammer
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - M M Ryals
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - S E Heuer
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - R D Robl
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - S Madamanchi
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - L S Davis
- Department of Internal Medicine, UTSW Medical Center at Dallas, Dallas, TX, USA
| | - B Lauwerys
- Université Catholique de Louvain, Brussels, Belgium
| | - M D Catalina
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| | - P E Lipsky
- AMPEL BioSolutions and RILITE Foundation, University of Virginia Research Park, Charlottesville, VA, USA
| |
Collapse
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
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.
Collapse
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.
| |
Collapse
|
39
|
Abstract
Antiviral therapy is one of the most exciting aspects of virology, since it has successfully employed basic science to generate very effective treatments for serious viral infections. Table 1 lists selected examples of those human viral diseases for which there are established antiviral drugs. Therapy for human immunodeficiency virus (HIV) infection has demonstrated that the potential impact antivirals can have on a lethal, chronic infection with lifesaving therapy administered to more than 12 million individuals by 2015. This dramatic advance is about to be recapitulated for the treatment of hepatitis C virus (HCV) infection. The development of new antiviral drugs is very much a work in progress, with active drug discovery programs for filoviruses, coronaviruses, dengue, and others.
Collapse
|
40
|
An J, Woodward JJ, Sasaki T, Minie M, Elkon KB. Cutting Edge: Antimalarial Drugs Inhibit IFN-β Production through Blockade of Cyclic GMP-AMP Synthase–DNA Interaction. J I 2015; 194:4089-93. [DOI: 10.4049/jimmunol.1402793] [Citation(s) in RCA: 123] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Accepted: 03/09/2015] [Indexed: 02/02/2023]
|
41
|
Abstract
Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development. Description of the “mode-of-action” of a small chemical compound against a protein target is essential for the drug discovery process. Such description relies on three main steps: i) the identification of the target protein within the thousands of proteins in an organism, ii) the localization of the binding interaction site in the identified target protein, and iii) the molecular characterization of the compound’s binding mode in the binding site of the target protein. Here, we introduce a new computational method, called nAnnoLyze, which uses graph theory principles to relate compounds and target proteins based on comparative principles. nAnnoLyze aims at addressing two of the three previous steps, that is, target identification and binding site localization. Our results suggest that the nAnnoLyze accuracy and proteome-wide applicability enables the large-scale annotation and analysis of compound–protein interaction and thus may benefit drug development.
Collapse
Affiliation(s)
- Francisco Martínez-Jiménez
- Genome Biology Group, Centre Nacional d’Aanàlisi Genòmica (CNAG), Barcelona, Spain
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Barcelona, Spain
| | - Marc A. Marti-Renom
- Genome Biology Group, Centre Nacional d’Aanàlisi Genòmica (CNAG), Barcelona, Spain
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- * E-mail:
| |
Collapse
|
42
|
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.
Collapse
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
| |
Collapse
|