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Zhou Y, Huang Z, Li W, Wei J, Jiang Q, Yang W, Huang J. Deep learning in preclinical antibody drug discovery and development. Methods 2023; 218:57-71. [PMID: 37454742 DOI: 10.1016/j.ymeth.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/20/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.
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Affiliation(s)
- Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wenzhen Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jinyi Wei
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qianhu Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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Ahmad B, Achek A, Farooq M, Choi S. Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations. Comput Struct Biotechnol J 2023; 21:4825-4835. [PMID: 37854633 PMCID: PMC10579963 DOI: 10.1016/j.csbj.2023.09.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/27/2023] [Accepted: 09/27/2023] [Indexed: 10/20/2023] Open
Abstract
Anomalous NLRP3 inflammasome responses have been linked to multiple health issues, including but not limited to atherosclerosis, diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disease. Thus, targeting NLRP3 and modulating its associated immune response might be a promising strategy for developing new anti-inflammatory drugs. Herein, we report a computational method for de novo peptide design for targeting NLRP3 inflammasomes. The described method leverages a long-short-term memory (LSTM) network based on a recurrent neural network (RNN) to model a valuable latent space of molecules. The resulting classifiers are utilized to guide the selection of molecules generated by the model based on circular dichroism spectra and physicochemical features derived from high-throughput molecular dynamics simulations. Of the experimentally tested sequences, 60% of the peptides showed NLRP3-mediated inhibition of IL-1β and IL-18. One peptide displayed high potency against NLRP3-mediated IL-1β inhibition. However, NLRC4 and AIM2 inflammasome-mediated IL-1β secretion was uninterrupted by this peptide, demonstrating its selectivity toward the NLRP3 inflammasome. Overall, these results indicate that deep learning and molecular dynamics can accelerate the discovery of NLRP3 inhibitors with potent and selective activity.
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Affiliation(s)
- Bilal Ahmad
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- S&K Therapeutics, Ajou University, Campus Plaza 418, Worldcup-ro 199, Yeongtong-gu, Suwon 16502, South Korea
| | - Asma Achek
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- Technology Development Platform, Institut Pasteur Korea, Seongnam 13488, Soouth Korea
| | - Mariya Farooq
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- S&K Therapeutics, Ajou University, Campus Plaza 418, Worldcup-ro 199, Yeongtong-gu, Suwon 16502, South Korea
| | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, South Korea
- S&K Therapeutics, Ajou University, Campus Plaza 418, Worldcup-ro 199, Yeongtong-gu, Suwon 16502, South Korea
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Cooray S, Price-Kuehne F, Hong Y, Omoyinmi E, Burleigh A, Gilmour KC, Ahmad B, Choi S, Bahar MW, Torpiano P, Gagunashvili A, Jensen B, Bellos E, Sancho-Shimizu V, Herberg JA, Mankad K, Kumar A, Kaliakatsos M, Worth AJJ, Eleftheriou D, Whittaker E, Brogan PA. Neuroinflammation, autoinflammation, splenomegaly and anemia caused by bi-allelic mutations in IRAK4. Front Immunol 2023; 14:1231749. [PMID: 37744344 PMCID: PMC10516541 DOI: 10.3389/fimmu.2023.1231749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
We describe a novel, severe autoinflammatory syndrome characterized by neuroinflammation, systemic autoinflammation, splenomegaly, and anemia (NASA) caused by bi-allelic mutations in IRAK4. IRAK-4 is a serine/threonine kinase with a pivotal role in innate immune signaling from toll-like receptors and production of pro-inflammatory cytokines. In humans, bi-allelic mutations in IRAK4 result in IRAK-4 deficiency and increased susceptibility to pyogenic bacterial infections, but autoinflammation has never been described. We describe 5 affected patients from 2 unrelated families with compound heterozygous mutations in IRAK4 (c.C877T (p.Q293*)/c.G958T (p.D320Y); and c.A86C (p.Q29P)/c.161 + 1G>A) resulting in severe systemic autoinflammation, massive splenomegaly and severe transfusion dependent anemia and, in 3/5 cases, severe neuroinflammation and seizures. IRAK-4 protein expression was reduced in peripheral blood mononuclear cells (PBMC) in affected patients. Immunological analysis demonstrated elevated serum tumor necrosis factor (TNF), interleukin (IL) 1 beta (IL-1β), IL-6, IL-8, interferon α2a (IFN-α2a), and interferon β (IFN-β); and elevated cerebrospinal fluid (CSF) IL-6 without elevation of CSF IFN-α despite perturbed interferon gene signature. Mutations were located within the death domain (DD; p.Q29P and splice site mutation c.161 + 1G>A) and kinase domain (p.Q293*/p.D320Y) of IRAK-4. Structure-based modeling of the DD mutation p.Q29P showed alteration in the alignment of a loop within the DD with loss of contact distance and hydrogen bond interactions with IRAK-1/2 within the myddosome complex. The kinase domain mutation p.D320Y was predicted to stabilize interactions within the kinase active site. While precise mechanisms of autoinflammation in NASA remain uncertain, we speculate that loss of negative regulation of IRAK-4 and IRAK-1; dysregulation of myddosome assembly and disassembly; or kinase active site instability may drive dysregulated IL-6 and TNF production. Blockade of IL-6 resulted in immediate and complete amelioration of systemic autoinflammation and anemia in all 5 patients treated; however, neuroinflammation has, so far proven recalcitrant to IL-6 blockade and the janus kinase (JAK) inhibitor baricitinib, likely due to lack of central nervous system penetration of both drugs. We therefore highlight that bi-allelic mutation in IRAK4 may be associated with a severe and complex autoinflammatory and neuroinflammatory phenotype that we have called NASA (neuroinflammation, autoinflammation, splenomegaly and anemia), in addition to immunodeficiency in humans.
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Affiliation(s)
- Samantha Cooray
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Fiona Price-Kuehne
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Ying Hong
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Ebun Omoyinmi
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Alice Burleigh
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
- Centre for Adolescent Rheumatology Versus Arthritis, University College London, London, United Kingdom
| | - Kimberly C. Gilmour
- Department of Immunology and Gene Therapy, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Bilal Ahmad
- Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea
| | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea
| | - Mohammad W. Bahar
- Division of Structural Biology, University of Oxford, The Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - Paul Torpiano
- Department of Immunology and Gene Therapy, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Andrey Gagunashvili
- Faculty of Life and Environmental Sciences, University of Iceland, Reykjavík, Iceland
| | - Barbara Jensen
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Evangelos Bellos
- Section of Paediatric Infectious Diseases, Imperial College London, London, United Kingdom
- Centre for Paediatrics and Child Health, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Vanessa Sancho-Shimizu
- Section of Paediatric Infectious Diseases, Imperial College London, London, United Kingdom
- Centre for Paediatrics and Child Health, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jethro A. Herberg
- Section of Paediatric Infectious Diseases, Imperial College London, London, United Kingdom
- Department of Paediatric Infectious Diseases, St Mary’s Hospital, Imperial College NHS Healthcare Trust, London, United Kingdom
| | - Kshitij Mankad
- Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Atul Kumar
- Department of Histopathology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Marios Kaliakatsos
- Department of Neurology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Austen J. J. Worth
- Department of Immunology and Gene Therapy, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
| | - Despina Eleftheriou
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Elizabeth Whittaker
- Section of Paediatric Infectious Diseases, Imperial College London, London, United Kingdom
- Department of Paediatric Infectious Diseases, St Mary’s Hospital, Imperial College NHS Healthcare Trust, London, United Kingdom
| | - Paul A. Brogan
- Infection, Immunity and Inflammation Department, University College London Great Ormond Street Institute of Child Health, London, United Kingdom
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Pirzada RH, Ahmad B, Qayyum N, Choi S. Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics. Front Endocrinol (Lausanne) 2023; 14:1084327. [PMID: 36950681 PMCID: PMC10025526 DOI: 10.3389/fendo.2023.1084327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 02/20/2023] [Indexed: 03/08/2023] Open
Abstract
Coronaviruses induce severe upper respiratory tract infections, which can spread to the lungs. The nucleocapsid protein (N protein) plays an important role in genome replication, transcription, and virion assembly in SARS-CoV-2, the virus causing COVID-19, and in other coronaviruses. Glycogen synthase kinase 3 (GSK3) activation phosphorylates the viral N protein. To combat COVID-19 and future coronavirus outbreaks, interference with the dependence of N protein on GSK3 may be a viable strategy. Toward this end, this study aimed to construct robust machine learning models to identify GSK3 inhibitors from Food and Drug Administration-approved and investigational drug libraries using the quantitative structure-activity relationship approach. A non-redundant dataset consisting of 495 and 3070 compounds for GSK3α and GSK3β, respectively, was acquired from the ChEMBL database. Twelve sets of molecular descriptors were used to define these inhibitors, and machine learning algorithms were selected using the LazyPredict package. Histogram-based gradient boosting and light gradient boosting machine algorithms were used to develop predictive models that were evaluated based on the root mean square error and R-squared value. Finally, the top two drugs (selinexor and ruboxistaurin) were selected for molecular dynamics simulation based on the highest predicted activity (negative log of the half-maximal inhibitory concentration, pIC50 value) to further investigate the structural stability of the protein-ligand complexes. This artificial intelligence-based virtual high-throughput screening approach is an effective strategy for accelerating drug discovery and finding novel pharmacological targets while reducing the cost and time.
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Affiliation(s)
- Rameez Hassan Pirzada
- Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea
- S&K Therapeutics, Ajou University Campus Plaza, Suwon, Republic of Korea
| | - Bilal Ahmad
- Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea
| | - Naila Qayyum
- Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea
| | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea
- S&K Therapeutics, Ajou University Campus Plaza, Suwon, Republic of Korea
- *Correspondence: Sangdun Choi,
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Madanat YF, Xie Z, Zeidan AM. Advances in myelodysplastic syndromes: promising novel agents and combination strategies. Expert Rev Hematol 2023; 16:51-63. [PMID: 36620919 DOI: 10.1080/17474086.2023.2166923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Myelodysplastic syndromes (MDS) are heterogeneous group of clonal hematopoietic stem cell neoplasms that have limited approved treatment options. Multiple novel agents are currently being tested in a clinical trial setting. From a therapeutic perspective, MDS is generally divided into lower-risk and higher-risk disease. In this review, we summarize some of the most prominent novel agents currently in development. AREAS COVERED This review focuses on select clinical trials in both lower- and higher-risk MDS, elucidating the mechanisms of action and rationale for drug combinations and summarizing early safety and efficacy data using novel agents in MDS. EXPERT OPINION Advances in understanding the innate immune system, telomere biology, as well as genomic drivers of the disease have led to the development of multiple novel agents that are currently in late stages of clinical development in MDS. Imetelstat is being tested in lower-risk disease and the phase III clinical trial recently completed accrual. Magrolimab, sabatolimab, and venetoclax in addition to novel oral hypomethylating agents (HMA) are being investigated in higher-risk MDS. These advances will hopefully bring better treatment options to patients and lead to a shift in the treatment paradigm. Post HMA therapy remains an area of dire unmet need.
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Affiliation(s)
- Yazan F Madanat
- Simmons Comprehensive Cancer Center, Division of Hematology/Oncology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Zhuoer Xie
- Department of Malignant Hematology, H. Lee Moffitt Cancer Center, Tampa, Florida, USA
| | - Amer M Zeidan
- Section of Hematology, Department of Internal Medicine, Yale Cancer Center, New Haven, Connecticut, USA
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