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Sridharan B, Sinha A, Bardhan J, Modee R, Ehara M, Priyakumar UD. Deep reinforcement learning in chemistry: A review. J Comput Chem 2024. [PMID: 38698628 DOI: 10.1002/jcc.27354] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 05/05/2024]
Abstract
Reinforcement learning (RL) has been applied to various domains in computational chemistry and has found wide-spread success. In this review, we first motivate the application of RL to chemistry and list some broad application domains, for example, molecule generation, geometry optimization, and retrosynthetic pathway search. We set up some of the formalism associated with reinforcement learning that should help the reader translate their chemistry problems into a form where RL can be used to solve them. We then discuss the solution formulations and algorithms proposed in recent literature for these problems, the advantages of one over the other, together with the necessary details of the RL algorithms they employ. This article should help the reader understand the state of RL applications in chemistry, learn about some relevant actively-researched open problems, gain insight into how RL can be used to approach them and hopefully inspire innovative RL applications in Chemistry.
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Affiliation(s)
- Bhuvanesh Sridharan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Animesh Sinha
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Jai Bardhan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Rohit Modee
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Masahiro Ehara
- Research Center for Computational Science, Institute for Molecular Science, Okazaki, Japan
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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Priyadarsinee L, Jamir E, Nagamani S, Mahanta HJ, Kumar N, John L, Sarma H, Kumar A, Gaur AS, Sahoo R, Vaikundamani S, Murugan NA, Priyakumar UD, Raghava GPS, Bharatam PV, Parthasarathi R, Subramanian V, Sastry GM, Sastry GN. Molecular Property Diagnostic Suite for COVID-19 (MPDS COVID-19): an open-source disease-specific drug discovery portal. GigaByte 2024; 2024:gigabyte114. [PMID: 38525218 PMCID: PMC10958779 DOI: 10.46471/gigabyte.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/11/2024] [Indexed: 03/26/2024] Open
Abstract
Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development. Availability & Implementation The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.
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Affiliation(s)
- Lipsa Priyadarsinee
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Esther Jamir
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Selvaraman Nagamani
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Hridoy Jyoti Mahanta
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Nandan Kumar
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Lijo John
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Himakshi Sarma
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Asheesh Kumar
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - Anamika Singh Gaur
- CSIR-Indian Institute of Toxicology Research, Lucknow, 226001, Uttar Pradesh, India
| | - Rosaleen Sahoo
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - S. Vaikundamani
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
| | - N. Arul Murugan
- Indraprastha Institute of Information Technology, Delhi, 110020, India
| | - U. Deva Priyakumar
- International Institute of Information Technology, Gachibowli, Hyderabad, 500032, India
| | - G. P. S. Raghava
- Indraprastha Institute of Information Technology, Delhi, 110020, India
| | - Prasad V. Bharatam
- National Institute of Pharmaceutical Education and Research, S.A.S. Nagar (Mohali), 160062, India
| | - Ramakrishnan Parthasarathi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- CSIR-Indian Institute of Toxicology Research, Lucknow, 226001, Uttar Pradesh, India
| | - V. Subramanian
- Department of Chemistry, Indian Institute of Technology, Chennai, 600036, India
| | - G. Madhavi Sastry
- Schrödinger Inc., Octave, Salarpuria Sattva Knowledge City, 1st Floor, Unit 3A, Hyderabad, 500081, India
| | - G. Narahari Sastry
- CSIR–North East Institute of Science and Technology, Jorhat, 785006, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- Indian Institute of Technology (IIT) Hyderabad, Kandi, Sangareddy, Telangana, 502284, India
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Korlepara DB, C S V, Srivastava R, Pal PK, Raza SH, Kumar V, Pandit S, Nair AG, Pandey S, Sharma S, Jeurkar S, Thakran K, Jaglan R, Verma S, Ramachandran I, Chatterjee P, Nayar D, Priyakumar UD. PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications. Sci Data 2024; 11:180. [PMID: 38336857 PMCID: PMC10858175 DOI: 10.1038/s41597-023-02872-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/21/2023] [Indexed: 02/12/2024] Open
Abstract
Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.
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Affiliation(s)
- Divya B Korlepara
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
- Divison of Physics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, 600127, India
| | - Vasavi C S
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
- Department of Artificial Intelligence, School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, 560035, India
| | - Rakesh Srivastava
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Pradeep Kumar Pal
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Saalim H Raza
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Vishal Kumar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shivam Pandit
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Aathira G Nair
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Sanjana Pandey
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shubham Sharma
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shruti Jeurkar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Kavita Thakran
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Reena Jaglan
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shivangi Verma
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Indhu Ramachandran
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Prathit Chatterjee
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Divya Nayar
- Department of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
| | - U Deva Priyakumar
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India.
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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Modee R, Mehta S, Laghuvarapu S, Priyakumar UD. MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning. J Phys Chem B 2023; 127:10295-10303. [PMID: 38013420 DOI: 10.1021/acs.jpcb.3c04771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Most optimization problems require the user to select an algorithm and, to some extent, also tune it for better performance. Although intuition and knowledge about the problem can speed up these selection and fine-tuning processes, users often use trial-and-error methodologies, which can be time-consuming and inefficient. With all of that in mind and much more, the concept of "learned optimizers", "learning to learn", and "meta-learning" has been gathering attention in recent years. In this article, we propose MolOpt that uses multiagent reinforcement learning (MARL) for autonomous molecular geometry optimization (MGO). Typically MGO algorithms are hand-designed, but MolOpt uses MARL to learn a learned optimizer (policy) that can perform MGO without the need for other hand-designed optimizers. We cast MGO as a MARL problem, where each agent corresponds to a single atom in the molecule. MolOpt performs MGO by minimizing the forces on each atom of the molecule. Our experiments demonstrate the generalizing ability of MolOpt for the MGO of propane, pentane, heptane, hexane, and octane when trained on ethane, butane, and isobutane. In terms of performance, MolOpt outperforms the MDMin optimizer and demonstrates performance similar to that of the FIRE optimizer. However, it does not surpass the BFGS optimizer. The results demonstrate that MolOpt has the potential to introduce innovative advancements in MGO by providing a novel approach using reinforcement learning (RL), which may open up new research directions for MGO. Overall, this work serves as a proof-of-concept for the potential of MARL in MGO.
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Affiliation(s)
- Rohit Modee
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - Sarvesh Mehta
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
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Viswanathan K, Goel M, Laghuvarapu S, Varma G, Priyakumar UD. Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines. Sci Rep 2023; 13:21069. [PMID: 38030689 PMCID: PMC10686981 DOI: 10.1038/s41598-023-42952-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 09/16/2023] [Indexed: 12/01/2023] Open
Abstract
The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with [Formula: see text]a 70% improvement in runtime compared to the baseline model by labeling only [Formula: see text]30% of molecules compared to the baseline oracle.
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Affiliation(s)
- Karthik Viswanathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Girish Varma
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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6
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Chaturvedi A, Borkar K, Priyakumar UD, Vinod P. PREHOST: Host prediction of coronaviridae family using machine learning. Heliyon 2023; 9:e13646. [PMID: 36816252 PMCID: PMC9922161 DOI: 10.1016/j.heliyon.2023.e13646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 02/05/2023] [Accepted: 02/06/2023] [Indexed: 02/13/2023] Open
Abstract
Coronavirus, a zoonotic virus capable of transmitting infections from animals to humans, emerged as a pandemic recently. In such circumstances, it is essential to understand the virus's origin. In this study, we present a novel machine-learning pipeline PreHost for host prediction of the family, Coronaviridae. We leverage the complete viral genome and sequences at the protein level (spike protein, membrane protein, and nucleocapsid protein). Compared with the current state-of-the-art approaches, the random forest model attained high accuracy and recall scores of 99.91% and 0.98, respectively, for genome sequences. In addition to the spike protein sequences, our study shows membrane and nucleocapsid protein sequences can be utilized to predict the host of viruses. We also identified important sites in the viral sequences that help distinguish between different host classes. The host prediction pipeline PreHost will cater as a valuable tool to take effective measures to govern the transmission of future viruses.
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Kanakala G, Aggarwal R, Nayar D, Priyakumar UD. Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets. ACS Omega 2023; 8:2389-2397. [PMID: 36687059 PMCID: PMC9850481 DOI: 10.1021/acsomega.2c06781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Drug design involves the process of identifying and designing molecules that bind well to a given receptor. A vital computational component of this process is the protein-ligand interaction scoring functions that evaluate the binding ability of various molecules or ligands with a given protein receptor binding pocket reasonably accurately. With the publicly available protein-ligand binding affinity data sets in both sequential and structural forms, machine learning methods have gained traction as a top choice for developing such scoring functions. While the performance shown by these models is optimistic, there are several hidden biases present in these data sets themselves that affect the utility of such models for practical purposes such as virtual screening. In this work, we use published methods to systematically investigate several such factors or biases present in these data sets. In our analysis, we highlight the importance of considering sequence, protein-ligand interaction, and pocket structure similarity while constructing data splits and provide an explanation for good protein-only and ligand-only performances in some data sets. Through this study, we provide to the community several pointers for the design of binding affinity predictors and data sets for reliable applicability.
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Affiliation(s)
| | - Rishal Aggarwal
- International
Institute of Information Technology, Hyderabad500 032, India
| | - Divya Nayar
- Department
of Materials Science and Engineering, Indian
Institute of Technology Delhi, Hauz Khas, New Delhi110016, India
| | - U. Deva Priyakumar
- International
Institute of Information Technology, Hyderabad500 032, India
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Dolai R, Kumar R, Elvers BJ, Pal PK, Joseph B, Sikari R, Nayak MK, Maiti A, Singh T, Chrysochos N, Jayaraman A, Krummenacher I, Mondal J, Priyakumar UD, Braunschweig H, Yildiz CB, Schulzke C, Jana A. Carbodicarbenes and Striking Redox Transitions of their Conjugate Acids: Influence of NHC versus CAAC as Donor Substituents. Chemistry 2023; 29:e202202888. [PMID: 36129127 PMCID: PMC10100033 DOI: 10.1002/chem.202202888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Indexed: 01/11/2023]
Abstract
Herein, a new type of carbodicarbene (CDC) comprising two different classes of carbenes is reported; NHC and CAAC as donor substituents and compare the molecular structure and coordination to Au(I)Cl to those of NHC-only and CAAC-only analogues. The conjugate acids of these three CDCs exhibit notable redox properties. Their reactions with [NO][SbF6 ] were investigated. The reduction of the conjugate acid of CAAC-only based CDC with KC8 results in the formation of hydrogen abstracted/eliminated products, which proceed through a neutral radical intermediate, detected by EPR spectroscopy. In contrast, the reduction of conjugate acids of NHC-only and NHC/CAAC based CDCs led to intermolecular reductive (reversible) carbon-carbon sigma bond formation. The resulting relatively elongated carbon-carbon sigma bonds were found to be readily oxidized. They were, thus, demonstrated to be potent reducing agents, underlining their potential utility as organic electron donors and n-dopants in organic semiconductor molecules.
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Affiliation(s)
- Ramapada Dolai
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Rahul Kumar
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Benedict J Elvers
- Institut für Biochemie, Universität Greifswald, Felix-Hausdorff-Strasse 4, 17489, Greifswald, Germany
| | - Pradeep Kumar Pal
- International Institute of Information Technology Gachibowli, Hyderabad, 500032, India
| | - Benson Joseph
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Rina Sikari
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Mithilesh Kumar Nayak
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Avijit Maiti
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Tejender Singh
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Nicolas Chrysochos
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - Arumugam Jayaraman
- Institute of Inorganic Chemistry and Institute for Sustainable Chemistry & Catalysis with Boron (ICB), Julius-Maximilians-Universität Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Ivo Krummenacher
- Institute of Inorganic Chemistry and Institute for Sustainable Chemistry & Catalysis with Boron (ICB), Julius-Maximilians-Universität Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Jagannath Mondal
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
| | - U Deva Priyakumar
- International Institute of Information Technology Gachibowli, Hyderabad, 500032, India
| | - Holger Braunschweig
- Institute of Inorganic Chemistry and Institute for Sustainable Chemistry & Catalysis with Boron (ICB), Julius-Maximilians-Universität Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Cem B Yildiz
- Department of Aromatic and Medicinal Plants, Aksaray University, Aksaray, 68100, Turkey
| | - Carola Schulzke
- Institut für Biochemie, Universität Greifswald, Felix-Hausdorff-Strasse 4, 17489, Greifswald, Germany
| | - Anukul Jana
- Tata Institute of Fundamental Research Hyderabad Gopanpally, Hyderabad, 500046, Telangana, India
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Mehta S, Goel M, Priyakumar UD. MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization. Front Med (Lausanne) 2022; 9:916481. [PMID: 36213671 PMCID: PMC9537730 DOI: 10.3389/fmed.2022.916481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
The pursuit of potential inhibitors for novel targets has become a very important problem especially over the last 2 years with the world in the midst of the COVID-19 pandemic. This entails performing high throughput screening exercises on drug libraries to identify potential “hits”. These hits are identified using analysis of their physical properties like binding affinity to the target receptor, octanol-water partition coefficient (LogP) and more. However, drug libraries can be extremely large and it is infeasible to calculate and analyze the physical properties for each of those molecules within acceptable time and moreover, each molecule must possess a multitude of properties apart from just the binding affinity. To address this problem, in this study, we propose an extension to the Machine learning framework for Enhanced MolEcular Screening (MEMES) framework for multi-objective Bayesian optimization. This approach is capable of identifying over 90% of the most desirable molecules with respect to all required properties while explicitly calculating the values of each of those properties on only 6% of the entire drug library. This framework would provide an immense boost in identifying potential hits that possess all properties required for a drug molecules.
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Nayak P, Murali AC, Pal PK, Priyakumar UD, Chandrasekhar V, Venkatasubbaiah K. Tetra-Coordinated Boron-Functionalized Phenanthroimidazole-Based Zinc Salen as a Photocatalyst for the Cycloaddition of CO 2 and Epoxides. Inorg Chem 2022; 61:14511-14516. [PMID: 36074754 DOI: 10.1021/acs.inorgchem.2c02693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A unique B-N coordinated phenanthroimidazole-based zinc salen was synthesized. The zinc salen thus synthesized acts as a photocatalyst for the cycloaddition of carbon dioxide with terminal epoxides under ambient conditions. DFT study of the cycloaddition of carbon dioxide with terminal epoxide indicates the preference of the reaction pathway when photocatalyzed by zinc salen. We anticipate that this strategy will help to design new photocatalysts for CO2 fixation.
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Affiliation(s)
- Prakash Nayak
- School of Chemical Sciences, National Institute of Science Education and Research (NISER), an OCC of Homi Bhaba National Institute, Bhubaneswar 752050, Odisha, India
| | - Anna Chandrasekar Murali
- School of Chemical Sciences, National Institute of Science Education and Research (NISER), an OCC of Homi Bhaba National Institute, Bhubaneswar 752050, Odisha, India
| | - Pradeep Kumar Pal
- International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- International Institute of Information Technology, Hyderabad 500 032, India
| | - Vadapalli Chandrasekhar
- Tata Institute of Fundamental Research Hyderabad, Gopanpally, Hyderabad 500 046, India.,Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Krishnan Venkatasubbaiah
- School of Chemical Sciences, National Institute of Science Education and Research (NISER), an OCC of Homi Bhaba National Institute, Bhubaneswar 752050, Odisha, India
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11
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Sreedharan R, Pal PK, Panyam PKR, Priyakumar UD, Gandhi T. Synthesis of α‐aryl ketones by harnessing the non‐innocence of toluene and its derivatives: Enhancing the acidity of methyl arenes by a Brønsted base and their mechanistic aspects. ASIAN J ORG CHEM 2022. [DOI: 10.1002/ajoc.202200372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Ramdas Sreedharan
- Vellore Institute of Technology: VIT University Department of Chemistry, School of Advanced Sciences INDIA
| | - Pradeep Kumar Pal
- International Institute of Information Technology Hyderabad Centre for Computational Natural Sciences and Bioinformatics INDIA
| | - Pradeep Kumar Reddy Panyam
- Vellore Institute of Technology: VIT University Department of Chemistry, School of Advanced Sciences INDIA
| | - U Deva Priyakumar
- International Institute of Information Technology Hyderabad Centre for Computational Natural Sciences and Bioinformatics INDIA
| | - Thirumanavelan Gandhi
- VIT University Materials Chemistry Division, School of Advanced Sciences VIT University 632014 Vellore INDIA
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12
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Balakrishnan K, Munusami P, Mohareer K, Priyakumar UD, Banerjee A, Luedde T, Mande SC, Münk C, Banerjee S. Staufen‐2 functions as a cofactor for enhanced Rev‐mediated nucleocytoplasmic trafficking of
HIV
‐1 genomic
RNA
via the
CRM1
pathway. FEBS J 2022; 289:6731-6751. [DOI: 10.1111/febs.16546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/21/2022] [Accepted: 06/01/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Kannan Balakrishnan
- Department of Biochemistry, School of Life Sciences University of Hyderabad India
- Clinic for Gastroenterology, Hepatology, and Infectiology Medical Faculty, Heinrich Heine University Düsseldorf Germany
| | - Punnagai Munusami
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India
- Department of Chemistry Arignar Anna Government Arts & Science College Karaikal Puducherry India
| | - Krishnaveni Mohareer
- Department of Biochemistry, School of Life Sciences University of Hyderabad India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad India
| | - Atoshi Banerjee
- Nevada Institute of Personalized Medicine University of Nevada Las Vegas NV USA
| | - Tom Luedde
- Clinic for Gastroenterology, Hepatology, and Infectiology Medical Faculty, Heinrich Heine University Düsseldorf Germany
| | - Shekhar C. Mande
- National Centre for Cell Science Pune India
- Council of Scientific and Industrial Research New Delhi India
| | - Carsten Münk
- Clinic for Gastroenterology, Hepatology, and Infectiology Medical Faculty, Heinrich Heine University Düsseldorf Germany
| | - Sharmistha Banerjee
- Department of Biochemistry, School of Life Sciences University of Hyderabad India
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13
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Sridharan B, Mehta S, Pathak Y, Priyakumar UD. Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure. J Phys Chem Lett 2022; 13:4924-4933. [PMID: 35635003 DOI: 10.1021/acs.jpclett.2c00624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Spectroscopy is the study of how matter interacts with electromagnetic radiation. The spectra of any molecule are highly information-rich, yet the inverse relation of spectra to the corresponding molecular structure is still an unsolved problem. Nuclear magnetic resonance (NMR) spectroscopy is one such critical technique in the scientists' toolkit to characterize molecules. In this work, a novel machine learning framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure given an NMR spectra. The proposed framework uses a combination of online Monte Carlo tree search (MCTS) and a set of graph convolution networks to build a molecule iteratively. Our method can predict the structure of the molecule ∼80% of the time in its top 3 guesses for molecules with <10 heavy atoms. We believe that the proposed framework is a significant step in solving the inverse design problem of NMR spectra.
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Affiliation(s)
- Bhuvanesh Sridharan
- Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - Sarvesh Mehta
- Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - Yashaswi Pathak
- Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - U Deva Priyakumar
- Centre for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
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14
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Chelur VR, Priyakumar UD. BiRDS - Binding Residue Detection from Protein Sequences Using Deep ResNets. J Chem Inf Model 2022; 62:1809-1818. [PMID: 35414182 DOI: 10.1021/acs.jcim.1c00972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-drug interactions play important roles in many biological processes and therapeutics. Predicting the binding sites of a protein helps to discover such interactions. New drugs can be designed to optimize these interactions, improving protein function. The tertiary structure of a protein decides the binding sites available to the drug molecule, but the determination of the 3D structure is slow and expensive. Conversely, the determination of the amino acid sequence is swift and economical. Although quick and accurate prediction of the binding site using just the sequence is challenging, the application of Deep Learning, which has been hugely successful in several biochemical tasks, makes it feasible. BiRDS is a Residual Neural Network that predicts the protein's most active binding site using sequence information. SC-PDB, an annotated database of druggable binding sites, is used for training the network. Multiple Sequence Alignments of the proteins in the database are generated using DeepMSA, and features such as Position-Specific Scoring Matrix, Secondary Structure, and Relative Solvent Accessibility are extracted. During training, a weighted binary cross-entropy loss function is used to counter the substantial imbalance in the two classes of binding and nonbinding residues. A novel test set SC6K is introduced to compare binding-site prediction methods. BiRDS achieves an AUROC score of 0.87, and the center of 25% of its predicted binding sites lie within 4 Å of the center of the actual binding site.
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Affiliation(s)
- Vineeth R Chelur
- Center for Computational Natural Sciences & Bioinformatics International Institute of Information Technology Hyderabad 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences & Bioinformatics International Institute of Information Technology Hyderabad 500032, India
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15
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Alle S, Kanakan A, Siddiqui S, Garg A, Karthikeyan A, Mehta P, Mishra N, Chattopadhyay P, Devi P, Waghdhare S, Tyagi A, Tarai B, Hazarik PP, Das P, Budhiraja S, Nangia V, Dewan A, Sethuraman R, Subramanian C, Srivastava M, Chakravarthi A, Jacob J, Namagiri M, Konala V, Dash D, Sethi T, Jha S, Agrawal A, Pandey R, Vinod PK, Priyakumar UD. COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits. PLoS One 2022; 17:e0264785. [PMID: 35298502 PMCID: PMC8929610 DOI: 10.1371/journal.pone.0264785] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 08/19/2021] [Accepted: 02/16/2022] [Indexed: 12/15/2022] Open
Abstract
The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.
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Affiliation(s)
- Shanmukh Alle
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Samreen Siddiqui
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Akshit Garg
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Akshaya Karthikeyan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
| | - Priyanka Mehta
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Neha Mishra
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Partha Chattopadhyay
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Intel Technology India Private Limited, Bangalore, Karnataka, India
| | - Priti Devi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Intel Technology India Private Limited, Bangalore, Karnataka, India
| | - Swati Waghdhare
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Akansha Tyagi
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Bansidhar Tarai
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Pranjal Pratim Hazarik
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Poonam Das
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Sandeep Budhiraja
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Vivek Nangia
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | - Arun Dewan
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
| | | | - C. Subramanian
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Mashrin Srivastava
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | | | - Johnny Jacob
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Madhuri Namagiri
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Varma Konala
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Debasish Dash
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Tavpritesh Sethi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Sujeet Jha
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - Anurag Agrawal
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India
- * E-mail: (SJ); (AA); (RP); (PKV); (UDP)
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16
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Choudhury C, Arul Murugan N, Deva Priyakumar U. Structure-based drug repurposing: traditional and advanced AI/ML-aided methods. Drug Discov Today 2022; 27:1847-1861. [PMID: 35301148 PMCID: PMC8920090 DOI: 10.1016/j.drudis.2022.03.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [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: 10/04/2021] [Revised: 02/16/2022] [Accepted: 03/10/2022] [Indexed: 02/08/2023]
Abstract
The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space. Teaser: This review highlights the importance of repurposable chemical space, and the contributions of conventional in silico approaches and modern machine-learning algorithms for rapid structure-based drug repurposing.
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Affiliation(s)
- Chinmayee Choudhury
- Department of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India
| | - N Arul Murugan
- Department of Computer Science, School of Electrical Engineering and Computer Sciences, KTH Royal Institute of Technology, S-100 44, Stockholm, Sweden; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India.
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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17
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Sridharan B, Goel M, Priyakumar UD. Modern Machine Learning for Tackling Inverse Problems in Chemistry: Molecular Design to Realization. Chem Commun (Camb) 2022; 58:5316-5331. [DOI: 10.1039/d1cc07035e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In the pursuit of finding molecules with desired properties, chemists have traditionally relied...
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18
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Goel M, Raghunathan S, Laghuvarapu S, Priyakumar UD. MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards. J Chem Inf Model 2021; 61:5815-5826. [PMID: 34866384 DOI: 10.1021/acs.jcim.1c01341] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches such as high-throughput virtual screening require extensive combing through existing data sets in the hope of finding possible matches. In this study, we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target and other desirable properties for druglike molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate druglike molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, quantitative estimate of drug likeliness, topological polar surface area, and hydration free energy along with the binding affinity. For multiobjective optimization, we have devised a novel strategy in which the property being used to calculate the reward is changed periodically. In comparison to the conventional approach of taking a weighted sum of all rewards, this strategy shows an enhanced ability to generate a significantly higher number of molecules with desirable properties.
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Affiliation(s)
- Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India.,École Centrale School of Engineering, Mahindra University, Hyderabad 500 043, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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19
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Abstract
Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences. Synopsis Recent surge in the application of machine learning (ML) methods in fundamental sciences has led to a perspective that these methods may become important tools in chemical science. This perspective provides an overview of the modern ML methods and their successful applications in chemistry during the last few years.
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Affiliation(s)
- Akshaya Karthikeyan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500 032 India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500 032 India
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20
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Modee R, Laghuvarapu S, Priyakumar UD. Benchmark study on deep neural network potentials for small organic molecules. J Comput Chem 2021; 43:308-318. [PMID: 34870332 DOI: 10.1002/jcc.26790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/13/2021] [Accepted: 11/15/2021] [Indexed: 11/06/2022]
Abstract
There has been tremendous advancement in machine learning (ML) applications in computational chemistry, particularly in neural network potentials (NNP). NNPs can approximate potential energy surface (PES) as a high dimensional function by learning from existing reference data, thereby circumventing the need to solve the electronic Schrödinger equation explicitly. As a result, ML accelerates chemical space exploration and property prediction compared to quantum mechanical methods. Novel ML methods have the potential to provide efficient means for predicting the properties of molecules. However, this potential has been limited by the lack of standard comparative evaluations. In this work, we compare four selected models, that is, ANI, PhysNet, SchNet, and BAND-NN, developed to represent the PES of small organic molecules. We evaluate these models for their accuracy and transferability on two different test sets (i) Small organic molecules of up to eight-heavy atoms on which ANI and SchNet achieve root mean square error (RMSE) of 0.55 and 0.60 kcal/mol, respectively. (ii) On random selection of molecules from the GDB-11 database with 10-heavy atoms, ANI achieves RMSE of 1.17 kcal/mol and SchNet achieves RMSE of 1.89 kcal/mol. We examine their ability to produce smooth meaningful surface by performing PES scans for bond stretch, angle bend, and dihedral rotations on relatively large molecules to assess their possible application in molecular dynamics simulations. We also evaluate their performance for yielding minimum energy structures via geometry optimization using various minimization algorithms. All these models were also able to accurately differentiate different isomers of the same empirical formula C 10 H 20 . ANI and PhysNet achieve an RMSE of 0.29 and 0.52 kcal/mol, respectively, on C 10 H 20 isomers.
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Affiliation(s)
- Rohit Modee
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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21
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Khatri B, Raghunathan S, Chakraborti S, Rahisuddin R, Kumaran S, Tadala R, Wagh P, Priyakumar UD, Chatterjee J. Desolvation of Peptide Bond by O to S Substitution Impacts Protein Stability. Angew Chem Int Ed Engl 2021; 60:24870-24874. [PMID: 34519402 DOI: 10.1002/anie.202110978] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/10/2021] [Indexed: 12/31/2022]
Abstract
Amino acid side chains are key to fine-tuning the microenvironment polarity in proteins composed of polar amide bonds. Here, we report that substituting an oxygen atom of the backbone amide bond with sulfur atom desolvates the thioamide bond, thereby increasing its lipophilicity. The impact of such local desolvation by O to S substitution in proteins was tested by synthesizing thioamidated variants of Pin1 WW domain. We observe that a thioamide acts in synergy with nonpolar amino acid side chains to reduce the microenvironment polarity and increase protein stability by more than 14 °C. Through favorable van der Waals and hydrogen bonding interactions, this single atom substitution significantly stabilizes proteins without altering the amino acid sequence and structure of the native protein.
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Affiliation(s)
- Bhavesh Khatri
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.,Present Address: École Centrale School of Engineering, Mahindra University, Hyderabad, 500043, India
| | - Sohini Chakraborti
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
| | - R Rahisuddin
- CSIR- Institute of Microbial Technology, Chandigarh 1, 60036, India
| | - S Kumaran
- CSIR- Institute of Microbial Technology, Chandigarh 1, 60036, India
| | | | | | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Jayanta Chatterjee
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
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22
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Khatri B, Raghunathan S, Chakraborti S, Rahisuddin R, Kumaran S, Tadala R, Wagh P, Priyakumar UD, Chatterjee J. Desolvation of Peptide Bond by O to S Substitution Impacts Protein Stability. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202110978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Bhavesh Khatri
- Molecular Biophysics Unit Indian Institute of Science Bangalore 560012 India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad 500032 India
- Present Address: École Centrale School of Engineering Mahindra University Hyderabad 500043 India
| | - Sohini Chakraborti
- Molecular Biophysics Unit Indian Institute of Science Bangalore 560012 India
| | - R. Rahisuddin
- CSIR- Institute of Microbial Technology Chandigarh 1 60036 India
| | - S. Kumaran
- CSIR- Institute of Microbial Technology Chandigarh 1 60036 India
| | | | | | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad 500032 India
| | - Jayanta Chatterjee
- Molecular Biophysics Unit Indian Institute of Science Bangalore 560012 India
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23
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Abstract
Application of deep learning techniques for de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables the usage of state of the art models in natural language processing, such as Transformers, for molecular design in general. Inspired by generative pre-training (GPT) models that have been shown to be successful in generating meaningful text, we train a transformer-decoder on the next token prediction task using masked self-attention for the generation of druglike molecules in this study. We show that our model, MolGPT, performs on par with other previously proposed modern machine learning frameworks for molecular generation in terms of generating valid, unique, and novel molecules. Furthermore, we demonstrate that the model can be trained conditionally to control multiple properties of the generated molecules. We also show that the model can be used to generate molecules with desired scaffolds as well as desired molecular properties by conditioning the generation on scaffold SMILES strings of desired scaffolds and property values. Using saliency maps, we highlight the interpretability of the generative process of the model.
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Affiliation(s)
- Viraj Bagal
- International Institute of Information Technology, Hyderabad 500 032, India.,Indian Institute of Science Education and Research, Pune 411 008, India
| | - Rishal Aggarwal
- International Institute of Information Technology, Hyderabad 500 032, India
| | - P K Vinod
- International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- International Institute of Information Technology, Hyderabad 500 032, India
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24
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Modee R, Agarwal S, Verma A, Joshi K, Priyakumar UD. DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers. Phys Chem Chem Phys 2021; 23:21995-22003. [PMID: 34569568 DOI: 10.1039/d1cp02956h] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol-1 (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.
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Affiliation(s)
- Rohit Modee
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India.
| | - Sheena Agarwal
- Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Dr Homi Bhabha Road, Pune-411008, India. .,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh-201002, India
| | - Ashwini Verma
- Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Dr Homi Bhabha Road, Pune-411008, India.
| | - Kavita Joshi
- Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Dr Homi Bhabha Road, Pune-411008, India. .,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh-201002, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India.
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25
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Abstract
Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new Stransitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.
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Affiliation(s)
- Yashas B L Samaga
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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26
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Dhara D, Pal PK, Dolai R, Chrysochos N, Rawat H, Elvers BJ, Krummenacher I, Braunschweig H, Schulzke C, Chandrasekhar V, Priyakumar UD, Jana A. Synthesis and reactivity of NHC-coordinated phosphinidene oxide. Chem Commun (Camb) 2021; 57:9546-9549. [PMID: 34546278 DOI: 10.1039/d1cc04421d] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Here we report the synthesis of an N-heterocyclic carbene (NHC)-stabilised phosphinidene oxide by the controlled oxygenation of a phosphinidene under ambient conditions. This compound can be further oxygenated to a phosphinidene dioxide. The stoichiometric reduction of a phosphinidene oxide with KC8 resembles the pinacol coupling reaction-the reduction of a carbonyl compound. We also looked at the stoichiometric oxidation of NHC-coordinated phosphinidene, phosphinidene oxide and phosphinidene dioxide with [NO][SbF6].
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Affiliation(s)
- Debabrata Dhara
- Tata Institute of Fundamental Research Hyderabad, Gopanpally, Hyderabad-500046, Telangana, India.
| | - Pradeep Kumar Pal
- International Institute of Information Technology, Gachibowli, Hyderabad-500032, India.
| | - Ramapada Dolai
- Tata Institute of Fundamental Research Hyderabad, Gopanpally, Hyderabad-500046, Telangana, India.
| | - Nicolas Chrysochos
- Institut für Biochemie, Universität Greifswald, Felix-Hausdorff-Straße 4, Greifswald D-17489, Germany.
| | - Hemant Rawat
- Tata Institute of Fundamental Research Hyderabad, Gopanpally, Hyderabad-500046, Telangana, India.
| | - Benedict J Elvers
- Institut für Biochemie, Universität Greifswald, Felix-Hausdorff-Straße 4, Greifswald D-17489, Germany.
| | - Ivo Krummenacher
- Institute of Inorganic Chemistry and Institute for Sustainable Chemistry & Catalysis with Boron (ICB), Julius-Maximilians-Universität Würzburg, Am Hubland, Würzburg 97074, Germany.
| | - Holger Braunschweig
- Institute of Inorganic Chemistry and Institute for Sustainable Chemistry & Catalysis with Boron (ICB), Julius-Maximilians-Universität Würzburg, Am Hubland, Würzburg 97074, Germany.
| | - Carola Schulzke
- Institut für Biochemie, Universität Greifswald, Felix-Hausdorff-Straße 4, Greifswald D-17489, Germany.
| | - Vadapalli Chandrasekhar
- Tata Institute of Fundamental Research Hyderabad, Gopanpally, Hyderabad-500046, Telangana, India. .,Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur-208016, India
| | - U Deva Priyakumar
- International Institute of Information Technology, Gachibowli, Hyderabad-500032, India.
| | - Anukul Jana
- Tata Institute of Fundamental Research Hyderabad, Gopanpally, Hyderabad-500046, Telangana, India.
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27
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Mehta S, Laghuvarapu S, Pathak Y, Sethi A, Alvala M, Priyakumar UD. MEMES: Machine learning framework for Enhanced MolEcular Screening. Chem Sci 2021; 12:11710-11721. [PMID: 34659706 PMCID: PMC8442698 DOI: 10.1039/d1sc02783b] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/24/2021] [Indexed: 01/20/2023] Open
Abstract
In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.
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Affiliation(s)
- Sarvesh Mehta
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
| | - Yashaswi Pathak
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
| | - Aaftaab Sethi
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Hyderabad 500 037 India
| | - Mallika Alvala
- School of Pharmacy and Technology Management, Narsee Monjee Institute of Management Sciences Hyderabad India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
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28
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Mehta P, Alle S, Chaturvedi A, Swaminathan A, Saifi S, Maurya R, Chattopadhyay P, Devi P, Chauhan R, Kanakan A, Vasudevan JS, Sethuraman R, Chidambaram S, Srivastava M, Chakravarthi A, Jacob J, Namagiri M, Konala V, Jha S, Priyakumar UD, Vinod PK, Pandey R. Clinico-Genomic Analysis Reveals Mutations Associated with COVID-19 Disease Severity: Possible Modulation by RNA Structure. Pathogens 2021; 10:1109. [PMID: 34578142 PMCID: PMC8464923 DOI: 10.3390/pathogens10091109] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 06/17/2021] [Revised: 08/07/2021] [Accepted: 08/10/2021] [Indexed: 12/23/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and preventionas the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support.
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Affiliation(s)
- Priyanka Mehta
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
| | - Shanmukh Alle
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India; (S.A.); (A.C.); (R.C.)
| | - Anusha Chaturvedi
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India; (S.A.); (A.C.); (R.C.)
| | - Aparna Swaminathan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
| | - Sheeba Saifi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
| | - Ranjeet Maurya
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Partha Chattopadhyay
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Priti Devi
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Ruchi Chauhan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India; (S.A.); (A.C.); (R.C.)
| | - Akshay Kanakan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
| | - Janani Srinivasa Vasudevan
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
| | - Ramanathan Sethuraman
- Intel Technology India Private Limited, Bangalore 530103, India; (R.S.); (S.C.); (M.S.); (A.C.); (J.J.); (M.N.); (V.K.)
| | - Subramanian Chidambaram
- Intel Technology India Private Limited, Bangalore 530103, India; (R.S.); (S.C.); (M.S.); (A.C.); (J.J.); (M.N.); (V.K.)
| | - Mashrin Srivastava
- Intel Technology India Private Limited, Bangalore 530103, India; (R.S.); (S.C.); (M.S.); (A.C.); (J.J.); (M.N.); (V.K.)
| | - Avinash Chakravarthi
- Intel Technology India Private Limited, Bangalore 530103, India; (R.S.); (S.C.); (M.S.); (A.C.); (J.J.); (M.N.); (V.K.)
| | - Johnny Jacob
- Intel Technology India Private Limited, Bangalore 530103, India; (R.S.); (S.C.); (M.S.); (A.C.); (J.J.); (M.N.); (V.K.)
| | - Madhuri Namagiri
- Intel Technology India Private Limited, Bangalore 530103, India; (R.S.); (S.C.); (M.S.); (A.C.); (J.J.); (M.N.); (V.K.)
| | - Varma Konala
- Intel Technology India Private Limited, Bangalore 530103, India; (R.S.); (S.C.); (M.S.); (A.C.); (J.J.); (M.N.); (V.K.)
| | - Sujeet Jha
- Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi 110017, India;
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India; (S.A.); (A.C.); (R.C.)
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India; (S.A.); (A.C.); (R.C.)
| | - Rajesh Pandey
- INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110017, India; (P.M.); (A.S.); (S.S.); (R.M.); (P.C.); (P.D.); (A.K.); (J.S.V.)
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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29
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Aggarwal R, Gupta A, Chelur V, Jawahar CV, Priyakumar UD. DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks. J Chem Inf Model 2021; 62:5069-5079. [PMID: 34374539 DOI: 10.1021/acs.jcim.1c00799] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilizes 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another data set SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 1st, 2018, until February 28th, 2020, for ligand binding site (LBS) detection. DeepPocket's results on various binding site data sets and SC6K highlight its better performance over current state-of-the-art methods and good generalization ability over novel structures.
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Affiliation(s)
- Rishal Aggarwal
- International Institute of Information Technology, Hyderabad 500 032, India
| | - Akash Gupta
- International Institute of Information Technology, Hyderabad 500 032, India
| | - Vineeth Chelur
- International Institute of Information Technology, Hyderabad 500 032, India
| | - C V Jawahar
- International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- International Institute of Information Technology, Hyderabad 500 032, India
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30
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Abstract
Synthetic ion channels are a promising technology in the medical and materials sciences because of their ability to conduct ions. Channels based on cyclodextrin, a cyclic oligomer of glucose, are of particular interest because of their nontoxicity and biocompatibility. Using molecular dynamics-based free energy calculations, this study identifies cyclodextrin channel types that are best suited to serve as synthetic ion channels. Free energy profiles show that the connectivity in the channel determines whether the channel is cation-selective or anion-selective. Furthermore, the energy barrier for ion transport is governed by the number of glucose molecules making up the cyclodextrin units of the channel. A detailed mechanism is proposed for ion transport through these channels. Findings from this study will aid in designing cyclodextrin-based channels that could be either cation-selective or anion-selective, by modifying the linkages of the channel or the number of glucose molecules in the cyclodextrin rings.
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Affiliation(s)
- Pratyusha Musunuru
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Siladitya Padhi
- TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad 500 081, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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31
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Karthikeyan A, Garg A, Vinod PK, Priyakumar UD. Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction. Front Public Health 2021; 9:626697. [PMID: 34055710 PMCID: PMC8149622 DOI: 10.3389/fpubh.2021.626697] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 04/06/2021] [Indexed: 12/14/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.
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Affiliation(s)
| | | | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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32
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Abstract
Solvation free energy is a fundamental property that influences various chemical and biological processes, such as reaction rates, protein folding, drug binding, and bioavailability of drugs. In this work, we present a deep learning method based on graph networks to accurately predict solvation free energies of small organic molecules. The proposed model, comprising three phases, namely, message passing, interaction, and prediction, is able to predict solvation free energies in any generic organic solvent with a mean absolute error of 0.16 kcal/mol. In terms of accuracy, the current model outperforms all of the proposed machine learning-based models so far. The atomic interactions predicted in an unsupervised manner are able to explain the trends of free energies consistent with chemical wisdom. Further, the robustness of the machine learning-based model has been tested thoroughly, and its capability to interpret the predictions has been verified with several examples.
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Affiliation(s)
- Yashaswi Pathak
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - Sarvesh Mehta
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500032, India
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33
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Pattnaik P, Raghunathan S, Kalluri T, Bhimalapuram P, Jawahar CV, Priyakumar UD. Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations. J Phys Chem A 2020; 124:6954-6967. [DOI: 10.1021/acs.jpca.0c03926] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Punyaslok Pattnaik
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Tarun Kalluri
- Center for Visual Information Technology, KCIS, International Institute of Information Technology, Hyderabad 500 032, India
| | - Prabhakar Bhimalapuram
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - C. V. Jawahar
- Center for Visual Information Technology, KCIS, International Institute of Information Technology, Hyderabad 500 032, India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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34
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Raghunathan S, Yadav K, Rojisha VC, Jaganade T, Prathyusha V, Bikkina S, Lourderaj U, Priyakumar UD. Transition between [R]- and [S]-stereoisomers without bond breaking. Phys Chem Chem Phys 2020; 22:14983-14991. [PMID: 32588839 DOI: 10.1039/d0cp02918a] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The fifty-year old proposal of a nondissociative racemization reaction of a tetracoordinated tetrahedral center from one enantiomer to another via a planar transition state by Hoffmann and coworkers has been explored by many research groups over the past five decades. A number of stable molecules with planar tetracoordinated and higher-coordinated centers have been designed and experimentally realized; however, there has not been a single example of a molecular system that can possibly undergo such racemization. Here we show examples of molecular species that undergo inversion of stereochemistry around tetrahedral centers (Si, Al- and P+) either via a planar transition state or an intermediate state using quantum mechanical, ab initio quasi-classical dynamics calculations, and Born-Oppenheimer molecular dynamics (BOMD) simulations. This work is expected to provide potential leads for future studies on this fundamental phenomenon in chemistry.
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Affiliation(s)
- Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology, Hyderabad 500 032, India.
| | - Komal Yadav
- School of Chemical Sciences, National Institute of Science Education and Research, Bhubaneswar, HBNI, P.O. Jatani, Khordha 752050, India.
| | - V C Rojisha
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology, Hyderabad 500 032, India.
| | - Tanashree Jaganade
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology, Hyderabad 500 032, India.
| | - V Prathyusha
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology, Hyderabad 500 032, India.
| | - Swetha Bikkina
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology, Hyderabad 500 032, India.
| | - Upakarasamy Lourderaj
- School of Chemical Sciences, National Institute of Science Education and Research, Bhubaneswar, HBNI, P.O. Jatani, Khordha 752050, India.
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology, Hyderabad 500 032, India.
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35
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Pathak Y, Laghuvarapu S, Mehta S, Priyakumar UD. Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules. ACTA ACUST UNITED AC 2020. [DOI: 10.1609/aaai.v34i01.5433] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents with high accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.
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36
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Abstract
Noncovalent interactions are key determinants in both chemical and biological processes. Among such processes, the hydrophobic interactions play an eminent role in folding of proteins, nucleic acids, formation of membranes, protein-ligand recognition, etc.. Though this interaction is mediated through the aqueous solvent, the stability of the above biomolecules can be highly sensitive to any small external perturbations, such as temperature, pressure, pH, or even cosolvent additives, like, urea-a highly soluble small organic molecule utilized by various living organisms to regulate osmotic pressure. A plethora of detailed studies exist covering both experimental and theoretical regimes, to understand how urea modulates the stability of biological macromolecules. While experimentalists have been primarily focusing on the thermodynamic and kinetic aspects, theoretical modeling predominantly involves mechanistic information at the molecular level, calculating atomistic details applying the force field approach to the high level electronic details using the quantum mechanical methods. The review focuses mainly on examples with biological relevance, such as (1) urea-assisted protein unfolding, (2) urea-assisted RNA unfolding, (3) urea lesion interaction within damaged DNA, (4) urea conduction through membrane proteins, and (5) protein-ligand interactions those explicitly address the vitality of hydrophobic interactions involving exclusively the urea-aromatic moiety.
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Affiliation(s)
- Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Tanashree Jaganade
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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37
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Abstract
The optical activity of a metal nanocluster (NC) is induced either by an asymmetric arrangement of constituents or by a dissymmetric field of a chiral ligand layer. Herein, we unveil the origin of chirality in Ag29 NCs, which is attributed to the intrinsically chiral atomic arrangement. The X-ray crystal structure of a Ag29(BDT)12(TPP)4 NC (BDT: 1,3-benzenedithiol; TPP: triphenylphosphine) manifested the presence of intrinsic chirality in the outer shell capping the icosahedral achiral Ag13 core. The enantiomers of the Ag29(BDT)12(TPP)4 NC are separated by high-performance liquid chromatography (HPLC) using a chiral column for the first time, showing mirror-image circular dichroism (CD) spectra. The CD spectra are reproduced by time-dependent density functional theory (TDDFT) calculations based on enantiomeric Ag29 models with achiral 1,3-propanedithiolate ligands. The mechanism of chiral induction in the synthesis of Ag29(DHLA)12 (DHLA: α-dihydrolipoic acid) NCs with a chiral ligand system is further discussed with the aid of DFT calculations. The use of the enantiomeric DHLA ligand preferentially leads to a one-handed atomic arrangement which is more stable than the opposite one, inducing the enantiomeric excess in the population of intrinsically chiral Ag29 NCs with CD activity. Enantioseparation of Ag29 nanoclusters with intrinsic chirality was performed by chiral HPLC, affording a pair of fractions with mirror image CD spectra.![]()
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Affiliation(s)
- Hiroto Yoshida
- Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST) Ikoma Nara 630-01921 Japan
| | - Masahiro Ehara
- Institute for Molecular Science, Research Center for Computational Science Myodai-ji Okazaki 444-8585 Japan
| | - U Deva Priyakumar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500032 India
| | - Tsuyoshi Kawai
- Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST) Ikoma Nara 630-01921 Japan
| | - Takuya Nakashima
- Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST) Ikoma Nara 630-01921 Japan
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38
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Abstract
A machine learning framework that generates material compositions exhibiting properties desired by the user.
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Affiliation(s)
- Yashaswi Pathak
- International Institute of Information Technology
- Hyderabad 500 032
- India
| | | | - Girish Varma
- International Institute of Information Technology
- Hyderabad 500 032
- India
| | - Masahiro Ehara
- Research Center for Computational Science
- Institute for Molecular Science
- Okazaki 444-8585
- Japan
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39
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Padhi S, Priyakumar UD. Selectivity and transport in aquaporins from molecular simulation studies. Vitamins and Hormones 2020; 112:47-70. [DOI: 10.1016/bs.vh.2019.10.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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40
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Laghuvarapu S, Pathak Y, Priyakumar UD. BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules. J Comput Chem 2019; 41:790-799. [DOI: 10.1002/jcc.26128] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 11/13/2019] [Accepted: 11/21/2019] [Indexed: 12/26/2022]
Affiliation(s)
- Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology Hyderabad 500 032 India
| | - Yashaswi Pathak
- Center for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology Hyderabad 500 032 India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology Hyderabad 500 032 India
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41
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Kumar S, Thakur J, Yadav K, Mitra M, Pal S, Ray A, Gupta S, Medatwal N, Gupta R, Mishra D, Rani P, Padhi S, Sharma P, Kapil A, Srivastava A, Priyakumar UD, Dasgupta U, Thukral L, Bajaj A. Cholic Acid-Derived Amphiphile which Combats Gram-Positive Bacteria-Mediated Infections via Disintegration of Lipid Clusters. ACS Biomater Sci Eng 2019; 5:4764-4775. [PMID: 33448819 DOI: 10.1021/acsbiomaterials.9b00706] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [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: 12/17/2022]
Abstract
Inappropriate and uncontrolled use of antibiotics results in the emergence of antibiotic resistance, thereby threatening the present clinical regimens to treat infectious diseases. Therefore, new antimicrobial agents that can prevent bacteria from developing drug resistance are urgently needed. Selective disruption of bacterial membranes is the most effective strategy for combating microbial infections as accumulation of genetic mutations will not allow for the emergence of drug resistance against these antimicrobials. In this work, we tested cholic acid (CA) derived amphiphiles tethered with different alkyl chains for their ability to combat Gram-positive bacterial infections. In-depth biophysical and biomolecular simulation studies suggested that the amphiphile with a hexyl chain (6) executes more effective interactions with Gram-positive bacterial membranes as compared to other hydrophobic counterparts. Amphiphile 6 is effective against multidrug resistant Gram-positive bacterial strains as well and does not allow the adherence of S. aureus on amphiphile 6 coated catheters implanted in mice. Further, treatment of wound infections with amphiphile 6 clears the bacterial infections. Therefore, the current study presents strategic guidelines in design and development of CA-derived membrane-targeting antimicrobials for Gram-positive bacterial infections.
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Affiliation(s)
- Sandeep Kumar
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India.,Manipal Academy of Higher Education, Tiger Circle Road, Madhav Nagar, Manipal-576104, Karnataka, India
| | - Jyoti Thakur
- Department of Chemistry, Indian Institute of Science Education and Research, Bhopal Bypass Road, Bhauri, Bhopal-462066, Madhya Pradesh, India
| | - Kavita Yadav
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India.,Manipal Academy of Higher Education, Tiger Circle Road, Madhav Nagar, Manipal-576104, Karnataka, India
| | - Madhurima Mitra
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Sanjay Pal
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India.,Kalinga Institute of Industrial Technology, KIIT Road, Patia, Bhubaneswar-751024, Odisha, India
| | - Arjun Ray
- CSIR-Institute of Genomics and Integrative Biology, South Campus, Mathura Road, Opp: Sukhdev Vihar Bus Depot, New Delhi-110025, India
| | - Siddhi Gupta
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Nihal Medatwal
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Ragini Gupta
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Deepakkumar Mishra
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Parul Rani
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Siladitya Padhi
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Professor CR Rao Road, Gachibowli, Hyderabad-500032, India
| | - Priyanka Sharma
- Department of Microbiology, All India Institute of Medical Sciences, Sri Aurobindo Marg, Ansari Nagar, New Delhi-110029, India
| | - Arti Kapil
- Department of Microbiology, All India Institute of Medical Sciences, Sri Aurobindo Marg, Ansari Nagar, New Delhi-110029, India
| | - Aasheesh Srivastava
- Department of Chemistry, Indian Institute of Science Education and Research, Bhopal Bypass Road, Bhauri, Bhopal-462066, Madhya Pradesh, India
| | - U Deva Priyakumar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Professor CR Rao Road, Gachibowli, Hyderabad-500032, India
| | - Ujjaini Dasgupta
- Amity Institute of Integrative Sciences and Health, Amity University, Amity Education Valley Gurugram, Panchgaon, Manesar, Gurugram-122413, Haryana, India
| | - Lipi Thukral
- CSIR-Institute of Genomics and Integrative Biology, South Campus, Mathura Road, Opp: Sukhdev Vihar Bus Depot, New Delhi-110025, India
| | - Avinash Bajaj
- Laboratory of Nanotechnology and Chemical Biology, Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
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42
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Jaganade T, Chattopadhyay A, Pazhayam NM, Priyakumar UD. Energetic, Structural and Dynamic Properties of Nucleobase-Urea Interactions that Aid in Urea Assisted RNA Unfolding. Sci Rep 2019; 9:8805. [PMID: 31217494 PMCID: PMC6584539 DOI: 10.1038/s41598-019-45010-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [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: 01/11/2019] [Accepted: 05/28/2019] [Indexed: 01/21/2023] Open
Abstract
Understanding the structure-function relationships of RNA has become increasingly important given the realization of its functional role in various cellular processes. Chemical denaturation of RNA by urea has been shown to be beneficial in investigating RNA stability and folding. Elucidation of the mechanism of unfolding of RNA by urea is important for understanding the folding pathways. In addition to studying denaturation of RNA in aqueous urea, it is important to understand the nature and strength of interactions of the building blocks of RNA. In this study, a systematic examination of the structural features and energetic factors involving interactions between nucleobases and urea is presented. Results from molecular dynamics (MD) simulations on each of the five DNA/RNA bases in water and eight different concentrations of aqueous urea, and free energy calculations using the thermodynamic integration method are presented. The interaction energies between all the nucleobases with the solvent environment and the transfer free energies become more favorable with respect to increase in the concentration of urea. Preferential interactions of urea versus water molecules with all model systems determined using Kirkwood-Buff integrals and two-domain models indicate preference of urea by nucleobases in comparison to water. The modes of interaction between urea and the nucleobases were analyzed in detail. In addition to the previously identified hydrogen bonding and stacking interactions between urea and nucleobases that stabilize the unfolded states of RNA in aqueous solution, NH-π interactions are proposed to be important. Dynamic properties of each of these three modes of interactions have been presented. The study provides fundamental insights into the nature of interaction of urea molecules with nucleobases and how it disrupts nucleic acids.
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Affiliation(s)
- Tanashree Jaganade
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Aditya Chattopadhyay
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Nila M Pazhayam
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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43
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Ehara M, Priyakumar UD. Gold‐Palladium Nanocluster Catalysts for Homocoupling: Electronic Structure and Interface Dynamics. CHEM REC 2019; 19:947-959. [DOI: 10.1002/tcr.201800177] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 12/11/2018] [Indexed: 12/28/2022]
Affiliation(s)
- Masahiro Ehara
- Institute for Molecular Science and Research Center for Computational Science 38 Nishigo-Naka, Myodaiji Okazaki 444-8585 Japan
- Elements Strategy Initiative for Catalysts and Batteries (ESICB)Kyoto University Kyoto 615-8245 Japan
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology Hyderabad 500 032 India
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44
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Bung N, Roy A, Priyakumar UD, Bulusu G. Computational modeling of the catalytic mechanism of hydroxymethylbilane synthase. Phys Chem Chem Phys 2019; 21:7932-7940. [DOI: 10.1039/c9cp00196d] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Hydroxymethylbilane synthase (HMBS), the third enzyme in the heme biosynthesis pathway, catalyzes the formation of 1-hydroxymethylbilane (HMB) by a stepwise polymerization of four molecules of porphobilinogen (PBG) using the dipyrromethane (DPM) cofactor.
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Affiliation(s)
- Navneet Bung
- TCS Innovation Labs – Hyderabad (Life Sciences Division)
- Tata Consultancy Services Limited
- Hyderabad 500081
- India
- Center for Computational Natural Sciences and Bioinformatics
| | - Arijit Roy
- TCS Innovation Labs – Hyderabad (Life Sciences Division)
- Tata Consultancy Services Limited
- Hyderabad 500081
- India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics
- International Institute of Information Technology
- Hyderabad 500032
- India
| | - Gopalakrishnan Bulusu
- TCS Innovation Labs – Hyderabad (Life Sciences Division)
- Tata Consultancy Services Limited
- Hyderabad 500081
- India
- Center for Computational Natural Sciences and Bioinformatics
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45
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Vibhute AM, Priyakumar UD, Ravi A, Sureshan KM. Correction: Model molecules to classify CHO hydrogen-bonds. Chem Commun (Camb) 2018; 54:8136. [PMID: 29975379 DOI: 10.1039/c8cc90298d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Correction for 'Model molecules to classify CHO hydrogen-bonds' by Amol M. Vibhute et al., Chem. Commun., 2018, 54, 4629-4632.
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Affiliation(s)
- Amol M Vibhute
- School of Chemistry, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram, Kerala-695551, India.
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46
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Chattopadhyay A, Zheng M, Waller MP, Priyakumar UD. A Probabilistic Framework for Constructing Temporal Relations in Replica Exchange Molecular Trajectories. J Chem Theory Comput 2018; 14:3365-3380. [PMID: 29791153 DOI: 10.1021/acs.jctc.7b01245] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Knowledge of the structure and dynamics of biomolecules is essential for elucidating the underlying mechanisms of biological processes. Given the stochastic nature of many biological processes, like protein unfolding, it is almost impossible that two independent simulations will generate the exact same sequence of events, which makes direct analysis of simulations difficult. Statistical models like Markov chains, transition networks, etc. help in shedding some light on the mechanistic nature of such processes by predicting long-time dynamics of these systems from short simulations. However, such methods fall short in analyzing trajectories with partial or no temporal information, for example, replica exchange molecular dynamics or Monte Carlo simulations. In this work, we propose a probabilistic algorithm, borrowing concepts from graph theory and machine learning, to extract reactive pathways from molecular trajectories in the absence of temporal data. A suitable vector representation was chosen to represent each frame in the macromolecular trajectory (as a series of interaction and conformational energies), and dimensionality reduction was performed using principal component analysis (PCA). The trajectory was then clustered using a density-based clustering algorithm, where each cluster represents a metastable state on the potential energy surface (PES) of the biomolecule under study. A graph was created with these clusters as nodes with the edges learned using an iterative expectation maximization algorithm. The most reactive path is conceived as the widest path along this graph. We have tested our method on RNA hairpin unfolding trajectory in aqueous urea solution. Our method makes the understanding of the mechanism of unfolding in the RNA hairpin molecule more tractable. As this method does not rely on temporal data, it can be used to analyze trajectories from Monte Carlo sampling techniques and replica exchange molecular dynamics (REMD).
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Affiliation(s)
- Aditya Chattopadhyay
- Centre for Computational Natural Sciences and Bioinformatics , International Institute of Information Technology , Hyderabad 500032 , India
| | - Min Zheng
- Centre for Multiscale Theory and Computation , Westfälische Wilhelms-Universität Münster , Münster , Germany
| | - Mark P Waller
- Department of Physics and International Centre for Quantum and Molecular Structures , Shanghai University , Shanghai , 200444 , People's Republic of China
| | - U Deva Priyakumar
- Centre for Computational Natural Sciences and Bioinformatics , International Institute of Information Technology , Hyderabad 500032 , India
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47
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Abstract
We developed a set of conformationally locked molecules each of which makes a single CHO H-bond/short contact and has different electron density at the acceptor oxygen atom. The downfield shift of the 1H NMR signals due to the hydrogen involved in the CHO H-bond varied from 0.93-1.6 ppm, and the magnitude of Δδ is in correlation with the hybridization state of the acceptor oxygen and with the CHO H-bond strengths quantified using a computational method.
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Affiliation(s)
- Amol M Vibhute
- School of Chemistry, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram, Kerala-695551, India.
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48
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Goyal S, Chattopadhyay A, Kasavajhala K, Priyakumar UD. Role of Urea–Aromatic Stacking Interactions in Stabilizing the Aromatic Residues of the Protein in Urea-Induced Denatured State. J Am Chem Soc 2017; 139:14931-14946. [DOI: 10.1021/jacs.7b05463] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Siddharth Goyal
- Center for Computational
Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Aditya Chattopadhyay
- Center for Computational
Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Koushik Kasavajhala
- Center for Computational
Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U. Deva Priyakumar
- Center for Computational
Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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49
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Padhi S, Reddy LK, Priyakumar UD. pH-mediated gating and formate transport mechanism in the Escherichia coli formate channel. Molecular Simulation 2017. [DOI: 10.1080/08927022.2017.1353691] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Siladitya Padhi
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Lekkala Karthik Reddy
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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50
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Padhi S, Priyakumar UD. Microsecond simulation of human aquaporin 2 reveals structural determinants of water permeability and selectivity. Biochimica et Biophysica Acta (BBA) - Biomembranes 2017; 1859:10-16. [DOI: 10.1016/j.bbamem.2016.10.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 10/19/2016] [Accepted: 10/21/2016] [Indexed: 02/06/2023]
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