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Article ; Online: Hexadecanoic acid analogs as potential CviR-mediated quorum sensing inhibitors in

Senthil, Renganathan / Archunan, Govindaraju / Vithya, Dharmaraj / Saravanan, Konda Mani

Journal of biomolecular structure & dynamics

2024 , Page(s) 1–10

Abstract: Chromobacterium ... ...

Abstract	Chromobacterium violaceum
Language	English
Publishing date	2024-01-02
Publishing country	England
Document type	Journal Article
ZDB-ID	49157-3
ISSN	1538-0254 ; 0739-1102
ISSN (online)	1538-0254
ISSN	0739-1102
DOI	10.1080/07391102.2023.2299945
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Article ; Online: Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Harihar, Balasubramanian / Saravanan, Konda Mani / Gromiha, Michael M / Selvaraj, Samuel

Molecular biotechnology

2024

Abstract: Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and ... ...

Abstract	Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.
Language	English
Publishing date	2024-03-18
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	1193057-3
ISSN	1559-0305 ; 1073-6085
ISSN (online)	1559-0305
ISSN	1073-6085
DOI	10.1007/s12033-024-01119-4
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Article ; Online: DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.

Zhang, Haiping / Saravanan, Konda Mani / Zhang, John Z H

Molecules (Basel, Switzerland)

2023 Volume 28, Issue 12

Abstract: The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by ... ...

Abstract	The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.
MeSH term(s)	Ligands ; Neural Networks, Computer ; Proteins/chemistry ; Protein Conformation ; Drug Evaluation, Preclinical ; Protein Binding
Chemical Substances	Ligands ; Proteins
Language	English
Publishing date	2023-06-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules28124691
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Article ; Online: Structural analysis of human G-protein-coupled receptor 17 ligand binding sites.

Konda Mani, Saravanan / Thiyagarajan, Ramesh / Yli-Harja, Olli / Kandhavelu, Meenakshisundaram / Murugesan, Akshaya

Journal of cellular biochemistry

2023 Volume 124, Issue 4, Page(s) 533–544

Abstract: The human G protein coupled membrane receptor (GPR17), the sensor of brain damage, is identified as a biomarker for many neurological diseases. In human brain tissue, GPR17 exist in two isoforms, long and short. While cryo-electron microscopy technology ... ...

Abstract	The human G protein coupled membrane receptor (GPR17), the sensor of brain damage, is identified as a biomarker for many neurological diseases. In human brain tissue, GPR17 exist in two isoforms, long and short. While cryo-electron microscopy technology has provided the structure of the long isoform of GPR17 with Gi complex, the structure of the short isoform and its activation mechanism remains unclear. Recently, we theoretically modeled the structure of the short isoform of GPR17 with Gi signaling protein and identified novel ligands. In the present work, we demonstrated the presence of two distinct ligand binding sites in the short isoform of GPR17. The molecular docking of GPR17 with endogenous (UDP) and synthetic ligands (T0510.3657, MDL29950) found the presence of two distinct binding pockets. Our observations revealed that endogenous ligand UDP can bind stronger in two different binding pockets as evidenced by glide and autodock vina scores, whereas the other two ligand's binding with GPR17 has less docking score. The analysis of receptor-UDP interactions shows complexes' stability in the lipid environment by 100 ns atomic molecular dynamics simulations. The amino acid residues VAL83, ARG87, and PHE111 constitute ligand binding site 1, whereas site 2 constitutes ASN67, ARG129, and LYS232. Root mean square fluctuation analysis showed the residues 83, 87, and 232 with higher fluctuations during molecular dynamics simulation in both binding pockets. Our findings imply that the residues of GPR17's two binding sites are crucial, and their interaction with UDP reveals the protein's hidden signaling and communication properties. Furthermore, this finding may assist in the development of targeted therapies for the treatment of neurological diseases.
MeSH term(s)	Humans ; Ligands ; Molecular Docking Simulation ; Cryoelectron Microscopy ; Receptors, G-Protein-Coupled/metabolism ; Binding Sites ; Uridine Diphosphate ; Protein Isoforms/metabolism
Chemical Substances	Ligands ; Receptors, G-Protein-Coupled ; Uridine Diphosphate (58-98-0) ; Protein Isoforms ; GPR17 protein, human
Language	English
Publishing date	2023-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	392402-6
ISSN	1097-4644 ; 0730-2312
ISSN (online)	1097-4644
ISSN	0730-2312
DOI	10.1002/jcb.30388
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Article ; Online: Waking Up the Sleep Field: An Overview on the Implications of Genetics and Bioinformatics of Sleep.

Pandi-Perumal, Seithikurippu R / Saravanan, Konda Mani / Paul, Sayan / Namasivayam, Ganesh Pandian / Chidambaram, Saravana Babu

Molecular biotechnology

2024

Abstract: Sleep genetics is an intriguing, as yet less understood, understudied, emerging area of biological and medical discipline. A generalist may not be aware of the current status of the field given the variety of journals that have published studies on the ... ...

Abstract	Sleep genetics is an intriguing, as yet less understood, understudied, emerging area of biological and medical discipline. A generalist may not be aware of the current status of the field given the variety of journals that have published studies on the genetics of sleep and the circadian clock over the years. For researchers venturing into this fascinating area, this review thus includes fundamental features of circadian rhythm and genetic variables impacting sleep-wake cycles. Sleep/wake pathway medication exposure and susceptibility are influenced by genetic variations, and the responsiveness of sleep-related medicines is influenced by several functional polymorphisms. This review highlights the features of the circadian timing system and then a genetic perspective on wakefulness and sleep, as well as the relationship between sleep genetics and sleep disorders. Neurotransmission genes, as well as circadian and sleep/wake receptors, exhibit functional variability. Experiments on animals and humans have shown that these genetic variants impact clock systems, signaling pathways, nature, amount, duration, type, intensity, quality, and quantity of sleep. In this regard, the overview covers research on sleep genetics, the genomic properties of several popular model species used in sleep studies, homologs of mammalian genes, sleep disorders, and related genes. In addition, the study includes a brief discussion of sleep, narcolepsy, and restless legs syndrome from the viewpoint of a model organism. It is suggested that the understanding of genetic clues on sleep function and sleep disorders may, in future, result in an evidence-based, personalized treatment of sleep disorders.
Language	English
Publishing date	2024-01-10
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	1193057-3
ISSN	1559-0305 ; 1073-6085
ISSN (online)	1559-0305
ISSN	1073-6085
DOI	10.1007/s12033-023-01009-1
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Article ; Online: DeepBindGCN

Haiping Zhang / Konda Mani Saravanan / John Z. H. Zhang

Molecules, Vol 28, Iss 4691, p

Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein–Ligand Interaction Prediction

2023 Volume 4691

Abstract	The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical–chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein–ligand interaction and can be used in many important large-scale virtual screening application scenarios.
Keywords	graph convolution network ; protein–ligand binding ; drug virtual screening ; deep learning ; DeepBindGCN ; Organic chemistry ; QD241-441
Subject code	540
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Benzenesulfonamide Analogs: Synthesis, Anti-GBM Activity and Pharmacoprofiling.

Murugesan, Akshaya / Konda Mani, Saravanan / Thiyagarajan, Ramesh / Palanivel, Suresh / Gurbanov, Atash V / Zubkov, Fedor I / Kandhavelu, Meenakshisundaram

International journal of molecular sciences

2023 Volume 24, Issue 15

Abstract: The tropomyosin receptor kinase A (TrkA) family of receptor tyrosine kinases (RTKs) emerge as a potential target for glioblastoma (GBM) treatment. Benzenesulfonamide analogs were identified as kinase inhibitors possessing promising anticancer properties. ...

Abstract	The tropomyosin receptor kinase A (TrkA) family of receptor tyrosine kinases (RTKs) emerge as a potential target for glioblastoma (GBM) treatment. Benzenesulfonamide analogs were identified as kinase inhibitors possessing promising anticancer properties. In the present work, four known and two novel benzenesulfonamide derivatives were synthesized, and their inhibitory activities in TrkA overexpressing cells, U87 and MEF cells were investigated. The cytotoxic effect of benzenesulfonamide derivatives and cisplatin was determined using trypan blue exclusion assays. The mode of interaction of benzenesulfonamides with TrkA was predicted by docking and structural analysis. ADMET profiling was also performed for all compounds to calculate the drug likeness property. Appropriate QSAR models were developed for studying structure-activity relationships. Compound 4-[2-(4,4-dimethyl-2,6-dioxocyclohexylidene)hydrazinyl]-
MeSH term(s)	Humans ; Cisplatin/pharmacology ; Glioblastoma/drug therapy ; Structure-Activity Relationship ; Antineoplastic Agents/chemistry ; Molecular Docking Simulation ; Cell Proliferation ; Molecular Structure ; Drug Screening Assays, Antitumor ; Benzenesulfonamides
Chemical Substances	Cisplatin (Q20Q21Q62J) ; antiglomerular basement membrane antibody ; Antineoplastic Agents
Language	English
Publishing date	2023-07-31
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms241512276
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Article ; Online: Design, synthesis and anticancer evaluation of novel arylhydrazones of active methylene compounds.

Murugesan, Akshaya / Konda Mani, Saravanan / Koochakkhani, Shabnaz / Subramanian, Kumar / Kandhavelu, Jayalakshmi / Thiyagarajan, Ramesh / Gurbanov, Atash V / Mahmudov, Kamran T / Kandhavelu, Meenakshisundaram

International journal of biological macromolecules

2023 Volume 254, Issue Pt 3, Page(s) 127909

Abstract: Nerve growth factor (NGF) and its receptor, tropomyosin kinase receptor kinase type A (TrkA) is emerging as an important target for Glioblastoma (GBM) treatment. TrkA is the cancer biomarker majorly involved in tumor invasion and migration into nearby ... ...

Abstract	Nerve growth factor (NGF) and its receptor, tropomyosin kinase receptor kinase type A (TrkA) is emerging as an important target for Glioblastoma (GBM) treatment. TrkA is the cancer biomarker majorly involved in tumor invasion and migration into nearby normal tissue. However, currently, available Trk inhibitors exhibit many adverse effects in cancer patients, thus demanding a novel class of ligands to regulate Trk signaling. Here, we exploited the role of TrkA (NTRK1) expression from the 651 datasets of brain tumors. RNA sequence analysis identified overexpression of NTRK1 in GBM, recurrent GBM as well in Oligoastrocytoma patients. Also, TrkA expression tends to increase over the higher grades of GBM. TrkA protein targeting hydrazone derivatives, R48, R142, and R234, were designed and their mode of interaction was studied using molecular docking and dynamic simulation studies. Ligands' stability and binding assessment reveals R48, 2 2-(2-(2-hydroxy-4-nitrophenyl) hydrazineylidene)-1-phenylbutane-1,3-dione, as a potent ligand that interacts well with TrkA's hydrophobic residues, Ile, Phe, Leu, Ala, and Val. R48- TrkA exhibits stable binding potentials with an average RMSD value <0.8 nm. R48 obeyed Lipinski's rule of five and possessed the best oral bioavailability, suggesting R48 as a potential compound with drug-likeness properties. In-vitro analysis also revealed that R48 exhibited a higher cytotoxicity effect for U87 GBM cells than TMZ with the IC
MeSH term(s)	Humans ; Receptor, trkA/genetics ; Receptor, trkA/metabolism ; Molecular Docking Simulation ; Neoplasm Recurrence, Local ; Signal Transduction ; Protein Kinase Inhibitors/pharmacology ; Glioblastoma/drug therapy ; Glioblastoma/pathology
Chemical Substances	Receptor, trkA (EC 2.7.10.1) ; Protein Kinase Inhibitors
Language	English
Publishing date	2023-11-10
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	282732-3
ISSN	1879-0003 ; 0141-8130
ISSN (online)	1879-0003
ISSN	0141-8130
DOI	10.1016/j.ijbiomac.2023.127909
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Article ; Online: Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Zhang, Haiping / Saravanan, Konda Mani / Wei, Yanjie / Jiao, Yang / Yang, Yang / Pan, Yi / Wu, Xuli / Zhang, John Z H

Journal of chemical information and modeling

2023 Volume 63, Issue 3, Page(s) 835–845

Abstract: Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, ...

Abstract	Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer,
MeSH term(s)	Humans ; Deep Learning ; Artificial Intelligence ; Drug Design ; SARS-CoV-2 ; COVID-19 ; Peptides/pharmacology
Chemical Substances	Peptides
Language	English
Publishing date	2023-02-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.2c01485
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Article: Hybrid drug-screening strategy identifies potential SARS-CoV-2 cell-entry inhibitors targeting human transmembrane serine protease.

Feng, Yufei / Cheng, Xiaoning / Wu, Shuilong / Mani Saravanan, Konda / Liu, Wenxin

Structural chemistry

2022 Volume 33, Issue 5, Page(s) 1503–1515

Abstract: The spread of coronavirus infectious disease (COVID-19) is associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has risked public health more than any other infectious disease. Researchers around the globe use multiple ... ...

Abstract	The spread of coronavirus infectious disease (COVID-19) is associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has risked public health more than any other infectious disease. Researchers around the globe use multiple approaches to identify an effective approved drug (drug repurposing) that treats viral infections. Most of the drug repurposing approaches target spike protein or main protease. Here we use transmembrane serine protease 2 (TMPRSS2) as a target that can prevent the virus entry into the cell by interacting with the surface receptors. By hypothesizing that the TMPRSS2 binders may help prevent the virus entry into the cell, we performed a systematic drug screening over the current approved drug database. Furthermore, we screened the Enamine REAL fragments dataset against the TMPRSS2 and presented nine potential drug-like compounds that give us clues about which kinds of groups the pocket prefers to bind, aiding future structure-based drug design for COVID-19. Also, we employ molecular dynamics simulations, binding free energy calculations, and well-tempered metadynamics to validate the obtained candidate drug and fragment list. Our results suggested three potential FDA-approved drugs against human TMPRSS2 as a target. These findings may pave the way for more drugs to be exposed to TMPRSS2, and testing the efficacy of these drugs with biochemical experiments will help improve COVID-19 treatment. Supplementary information: The online version contains supplementary material available at 10.1007/s11224-022-01960-w.
Language	English
Publishing date	2022-05-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	2018832-8
ISSN	1572-9001 ; 1040-0400
ISSN (online)	1572-9001
ISSN	1040-0400
DOI	10.1007/s11224-022-01960-w
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