Machine learning modeling to identify affinity improved biobetter anticancer drug trastuzumab and the insight of molecular recognition of trastuzumab towards its antigen HER2

Balakrishnan, Nataraj; Baskar, G.; Balaji, Sathyanarayan; Kullappan, Malathi; Mohan, Surapaneni Krishna

doi:10.1080/07391102.2021.1961866

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Subsequently, the energy-minimized system was equilibrated using the NVT and NPT ensembles for 100ps. The temperature was maintained at 310K with constant pressure at 1.01325 bar [ 74 , 75 ]. Finally, 100 ns simulations for each system were performed for further analysis.…”

Section: Methodsmentioning

confidence: 99%

Plant-Derived Antiviral Compounds as Potential Entry Inhibitors against Spike Protein of SARS-CoV-2 Wild-Type and Delta Variant: An Integrative in SilicoApproach

Surapaneni¹,

Kullappan²,

Patil

et al. 2022

Molecules

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The wild-type SARS-CoV-2 has continuously evolved into several variants with increased transmissibility and virulence. The Delta variant which was initially identified in India created a devastating impact throughout the country during the second wave. While the efficacy of the existing vaccines against the latest SARS-CoV-2 variants remains unclear, extensive research is being carried out to develop potential antiviral drugs through approaches like in silico screening and drug-repurposing. This study aimed to conduct the docking-based virtual screening of 50 potential phytochemical compounds against a Spike glycoprotein of the wild-type and the Delta SARS-CoV-2 variant. Subsequently, molecular docking was performed for the five best compounds, such as Lupeol, Betulin, Hypericin, Corilagin, and Geraniin, along with synthetic controls. From the results obtained, it was evident that Lupeol exhibited a remarkable binding affinity towards the wild-type Spike protein (−8.54 kcal/mol), while Betulin showed significant binding interactions with the mutated Spike protein (−8.83 kcal/mol), respectively. The binding energy values of the selected plant compounds were slightly higher than that of the controls. Key hydrogen bonding and hydrophobic interactions of the resulting complexes were visualized, which explained their greater binding affinity against the target proteins—the Delta S protein of SARS-CoV-2, in particular. The lower RMSD, the RMSF values of the complexes and the ligands, Rg, H-bonds, and the binding free energies of the complexes together revealed the stability of the complexes and significant binding affinities of the ligands towards the target proteins. Our study suggests that Lupeol and Betulin could be considered as potential ligands for SARS-CoV-2 spike antagonists. Further experimental validations might provide new insights for the possible antiviral therapeutic interventions of the identified lead compounds and their analogs against COVID-19 infection.

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Section: Methodsmentioning

confidence: 99%

Plant-Derived Antiviral Compounds as Potential Entry Inhibitors against Spike Protein of SARS-CoV-2 Wild-Type and Delta Variant: An Integrative in SilicoApproach

Surapaneni¹,

Kullappan²,

Patil

et al. 2022

Molecules

Self Cite

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“…Structural characterization has also enabled the determination of the mechanism behind the inhibitory synergy of Pertuzumab and Trastuzumab, showing with cryo-EM structures that they do induce cooperative binding [59]. ML models based on structural signatures have been leveraged to improve the design of mAbs [60,61]. The structural analysis of the kinase domain, on the other hand, has been very useful to identify and prioritize small molecules targeting this domain, and to explain the reasons for resistance [62].…”

Section: Receptor Tyrosine Kinasesmentioning

confidence: 99%

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Gorostiola González,

Rakers,

Jespers

et al. 2024

IJMS

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Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of “wet-lab” experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

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A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management

Deng,

Yan,

Zhan

et al. 2024

iMetaOmics

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Breast cancer (BC), specifically HER2‐positives subtype, has a poor prognosis. Nevertheless, the development of anti‐HER2 therapy yielded satisfactory outcomes. Therefore, evaluating patient HER2 status and ascertaining responsiveness to anti‐HER2 therapy is crucial. The advent of deep learning has propelled the artificial intelligence (AI) revolution, leading to an increased applicability of AI in predictive models. In the field of medicine, AI is an emerging modality that is gaining momentum for facilitating cancer diagnosis and treatment, particularly in the effective management of breast cancer. This study aims to provide a comprehensive review of current diagnostic and predictive models that utilize data obtained from histopathological slides, radiomics, and HER2 binding sites. Advancements and practical applications of these models were also evaluated. Additionally, we examined existing obstacles that AI encounters for anti‐HER2 therapy. We also proposed future directions for integrating AI in assessing and managing anti‐HER2 therapy. The findings of this study offer valuable insights into the evaluation of AI‐based anti‐HER2 therapy, emphasizing key concepts and obstacles that, if addressed, could facilitate the integration of AI‐assisted anti‐HER2 therapy. The integration of AI has the potential to enhance the precision and customization of screening and treatment protocols for HER2+ breast cancer.

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Machine learning modeling to identify affinity improved biobetter anticancer drug trastuzumab and the insight of molecular recognition of trastuzumab towards its antigen HER2

Cited by 5 publications

References 33 publications

Plant-Derived Antiviral Compounds as Potential Entry Inhibitors against Spike Protein of SARS-CoV-2 Wild-Type and Delta Variant: An Integrative in SilicoApproach

Plant-Derived Antiviral Compounds as Potential Entry Inhibitors against Spike Protein of SARS-CoV-2 Wild-Type and Delta Variant: An Integrative in SilicoApproach

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management

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