Recep Adiyaman
Vision About Experience Education Skills Publications Tools Newsletter Updates Podcasts Blog Contact CV Subscribe
All Issues
Older Newer
Daily Signal December 29, 2025 · 9 min read

Issue #7: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Protein Design Digest - 2025-12-29 - Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Share X LinkedIn
Protein Design Daily

Building something in Protein Design?

I love collaborating on new challenges. Let's build together.

LinkedIn Email

Subscribe to Protein Design Digest

Daily curated signals from arXiv, PubMed, and BioRxiv.

Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Why this matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

Also Worth Reading

Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

A novel series of dihydropyridine-sulfonyl derivatives (AG-CHO and analogues A1-A7) were synthesized and structurally characterized. Molecular docking demonstrated favorable binding of these compounds to autophagy-associated and cancer-related targets, while molecular dynamics simulations confirmed A5 as the most stable ligand protein interactions. Functional assays in SKOV-3, MCF-7, A549, and EA.hy.926 cells using acridine orange staining and flow cytometry revealed significant autophagy induction. Among all tested compounds AG-CHO emerged as the most potent inducer of autophagy. Notably, derivatives such as A6 and A7 showed selective potency in endothelial cells, whereas A1, A5, and A7 were effective in A549 cells, indicating cell-specific activity. Collectively, this integrated computational and experimental study identifies A5 as the lead compound and highlights dihydropyridine-sulfonyl scaffolds as promising autophagy modulators and potential anticancer candidates for further preclinical development.

Geometric deep learning assists protein engineering. Opportunities and Challenges.

Protein engineering is experiencing a paradigmatic transformation through the integration of geometric deep learning (GDL) into computational design workflows. While traditional approaches such as rational design and directed evolution have achieved significant progress, they remain constrained by the vastness of sequence space and the cost of experimental validation. GDL overcomes these limitations by operating on non-Euclidean domains and by capturing the spatial, topological, and physicochemical features that govern protein function. This perspective provides a comprehensive and critical overview of GDL applications in stability prediction, functional annotation, molecular interaction modeling, and de novo protein design. It consolidates methodological principles, architectural diversity, and performance trends across representative studies, emphasizing how GDL enhances interpretability and generalization in protein science. Aimed at both computational method developers and experimental protein engineers, the review bridges algorithmic concepts with practical design considerations, offering guidance on data representation, model selection, and evaluation strategies. By integrating explainable artificial intelligence and structure-based validation within a unified conceptual framework, this work highlights how GDL can serve as a foundation for transparent, interpretable, and autonomous protein design. As GDL converges with generative modeling, molecular simulation, and high-throughput experimentation, it is poised to become a cornerstone technology for next-generation protein engineering and synthetic biology.

Investigating the olfactory function of microplusin-like proteins in Rhipicephalus microplus through molecular docking and dynamics simulations.

Ticks are responsible for transmitting infectious pathogens of public health and veterinary importance worldwide. Chemosensory perception in ticks constitutes a fundamental pathway in host location and disease transmission. This study aims to analyze the function of the Rhipicephalus microplus microplusin-like protein (MLP) in the perception of volatile organic compounds. To obtain the results, AlphaFold2, Swiss Model, and AlphaFold3 were utilized for protein prediction. UCSF Chimera, AutoDock Vina in Linux, and Discovery Studio Visualizer were employed for docking analyses and interaction visualizations. The GROMACS software in a virtual Linux environment was used for molecular dynamics simulations. Out of 46 volatile molecules selected based on literature and used for docking, the four top compounds were evaluated for their interaction, including squalene with a binding energy of -5.183 kcal/mol, uric acid with -5.169 kcal/mol, beta-ionone with -5.037 kcal/mol, and 2,4-Di-tert-butylphenol with -5.035 kcal/mol. The stability of MLP with the top two compounds, squalene and uric acid, was evaluated through molecular dynamics simulations. The uric acid complex was more stable. It showed lower and more stable root-mean-square deviation (∼2 nm), as well as hydrogen bonding (2-4 bonds), smoother solvent-accessible surface area, and gyration radius profiles. In contrast, the squalene complex showed greater conformational variability, lacking hydrogen bonding. The Gibbs free energy landscape and principal component analysis revealed that squalene had stabilization at the start of the simulation. In contrast, uric acid showed stronger long-term conformational convergence and stabilization by the end of the simulation. This study demonstrated the potential role of microplusin-like protein in recognizing volatile organic compounds. It provides insights into the potential to develop new tick-control strategies.

Research & AI Updates

From the Industry

Quick Reads

Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.

Background Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, insomnia, and increased suicide risk, posing a significant burden on global health. Read more →

UPLC-Q-TOF/MS-based spectrum-effect correlation combined with chemometrics and molecular docking for quality assessment and screening of bioactive components with hemostatic, antinociceptive, and anti-inflammatory activities in Liparis nervosa.

Ethnopharmacological relevance Liparis nervosa (LN) occurs in Southwest China and is traditionally used as a hemostatic and detoxifying agent; however, the pharmacodynamic basis for its medicinal properties is unclear; this impedes the quality standardization and clinical application of this herb. Read more →

Exploring the Mechanism of Platycladi Cacumen in Intervening Androgenetic Alopecia Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation

Abstract As a traditional hair-growth-promoting herb, Platycladi Cacumen(PC) has a long history of folk application in the field of hair loss improvement. Read more →

Unraveling the mechanism of curcumin in coronary slow flow phenomenon through network pharmacology and molecular docking.

The coronary slow flow phenomenon (CSFP) is associated with an increased risk of adverse cardiovascular events, yet standardized treatment is lacking. Read more →

A screening strategy for bioactive components from Amaranth: An integrated approach of network pharmacology, molecular docking and molecular dynamics simulation.

Amaranth is a traditional medicinal and forage plant with promising anti-inflammatory properties. Read more →

Molecular docking and molecular dynamics study of PUFAs from <i>Navicula salinicola</i>: prospective antiviral strategies targeting the SARS-CoV-2 spike protein.

The emergence of novel viral infections, such as SARS-CoV-2, H5N2, and H7N9, among recently identified viruses, has highlighted the urgent need for new antiviral therapeutics. Read more →

Computational insights into Ru(II)-coumarin complexes as potential anticancer agents: a DFT, QTAIM, NCI-RDG, molecular docking and molecular dynamics approach.

Ru(II) complexes have been explored as promising candidates for novel anticancer agents, due to their significant bioactivity, selective cytotoxicity, and ability to induce apoptosis via multiple signalling pathways, with coumarin derivatives serving as effective ligands to enhance their therapeutic efficacy. Read more →

Targeting myeloid cell leukemia-1 protein to identify potential compounds for chronic myeloid leukemia treatment: Molecular docking and molecular dynamics simulation approaches.

Myeloid cell leukemia-1 (Mcl-1), an anti-apoptotic member of the Bcl-2 family, is frequently overexpressed and amplified in chronic myeloid leukemia (CML) as well as in several other malignancies, contributing to tumor progression and therapeutic resistance. Read more →

Pipeline Tip

Use GPU-accelerated MD refinement to lift model quality in under 2 hours.

Resources & Tools

The protein structure is the language of life; design is its poetry. — Recep Adiyaman

BS HF DK