Recep Adiyaman
Vision About Experience Education Skills Publications Tools Newsletter Updates Podcasts Blog Contact CV Subscribe
All Issues
Older Newer
Daily Signal January 10, 2026 · 9 min read

Issue #18: Integrated QSAR, molecular docking, and dynamics-based discovery of a potent selective HDAC1 inhibitor with therapeutic potential in aggressive cancers.

Protein Design Digest - 2026-01-10 - Integrated QSAR, molecular docking, and dynamics-based discovery of a potent selective HDAC1 inhibitor with therapeutic potential in aggressive cancers.

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 QSAR, molecular docking, and dynamics-based discovery of a potent selective HDAC1 inhibitor with therapeutic potential in aggressive cancers.

This research introduces a comprehensive computational and experimental approach aimed at the systematic design of selective Histone Deacetylase 1 (HDAC1) inhibitors, which hold therapeutic promise for treating aggressive cancers. A comprehensive Quantitative Structure-Activity Relationship (QSAR) model was constructed utilizing 1168 experimentally validated HDAC1 inhibitors, incorporating molecular descriptors associated with hydrogen bonding, steric, and electronic properties. The validated model, with a R 2 of 0.80 and a Q 2 of 0.80, was utilized for the virtual screening of the ChemDiv HDAC library, successfully identifying high-potential hits. The leading compounds underwent receptor-based molecular docking with the HDAC1 crystal structure (PDB ID: 4BKX), which highlighted essential interactions such as zinc ion coordination and π-π stacking. Notably, compound 0356-0096 demonstrated a higher binding affinity than the reference inhibitor vorinostat. Molecular dynamics (MD) simulations conducted over a duration of 500 ns demonstrated the stability of the complex and a decrease in flexibility, as evidenced by analyses of Root Mean Square Deviation (RMSD) and Fluctuation (RMSF). The analysis of simulation trajectories through Principal Component Analysis (PCA) and the mapping of the Free Energy Landscape (FEL) revealed stable low-energy conformations that align with thermodynamically favorable binding conditions. The results of ADMET profiling demonstrated that the lead compounds exhibit good oral bioavailability, low toxicity, and favorable metabolic stability. Validation through in vitro methods using the MTT assay on MDA-MB-231 (triple-negative breast cancer) and A431 (epidermoid carcinoma) cell lines revealed significant, dose-dependent cytotoxic effects, with IC 50 values of 2.7 μM and 91.6 nM, respectively. The computed Selectivity Index (SI) demonstrated a preferential cytotoxic effect on cancer cells in comparison to normal NRK kidney cells. This integrative QSAR-docking-MD-FEL-MTT approach effectively identified compound 0356-0096 as a potent and selective HDAC1 inhibitor. By combining predictive computational models with empirical validation, it provides a structured pathway for the preclinical development of targeted epigenetic cancer therapeutics.

Why this matters: Expands the searchable sequence space for novel folds and high-affinity binders.

Also Worth Reading

Synthesis, Anticancer Evaluation, and Molecular Docking of Triazolylmethyl-Dihydroquinazolinyl Benzoate Derivatives as Potential PARP-1 Inhibitors.

Quinazolinone derivatives have emerged as promising scaffolds in medicinal chemistry due to their broad spectrum of biological activities, including anticancer potential. Incorporation of triazole rings through click chemistry has further boosted the pharmacological relevance of such compounds, due to the triazole’s stability, bioisosterism, and ability to engage in key interactions with biological targets. Motivated by these properties, a library of 24 triazolylmethyl-dihydroquinazolinyl benzoate (TDB) derivatives (7a-x) was synthesized using a click chemistry strategy, starting from anthranilamide and phthalic anhydride. The structures of the synthesized compounds were established through IR, 1 H NMR, 13 C NMR, and HRMS spectral analysis. The anticancer potential of all derivatives was evaluated by using SRB assay, with compounds 7j and 7q displaying notable activity, with GI 50 values of 22 and 48 µg/mL, respectively. In addition, compounds 7a, 7e, 7f, 7l, 7u, 7v, and 7x displayed moderate activity, with GI 50 values ranging from 58 to 77 µg/mL. In addition, molecular docking studies were performed using poly(ADP-ribose) polymerase-1 as the target enzyme, and the results confirmed that the TDB derivatives exhibited strong binding affinity. Furthermore, molecular dynamics simulations were conducted to evaluate the stability of the docked complexes, specifically for compounds 7j and 7q, which confirmed that the TDB derivatives formed stable interactions with poly(ADP-ribose) polymerase-1.

Mechanistic study of plastic monomers in gestational diabetes mellitus: A network toxicology and molecular docking approach.

Plastics are widely used in various fields such as food packaging, textile fibers, building materials, and transportation. Although the relationship between plastic additives and diseases has been reported, there is limited research on the association between plastic monomers (PM) and gestational diabetes mellitus (GDM). This study aims to investigate the link between environmental PM and GDM. By employing advanced network toxicology and molecular docking techniques, we successfully elucidated the molecular mechanisms by which PM may induce GDM. Utilizing databases such as PubChem, SEA, Super-PRED, SwissTargetPrediction, PharmMapper, Gene Cards, and OMIM, we identified potential targets associated with the disease. Further analysis using STRING and Cytoscape software helped determine the core targets most significantly related to these metabolic disorders. Additionally, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the David database to characterize these core targets. Finally, molecular docking with CB-Dock2 was used to validate the binding affinity of PM to these target proteins. Our findings suggest that PM may potentially induce GDM by modulating the insulin signaling pathway through STAT3, AKT1, and TP53. In summary, this work provides novel insights into the mechanisms by which environmental pollutants may trigger GDM, thereby laying a theoretical foundation for disease prevention and treatment. It offers valuable references for the safety evaluation of plastics, urging food safety regulatory agencies to strengthen oversight and encouraging the public to reduce plastic usage.

Protein Language Models Trained on Biophysical Dynamics Inform Mutation Effects

Structural dynamics are fundamental to protein functions and mutation effects. Current protein deep learning models are predominantly trained on sequence and/or static structure data, which often fail to capture the dynamic nature of proteins. To address this, we introduce SeqDance and ESMDance, two protein language models trained on dynamic biophysical properties derived from molecular dynamics simulations and normal mode analyses of over 64,000 proteins. Both models can be directly applied to predict dynamic properties of unseen ordered and disordered proteins. SeqDance, trained from scratch, has attentions that capture dynamic interaction and co-movement between residues, and its embeddings encode rich representations of protein dynamics that can be further utilized to predict conformational properties beyond the training tasks via transfer learning. SeqDance predicted dynamic property changes reflect mutation effect on protein folding stability. ESMDance, built upon ESM2 (Evolutionary Scale Model II) outputs, substantially outperforms ESM2 in zero-shot prediction of mutation effects for designed and viral proteins which lack evolutionary information. Together, SeqDance and ESMDance offer a new framework for integrating protein dynamics into language models, enabling more generalizable predictions of protein behavior and mutation effects. Significance StatementThe sequence–structure (ensemble)–function relationship is central to biology. Protein dynamics in the structure ensemble play a decisive role in determining function and mutation effects, and are widely used to study thermodynamics, folding pathways, and dynamic interactions of ordered proteins, as well as the conformational variability of intrinsically disordered proteins. However, current state-of-the-art protein deep learning models, such as AlphaFold2,3 and ESM, focus on static structures and sequences, which failed to directly capture protein dynamics. Here, we address this gap by developing protein language models to learn dynamic properties of over 64,000 proteins. We show that the models Transformer attentions capture protein dynamic interactions, and our model can be applied to predict conformational properties and mutation effects.

Research & AI Updates

From the Industry

Quick Reads

GC-MS phytochemical screening, molecular docking, MD simulation, and in vitro antioxidant assay of Euphorbia thymifolia L. extract.

Euphorbia thymifolia L., a medicinal herb traditionally recognized for its many medicinal properties, was investigated in the present study to evaluate the antioxidant potential of its hexane fraction and to elucidate its possible mechanism of action through Keap1-Nrf2 pathway modulation using in silico analyses, along with validation through in vitro assays. Read more →

In-silico screening of marine fungal metabolites identifies potential FtsZ inhibitors against MDR-tuberculosis through docking and molecular dynamics analysis.

Tuberculosis (TB) remains a major global health challenge, intensified by the rise of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis. Read more →

Anticancer potential of Dendrocnide meyeniana through phytochemical profiling, ADMET analysis, molecular docking, and in silico cytotoxicity evaluation.

Phytochemicals are widely explored for cancer therapeutics due to their structural diversity and broad pharmacological activities. Read more →

Multiparameter optimization of broad-spectrum thymidine phosphorylase inhibition, ADMET properties, molecular docking, and DFT screening of novel pyrazole-based thiadiazole/thiazole derivatives.

Thymidine phosphorylase (TP) is a key enzyme involved in pyrimidine nucleoside metabolism and plays a significant role in angiogenesis, tumor progression, and metastasis. Read more →

Exploring the Therapeutic Potential of Secondary Metabolites from Pogostemon cablin (Aceh Patchouli Oil) as Anti-Breast Cancer Agents: A Network Pharmacology, ADMET, and Molecular Docking Approach

Abstract Breast cancer remains a major global health challenge with increasing incidence and mortality. Read more →

Integrated network pharmacology, and molecular docking characterization of the hepatoprotective properties of dendrobine

Abstract Dendrobium has long been used in traditional medicine to manage liver disorders. Read more →

Exploring the Scoring Function Space with Lasso Regression.

Artificial intelligence (AI) successfully integrates several emerging and established techniques to build models to address complex systems, including those from biological sources. Read more →

Deep contrastive learning enables genome-wide virtual screening.

Recent breakthroughs in protein structure prediction have opened new avenues for genome-wide drug discovery, yet existing virtual screening methods remain computationally prohibitive. Read more →

Pipeline Tip

Index your BigWig files before visualization to save memory.

Resources & Tools

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

BS HF DK