Recep Adiyaman
Vision About Experience Education Skills Publications Tools Newsletter Updates Podcasts Blog Contact CV Subscribe
All Issues
Older Newer
Daily Signal January 25, 2026 · 9 min read

Issue #31: Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Protein Design Digest - 2026-01-25 - Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

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

Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Objective This study presents a methodology employing virtual screening to identify curcumin derivatives with selective affinity for the HER2 mutations L755S, T798I, and T798M. Methods Curcumin derivatives were retrieved from the ChEMBL database and filtered using KNIME. HER2 mutations were modeled in silico using MOE software with PDB ID 3RCD. Molecular docking and dynamics simulations were conducted to screen high-affinity compounds and evaluate binding interactions. Result From 505 curcumin derivatives, the RDKit module implemented in KNIME successfully filtered 317 compounds. Subsequent molecular docking against wild-type HER2 identified 100 curcumin derivatives with low docking scores, among which the top 20 compounds exhibited better binding affinities than Lapatinib. Further molecular docking screening against the three HER2 mutations identified five lead compounds with the lowest docking scores. Molecular docking and molecular dynamics simulation revealed critical binding interactions with residues essential for kinase domain stability. Chemical structural analysis revealed key modifications, such as geranyl and tripeptide modifications. CHEMBL3758656 and CHEMBL3827366, two curcumin derivatives, demonstrated consistent binding across HER2 mutations and a favorable ADMET profile. Conclusion This study successfully identified CHEMBL3758656 and CHEMBL3827366 as promising HER2 inhibitors through comprehensive virtual screening. Their high binding affinity against L755S, T798I, and T798M mutations and favorable ADME and toxicity properties underscore their potential as alternative therapeutics for HER2-positive breast cancer.

Why this matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

Also Worth Reading

Energy-Driven Innovations in Computational De Novo Protein Engineering.

Energy models play a crucial role in the advancement of computational de novo protein engineering, enabling the design of novel proteins with tailored functionalities. Proteins serve as the foundation of biochemical processes, making their precise engineering essential for applications in biotechnology, medicine, and synthetic biology. Unlike traditional approaches that focus on modifying existing proteins, de novo engineering introduces entirely new constructs, a paradigm shift driven by energy-based strategies that guide protein folding, stability, and functionality through comprehensive simulations of energy landscapes. Computational techniques such as molecular dynamics (MD), thermodynamic integration, and Monte Carlo sampling are fundamental in evaluating designed proteins’ stability and dynamic behavior. Widely used tools such as CHARMM, Amber, and Rosetta leverage advanced energy functions to optimize protein structures, facilitating accurate predictions of folding pathways and binding affinities. Additionally, the integration of machine learning (ML) and deep learning (DL) has significantly improved the speed and precision of energy-based modeling, enhancing the design and optimization process. This review systematically analyzes recent studies, provides quantitative benchmarking of major computational platforms, and presents a decision framework for method selection based on accuracy-cost-throughput trade-offs. By integrating classical force fields, quantum mechanical approaches, and AI-driven predictions with experimental validation, this work outlines a roadmap for advancing therapeutic and industrial protein design through synergistic physics-based and data-driven strategies.

Tailored pyrrole-based imidazothiazole scaffolds: Synthetic elaboration, enzyme kinetic profiling and DFT-guided molecular docking toward Antidiabetic therapeutics.

The current research study highlights the successful biological evaluation of novel imidazo-thiadiazole based pyrrole derivatives, with the aim of targeting diabetes mellitus through alpha-amylase and alpha-glucosidase inhibition. These compounds exhibited promising anti-diabetic activity, notably compound 8 emerged as a leading candidate (3.50 ± 0.20, and 4.10 ± 0.10 µM) which outperformed the potential of acarbose (6.20 ± 0.10 and 6.70 ± 0.20 µM), a reference drug. The enhanced biological potential of compound 8 is likely due to incorporation of hydroxyl substituents, which may strengthen its binding affinity and selectivity towards the targeted enzymes. Molecular docking revealed stable interactions with key amino acids residues of targeted enzymes, providing mechanistic basis for its potent inhibitory activity. To further established their therapeutic relevance, enzyme kinetic study was conducted which confirmed their mode of inhibition while ADMET analysis indicated favorable pharmacokinetics and safety profiles. Moreover, pharmacophore modeling and molecular dynamics simulations reinforced the stability and binding efficiency of lead compounds under dynamic biological conditions. All the experimental results and in silico validations demonstrate that potent compounds possess significant anti-diabetic activity profile. Their ability to outperform an existing diabetes mellitus inhibitor and maintaining a favorable safety profile suggest that these compounds have potential to be further used in drug development and optimization against Diabetes Mellitus.

Identification of novel umami peptides in fermented milk and elucidation of their umami mechanism via molecular docking and molecular dynamics simulations.

A streamlined workflow integrating multi-model machine learning, bioinformatics filtering, sensory evaluation, molecular docking and dynamics simulations was applied to mine umami peptides in fermented milk. Based on dual selection criteria-(i) unanimous umami prediction by UMPred-FRL, Umami_YYDS, Umami-MRNN, Mlp4Umami, Umami_TD, (ii) favorable in silico properties (non-toxicity, non-allergenicity, good solubility, stability, potential bioactivity)-ten out of the 1505 peptides identified by peptidomics were shortlisted as umami peptide candidates. Sensory evaluation confirmed that eight imparted an umami taste. Molecular docking revealed that umami peptides interact with TAS1R1/TAS1R3 primarily through hydrogen bonds formed between their hydrophilic residues (predominantly Lys, Tyr) and receptor hydrophilic residues (notably Lys/Arg in TAS1R1, Asn in TAS1R3). Residues Arg307/Met375/Lys379 of TAS1R1, and Arg327/443/Ala329/Val437/Met452 of TAS1R3 were key interaction sites. Molecular dynamics simulations showed that the three peptides with the highest umami taste-EVFTKK, SKKTVDME, VMGVSKVKE-formed stable and compact complexes with TAS1R1/TAS1R3. This work enhances understanding of the umami characteristics of fermented milk.

Research & AI Updates

From the Industry

Quick Reads

Screening of active constituents in camellia oil against atopic dermatitis via molecular docking and experimental validation: elucidation of the underlying molecular mechanism.

Objective Atopic dermatitis (AD) is a chronic inflammatory skin disease. Read more →

ViralBindPredict: Empowering Viral Protein-Ligand Binding Sites through Deep Learning and Protein Sequence-Derived Insights.

The development of a single therapeutic compound can exceed 1.8 billion USD and take more than a decade, underscoring the urgent need to accelerate drug discovery. Read more →

Based on 3D-QSAR modeling and molecular dynamics of novel peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 inhibitors design and screening.

Background This study investigates the role of peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1) in tumorigenesis and evaluates the potential of novel PIN1 inhibitors for cancer therapeutics. Read more →

DDI-AttendNet: cross attention with structured graph learning for inter-drug connectivity analysis.

Introduction In the context of interdisciplinary computational science and its increasingly vital role in advancing applied computer-aided drug discovery, the accurate characterization of inter-drug connectivity is essential for identifying synergistic therapeutic effects, mitigating adverse reactions, and optimizing polypharmacy strategies. Read more →

Study of the multitarget mechanism of Zao Ren Gan Cao Da Mai decoction in the treatment of insomnia comorbid with depression based on network pharmacology and molecular docking technology.

Based on network pharmacology and molecular docking to explore the mechanism of “Zao Ren Gan Cao Da Mai Decoction” in the treatment of insomnia comorbid with depression. Read more →

Rational discovery of testosterone-enhancing peptide (AGNYGLPT) from sea cucumber: targeting T-type calcium channels through docking, molecular dynamics simulations, and cellular validation.

Calcium ions (Ca 2+ ) are a crucial signaling factor in testosterone synthesis. Read more →

An evaluation of Roluperidone as a promising repurposing candidate for Alzheimer’s Disease: A Computational Investigation.

Alzheimer’s disease (AD) is the most dominant and prevalent form of dementia. Read more →

Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance

Abstract Antibiotic resistance among pathogens common to human beings and animals, which include Brucella melitensis , has end up a significant worldwide health task. Read more →

Pipeline Tip

Use local MSA generation (colabfold_search) to bypass speed bottlenecks.

Resources & Tools

Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

BS HF DK