Recep Adiyaman
Vision About Experience Education Skills Publications Tools Newsletter Updates Podcasts Blog Contact CV Subscribe
All Issues
Older Newer
Daily Signal March 12, 2026 · 8 min read

Issue #66: A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

Protein Design Digest - 2026-03-12 - A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

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

A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

Binding affinity is a critical parameter that can influence the state of the drug in vivo and help to define the formulation strategy. The current study implements a multimodal approach to analyse the binding affinity between human serum albumin (HSA) and pioglitazone. Ultraviolet (UV) absorbance and fluorescence spectrometry analyses were performed on different combinations of HSA and pioglitazone complexes, and the absorbance and fluorescence intensities were mapped to calculate the binding constant. DynamicBind, a distinct deep-learning artificial intelligence tool, was implemented to perform in silico docking studies using a non-conventional approach. Furthermore, molecular dynamics simulation was also performed to generate root mean square deviation, radius of gyration, and root mean square fluctuation values, followed by principal component analysis, probability distribution function, and free energy landscape analysis. The simulation output was analysed to interpret the binding affinity and associated conformation of the protein-active pharmaceutical ingredient (API) complex. The binding constant calculated through UV analysis was 1.1 × 10 4 M -1 . Fluorescence spectroscopic analysis derived a value of 1.7 × 10 5 M -1 . At the same time, DynamicBind predicted the cLDDT score for the top predicted model to be 0.634, and a binding affinity value of greater than 5, indicating a relatively moderate binding between pioglitazone and HSA. The results from molecular dynamics simulations further complemented our earlier observations, indicating non-covalent binding interactions and a stable protein-API complex, which is desirable for developing a formulation using HSA as a carrier polymer. This orthogonal approach also provided critical information on the fate of the API and possible considerations that needed to be made during the design of the formulation process, highlighting the need for similar approaches that could provide multifaceted advantages and help in optimising R&D costs and timelines.

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

Also Worth Reading

Binding interactions of Trametes villosa and Trametes lactinea laccases with 4-nonylphenol and its intermediates: molecular docking and molecular dynamics approaches.

Emerging pollutants such as 4-nonylphenol (4-NP) act as endocrine disruptors and have been associated with reproductive toxicity in humans and wildlife, as well as with physiological disturbances in aquatic, terrestrial, and plant organisms. Laccases are oxidoreductases with notable biotechnological relevance and the ability to oxidize phenolic pollutants, making them attractive candidates for biodegradation strategies. This study investigated the interactions between laccases from Trametes villosa and Trametes lactinea and 4-NP and its degradation intermediates via molecular docking and molecular dynamics simulations (MDS). Ligands were geometrically optimized using the PM7 semiempirical method, and their global reactivity descriptors were computed to explore correlations between electronic properties and laccase binding affinity. Docking revealed favorable binding energies (ΔG bind ≈ -6 kcal·mol -1 ) and recurrent interactions with key amino acid residues, including Ala, Glu, Leu, Phe, Pro, Ser, Val, and His, mainly through hydrogen bonding and hydrophobic contacts. The MDS confirmed the stability of the enzyme-ligand complexes, as indicated by low root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values, along with consistent radius of gyration and solvent-accessible surface areas throughout the trajectories. Binding free energy calculations using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method indicated stronger binding affinity under solvation, with ΔG bind values of -26.45 and -17.73 kcal·mol -1 for T. villosa and T. lactinea, respectively, highlighting hydrophobic and van der Waals contributions as the primary stabilizing forces. Overall, these results provide computational evidence that laccases from T. villosa and T. lactinea have potential for application in the oxidative biodegradation of 4-NP. These findings advance the molecular understanding of fungal laccase‒pollutant interactions and support future in vitro validation and protein engineering strategies aimed at enhancing biodegradation efficiency.

Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.

The integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling highly precise disease diagnosis and targeted therapy. AI-driven methodologies, including machine learning and deep learning, facilitate the rapid analysis of complex biological and chemical datasets, accelerating protein structure prediction, molecular docking, and structure-activity relationship modeling. These capabilities support the rational design of proteins and peptides with enhanced specificity, therapeutic efficacy, and safety, while enabling personalized treatment strategies tailored to individual molecular profiles. In parallel, sustainable nanomedicine focuses on the development of biodegradable, biocompatible, and environmentally benign nanomaterials to improve drug bioavailability, stability, and controlled release. AI-assisted optimization further refines nanocarrier design by balancing therapeutic performance with safety and environmental impact. Advanced intelligent nanocarriers capable of real-time monitoring, adaptive drug release, and degradation into non-toxic by-products represent a significant advancement over conventional static systems. The theranostic paradigm has become central to precision medicine, particularly in oncology, especially where AI-designed nanoplatforms enable targeted delivery of imaging agents and therapeutics to tumors, while allowing continuous treatment monitoring and minimizing off-target effects. Emerging applications in neurological, infectious, and cardiovascular diseases further highlight the broad clinical potential of this approach. Accordingly, this review summarizes AI-driven protein design strategies, sustainable nanocarrier engineering, and their convergence in next-generation theranostic systems, critically discussing mechanistic insights, translational challenges, and design principles required for developing safe, scalable, and clinically adaptable intelligent nanomedicines.

Establishing FDA-approved oncology drugs as GPR176 inhibitor through homology modelling, molecular docking, MMGBSA, DFT, and molecular dynamics simulation.

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 →

Synthesis of acyl hydrazide derivatives of α-naphthalene acetic acid as COX-2 inhibitors: in vitro evaluation, molecular docking and DFT studies.

Cyclooxygenase-2 (COX-2) is a key enzyme in pain perception and inflammatory cascade that make it an attractive target to develop safer anti-inflammatory agents. Read more →

Bioactivity profiling of Cornus mas L. leaf, bark, and fruit extracts from different locations in Bosnia and Herzegovina with molecular docking studies.

Cornus mas L. 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 →

CF2H: a cell-free two-hybrid platform for rapid protein binder screening.

Protein binders that detect, activate, inhibit, or otherwise modulate their targets are pivotal for biomedical applications. Read more →

Computational analysis of umami and bitter taste interactions: orthogonality in receptor-ligand binding.

We investigated the in silico binding of ligands relevant to the bitter taste in wine (flavan-3-ol) and the umami taste (glutamate) with bitter and umami receptors (T1R1 and T2R). Read more →

Mechanistic insights into Grifola frondosa-driven fermentation of Rice-wheat bran: Metabolomic profiling, molecular dynamics, and enhanced antioxidant efficacy.

Rice bran (RB) and wheat bran (WB) are rich in bioactives but poorly utilized due to limited bioaccessibility. Read more →

Generative Protein Design for Physiological Intervention: Progress and Horizons.

Pipeline Tip

Use Snakemake for reproducible end-to-end protein design workflows.

Resources & Tools

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

BS HF DK