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Daily Signal April 20, 2026 · 9 min read

Issue #92: Advancing protein engineering via organic chemistry.

Protein Design Digest #92: Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors…

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Advancing protein engineering via organic chemistry.

Proteins are central to nearly all biological processes; their functions are tightly regulated by dynamic mechanisms such as covalent alterations; e.g., post-translational modifications (PTMs). These modifications can influence the protein’s structure, localization, and activity. Inspired by this diversity and regulation, advances in synthetic organic chemistry have enabled the production of a plethora of novel proteins for both basic research and biomedical applications. Recent progress in structural elucidation technologies and modern organic chemistry has enabled atom-level modifications, significantly enhancing our ability to tailor protein function. These approaches greatly expand the toolkit currently available for generating complex proteins with unique structural and functional properties. In this review, we summarize recent progress in chemical protein engineering and highlight its emerging applications in catalysis, functional studies, and drug development.

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Also Worth Reading

Exploring quantum frontiers in protein structure prediction: techniques, challenges, and opportunities.

Protein folding is governed by the principle of free energy minimization, where a protein’s native tertiary structure corresponds to the global minimum on an energy landscape shaped by quantum mechanical interactions such as hydrogen bonding, van der Waals forces, and electron delocalization. Despite significant advances in template-based modeling (TBM), ab-initio simulations, and deep learning approaches, classical methods continue to face challenges due to the exponential complexity of the conformational search space and the approximations involved in modeling molecular interactions. Although AlphaFold, a deep learning-based protein modeling tool, has achieved a remarkable score of 92.4 in the critical assessment of protein structure prediction (CASP), classical protein structure prediction (PSP) remains hindered by the computational limitations of conventional binary architecture in representing the physical constraints of biomolecular systems. By representing the combinatorial explosion of possible conformations as a more tractable optimization problem, quantum computing offers a fundamentally new paradigm for protein three-dimensional (3D) structure prediction. In this review, we explore how quantum computing (QC) techniques including quantum annealing, quantum optimization algorithms, and hybrid quantum-classical approaches can leverage quantum properties such as superposition, entanglement, and tunneling to more efficiently navigate the complex energy landscapes associated with protein folding. While current challenges, including limited qubit fidelity, error correction, and scalability, remain, the integration of quantum algorithms with classical strategies holds significant promise for advancing structural biology, with profound implications for drug discovery and the understanding of complex biomolecular systems.

Predicting the Mechanism of Action of Bawei Chufan Soup in Treating Teen Depression through Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation.

Introduction The Bawei Chufan Soup (BWCFS) in Traditional Chinese Medicine (TCM) offers unique advantages in treating Teen Depression (TD). This study utilizes network pharmacology, molecular docking, and molecular dynamics simulations to predict the material basis and mechanism of action of the decoction. Methods The TCMSP, SwissADME, and SwissTargetPrediction databases were utilized to obtain the active ingredients and targets of the BWCFS. The GeneCards, OMIM, and Disgenet databases were used to identify disease targets, and the intersection of these sets was determined using the VENNY tool. The intersecting targets were imported into the String database for protein- protein interaction analysis and the screening of core targets. GO and KEGG enrichment analyses of the intersecting targets were conducted using the David database, and drugcomponent- target-pathway network diagrams were constructed using Cytoscape 3.10.0 software. The molecular docking models of the core components and key targets were generated using AutoDock Vina, and kinetic simulations were conducted using GROMACS 2020.3, paired with the best docking models. Results After screening, the study identified the core components of BWCFS as Baicalein, Kaempferol, Quercetin, Cerevisterol, and Cavidine, with the key targets for TD being AKT1, IL6, TNF, ESR1, and IL1B. GO enrichment analysis revealed that BWCFS may affect signal transduction in the treatment of TD, and is associated with cellular components such as the plasma membrane and dendrites, as well as the regulation of protein binding. KEGG analysis suggested that the intersecting genes are primarily enriched in the cyclic adenosine monophosphate (cAMP) signaling pathway. Molecular docking results indicated that AKT1 shows good binding affinity with Baicalein, Cavidine, Kaempferol, and Quercetin, while Cerevisterol exhibits strong binding with TNF. The molecular dynamics simulations were stable and reliable. During the protein-ligand complex simulation, the binding between the protein and ligand was stable, with van der Waals interactions as the primary force, while hydrogen bonds were present between both the protein and ligand. Discussion Though this study has several common limitations associated with network pharmacology, and no animal experiments have been conducted for verification, the study has successfully explored and validated the mechanism of action of BWCFS in treating TD using scientific computational methods. This study provides new perspectives and methods for the development and management of pharmacological treatments for TD, offering innovative insights into TCM approaches for its treatment. Conclusion Through network pharmacology, this study preliminarily predicted the material basis and mechanism of action of BWCFS in treating TD. Furthermore, the therapeutic effects of BWCFS on TD may be associated with neuroinflammation and structural and functional changes in neuronal dendrites. The cAMP-PKA-NF-κB and cAMP-PI3K-AKT-NF-κB pathways are proposed as potential therapeutic targets.

Unraveling the molecular interaction of hydroxytyrosol with human serum albumin via multi-spectroscopy, thermodynamic analysis, molecular docking and molecular dynamics simulation.

Hydroxytyrosol (HT) is a powerful antioxidant that scavenges free radicals and protects cells and also is one of the main active ingredients in functional foods. Recently, the consumption of HT has been increasing due to its excellent biological and pharmacological effects. However, the interaction of HT and major proteins in the circulatory system remains unclear. Herein, Human Serum Albumin (HSA) binding interactions with HT were analyzed on a molecular level through multi-spectroscopy analysis, thermodynamic analysis, molecular docking and molecular dynamics (MD) simulation in the present study. UV-vis absorption spectroscopy, three-dimensional (3D) fluorescence spectroscopy, synchronous fluorescence spectroscopy, circular dichroism (CD) spectroscopy, FT-IR spectroscopy and surface hydrophobicity experiment revealed that HT induced conformational changes and a slight secondary structure changes in HSA. Thermodynamic analysis and site competition experiments demonstrated that HT was bound predominantly to HSA’s Sudlow site I via hydrophobic forces (ΔH > 0, ΔS > 0) and was a spontaneous process (ΔG -1 ) than Sudlow site III and Sudlow site II. Moreover, HT binds to HSA via hydrophobic and hydrogen bond interactions, as also validated by molecular docking and MD simulations. In summary, this study contributes to an advanced understanding of HSA-HT interactions as well as to a theoretical understanding of the interplay between HT absorption, distribution, and transport.

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Marine algal TNF-α inhibitors explored by comparative docking and molecular dynamics simulations.

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Hepatotoxic Mechanisms of Polyethylene Terephthalate Microplastics Revealed by Network Toxicology, Molecular Docking, and In Vivo Validation.

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Deciphering enzyme inhibition of thiazole assemblies for diabetes management via molecular docking, dynamic simulation, DFT and kinetic study: A computational therapeutic strategy.

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Mechanistic insights into PFAS derivatives-induced coronary heart disease and atherosclerotic renal artery stenosis via integrated network toxicology and molecular modeling.

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Advancing protein engineering via organic chemistry.

Proteins are central to nearly all biological processes; their functions are tightly regulated by dynamic mechanisms such as covalent alterations; e.g., post-translational modifications (PTMs). Read more →

LMO7-mediated ubiquitination of SIRT3 promotes osteoarthritis progression: an investigation using machine learning and molecular dynamics simulations.

Background This study aims to inform clinical decision-making by identifying metabolism-related biomarkers involved in the progression of osteoarthritis (OA). Read more →

In silico and in vitro insights into stigmasterol targeting Keap1/Nrf2, Bcl-2/Bax and IKKβ/IκBα protein-protein interactions in Arsenic-induced toxicity.

Inorganic arsenic is a known neurotoxin that contributes to neurodevelopmental and neurodegenerative disorders by promoting oxidative stress, mitochondrial dysfunction and inflammation. Read more →

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

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