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Daily Signal April 17, 2026 · 10 min read

Issue #91: From Atoms to Fragments: A Coarse Representation for Efficient and Functional Protein Design.

Protein Design Digest #91: Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors…

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From Atoms to Fragments: A Coarse Representation for Efficient and Functional Protein Design.

Although deep learning has accelerated protein design, current protein representations such as sequences or full-atom structures scale non-linearly with protein length. We propose a sparse and interpretable representation for proteins, based on evolutionarily conserved fragments. Specifically, we use a curated set of 40 functional and evolutionarily conserved fragments as an alphabet to build Fragment Graphs and Fragment Sets. These fragment-based representations are both lightweight and functionally informative, capturing up to 55% more variance using fewer than 13 of the dimensions required by traditional methods. On a dataset of 215 functionally diverse proteins, our approach creates more coherent functional clusters than traditional sequence- and structure-based methods, even among proteins with ≤30% sequence identity. Fragment-based searches of protein databases achieve accuracies comparable to traditional methods, while using 90% fewer tokens per protein. These searches execute ∼68.7× faster than RMSD-based structural methods and ∼1.64× faster than sequence-based methods, even including fragment pre-processing overhead. Additionally, we show that our representation effectively guides RFDiffusion for protein backbone generation with functional recovery rates higher than 40%. In summary, our fragment-based representation offers a scalable and interpretable alternative for the next generation of protein design tools for backbone design, sequence design, and functional similarity searches within protein structure databases. https://github.com/wells-wood-research/tessera.

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Also Worth Reading

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.

Exploring the mechanism of Jiaotai Pill in treating insomnia and erectile dysfunction based on network pharmacology, molecular docking, and molecular dynamics simulations.

Insomnia and erectile dysfunction (ED) exhibit a closely linked bidirectional association, often forming a vicious cycle. Current treatments focusing on single disorders show limited efficacy for this comorbidity. Jiaotai Pill, a classic traditional Chinese medicine formula, has shown potential in alleviating both conditions, aligning with the principle of “treating different diseases with the same therapy.” However, its mechanisms of action remain unclear. This study employed an integrated strategy combining network pharmacology, molecular docking, and molecular dynamics (MD) simulations to systematically explore Jiaotai Pill multi-component, multi-target, and multi-pathway therapeutic mechanisms against comorbid insomnia and ED. First, the active components of Jiaotai Pill and disease-related targets for insomnia and ED were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and relevant disease databases. Subsequently, the overlapping targets between Jiaotai Pill active components and insomnia/ED-related targets were identified. Both protein-protein interaction networks and compound-target networks were constructed based on these overlapping targets. Core targets and active components were further screened via topological analysis of the constructed networks. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were subsequently performed to explore the biological functions and potential regulatory pathways of the core targets. Finally, molecular docking and MD simulations were carried out to validate and key binding interactions between the screened core active components and their corresponding core targets. A total of 21 potential active components of Jiaotai Pill and 123 overlapping targets between these components and insomnia/ED-related disease targets were successfully identified. AKT1, INS, IL-6, TNF, and TP53 were identified as core targets. Enrichment analysis highlighted TNF, IL-17, and PI3K/Akt signaling pathways. Molecular docking confirmed stable binding affinity, and MD simulations demonstrated high structural stability of the quercetin-AKT1 complex. This study elucidates that Jiaotai Pill may treat comorbid insomnia and ED through multi-component, multi-target, and multi-pathway therapeutic mechanisms.

Phytochemical analysis, pharmacological screening, and molecular docking studies of fresh and dry Saudi Ocimum basilicum L.

The volatile oil composition and ethanolic extracts of Ocimum basilicum L. grown in Saudi Arabia were examined, with emphasis on their antioxidant and antifungal properties. GC-MS analysis identified 111 volatile components in fresh basil and 125 in dried basil, representing 98% and 96% of the total essential oil content. Methyl chavicol was the major volatile constituent in the whole plant, including leaves, flowers, and stems, showing higher percentages in fresh samples than in dried ones. Analysis of phenolic compounds in ethanolic extracts showed that rutin was the predominant compound in fresh whole plants. Rosmarinic acid was the main phenolic constituent in fresh flowers, leaves, and stems. In contrast, luteolin was the most abundant phenolic compound in extracts from the entire dried plant. Catechin was the dominant compound in dried flowers, while quercetin prevailed in dried leaves and stems. The ethanolic extracts exhibited stronger antioxidant activity than the essential oils, particularly in scavenging DPPH radicals. In addition, notable antifungal activity was recorded against Penicillium aurantiogriseum. Molecular docking analysis confirmed the experimental results, revealing strong interactions between flavonoids and fungal enzymes related to metabolic pathways and reactive oxygen species production. Quercetin and rutin demonstrated the highest binding affinities. These findings emphasize the potential use of Ocimum basilicum L. as a natural antioxidant, antifungal agent, and flavoring source.

Research & AI Updates

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Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Read more →

From Atoms to Fragments: A Coarse Representation for Efficient and Functional Protein Design.

Although deep learning has accelerated protein design, current protein representations such as sequences or full-atom structures scale non-linearly with protein length. Read more →

Targeting KasA: isosakuranetin derivatives as promising scaffolds for novel anti-tuberculosis agents against drug-resistant Mycobacterium tuberculosis.

Mycobacterium tuberculosis remains a major global health threat due to the rising prevalence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, which limit the effectiveness of current therapies. Read more →

A multi-stage computational pipeline for repurposing FDA-approved drugs: application to EGFR C797S-mutant NSCLC.

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Multiomics Mendelian Randomization of GWAS, eQTL, and pQTL Combined With Drug Analysis to Predict Drug Targets in Myocardial Infarction.

Despite significant advances in the management of myocardial infarction (MI), therapeutic options targeting upstream pathogenic mechanisms remain scarce. Read more →

Rational design of novel α-amylase inhibitor peptide derived from quinoa seed peptide with <i>in silico</i> approaches.

Diabetes mellitus (DM) has become a global concern that is responsible for millions of deaths in the world. Read more →

Effects of single synonymous substitutions on fold efficiency demonstrate the influence of rare codons and protein structure.

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Normalise thermal B-factors when comparing different crystal structures.

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

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