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Daily Signal April 13, 2026 · 9 min read

Issue #87: De novo protein design: a transformative frontier in clinical protein applications.

Protein Design Digest #87: Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of L…

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De novo protein design: a transformative frontier in clinical protein applications.

Background Protein biologics are indispensable in disease prevention, diagnosis, and therapy, yet their development remains largely constrained by reliance on native protein scaffolds, resulting in long development timelines, limited structural and functional tunability, challenges in manufacturing consistency, and high production costs. Main body De novo protein design moves beyond the structural and functional constraints inherent to traditional approaches, enabling the direct creation of proteins with tailored structures and functions and offering a new avenue to address these challenges. In this review, we summarize the principal computational strategies underlying de novo protein design and the contribution of deep learning to its recent progress, and highlight prospective applications, major translational barriers, and the current limitations and future challenges of the field. Conclusions Despite notable methodological progress in de novo protein design, its path toward clinical application continues to be limited by a range of biological, technical, and translational considerations. Future work will need closer coordination between computational design, experimental validation, engineering optimization, and clinical needs, with clinical feasibility considered early and refined throughout development.

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Also Worth Reading

Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.

De novo protein design has enabled the creation of proteins with diverse functionalities that are not found in nature. Despite recent advances, experimental success rates remain inconsistent and context-dependent, posing a bottleneck for broader applications of de novo design. To overcome this, structure and sequence prediction models have been applied to assess design quality prior to experimental testing to save time and resources. In this study, we examined the extent to which AlphaFold, Protein MPNN, and ESMFold can discriminate between experimentally successful and unsuccessful designs. We first curated a benchmark dataset of 614 experimentally characterized de novo designed monomers from 11 different design studies between 2012 and 2021. All predictive models demonstrated moderate ability to discriminate experimental successes (expressed, soluble, monomeric, and fold with the correct secondary structure) from failures. Still, many failed designs have better confidence metrics than successful designs, and confidence metrics were topology-dependent. Among all computational models evaluated, ESMFold average predicted local-distance difference test (pLDDT) yielded the best individual performance at distinguishing between successful and unsuccessful designs. A logistic regression model combining all confidence metrics provided only modest improvement over ESMFold pLDDT alone. Overall, these results show that these models can serve as an initial filtering strategy prior to experimental validation; however, their utility at accurately predicting experimentally successful designs remains limited without task-specific training.

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.

Exploring the Mechanism of Oral Cancer With Shikonin Based on the Network Pharmacology and Molecular Docking Technology.

Objectives To explore the underlying mechanisms of shikonin in treating oral cancer using network pharmacology and molecular docking methods. Materials and methods Targets of shikonin were obtained from the TCMSP, BATMAN, ChEMBL, PharmMapper and HERB databases. Targets of oral cancer were gathered from the OMIM, STITCH, GeneCards and Drugbank databases. The intersection targets of shikonin and oral cancer were obtained for subsequent analysis. The intersecting targets of shikonin and oral cancer were entered into the DAVID database and used its functions to perform Gene Ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis on the intersection targets to obtain the relevant pathways and biological functions of shikonin in the treatment of oral cancer. The protein-protein interaction (PPI) network of shikonin and oral cancer targets was constructed in STRING platform. Subsequently, using Cytoscape 3.8.0 to obtain the key targets of shikonin and oral cancer. Finally, molecular docking and molecular dynamics simulations were used to evaluate the strength of binding between shikonin and key targets, as well as the hydrogen bonds involved. Results In total, 481 targets were screened for shikonin, and 10,058 targets were identified for oral cancer. By GO and KEGG analysis, the targets of shikonin and oral cancer may be involved in the mediation of apoptosis, inflammation and immune response. And the associated signalling pathways that targets may be involved in the treatment of oral cancer, including the FoxO signalling pathway, HIF-1 signalling pathway, TNF signalling pathway, and Th17 cell differentiation, etc. Cytoscape software screened the key genes including AKT1, MAPK1, CXCR4, CXCL8, CCL3, CCL4, CCL5, CYBB, BCL2, NOX1, HIF-1, TP53. The results of molecular docking and molecular dynamics simulations showed that shikonin exhibits good binding interactions with CCL3, AKT1 and NOX1. Conclusions Mulitple molecular mechanisms involved in oral cancer management with shikonin have been elucidated providing a glimpse og the underlying therapeutic targets for the disease.

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De novo protein design: a transformative frontier in clinical protein applications.

Background Protein biologics are indispensable in disease prevention, diagnosis, and therapy, yet their development remains largely constrained by reliance on native protein scaffolds, resulting in long development timelines, limited structural and functional tunability, challenges in manufacturing consistency, and high production costs. Read more →

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Employ HADDOCK for ambiguous restraints in protein-protein docking.

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

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