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Daily Signal February 06, 2026 · 8 min read

Issue #42: De novo protein design enables targeting of intractable oncogenic interfaces

Protein Design Digest - 2026-02-06 - A New Insight into the Study of Neural Cell Adhesion Molecule (NCAM) Polysialylation Inhibition Incorporated the Molecular Docking Models into the NMR Spectroscopy of a Crucial Peptide-Ligand Interaction.

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De novo protein design enables targeting of intractable oncogenic interfaces

ABSTRACT Protein-protein interactions (PPIs) involving oncogenic drivers remain among the most intractable targets in cancer biology due to their dynamic conformations and limited accessibility to conventional small molecules. Although antibodies and indirect inhibitors have achieved clinical success against targets such as PD-1/PD-L1 and MYC, challenges persist related to tissue penetration, intracellular delivery, resistance, and incomplete blockade of key interface hotspots. Here, we present DesignForge, an integrated de novo protein design framework that combines deep-learning-based structure generation, sequence optimization, and energetic hotspot mapping to create compact miniprotein binders for PPIs. Using this approach, we engineered PD-1 mimetics predicted to disrupt the PD-1/PD-L1 immune checkpoint, designed scaffolds targeting the MYC/MAX dimerization interface, and generated KRAS binders in a manner predicted to occlude RAF interaction. The top designs showed high structural confidence by AlphaFold2, favorable stability metrics, and consistent hotspot engagement identified through MOE-based analyses. Collectively, these results establish DesignForge as a generalizable in silico platform for rational design of therapeutic protein binders that extend beyond antibody and small-molecule modalities to systematically target intractable oncogenic PPIs.

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Also Worth Reading

Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds.

Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy for drug development. In this study, we utilized AlphaFold to predict the structures of target proteins from Schistosoma mansoni and S. japonicum, followed by virtual molecular screening to identify potential inhibitors. Among 202 potential therapeutic targets, we identified 37 proteins with high-accuracy structural predictions suitable for molecular docking with 14,600 compounds. This screening yielded 268 candidate compounds, which were further evaluated ex vivo for activity against both adult and juvenile S. mansoni and S. japonicum. Seven compounds exhibited strong anti-schistosomal activity, with HY-B2171A (Carubicin hydrochloride, CH) emerging as the most potent. CH was predicted to target the splicing factor U2AF65, and knockdown of its coding gene Smp_019690 resulted in a phenotype similar to CH treatment. RNA sequencing revealed that both CH treatment and Smp_019690 RNA interference (RNAi) disrupted splicing events in the parasites. Further studies demonstrated that CH impairs parasite viability by inhibiting U2AF65 function in mRNA splicing regulation. By integrating RNAi-based target identification with structure-based virtual screening, alongside ex vivo phenotypic and molecular analyses of compound-treated schistosomes, our study provides a comprehensive framework for anti-schistosomal drug discovery and identifies promising candidates for further preclinical development.

Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.

Recent advances in protein structure prediction have highlighted the importance of a longstanding problem: given multiple structural models of a protein, how does one select the best model to use when predicting interactions between that protein and candidate drug molecules? Here we demonstrate the value of a previously unutilized source of information in addressing this problem. We show that given multiple ligands known to bind the protein, one can perform effective model selection by comparing the predicted binding poses of multiple ligands at each model. We introduce a method, RevBind, that exploits this information, leveraging the statistical tendency of different ligands to form similar chemical interactions with a protein’s binding pocket. RevBind can be used, for example, to select among variants of AlphaFold models, identifying those that are most useful for molecular docking. Our findings pave the way for the development of even better model selection methods that draw simultaneously on the information used by RevBind and the information used by previous methods.

Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer’s Disease.

Introduction Alzheimer’s disease (AD) is a debilitating neurodegenerative condition marked by progressive cognitive decline and memory impairment, affecting millions worldwide. Despite extensive research, no definitive cure exists, underscoring the need for innovative approaches to drug discovery and development. Methods This review focuses on the application of molecular docking techniques in the context of AD drug discovery. The methodology involves the use of computational modeling tools to predict and analyze the interactions between small drug-like molecules and key protein targets implicated in AD pathogenesis, particularly amyloid-beta (Aβ) and tau proteins. Results Molecular docking has enabled the virtual screening of large chemical libraries to identify potential inhibitors of Aβ aggregation and tau hyperphosphorylation. Numerous studies have validated docking-predicted interactions with in vitro and in vivo experiments, resulting in the discovery of novel compounds with promising pharmacological profiles. Docking has also aided in the optimization of ligand binding affinity and selectivity toward AD-relevant targets. Discussion The integration of molecular docking with experimental techniques enhances the reliability and efficiency of the drug discovery process. Docking allows for the early identification of bioactive molecules, reducing time and cost compared to traditional methods. However, limitations such as rigid receptor assumptions and scoring function inaccuracies require further refinement. Conclusion Molecular docking stands out as a powerful computational tool in the quest for effective AD therapies. Simulating protein-ligand interactions accelerates the identification of potential drug candidates and supports the rational design of targeted interventions, paving the way for future clinical applications in combating Alzheimer’s disease.

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De novo protein design enables targeting of intractable oncogenic interfaces

ABSTRACT Protein-protein interactions (PPIs) involving oncogenic drivers remain among the most intractable targets in cancer biology due to their dynamic conformations and limited accessibility to conventional small molecules. Read more →

Curcumin attenuates LPS-induced inflammation in RAW 264.7 cells: A multifaceted study integrating network pharmacology, molecular docking, molecular dynamics simulation, and experimental validation.

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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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