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Daily Signal April 14, 2026 · 10 min read

Issue #88: Evaluation of protein-RNA Docking Web Servers for Template-Free Docking and Comparison with the AlphaFold Server.

Protein Design Digest #88: Evaluation of protein-RNA Docking Web Servers for Template-Free Docking …

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Evaluation of protein-RNA Docking Web Servers for Template-Free Docking and Comparison with the AlphaFold Server.

Protein-RNA docking is a valuable tool for predicting the structures of protein-RNA complexes, which allow us to understand the structural basis for gene expression and regulation, thus facilitating drug development. Despite the development of several protein-RNA docking programs, the field remains relatively underdeveloped compared to protein-protein docking, and a systematic comparison of these programs in terms of accuracy and efficiency is still lacking. Recent advances in deep learning-based structure prediction, such as AlphaFold 3, offer a promising alternative for modeling protein-RNA complexes. Here, we have compiled a consolidated benchmark data set of 235 protein-RNA complexes (freely available at https://github.com/tanys-group/protein-rna-docking-benchmark), which were curated from PDB structures deposited up to July 2024, to assess the performance of five template-free docking web servers and the AlphaFold Server. Among the docking web servers, HDOCK performed the best, achieving success rates of 31.1% and 44.7% within the top 1 and top 5 predictions, respectively, as assessed by CAPRI (Critical Assessment of PRedicted Interactions) metrics. Although AlphaFold 3 outperformed all the docking web servers with an overall success rate of 87.0% in its top 5 predictions, it failed in nine cases where docking approaches succeeded and showed a markedly lower success rate of 40% for protein-RNA complexes outside its training set, comparable to that of HDOCK (35%). Our study provides valuable insights into the strengths and limitations of current protein-RNA docking servers and AlphaFold 3, offering practical guidance for selecting the appropriate tool for protein-RNA complex structure prediction. These results also suggest that hybrid approaches combining physics-based and machine learning methods hold significant promise for achieving higher prediction accuracy.

Why this matters: Critical for improving fold accuracy and reducing structural uncertainty in de novo design.

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.

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.

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Pipeline Tip

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

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