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Daily Signal June 08, 2026 · 9 min read

Issue #125: Decoding the Grammar of Protein–Protein Interaction Interfaces with Multimodal Representations

Protein Design Digest #125: BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accura…

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Decoding the Grammar of Protein–Protein Interaction Interfaces with Multimodal Representations

Protein-protein interactions (PPI) govern essential cellular processes, making the computational identification of interacting sites a central challenge in structural biology, with important implications for protein engineering and the development of targeted therapeutics. Existing prediction algorithms include sequence-based methods, which lack structural information, or structure-based approaches, which often struggle to effectively integrate evolutionary context. Here, we present ESM3-PPISites, a supervised model for residue-level classification of PPI interfaces, leveraging the multimodal representations of the ESM3 Protein Language Model. To ensure a bias-free evaluation, we adopt a stringent redundancy filtering protocol, systematically eliminating latent homology between the training data and a curated benchmark set in both sequence and structural space. Our findings demonstrate that while ESM3 largest proprietary version yields the highest predictive power, targeted fine-tuning of its small open-weight counterpart significantly narrows the performance gap. Requiring only primary sequence data at inference, ESM3-PPISites achieves unprecedented accuracy, vastly outperforming current approaches. Crucially, we demonstrate the practical impact of these predictions by integrating them as spatial restraints within the HADDOCK3 docking platform. When evaluated on an independent subset of 12 complexes from the Docking Benchmark v5, our prediction-guided pipeline strongly enhances the identification of near-native binding poses over ab initio blind docking, while reducing computational runtime by an order of magnitude. This framework establishes a scalable paradigm for high-throughput structural interactomics.

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Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling

Proteins perform essential roles across nearly all cellular processes, and accurate three-dimensional structures remain critical for elucidating structure-function relationships and studies on drug discovery. Cryo-electron microscopy (cryo-EM), X-ray crystallography, and nuclear magnetic resonance can provide detailed structural information. However, for many proteins, structural information is available only as lower-resolution experimental data or sparse data. Such information is more difficult to translate into accurate atomic coordinates; a common example is low-resolution cryo-EM density maps. In parallel, mass spectrometry-based methods, including ion mobility (IM-MS), offer rapid, broadly applicable structural descriptors such as collisional cross section (CCS), a global measure of molecular shape and size, but CCS values also do not provide atomistic detail. Here we present CRIM (cryo-EM + IM-MS), an integrative Rosetta scoring function that combines low-resolution cryo-EM density information with IM-MS derived CCS as restraints to improve monomeric protein structure prediction. CRIM incorporates the Rosetta REF2015 (RS) energy with a CCS agreement penalty (computed via PARCS) and an electron-density agreement term (elec_dens_fast). We tested CRIM on an ideal dataset of 60 monomeric proteins using simulated CCS values and density maps. Across the ideal dataset, the CRIM score function improved or maintained prediction quality for many targets, reducing the mean RMSD from 3.65 [A] (RS) to 2.90 [A] and increasing the mean TM-score from 0.88 to 0.90. Furthermore, an experimental benchmark dataset of 54 proteins was curated to include either experimental cryo-EM maps or published CCS values. On the experimental dataset, CRIM similarly improved model selection, lowering the mean RMSD from 6.65 [A] to 4.38 [A] and raising the mean TM-score from 0.73 to 0.79. In comparison to AlphaFold3 predictions, CRIM frequently yielded competitive predictions and was able to substantially outperform AlphaFold3 for select difficult targets where sparse experimental restraints provide strong discriminatory power. The CRIM score function is freely available within the Rosetta software suite and provides a practical framework for leveraging complementary IM-MS and cryo-EM data to improve monomeric protein structure prediction.

Computational Assessment of Phytochemical Inhibitors of Cytochrome P450 2E1 (CYP2E1) Implicated in Hepatotoxicity: Comparative Molecular Docking and Interaction Analysis Using Silymarin as a Benchmark

Abstract Liver diseases remain a major global health challenge, often driven by oxidative stress and reactive metabolites generated during xenobiotic metabolism. Cytochrome P450 2E1 (CYP2E1) plays a central role in hepatotoxicity through the bioactivation of toxic compounds and excessive production of reactive oxygen species. This study aimed to evaluate the inhibitory potential of selected phytochemicals (curcumin, quercetin, and kaempferol) against CYP2E1 using molecular docking, with silymarin as a benchmark compound. Molecular docking was performed using SwissDock, while UCSF Chimera was used for three-dimensional visualization and interaction confirmation. BIOVIA Discovery Studio Visualizer was employed for detailed 2D interaction profiling, including hydrogen bonding and hydrophobic interaction analysis. Binding affinities were evaluated based on estimated free energy values (ΔG), and interaction patterns were analyzed to assess ligand stability within the CYP2E1 active site. Results revealed that curcumin exhibited the strongest binding affinity (− 6.9889 kcal/mol), outperforming the benchmark ligand silymarin (− 6.5559 kcal/mol), followed by kaempferol (− 6.3145 kcal/mol) and quercetin (− 6.0048 kcal/mol). Curcumin also formed the highest number of hydrogen bonds and demonstrated favorable hydrophobic and short-range interactions within the active site. These combined interactions suggest strong ligand–enzyme stability and effective binding within the CYP2E1 catalytic pocket. On the whole curcumin demonstrated the most promising inhibitory potential against CYP2E1 among the evaluated phytochemicals, suggesting its possible role as a natural hepatoprotective agent. However, further molecular dynamics simulations, in vitro enzymatic studies, and in vivo validation are required to confirm its therapeutic efficacy.

Investigation of in vitro anticancer and antioxidant activities of various extracts of Bayramiç Beyazı nectarine, and molecular docking, molecular dynamics simulation, and protein-protein interaction network

Nectarine (Prunus persica var. nucipersica), due to its high phenolic content and antioxidant properties, holds significance for human health. The aim of this study was to evaluate the in vitro anticancer and antioxidant effects of the extracts obtained from the fruit and kernel of “Bayramiç Beyazı” nectarine, a geographically indicated fruit grown in Bayramiç district of Çanakkale. The anticancer effects of the methanol and aqueous ethanol extracts were evaluated on breast and colon cancer cell lines. Apoptotic fragmentation and mitochondrial membrane potential of fruit and kernel extracts were examined under fluorescence microscopy. Antioxidant activity and phenolic content were determined using DPPH, ABTS, and Folin-Ciocalteu (F-C) methods, respectively. Kernel extract has the highest antioxidant activity (DPPH IC₅₀= 0.15 ± 0.001 mg/mL). The fruit methanol, aqueous ethanol, and kernel aqueous ethanol extracts significantly reduced the fluorescent intensity of the cells. A combination study was conducted between the extracts and doxorubicin. Molecular docking and molecular dynamics (MD) simulation studies of some of the identified components were performed using the Glide/SP and Desmond against a drug target PRK1. The highest binding affinity with quercetin for targeting PRK1 was calculated as -8.789 kcal/mol. The average RMSD values were calculated between 3.43 ± 0.31 and 2.22 ± 0.30 Å throughout 500 ns MD simulations. A protein-protein interaction network analysis was performed for PRK1 using a systems biology approach to identify the highest scoring predicted proteins such as RHOA, MAP2K3, and MEFV. The investigation of the in vitro anticancer effects of “Bayramiç Beyazı” extracts and combined in silico analyses were carried out for the first time, and the outcomes of this study have promising potential for future studies.

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Decoding the Grammar of Protein–Protein Interaction Interfaces with Multimodal Representations

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

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