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Daily Signal February 05, 2026 · 8 min read

Issue #41: High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

Protein Design Digest - 2026-02-05 - 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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High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 makes a revolutionary breakthrough in predicting protein monomeric structures, accurately capturing inter-chain interaction signals and modeling the structures of protein complexes remain a formidable challenge. In this work, we report DeepSCFold, a pipeline for improving protein complex structure modeling. DeepSCFold uses sequence-based deep learning models to predict protein-protein structural similarity and interaction probability, providing a foundation for identifying interaction partners and constructing deep paired multiple-sequence alignments (MSAs) for protein complex structure prediction. Benchmark results show that DeepSCFold significantly increases the accuracy of protein complex structure prediction compared with state-of-the-art methods. For multimer targets from CASP15, DeepSCFold achieves an improvement of 11.6% and 10.3% in TM-score compared to AlphaFold-Multimer and AlphaFold3, respectively. Furthermore, when applied to antibody-antigen complexes from the SAbDab database, DeepSCFold enhances the prediction success rate for antibody-antigen binding interfaces by 24.7% and 12.4% over AlphaFold-Multimer and AlphaFold3, respectively. These results demonstrate that DeepSCFold effectively captures intrinsic and conserved protein-protein interaction patterns through sequence-derived structure-aware information, rather than relying solely on sequence-level co-evolutionary signals.

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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.

From MM-PBSA to H-MMGB: Multiscale Modeling for Biomolecular Structure and Drug Discovery.

From early efforts to predict protein structure from simplified models, computational biophysics has progressed toward increasingly physics-based approaches for evaluating biomolecular structure, molecular interactions, and energetics. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method provided one of the first broadly accessible ways to evaluate binding and folding energetics from molecular dynamics (MD) trajectories, with applications ranging from protein structure prediction benchmarks to protein-ligand affinity ranking. Building on this foundation, the hierarchical Molecular Mechanics Generalized Born (H-MMGB) approach was developed to provide MMGB-based binding free energy estimates more efficiently, employing the Generalized Born model in contrast to the Poisson-Boltzmann framework of MM-PBSA and thereby enabling prospective applications to ligand design. Case studies illustrate how these methods, ranging from protein folding assessment to intact-ligand modeling and to a deconstruction-reconstruction strategy using picofragments, enable hypothesis generation in the absence of experimental structures and in challenging protein-protein interaction targets. Together, these developments support a guiding principle: gradual incorporation of more physics into modeling workflows increases the probability of successfully meeting objectives across diverse computational simulation problems.

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Study on the Mechanism of Ku Diding in the Treatment of Diabetes based on Network Pharmacology, Molecular Docking Technology, and Molecular Dynamics.

Introduction To explore how Ku Diding (KDD) works in managing Diabetes Mellitus (DM), researchers utilized network pharmacology, molecular docking, and molecular dynamics methodologies. Read more →

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Read more →

Exploring toxicity and mechanisms of DDTs in Alzheimer’s disease through network toxicology and molecular docking insights.

BackgroundDichlorodiphenyltrichloroethane (DDT) and its metabolites (DDTs), such as dichlorodiphenyldichloroethylene (DDE) and dichlorodiphenyldichloroethane (DDD), are synthetic organochlorine pesticides with long environmental persistence. Read more →

Molecular docking and dynamics in protein serine/threonine kinase drug discovery: advances, challenges, and future perspectives.

Protein serine/threonine kinases (STKs) regulate critical signaling pathways involved in cell growth, proliferation, metabolism, and apoptosis. Read more →

High-resolution protein modeling through Cryo-EM and AI: current trends and future perspectives - a review.

The structural elucidation of proteins is fundamental to understanding their biological functions and advancing drug discovery. Read more →

The promise of AlphaFold for gene structure annotation

Background As sequencing technology improves, more genomes become available. Read more →

Assessing the Neurotoxicity of Bisphenol A Using Network Toxicology, Molecular Docking, and Molecular Dynamics Simulation.

Bisphenol A (BPA) is a prevalent environmental endocrine disruptor with potential impacts to the neurological system in humans. Read more →

Network pharmacology, molecular docking, and molecular dynamics simulations to explore the effects of sinomenine on thyroid dysfunction.

Thyroid dysfunction is a disease closely associated with autoinflammatory responses and immune imbalances. Read more →

Pipeline Tip

Always validate pLDDT scores before using AlphaFold models for docking.

Resources & Tools

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

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