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Weekly Digest April 24, 2026 · 14 min read

Weekly Digest: Apr 20 - Apr 24, 2026

A curated summary of the top protein engineering and structure prediction signals from Apr 20 - Apr 24, 2026.

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Daily curated signals from arXiv, PubMed, and BioRxiv.

🧬 Weekly Recap

Apr 20 - Apr 24, 2026

Missed a day? Here are the top research signals and tools from Monday to Friday, summarized in one place.

🏆 Top Signals of the Week

🗓️ Monday, Apr 20

Advancing protein engineering via organic chemistry.

🧬 Abstract

Proteins are central to nearly all biological processes; their functions are tightly regulated by dynamic mechanisms such as covalent alterations; e.g., post-translational modifications (PTMs). These modifications can influence the protein’s structure, localization, and activity. Inspired by this diversity and regulation, advances in synthetic organic chemistry have enabled the production of a plethora of novel proteins for both basic research and biomedical applications. Recent progress in structural elucidation technologies and modern organic chemistry has enabled atom-level modifications, significantly enhancing our ability to tailor protein function. These approaches greatly expand the toolkit currently available for generating complex proteins with unique structural and functional properties. In this review, we summarize recent progress in chemical protein engineering and highlight its emerging applications in catalysis, functional studies, and drug development.

🗓️ Thursday, Apr 23

NNDock2: A neural network-based scoring function for ranking protein-protein docking models.

🧬 Abstract

Protein-protein interactions (PPIs) play crucial roles in diverse cellular functions and biological processes, and structural knowledge of the protein complexes is valuable for the elucidation of those functions and designing new drugs. Due to the limitations of experimental methods, computational modeling approaches capable of producing reliable protein complex models using molecular docking tools are of considerable practical interest. The success of protein docking largely depends on an accurate scoring function that can pick out good protein docking models. In this work, we present a neural network-based scoring function for scoring protein-protein docking models, NNDock2, the updated version of our previous scoring function, NNDock1. To improve NNDock1, we augmented the training decoys by adding a large number of more distant decoys. In addition, instead of interface root mean square deviation (iRMSD) in NNDock1, we used the fraction of native contact ([Formula: see text] as a target function, which shows better correlation with true model quality. We also applied regularization during training to avoid overfitting. We tested NNDock2 on the protein-protein docking benchmark version 5.0 (BM5), DOCKGROUND dataset, and the CAPRI score set and compared the performance of NNDock2 with other state-of-the-art scoring functions. NNDock2 performed comparably to other state-of-the-art scoring functions, despite the simplicity of the method and low computational costs. We envision that NNDock2 could be used as an independent scoring function or as an element or feature of composite or deep learning-based scoring functions for protein complex model quality estimation.

Why it matters: Expands the searchable sequence space for novel folds and high-affinity binders.

🗓️ Friday, Apr 24

Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

🧬 Abstract

Predicting cytochrome P450 (CYP450) ligand binding is critical in early-stage drug discovery as CYP450-mediated metabolism profoundly influences drug efficacy, safety, and adverse reaction risks. However, experimental determination of CYP450-ligand interactions remains resource- and time-intensive, underscoring the need for robust computational alternatives. While ligand-based methods are commonly employed, they often fail to fully account for structural intricacies governing protein-ligand interactions. To address this gap, we developed a hybrid machine learning framework integrating ligand descriptors, protein descriptors, and protein-ligand interaction descriptors that include molecular docking-derived parameters, rescoring function components from multiple algorithms, and structural interaction fingerprints (SIFt). Evaluated on CYP1A2 and CYP17A1 isoforms, our model demonstrated superior predictive accuracy in cross-validation compared with stand-alone molecular docking and ligand-based approaches. Furthermore, benchmarking against state-of-the-art tools (SwissADME and ADMETlab 3.0) revealed enhanced performance in binding prediction. This work establishes a versatile framework for advancing computational tools to prioritize CYP450 binding assessments during drug discovery.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

📚 All Papers & Quick Reads

🗓️ Monday, Apr 20

🗓️ Thursday, Apr 23

🗓️ Friday, Apr 24

🛠️ Tools & Datasets

  • 🛠 Tool: FunFOLD5 - Automated system for protein ligand-binding site prediction and function annotation.
  • 🛠 Tool: MultiFOLD/IntFOLD - High-performance protein structure prediction and quality assessment server.
  • 💾 Dataset: UniRef - Clustered protein sequence sets for fast similarity searches.
  • 💾 Dataset: BFD - Big Fantastic Database for deep learning protein modeling.
  • 🛠 Tool: Chai-1 - Multi-modal foundation model for molecular structure prediction.
  • 🛠 Tool: Boltz-1 - Open-source biomolecular structure prediction model.
  • 💾 Dataset: PDBbind - Binding affinity data with 3D structures of protein-ligand complexes.
  • 💾 Dataset: BioLiP - Verified biologically relevant ligand-protein interactions.
  • 🛠 Tool: ProteinSolver - Graph-based neural network for protein sequence design.
  • 💾 Dataset: SIFTS - Residue-level mapping between PDB, UniProt, and other resources.

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