Recep Adiyaman
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Weekly Digest: Jan 12 - Jan 16, 2026

January 16, 2026 Daily Intelligence
Protein Design Daily

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🧬 Protein Design Digest

Curated protein signals by Recep Adiyaman

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🧬 Weekly Recap

Jan 12 - Jan 16, 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, Jan 12

Synthesis, Anticancer Evaluation, and Molecular Docking of Triazolylmethyl-Dihydroquinazolinyl Benzoate Derivatives as Potential PARP-1 Inhibitors.

🧬 Abstract

Quinazolinone derivatives have emerged as promising scaffolds in medicinal chemistry due to their broad spectrum of biological activities, including anticancer potential. Incorporation of triazole rings through click chemistry has further boosted the pharmacological relevance of such compounds, due to the triazole’s stability, bioisosterism, and ability to engage in key interactions with biological targets. Motivated by these properties, a library of 24 triazolylmethyl-dihydroquinazolinyl benzoate (TDB) derivatives (7a-x) was synthesized using a click chemistry strategy, starting from anthranilamide and phthalic anhydride. The structures of the synthesized compounds were established through IR, 1 H NMR, 13 C NMR, and HRMS spectral analysis. The anticancer potential of all derivatives was evaluated by using SRB assay, with compounds 7j and 7q displaying notable activity, with GI 50 values of 22 and 48 µg/mL, respectively. In addition, compounds 7a, 7e, 7f, 7l, 7u, 7v, and 7x displayed moderate activity, with GI 50 values ranging from 58 to 77 µg/mL. In addition, molecular docking studies were performed using poly(ADP-ribose) polymerase-1 as the target enzyme, and the results confirmed that the TDB derivatives exhibited strong binding affinity. Furthermore, molecular dynamics simulations were conducted to evaluate the stability of the docked complexes, specifically for compounds 7j and 7q, which confirmed that the TDB derivatives formed stable interactions with poly(ADP-ribose) polymerase-1.

Why it matters: Provides actionable mutations to enhance catalytic efficiency or thermostability.

🗓️ Tuesday, Jan 13

Geometric deep learning assists protein engineering. Opportunities and Challenges.

🧬 Abstract

Protein engineering is experiencing a paradigmatic transformation through the integration of geometric deep learning (GDL) into computational design workflows. While traditional approaches such as rational design and directed evolution have achieved significant progress, they remain constrained by the vastness of sequence space and the cost of experimental validation. GDL overcomes these limitations by operating on non-Euclidean domains and by capturing the spatial, topological, and physicochemical features that govern protein function. This perspective provides a comprehensive and critical overview of GDL applications in stability prediction, functional annotation, molecular interaction modeling, and de novo protein design. It consolidates methodological principles, architectural diversity, and performance trends across representative studies, emphasizing how GDL enhances interpretability and generalization in protein science. Aimed at both computational method developers and experimental protein engineers, the review bridges algorithmic concepts with practical design considerations, offering guidance on data representation, model selection, and evaluation strategies. By integrating explainable artificial intelligence and structure-based validation within a unified conceptual framework, this work highlights how GDL can serve as a foundation for transparent, interpretable, and autonomous protein design. As GDL converges with generative modeling, molecular simulation, and high-throughput experimentation, it is poised to become a cornerstone technology for next-generation protein engineering and synthetic biology.

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

🗓️ Wednesday, Jan 14

Advantages and Limitations of AlphaFold in Structural Biology: Insights from Recent Studies.

🧬 Abstract

Over the past three years, AlphaFold-a deep learning-based protein structure prediction system-has transformed structural biology by providing near-experimental accuracy models directly from amino acid sequences. This narrative review synthesizes applications reported in the 2022-2025 literature across human, microbial, and viral systems, drawing on peer-reviewed studies as our data source. Representative examples include modeling of SARS-CoV-2 spike and nucleocapsid proteins in virology, assisting cryo-EM interpretation of bacterial ribosomal and membrane-protein complexes in microbiology, and refining conformational hypotheses for human GPCRs in biomedicine. Across these cases, AlphaFold predictions have complemented experimental workflows by accelerating hypothesis generation, improving model fitting within ambiguous density regions (poorly resolved areas of cryo-EM maps), and guiding mutagenesis strategies to probe dynamic conformational states. We also summarize recent method extensions: AlphaFold-Multimer improves multi-chain complex assembly prediction, while molecular dynamics (MD) simulations augment AlphaFold’s static models by sampling conformational flexibility and testing stability. Despite these advances, important limitations remain-particularly for intrinsically disordered regions, protein-ligand and protein-cofactor interactions, and very large or transient assemblies-and current community benchmarks indicate that approximately one-third of residues may lack atomistic precision, underscoring uncertainty in flexible or modified segments. Framed within a clear chronological window and evidence base, our analysis highlights both the practical impact and the remaining challenges of integrating AlphaFold with experiment, outlining priorities where further methodological innovation and orthogonal validation are needed.

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

🗓️ Thursday, Jan 15

Benchmarking co-folding methods to predict the structures of covalent protein-ligand complexes.

🧬 Abstract

Targeted covalent inhibitors (TCIs) are emerging as a new modality in drug discovery because of their strong binding affinity and prolonged target engagement. However, the rational design of TCIs remains a significant challenge and is hindered by the lack of methods that accurately predict the structures of covalent protein-ligand complexes. Recent advances in co-folding approaches have made substantial strides in modeling complex biomolecular structures. Despite significant progress, their performance profiles for predicting the structures of covalent protein-ligand complexes remain largely unexplored because of the absence of rigorous benchmarks. Here, we introduce CoFD-Bench, a comprehensive benchmark dataset comprising 218 recently resolved covalent complexes designed to systematically evaluate both classical docking methods (AutoDock-GPU, CovDock, and GNINA) and deep learning co-folding models (AlphaFold3 (AF3), Chai-1, and Boltz-1x). Our results demonstrate that co-folding methods achieve superior ligand RMSD accuracy and protein-ligand interaction recovery. However, their performance markedly declines for novel pocket-ligand pairs. In contrast, classical docking methods exhibit stable but modest performance, which is primarily limited by target conformations. Furthermore, computational efficiency evaluations show that co-folding methods are slower than classical approaches, posing challenges for large-scale predictions. We also reveal that AF3 has the potential to identify native covalent residues through noncovalent co-folding, with a ligand RMSD comparable to that of covalent co-folding. These findings offer a possible route to explore covalent binding without prior specification of reactive residues, which are often unknown in real-world scenarios. Our study provides crucial insights and new opportunities for future co-folding-based TCI design, informing future model applications and improvements. CoFD-Bench offers rigorous evaluation criteria, diverse docking scenarios, and various methodological baselines, positioning it as an important benchmark for future model development and assessment.

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

🗓️ Friday, Jan 16

In Silico Discovery of RIOK3 Inhibitors Against Pancreatic Ductal Adenocarcinoma Using Homology Modelling, Molecular Docking, Molecular Dynamics Simulations, ADMET Prediction, and MTT assay

🧬 Abstract

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer strongly linked to RIO Kinase 3 (RIOK3), which promotes progression by stabilizing and phosphorylating Focal Adhesion Kinase (FAK). Advances in protein structure prediction, particularly AlphaFold2, have significantly enhanced our understanding of protein dynamics, aiding in the identification of potential inhibitors for targeted therapies. This study used structure-based virtual screening, molecular dynamics simulations, ADMET/toxicity prediction, and in vitro validation to identify potential inhibitors of RIOK3 for PDAC treatment. The 3D structure of RIOK3 was predicted using AlphaFold2 and docked with FDA-approved drugs via AutoDock Vina. Pharmacokinetic and pharmacodynamic properties were assessed with SwissADME, and in vitro validation was performed using MTT assays to assess cell viability and growth inhibition. Four top-scoring compounds were identified, with binding energies between − 11.3 and − 10.4 kcal/mol. Venetoclax showed the most stable complex with RIOK3, followed by Conivaptan and Irinotecan. Drospirenone showed weaker binding. Molecular dynamics simulations and MM/GBSA analysis supported the stability of these complexes. SwissADME and ProTox-II confirmed that the compounds met drug-likeness criteria but exhibited distinct pharmacokinetic and toxicity profiles. In vitro MTT assays showed concentration-dependent growth inhibition in PANC-1 cells, with Conivaptan having the lowest IC₅₀ value. This study identifies RIOK3 as a promising therapeutic target for PDAC, with Venetoclax, Conivaptan, Drospirenone, and Irinotecan as repurposable candidates for further research. Further studies should include biochemical assays, expanded cytotoxicity profiling in multiple PDAC cell lines, and in vivo evaluations to validate RIOK3-targeted therapies for PDAC treatment.

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

📚 All Papers & Quick Reads

🗓️ Monday, Jan 12

🗓️ Tuesday, Jan 13

🗓️ Wednesday, Jan 14

🗓️ Thursday, Jan 15

🗓️ Friday, Jan 16

🛠️ Tools & Datasets

  • 🛠 Tool: RFdiffusion - State-of-the-art generative model for de novo protein design.
  • 🛠 Tool: ProteinMPNN - High-speed sequence design optimized for fixed-backbone folding.
  • 💾 Dataset: UniRef - Clustered protein sequence sets for fast similarity searches.
  • 💾 Dataset: BFD - Big Fantastic Database for deep learning protein modeling.
  • 🛠 Tool: OpenFold - Fast, trainable, and open implementation of AlphaFold2.
  • 💾 Dataset: MGnify - Metagenomics resource for microbiome sequence data.
  • 🛠 Tool: ChimeraX - Next-gen molecular visualization for large data sets.
  • 💾 Dataset: PDBbind - Binding affinity data with 3D structures of protein-ligand complexes.
  • 🛠 Tool: AlphaFold2 - Deep learning system for high-accuracy protein structure prediction.
  • 💾 Dataset: BioLiP - Verified biologically relevant ligand-protein interactions.
  • 🛠 Tool: ColabFold - Fast AlphaFold2/MMseqs2 pipeline for large-scale predictions.
  • 💾 Dataset: SIFTS - Residue-level mapping between PDB, UniProt, and other resources.

🤖 AI in Research Recap

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