🧬 Weekly Recap

May 04 - May 08, 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

🗓️ Friday, May 08

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

🧬 Abstract

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

📚 All Papers & Quick Reads

🗓️ Friday, May 08

• Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.: The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced drug discovery. The current study employed a robust computational pipeline integrating Machine Learning (ML)…
• Advances in Molecular Docking Methodologies: Computer-aided drug design (CADD) is undergoing a fundamental paradigm shift driven by the transition from classical biophysical methods to deep learning architectures and generative artificial intelligence. This review analyzes the evolution of molecular…
• Integrative structural and physicochemical characterization of chalcone synthase enzymes from medicinal plants using AlphaFold, molecular docking, and molecular dynamics.: Chalcone synthase (CHS) is the entry-point enzyme of the flavonoid biosynthetic pathway, catalyzing the first committed step toward the production of diverse bioactive metabolites with antioxidant, anti-inflammatory, and anticancer properties. Here, we…
• The past, present and future of de novo protein design.: With deep-learning-powered advances in protein design methods, there is an ongoing paradigm shift in protein engineering from random selection to intentional computational design methods. Here we describe the current state of de novo protein design. While…
• Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.: 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…
• The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.: The AlphaFold (AF) initiative profoundly impacted structural biology, evidenced by its 2024 Nobel Prize. AlphaFold progressed from AF1 to AF2, which achieved near-experimental accuracy in single-chain protein folding, and then to AF3, expanding predictions…
• NNDock2: A neural network-based scoring function for ranking protein-protein docking models.: 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…
• Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.: Psoriasis is a chronic inflammatory skin disease with limited safe and effective treatments. Methylparaben, a widely used preservative in cosmetics, pharmaceuticals, and food, is an emerging environmental pollutant linked to immune-related skin disorders,…

🛠️ Tools & Datasets

• 🛠 Tool: MAFFT - Multiple sequence alignment with high speed and accuracy.
• 🛠 Tool: Clustal Omega - Scalable multiple sequence alignment for protein families.
• 💾 Dataset: BioLiP - Verified biologically relevant ligand-protein interactions.
• 💾 Dataset: SIFTS - Residue-level mapping between PDB, UniProt, and other resources.

🤖 AI in Research Recap

• Bruker Unveils New NMR Products and Workflow Solutions at ENC 2026 - AZoM: Bruker Unveils New NMR Products and Workflow Solutions at ENC 2026    AZoM
• AI Pharma Tech eyes Isomorphic Labs clinical leap - AI CERTs: AI Pharma Tech eyes Isomorphic Labs clinical leap    AI CERTs
• AlphaFold’s Surprising Revelation: AI Shifts Bottlenecks, Not Workers - American Enterprise Institute - AEI: AlphaFold’s Surprising Revelation: AI Shifts Bottlenecks, Not Workers    American Enterprise Institute - AEI
• Donald Becker Reappointed as Head of UNL Biochemistry Department - IANR News: Donald Becker Reappointed as Head of UNL Biochemistry Department    IANR News
• Google Cloud’s Shweta Maniar on moving life-sciences AI from research to reality - R&D World: Google Cloud’s Shweta Maniar on moving life-sciences AI from research to reality    R&D World

🏢 Industry & Real-World Applications

• Israeli-founded AI biotech Immunai expands AstraZeneca cancer collaboration - The Jerusalem Post: Israeli-founded AI biotech Immunai expands AstraZeneca cancer collaboration    The Jerusalem Post
• AstraZeneca expands AI partnership with Immunai in deal worth up to $37.5 million - CTech: AstraZeneca expands AI partnership with Immunai in deal worth up to $37.5 million    CTech
• Autoimmune disease biotech Odyssey sets terms for $225m IPO - pharmaphorum: Autoimmune disease biotech Odyssey sets terms for $225m IPO    pharmaphorum
• BRIGHT and LanzaTech launch new partnership to accelerate carbon to value biotechnology in Europe - EurekAlert!: BRIGHT and LanzaTech launch new partnership to accelerate carbon to value biotechnology in Europe    EurekAlert!
• Swiss biotech’s strong performance setting new records - The Pharma Letter: Swiss biotech’s strong performance setting new records    The Pharma Letter
• Epicrispr Biotechnologies and Forge Biologics Announce AAV Development and cGMP Manufacturing Partnership - BioSpace: Epicrispr Biotechnologies and Forge Biologics Announce AAV Development and cGMP Manufacturing Partnership    BioSpace
• Arzeda Partners With KENT Specialty Milling on AI-Driven Protein Development - TipRanks: Arzeda Partners With KENT Specialty Milling on AI-Driven Protein Development    TipRanks

💼 Jobs & Opportunities

• STEM Sync AI hiring Bioinformatics & Single-Cell Genomics Evaluation Expert | Remote in United States - LinkedIn (Bioinformatics Careers)
• Mercor hiring Structural Biologist - Protein Design in London, England, United Kingdom - LinkedIn (Bioinformatics Careers)

📅 Events

• Protein Design Hub (LinkedIn Group)
• Structural Biology Events

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