Issue #46: Unfreezing structural biology for drug discovery.

Signal of the Day

Unfreezing structural biology for drug discovery.

Structure-based drug discovery relies on three-dimensional protein structures to provide the atomic blueprints for small-molecule design, indicating where to place each atom to maximize favorable interactions. The advent of cryo-cooling crystals in crystallography greatly accelerated the ease and accessibility of structural data, making it a mainstay of most drug discovery efforts. However, despite its successes, including producing numerous clinically successful molecules, cryo-cooled samples only tell part of the structural story: they may leave out dynamic details or introduce artifacts that may lead drug discovery campaigns astray. In this Perspective, we highlight recent studies characterizing temperature-sensitive structural phenomena observed by crystallography. We showcase how leveraging information on rare, hidden conformational states informs ligand discovery via molecular docking. This demonstrates the value of performing structural studies at elevated temperatures, closer to where biology occurs, to ‘unfreeze’ structural ensembles for drug discovery and design.

Why this matters:

Also Worth Reading

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, but its role and mechanism in psoriasis remain unclear. This study explored its potential mechanism using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms. Methylparaben targets were retrieved from GeneCards and TCMSP, and psoriasis-related targets from CTD and GeneCards. Overlapping targets were screened with Venny 2.1.0. A PPI network was constructed via STRING, and core targets identified using Cytoscape 3.10.2. GO and KEGG enrichment analyses were performed on DAVID. Molecular docking evaluated the binding affinity of methylparaben with key targets. A total of 138 compound-related and 5,592 psoriasis-related targets were identified. Core targets such as INS, HIF1A, and PPARG are involved in regulating immune-inflammatory responses, keratinocyte proliferation and differentiation, and oxidative stress. GO analysis revealed enrichment in xenobiotic metabolism, lipopolysaccharide response, and metal ion binding. KEGG analysis highlighted pathways related to cancer, chemical carcinogenesis from reactive oxygen species, and drug metabolism via cytochrome P450 enzymes. Molecular docking showed stable binding of methylparaben to INS (-4.5 kcal/mol), HIF1A (-5.9 kcal/mol), and PPARG (-5.5 kcal/mol), primarily through hydrogen bonds and hydrophobic interactions. Methylparaben may exert its effects on psoriasis via multi-target and multi-pathway mechanisms, influencing inflammation, oxidative stress, and cellular regulation. These findings provide valuable insight into its toxicological mechanism and potential therapeutic application.

Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer’s Disease.

Introduction Alzheimer’s disease (AD) is a debilitating neurodegenerative condition marked by progressive cognitive decline and memory impairment, affecting millions worldwide. Despite extensive research, no definitive cure exists, underscoring the need for innovative approaches to drug discovery and development. Methods This review focuses on the application of molecular docking techniques in the context of AD drug discovery. The methodology involves the use of computational modeling tools to predict and analyze the interactions between small drug-like molecules and key protein targets implicated in AD pathogenesis, particularly amyloid-beta (Aβ) and tau proteins. Results Molecular docking has enabled the virtual screening of large chemical libraries to identify potential inhibitors of Aβ aggregation and tau hyperphosphorylation. Numerous studies have validated docking-predicted interactions with in vitro and in vivo experiments, resulting in the discovery of novel compounds with promising pharmacological profiles. Docking has also aided in the optimization of ligand binding affinity and selectivity toward AD-relevant targets. Discussion The integration of molecular docking with experimental techniques enhances the reliability and efficiency of the drug discovery process. Docking allows for the early identification of bioactive molecules, reducing time and cost compared to traditional methods. However, limitations such as rigid receptor assumptions and scoring function inaccuracies require further refinement. Conclusion Molecular docking stands out as a powerful computational tool in the quest for effective AD therapies. Simulating protein-ligand interactions accelerates the identification of potential drug candidates and supports the rational design of targeted interventions, paving the way for future clinical applications in combating Alzheimer’s disease.

Unveiling the therapeutic potential of Resveratrol against neutrophil-mediated hyperinflammation in sepsis: An in-silico, molecular docking, and ex-vivo approach.

Sepsis is a serious condition characterized by hyperinflammation, often leading to organ dysfunction and mortality. Currently employed drugs are non-specific and lead to severe side effects. Hence, there is a need to search for patient-specific drugs targeting hyperinflammation. An in-silico analysis of multiple GEO datasets [GSE63311, GSE95233, and GSE28750] to identify differentially expressed genes [DEGs] in sepsis was performed. In-silico tools, including hub gene identification using Cytoscape, protein-protein interaction [PPI] network analysis using STRING, network analysis and functional enrichment, KEGG pathway enrichment analysis, and gene ontology, were performed. Molecular docking was performed to assess the interactions between the ligand and target protein. Furthermore, we performed an ex-vivo study by challenging neutrophils from healthy volunteers and sepsis patients with and without E. coli and S. aureus, involving free radical formation, NO generation, and neutrophil extracellular traps [NETs] formation in the presence or absence of Resveratrol [RSV]. Nine potential hub genes, namely S100A12, S100A9, S100A8, HK3, HP, CD177, MCEMP1, ARG1, and ANXA3, having roles in pro-inflammatory, immune regulation, and metabolic processes, were identified as DEGs. PPI networks generated using the Cytoscape CytoHubba plugin identified these genes as central nodes, critical for sepsis progression. Molecular docking studies confirmed the binding affinity of RSV with S100A12. The free radical and NO generations, NETs release, and p38 MAPK phosphorylation from human neutrophils were significantly attenuated in the presence of RSV. The present study identified S100A12 as a potential therapeutic target for RSV to attenuate neutrophil-driven hyperinflammation in sepsis. This revealed the therapeutic potential of RSV in combating sepsis.

Research & AI Updates

• Isomorphic Labs New AI Doubles AlphaFold 3’s Accuracy in Protein-Ligand Predictions - WinBuzzer — Isomorphic Labs New AI Doubles AlphaFold 3’s Accuracy in Protein-Ligand Predictions    WinBuzzer.
• How Demis Hassabis is leading Google through an innovator’s dilemma—and made OpenAI declare ‘code red’ - Fortune — How Demis Hassabis is leading Google through an innovator’s dilemma—and made OpenAI declare ‘code red’    Fortune.
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From the Industry

• Swiss-Japanese ADC player Araris expands Chugai pact with $780m licensing agreement - European Biotechnology Magazine — Swiss-Japanese ADC player Araris expands Chugai pact with $780m licensing agreement    European Biotechnology Magazine.
• South Korea AI biotech Galux raises $29m in series B - Tech in Asia — South Korea AI biotech Galux raises $29m in series B    Tech in Asia.
• Bristol Myers and Evinova pen global development partnership - Fierce Biotech — Bristol Myers and Evinova pen global development partnership    Fierce Biotech.
• FDA Modernizes Draft Guidance on Statistical Methods to Support Clinical Trial Data - JD Supra — FDA Modernizes Draft Guidance on Statistical Methods to Support Clinical Trial Data    JD Supra.
• AI in drug discovery: 2025 in review - Drug Target Review — AI in drug discovery: 2025 in review    Drug Target Review.
• Takeda, Iambic Launch Up-to-$1.7B AI Collaboration - GEN - Genetic Engineering and Biotechnology News — Takeda, Iambic Launch Up-to-$1.7B AI Collaboration    GEN - Genetic Engineering and Biotechnology News.
• Genetically modified organism - Medicine, Research, Biotechnology - Britannica — Genetically modified organism - Medicine, Research, Biotechnology    Britannica.

Quick Reads

Mechanisms of hesperetin in alleviating diabetic nephropathy: Network pharmacology, molecular docking, and experimental validation.

Diabetic nephropathy (DN) accounts for approximately 50% of chronic kidney disease cases. Read more →

Osteoarthritis and chondrosarcoma: Bioinformatics analysis based on single-cell RNA sequencing and molecular docking.

Osteoarthritis (OA) and chondrosarcoma (CHS) are joint-disabling diseases that differ in their clinical manifestations, pathobiological mechanisms and management strategies. Read more →

Molecular docking and dynamic simulation of marine natural products from soft coral-derived microbes against SARS-CoV-2 main protease and spike protein.

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Read more →

Unfreezing structural biology for drug discovery.

Structure-based drug discovery relies on three-dimensional protein structures to provide the atomic blueprints for small-molecule design, indicating where to place each atom to maximize favorable interactions. Read more →

Deciphering the Mechanisms of Cupferron Reproductive Toxicity: Insights from In Vitro Assays, Network Toxicology, and Molecular Docking.

Cupferron, widely used in industrial and analytical contexts, has been proposed as a potential nitric oxide (NO) donor; however, its effects on the male reproductive system remain unclear. Read more →

Antibacterial potential of endophytic Streptomyces spp. isolated from peanut (Arachis hypogaea) roots: bioactiveprofiling and molecular docking studies.

The worldwide escalation of antimicrobial resistance (AMR) necessitates the search for new bioactive agents from natural sources. Read more →

A Computational Workflow for Membrane Protein-Ligand Interaction Studies: Focus on α5-Containing GABA (A) Receptors.

In neuropharmacology and drug development, in silico methods have become increasingly vital, particularly for studying receptor-ligand interactions at the molecular level. Read more →

Structural and immunological impacts of TOLLIP nsSNPs: A computational biology approach to drug discovery and immune system modulation.

Toll-Interacting Protein (TOLLIP) serves as key adaptor molecule in innate immune signaling, modulating toll-like receptors (TLRs) and interleukin-1 (IL-1) pathway. Read more →

Pipeline Tip

Employ HADDOCK for ambiguous restraints in protein-protein docking.

Resources & Tools

• Dataset: PDBbind - Binding affinity data with 3D structures of protein-ligand complexes.
• Dataset: BioLiP - Verified biologically relevant ligand-protein interactions.
• Tool: ColabFold - Fast AlphaFold2/MMseqs2 pipeline for large-scale predictions. View all tools →
• Tool: RoseTTAFold - End-to-end neural network for protein structure prediction. View all tools →
• Event: Structural Biology Events (Open)
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Job: 2026 Summer Intern - Computational Biology and Medicine - Immunology - Indeed at Indeed Jobs
• Job: Computational Biology & AI Intern - Summer 2026 - Indeed at Indeed Jobs

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