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Daily Signal March 18, 2026 · 9 min read

Issue #70: Molecular Dynamics-Guided Design and Chemoproteomic Profiling of Covalent Kinase Activity Probes.

Protein Design Digest - 2026-03-18 - Mechanisms of Okanin against wound healing based on network pharmacology, molecular docking and molecular dynamics simulation.

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Molecular Dynamics-Guided Design and Chemoproteomic Profiling of Covalent Kinase Activity Probes.

Covalent small molecule probes can be powerful tools to interrogate protein activity state in native cellular environments. The design of familywide activity probes requires an understanding of the molecular sources of conserved and target-specific small molecule targeting across protein family members. Here, we developed and applied a multifaceted docking and molecular dynamics (MD) simulation pipeline to design cell-permeable covalent kinase activity probes from a set of hinge-binding pharmacophores. This computationally-guided approach yielded a series of cell-active indazole sulfonylfluorides that target a conserved catalytic lysine in active protein kinases. Chemoproteomic profiling of a lead probe, K60P, confirmed engagement of more than 100 unique native kinases across several cancer cell lines. Competitive profiling identified kinases as the predominant class of specific targets for K60P but also highlighted significant nonkinase targets for K60P and the established covalent kinase probe, XO44, underscoring the utility of native kinase profiling in situ to identify relevant targets of small molecule kinase inhibitors in cells. Dose-, time- and site-specific proteomic mapping with a known target kinase, ABL1, coupled with a Bayesian Metropolis Monte Carlo (BMMC) kinetic modeling method showed that key descriptors of covalent probe efficiency could be predicted with straightforward dose- and time-dependent covalent engagement studies and highlighted kinact/KI as a key variable to optimize for specific and broad kinase engagement. Finally, focused molecular dynamics simulations revealed that K60P, as well as the comparator probe XO44, preferentially engage with target kinases in their active, DFG-in conformations, which is driven by increasing population of reaction-ready small molecule conformation. These results together establish a computational and kinetic modeling framework for designing covalent activity probes and highlight the balance of target selectivity and kinetic efficiency as a key factor in determining their proteome-wide reactivity.

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Also Worth Reading

Integrative gene target mapping, RNA sequencing, in silico molecular docking, ADMET profiling and molecular dynamics simulation study of marine derived molecules for type 1 diabetes mellitus.

Type 1 diabetes mellitus (T1DM) is a metabolic disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in T1DM through adopting integrated bioinformatics tools. The gene expression Omnibus (GEO) database was used to obtain next generation sequencing data (GSE270484) of T1DM and normal control samples. Furthermore, differentially expressed genes (DEGs) were screened using the DESeq2 package in R bioconductor package. Gene Ontology (GO) and pathway enrichment analyses were performed by g:Profiler. The protein-protein interaction (PPI) network was plotted with IID PPI database and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC. Then, microRNAs (miRNAs) and transcription factors (TFs) in T1DM were screened out from the miRNet and NetworkAnalyst database. Then, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Moreover, a drug-hub gene interaction network of the hub genes was constructed and predicted the drug molecule against hub genes. The receiver operating characteristic (ROC) curves were generated to predict diagnostic value of hub genes. Finally we performed molecular docking, ADMET profiling and molecular dynamics simulation studies of marine derived chemical constituents using Schrodinger Suite 2025-1. A total of 958 DEGs were screened: 479 up regulated genes and 479 down regulated genes. DEG were mainly enriched in the terms of developmental process, membrane, cation binding, response to stimulus, cell periphery, ion binding, neuronal system and metabolism. Based on the data of protein-protein interaction (PPI), the top 10 hub genes (5 up regulated and 5 down regulated) were ranked, including FN1, GSN, ADRB2, CEP128, FLNA, CD74, EFEMP2, POU6F2, P4HA2 and BCL6. The miRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-657, hsa-miR-1266-5p, NOTCH1 and GTF3C2 might play an important role in the pathogenesis of T1DM. The drug-hub gene interaction network showed that Clenbuterol, Diethylstilbestrol, Selegiline and Isoflurophate predicted therapeutic drugs for the T1DM. Molecular docking and molecular dynamics simulation study revealed that CMNPD5805 and CMNPD30286 as potential inhibitors of FN1 (pdb id: 3M7P) a key biomarker in pathogenesis of T1DM. These findings promote the understanding of the molecular mechanism and clinically related molecular targets for T1DM.

Innovative integration of molecular docking and machine learning for drug discovery: from virtual screening to nanomolar inhibitors.

This review highlights our group’s systematic approach to integrating molecular docking, pharmacophore modeling, and machine learning methodologies for the rational discovery of bioactive leads. We describe innovative strategies including docking-based data augmentation, ligand-receptor contact fingerprints, genetic algorithm-guided feature selection, and SHAP-based model interpretation. These approaches have enabled the discovery of nanomolar inhibitors against multiple therapeutic targets including STAT3, TTK, LSD-1, and HER2. The presented workflow demonstrates how machine learning (ML) can be synergistically combined with traditional computer-aided drug design methods to achieve efficient scaffold hopping and identify novel chemotypes with potent biological activities.

Evaluation of drug-excipient compatibility of ibuprofen with eggshell-derived calcium citrate using FTIR, DSC, and molecular docking studies.

Eggshells hold long-lasting nutritional and medicinal relevance in African folklore, often administered traditionally in its crushed or powdered form to ameliorate bone issues, treat calcium deficiency, and promote well-being. However, not much has been achieved in translating this folklore practice into pharmaceutical exploration and formulation science. Drug-excipient incompatibilities are critical considerations in the development of stable and effective pharmaceutical formulations. This study investigated the compatibility of ibuprofen with eggshell-derived calcium citrate using Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and molecular docking approaches. Calcium citrate was prepared from chicken eggshells via reaction with citric acid and characterised. Binary mixtures of ibuprofen and calcium citrate were evaluated for potential interactions using FTIR and DSC. In silico molecular docking studies were conducted using AutoDock Vina, and docking methodology was validated using re-docking of a known ibuprofen-calcium interaction. FTIR spectra of the binary mixtures showed minor peak shifts, particularly at 1710 cm -1 (C=O) and 3300 cm -1 (O-H), suggesting weak physical interactions. DSC thermograms demonstrated slight broadening and depression of the ibuprofen melting endotherm, indicating no significant incompatibility. Molecular docking revealed a binding affinity of - 4.7 kcal/mol, primarily mediated by ionic interactions between ibuprofen’s carboxyl group and calcium ions. Ibuprofen exhibits acceptable compatibility with eggshell-derived calcium citrate. These findings suggest its potential as a sustainable and cost-effective pharmaceutical filler in oral drug formulations.

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Pipeline Tip

Normalise thermal B-factors when comparing different crystal structures.

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

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