Issue #81: Molecular Dynamics Simulations: Principles, Algorithms, and Emerging Applications

Signal of the Day

Molecular Dynamics Simulations: Principles, Algorithms, and Emerging Applications

Molecular dynamics (MD) simulation is a fundamental technique for resolving biomolecular structures and functions at atomic resolution. Accelerated by GPU computing and machine learning-integrated force fields (FF), modern MD simulation facilitates the study of large-scale systems and rare biological events, such as protein folding, allosteric transitions, etc. While advanced sampling methods and AI integration have significantly enhanced efficiency in drug discovery and protein engineering, the field still faces challenges regarding FF accuracy, timescale constraints, and quantum effects. Continued development of hybrid quantum and molecular mechanics methods and standardized workflows is essential to further improve the predictive power and reproducibility of MD in biotechnological research. In this review, we attempted to provide the latest developments in the MD simulations.

Why this matters:

Also Worth Reading

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 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.

Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Objective This study presents a methodology employing virtual screening to identify curcumin derivatives with selective affinity for the HER2 mutations L755S, T798I, and T798M. Methods Curcumin derivatives were retrieved from the ChEMBL database and filtered using KNIME. HER2 mutations were modeled in silico using MOE software with PDB ID 3RCD. Molecular docking and dynamics simulations were conducted to screen high-affinity compounds and evaluate binding interactions. Result From 505 curcumin derivatives, the RDKit module implemented in KNIME successfully filtered 317 compounds. Subsequent molecular docking against wild-type HER2 identified 100 curcumin derivatives with low docking scores, among which the top 20 compounds exhibited better binding affinities than Lapatinib. Further molecular docking screening against the three HER2 mutations identified five lead compounds with the lowest docking scores. Molecular docking and molecular dynamics simulation revealed critical binding interactions with residues essential for kinase domain stability. Chemical structural analysis revealed key modifications, such as geranyl and tripeptide modifications. CHEMBL3758656 and CHEMBL3827366, two curcumin derivatives, demonstrated consistent binding across HER2 mutations and a favorable ADMET profile. Conclusion This study successfully identified CHEMBL3758656 and CHEMBL3827366 as promising HER2 inhibitors through comprehensive virtual screening. Their high binding affinity against L755S, T798I, and T798M mutations and favorable ADME and toxicity properties underscore their potential as alternative therapeutics for HER2-positive breast cancer.

Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical applications.

The three-dimensional structure of a protein underpins its biological function, making structure determination and prediction central challenges in structural biology. Although experimental techniques such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryo-EM) can yield high-resolution structures, they are limited by low throughput, high cost, and demanding sample preparation. Likewise, traditional computational methods often perform poorly in the absence of homologous templates or under complex folding dynamics. Recent advances in deep learning and large-scale protein language models have transformed protein structure prediction. Models such as AlphaFold3 and RoseTTAFold achieve near-experimental accuracy by integrating evolutionary information, geometric constraints, and end-to-end neural architectures, while single-sequence approaches such as ESMFold offer substantial gains in speed and scalability. This review summarizes the biochemical foundations of protein folding, recent AI-driven methodological advances, and representative applications in drug discovery, enzyme engineering, and disease research, and discusses current challenges and future directions.

Research & AI Updates

• Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Data - Simons Foundation — Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Data    Simons Foundation.
• AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network - Nature — AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network    Nature.
• Two Undervalued Healthcare Stocks Poised for a 60% Upside - NAI500 — Two Undervalued Healthcare Stocks Poised for a 60% Upside    NAI500.

From the Industry

• Orionis Biosciences Appoints Dr. Giulio Draetta as Chief Strategy Officer - citybiz.co — Orionis Biosciences Appoints Dr.
• Boehringer doubles down on OpenProtein.AI antibody discovery pact - FirstWord Pharma — Boehringer doubles down on OpenProtein.AI antibody discovery pact    FirstWord Pharma.
• First batch of foot-ulcer therapy made for planned human trial - Stock Titan — First batch of foot-ulcer therapy made for planned human trial    Stock Titan.
• Scholar Rock Resubmits Biologics License Application (BLA) to FDA for Apitegromab for Treatment of Children and Adults with Spinal Muscular Atrophy (SMA) - BioSpace — Scholar Rock Resubmits Biologics License Application (BLA) to FDA for Apitegromab for Treatment of Children and Adults with Spinal Muscular Atrophy (SMA)    BioSpace.
• Advancing the Manufacture of Patient Accessible Cell and Gene Therapies at Place-of-Care - Genetic Engineering and Biotechnology News — Advancing the Manufacture of Patient Accessible Cell and Gene Therapies at Place-of-Care    Genetic Engineering and Biotechnology News.
• OpenProtein.AI Expands Strategic Partnership with Boehringer Ingelheim to Advance AI-Driven Antibody Discovery - PR Newswire — OpenProtein.AI Expands Strategic Partnership with Boehringer Ingelheim to Advance AI-Driven Antibody Discovery    PR Newswire.
• Biogen to acquire immunology biotech Apellis for $5.6 billion - statnews.com — Biogen to acquire immunology biotech Apellis for $5.6 billion    statnews.com.

Quick Reads

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. Read more →

Hybrid deep learning with protein language models and dual-path architecture for predicting IDP functions.

Intrinsically disordered regions (IDRs) drive essential cellular functions but resist conventional structural-function annotation due to their dynamic conformations. Read more →

Molecular Dynamics Simulations: Principles, Algorithms, and Emerging Applications

Molecular dynamics (MD) simulation is a fundamental technique for resolving biomolecular structures and functions at atomic resolution. Read more →

Multitarget Enzyme Inhibition and Anticancer Potential of 3-Substituted Indole Derivatives Supported by Molecular Docking Studies.

The enzymes and anticancer properties of disubstituted indole, indole-imidazole, and indole-pyrazole derivatives were determined. Read more →

Antimicrobial and anticancer evaluation of Juniperus chinensis seed oil with molecular docking of α-cedrol-AKT1.

In this study, the essential oil of Juniperus chinensis L. Read more →

Biological, electrochemical, molecular docking and DFT studies of the asymmetric Schiff base: synthesis of 2-hydroxy-6-methyl-3-((E)-1-((4-methyl-3-((E)-(pyridin-3-ylmethylene)amino)phenyl)imino)ethyl)-4H-pyran-4-one.

The asymmetric Schiff base 2-hydroxy-6-methyl-3-((E)-1-((4-methyl-3-((E)-(pyridin-3-ylmethylene)amino)phenyl)imino)ethyl)-4H-pyran-4-one (AL) was synthesized, and characterized by LC-MS, FT-IR, UV-Vis, 1H and 13C NMR techniques as well as cyclic voltammetry. Read more →

Molecular and pharmacological impacts of phytochemicals on the enoyl-acyl carrier protein reductase: potential therapeutic implications in tuberculosis.

Tuberculosis (TB) remains a major global health challenge, necessitating the development of novel therapeutic interventions. Read more →

AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.

Drug-target interactions (DTIs) are fundamental to drug discovery, development, and repositioning. Read more →

Pipeline Tip

Check for missing residues in PDB files using PDB-Fixer before simulation.

Resources & Tools

• Dataset: CATH - Hierarchical protein domain classification for structure and function.
• Dataset: SCOPe - Curated structural classification of proteins for fold analysis.
• Tool: OpenFold - Fast, trainable, and open implementation of AlphaFold2. View all tools →
• Tool: ChimeraX - Next-gen molecular visualization for large data sets. View all tools →
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Event: Structural Biology Events (Open)
• Job: Job Application for Director, Data Science at Glyphic Biotechnologies - Greenhouse at Greenhouse
• Job: Job Application for Associate Director, Regulatory CMC at Dianthus Therapeutics - Greenhouse at Greenhouse Boards

The protein structure is the language of life; design is its poetry. — Recep Adiyaman