Author: OneAngstrom

Anyone working regularly in molecular modeling knows how repetitive and time-consuming setting up a clean, clear visualization can be. You may spend several minutes choosing representations, hiding solvent atoms, coloring chains, creating attractive figures—only to repeat the same steps a…

Molecular modelers using umbrella sampling often face a common challenge: selecting meaningful and well-distributed initial conformations. Poor choices at this early stage can lead to inefficient sampling or misleading Potential of Mean Force (PMF) results. Fortunately, the GROMACS Wizard in…

Reproducibility and automation are essential in molecular modeling, yet many modelers struggle when it comes to managing Python environments. Switching between systems, sharing workflows, or simply setting up dependencies for simulations or AI-based modeling can often lead to frustrating environment…

When setting up coarse-grained (CG) molecular dynamics simulations for systems containing multiple copies of the same protein—such as membrane assemblies or multimeric complexes—one common pain point is efficiently and correctly preparing these replicas. This involves not just duplicating the molecular…

Switching between different tools and windows is one of the biggest time sinks when modeling molecular systems. Whether you’re analyzing a protein structure, building ligands, or running simulations, flipping repeatedly between visualizers, inspectors, history panels, and asset libraries can disrupt…

When working with protein systems that include multiple replicas—either for crowding simulations, protein-protein interaction analysis, or other ensemble-based studies—generating coarse-grained (CG) models using the MARTINI force field can be challenging if your atoms, chains, or residues aren’t uniquely identified. One…

For many molecular modelers, the initial stages of a simulation can often be frustratingly slow. Before any meaningful analysis can begin, structures must be relaxed and optimized — and this step can become a bottleneck, especially for large or flexible…

When preparing to model protein transitions—such as conformational changes or reaction pathways—many molecular modelers face a frustrating issue: their interpolation algorithm fails to run, or produces unrealistic outputs. A frequent cause? Poorly prepared structures. If you’re using the As-Rigid-As-Possible (ARAP)…

When preparing molecular animations, it’s easy to focus on moving objects—proteins folding, ligands binding, conformational changes. But one often overlooked element is just as important: the virtual camera. If your perspective shifts unexpectedly between frames where no camera path has…