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Evidence-based bioinformatics articles, computational biology methods, and practical guidance for molecular research.
Find a method, workflow, scientific concept, or software decision.
Use mk_prepare_receptor to create AutoDock Vina receptor PDBQT and box files while documenting parsers, residue decisions, outputs, validation, and provenance.
Read the analysisFilter the publication by research area or keyword.
Evidence-led articles on scientific methods, reproducible workflows, and software decisions.
Learn how a sequence dot plot works, what diagonal patterns can suggest, how window and threshold settings affect noise and sensitivity, and when to follow the visual pattern with an alignment.
Learn when local protein alignment is more appropriate than an end-to-end comparison, how Smith-Waterman defines the matching region, and how to interpret score, coordinates, identity, and gaps without overclaiming.
Learn when end-to-end protein alignment is appropriate, how it differs from local alignment, why substitution matrices and gap penalties change the result, and how to interpret score and identity responsibly.
Choose the right random-sequence model, distinguish uniform generation from shuffling, preserve the generated FASTA and metadata, and avoid treating synthetic controls as natural proteins.
Learn what a PDBQT file stores, how ligand torsion trees differ from rigid receptor records, why bond orders can be lost, and what to inspect before an AutoDock Vina run.
Decide which waters, metals, ions, and cofactors belong in a docking receptor by biological role, structural evidence, ligand mechanism, force-field compatibility, and controlled validation.
Compare local, cloud, and hybrid molecular docking by real workload, data boundaries, runtime continuity, reproducibility, collaboration, governance, and total cost - not by deployment label alone.
A practical AutoDock Vina reporting checklist for preserving prepared inputs, software versions, search-box geometry, parameters, seeds, raw poses, failures, and the evidence needed to re-execute a docking study.
Choose a receptor by target state, local pocket evidence, ligand context, and validation. Compare when a holo, apo, AlphaFold, or receptor ensemble best fits a reproducible protein-small-molecule docking campaign.