BACKGROUND: Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. RESULTS: We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. CONCLUSIONS: P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.

• Keywords
• Binding site prediction, Ligand binding sites, Machine learning, Protein pockets, Protein surface descriptors, Random forests,
• Publication type
• Journal Article MeSH

There is great interest in increasing proteins' stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt's reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.

• MeSH
• Point Mutation genetics   physiology   MeSH
• Databases, Genetic MeSH
• Lyases chemistry   genetics   metabolism   MeSH
• Models, Molecular MeSH
• Computer Simulation MeSH
• Protein Engineering methods   MeSH
• Enzyme Stability genetics   MeSH
• Temperature MeSH
• Computational Biology methods   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Lyases MeSH

We emphasize the importance of dynamics and hydration for enzymatic catalysis and protein design by transplanting the active site from a haloalkane dehalogenase with high enantioselectivity to nonselective dehalogenase. Protein crystallography confirms that the active site geometry of the redesigned dehalogenase matches that of the target, but its enantioselectivity remains low. Time-dependent fluorescence shifts and computer simulations revealed that dynamics and hydration at the tunnel mouth differ substantially between the redesigned and target dehalogenase.

• MeSH
• Hydrocarbons, Brominated chemistry   MeSH
• Spectrometry, Fluorescence MeSH
• Hydrolases chemistry   genetics   MeSH
• Catalytic Domain MeSH
• Catalysis MeSH
• Protein Conformation MeSH
• Crystallography, X-Ray MeSH
• Molecular Sequence Data MeSH
• Mutagenesis, Site-Directed MeSH
• Protein Engineering * MeSH
• Amino Acid Sequence MeSH
• Molecular Dynamics Simulation * MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization MeSH
• Stereoisomerism MeSH
• Water chemistry   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Hydrocarbons, Brominated MeSH
• haloalkane dehalogenase MeSH Browser
• Hydrolases MeSH
• Water MeSH