LAHMA: structure analysis through local annotation of homology-matched amino acids

AlphaFill: enriching AlphaFold models with ligands and cofactors

Artificial intelligence-based protein structure prediction approaches have had a transformative effect on biomolecular sciences. The predicted protein models in the AlphaFold protein structure database, however, all lack coordinates for small molecules, essential for molecular structure or function: hemoglobin lacks bound heme; zinc-finger motifs lack zinc ions essential for structural integrity and metalloproteases lack metal ions needed for catalysis. Ligands important for biological function are absent too; no ADP or ATP is bound to any of the ATPases or kinases. Here we present AlphaFill, an algorithm that uses sequence and structure similarity to 'transplant' such 'missing' small molecules and ions from experimentally determined structures to predicted protein models. The algorithm was successfully validated against experimental structures. A total of 12,029,789 transplants were performed on 995,411 AlphaFold models and are available together with associated validation metrics in the alphafill.eu databank, a resource to help scientists make new hypotheses and design targeted experiments.

CCD2: design constructs for protein expression, the easy way

Studying the function or structure of proteins usually requires the generation of many protein-truncation constructs for recombinant expression, which is a tedious and error-prone job. CCD2 is a software tool designed to facilitate and automate this task. CCD2 helps scientists by aggregating the information necessary to design protein-expression constructs. This information includes sequence conservation, secondary structure prediction, domain(s) and disorder detection, post-translational modifications and information on similar (domain) structures that are available in the Protein Data Bank. CCD2 then allows users to easily choose the boundaries for protein constructs and automatically generates the primers necessary for construct amplification by polymerase chain reaction. Finally, CCD2 provides a quick analysis of the properties of the chosen constructs, together with their DNA vector maps for bookkeeping. The features of CCD2 are discussed step by step, showing that it can be a useful tool for laboratories that engage in recombinant protein production for any type of experiment, and in particular for structural biology studies.

Modelling covalent linkages in CCP4

In this contribution, the current protocols for modelling covalent linkages within the CCP4 suite are considered. The mechanism used for modelling covalent linkages is reviewed: the use of dictionaries for describing changes to stereochemistry as a result of the covalent linkage and the application of link-annotation records to structural models to ensure the correct treatment of individual instances of covalent linkages. Previously, linkage descriptions were lacking in quality compared with those of contemporary component dictionaries. Consequently, AceDRG has been adapted for the generation of link dictionaries of the same quality as for individual components. The approach adopted by AceDRG for the generation of link dictionaries is outlined, which includes associated modifications to the linked components. A number of tools to facilitate the practical modelling of covalent linkages available within the CCP4 suite are described, including a new restraint-dictionary accumulator, the Make Covalent Link tool and AceDRG interface in Coot, the 3D graphical editor JLigand and the mechanisms for dealing with covalent linkages in the CCP4i2 and CCP4 Cloud environments. These integrated solutions streamline and ease the covalent-linkage modelling workflow, seamlessly transferring relevant information between programs. Current recommended practice is elucidated by means of instructive practical examples. By summarizing the different approaches to modelling linkages that are available within the CCP4 suite, limitations and potential pitfalls that may be encountered are highlighted in order to raise awareness, with the intention of improving the quality of future modelled covalent linkages in macromolecular complexes.