The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.

SUMMARY: HTHquery is a web-based service to determine if a protein structure has a helix-turn-helix structural motif which could bind to DNA. It is based on a similarity with a set of structural templates, the accessibility of a putative structural motif and a positive electrostatic potential in the neighbourhood of the putative motif. A set of scores are computed, based on each template, using a linear predictor. From the training set used, the predictor has a true positive rate of 83.5% and a false positive rate of 0.8%. The emphasis for the website is on providing a straightforward interface which can be easily used by a bench-based scientist. AVAILABILITY: HTHquery is implemented using a set of Perl scripts and C program and can be accessed freely on the website http://www.ebi.ac.uk/thornton-srv/databases/HTHquery.

MOTIVATION: The solubility of a protein is crucial for its function and is therefore an evolutionary constraint. As the solubility of a protein is related to the distribution of polar and hydrophobic residues on its solvent accessible surface, such a constraint should provide a valuable insight into the evolution of protein surfaces. We examine how the surfaces of proteins have evolved by considering how the average hydrophobicities of patches of surface residues vary across homologous proteins. We derive distributions for the average hydrophobicity/philicity of surface patches at a residue-based level-which we refer to as the residue hydrophobic density. This is computed for a set of 28 monomeric proteins and their homologues. The resulting distributions are compared with a set of randomized sequences, with the same residue content. RESULTS: We find that the patches, involving typically more than 10 residues, maintain a more hydrophilic surface than one would expect from a random substitution model, indicating a cooperative behaviour for these surfaces residues in terms of this single variable. SUPPLEMENTARY INFORMATION: Additional plots for all of the proteins examined in this paper can be found at: http://www.ebi.ac.uk/~shanahan/PCon/index.html