A syntax-correcting CIF parser,COD::CIF::Parser, is presented that can parse CIF 1.1 files and accurately report the position and the nature of the discovered syntactic problems. In addition, the parser is able to automatically fix the most common and the most obvious syntactic deficiencies of the input files. Bindings for Perl, C and Python programming environments are available. Based onCOD::CIF::Parser, thecod-toolspackage for manipulating the CIFs in the Crystallography Open Database (COD) has been developed. Thecod-toolspackage has been successfully used for continuous updates of the data in the automated COD data deposition pipeline, and to check the validity of COD data against the IUCr data validation guidelines. The performance, capabilities and applications of different parsers are compared.

Introduction: In pharmaceutical design where future drugs are developed by targeting a specific chosen protein, the evaluation of ligand affinity is crucial. For this very purpose are a multitude of diverse methods which are continuously being improved, which, in turn, makes it difficult to choose which techniques to use in practice. Areas covered: In this review, the authors discuss both experimental and computational approaches for affinity evaluation. Basic principles, general limitations and advantages, as well as main areas of application in drug discovery, are overviewed for some of the most popular ligand binding assays. The authors further provide a guide to affinity predictions, collectively covering several techniques that are used in the first stages of rational drug design. Expert opinion: All affinity estimation methods have limitations and advantages that partially overlap and complement one another. Some of the suggested best practices include cross-verification of data using at least two different techniques and careful data interpretation.

Drug resistance mutations in HIV-1 protease selectively alter inhibitor binding without significantly affecting substrate recognition and cleavage. This alteration in molecular recognition led us to develop the substrate-envelope hypothesis which predicts that HIV-1 protease inhibitors that fit within the overlapping consensus volume of the substrates are less likely to be susceptible to drug-resistant mutations, as a mutation impacting such inhibitors would simultaneously impact the processing of substrates. To evaluate this hypothesis, over 130 HIV-1 protease inhibitors were designed and synthesized using three different approaches with and without substrate-envelope constraints. A subset of 16 representative inhibitors with binding affinities to wild-type protease ranging from 58 nM to 0.8 pM was chosen for crystallographic analysis. The inhibitor-protease complexes revealed that tightly binding inhibitors (at the picomolar level of affinity) appear to “lock” into the protease active site by forming hydrogen bonds to particular active-site residues. Both this hydrogen bonding pattern and subtle variations in protein-ligand van der Waals interactions distinguish nanomolar from picomolar inhibitors. In general, inhibitors that fit within the substrate envelope, regardless of whether they are picomolar or nanomolar, have flatter profiles with respect to drug-resistant protease variants than inhibitors that protrude beyond the substrate envelope; this provides a strong rationale for incorporating substrate-envelope constraints into structure-based design strategies to develop new HIV-1 protease inhibitors.