Structural complementarity of distance constraints obtained from chemical cross-linking and amino acid coevolution.

The analysis of amino acid coevolution has emerged as a practical method for protein structural modeling by providing structural contact information from alignments of amino acid sequences. In parallel, chemical cross-linking/mass spectrometry (XLMS) has gained attention as a universally applicable method for obtaining low-resolution distance constraints to model the quaternary arrangements of proteins, and more recently even protein tertiary structures. Here, we show that the structural information obtained by XLMS and coevolutionary analysis are effectively complementary: the distance constraints obtained by each method are almost exclusively associated with non-coincident pairs of residues, and modeling results obtained by the combination of both sets are improved relative to considering the same total number of constraints of a single type. The structural rationale behind the complementarity of the distance constraints is discussed and illustrated for a representative set of proteins with different sizes and folds.

Practices in Molecular Docking and Structure-Based Virtual Screening.

Drug discovery has evolved significantly over the past two decades. Progress in key areas such as molecular and structural biology has contributed to the elucidation of the three-dimensional structure and function of a wide range of biological molecules of therapeutic interest. In this context, the integration of experimental techniques, such as X-ray crystallography, and computational methods, such as molecular docking, has promoted the emergence of several areas in drug discovery, such as structure-based drug design (SBDD). SBDD strategies have been broadly used to identify, predict and optimize the activity of small molecules toward a molecular target and have contributed to major scientific breakthroughs in pharmaceutical R&D. This chapter outlines molecular docking and structure-based virtual screening (SBVS) protocols used to predict the interaction of small molecules with the phosphatidylinositol-bisphosphate-kinase PI3Kδ, which is a molecular target for hematological diseases. A detailed description of the molecular docking and SBVS procedures and an evaluation of the results are provided.

Discovery of new inhibitors of Mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach.

Mycobacterium tuberculosis InhA (MtInhA) is an attractive enzyme to drug discovery efforts due to its validation as an effective biological target for tuberculosis therapy. In this work, two different virtual-ligand-screening approaches were applied in order to identify new InhA inhibitors' candidates from a library of ligands selected from the ZINC database. First, a 3-D pharmacophore model was built based on 36 available MtInhA crystal structures. By combining structure-based and ligand-based information, four pharmacophoric points were designed to select molecules able to satisfy the binding features of MtInhA substrate-binding cavity. The second approach consisted of using four well established docking programs, with different search algorithms, to compare the binding mode and score of the selected molecules from the aforementioned library. After detailed analyses of the results, six ligands were selected for in vitro analysis. Three of these molecules presented a satisfactory inhibitory activity with IC50 values ranging from 24 (±2) µM to 83 (±5) µM. The best compound presented an uncompetitive inhibition mode to NADH and 2-trans-dodecenoyl-CoA substrates, with Ki values of 24 (±3) µM and 20 (±2) µM, respectively. These molecules were not yet described as antituberculars or as InhA inhibitors, making its novelty interesting to start efforts on ligand optimization in order to identify new effective drugs against tuberculosis having InhA as a target. More studies are underway to dissect the discovered uncompetitive inhibitor interactions with MtInhA.