A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein–ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein–ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured to better fit the needs of the community are presented. Many groups generously participated in improving the data set, and this underscores the value of a supportive, collaborative effort in moving our field forward.

A traditional technique for structure-based drug design (SBDD) is mapping protein surfaces with probe molecules to identify "hot spots" where key functional groups can best complement the receptor. Common methods, such as minimizing probes or calculating grids, use a fixed protein structure in the gas phase, ignoring both protein flexibility and proper competition between the probes and water. As a result, the potential surface is quite rugged and many spurious, local minima are identified. Here, we compare rigid and fully flexible protein in mixed-solvent molecular dynamics (MixMD), which allows for flexibility and full solvent effects. We were surprised to find that that the many local minima are still found when a protein's conformational sampling is restricted; the dynamic averaging of probes and competition with water does not smooth the potential surface as one might expect. Only when a protein is allowed to be fully flexible in the simulation are the proper minima located and the spurious ones eliminated. Our results indicate that inclusion of full protein flexibility is critical to accurate hot-spot mapping for SBDD.Protein flexibility is an important component of protein-ligand binding, but it is often neglected in structure-based drug design (SBDD). Many traditional techniques for SBDD rely upon solvent mapping performed through grids or probe minimization. Most computational solvent-mapping techniques1 -4 do not account for the impact of protein flexibility on ligand binding, which prevents accurate mapping of hot spots. Also, they typically do not allow for active competition between solvent probes and water, ignoring proper solvation effects. In this communication, we demonstrate that the conformational diversity inherent to proteins strongly affects the outcome of hot-spot mapping.An experimental method that explores protein surfaces using water and organic solvent as probes is the multiple solvent crystal structure (MSCS)5 technique. Potential protein unfolding is typically prevented through cross-linking. The results of this procedure, performed with various solvents, can be superimposed to design custom ligands by linking fragments. We have developed a protocol for using MixMD to map hot spots in a way similar to MSCS. Our multiple protein structure (MPS) method6 -8 for creating binding-site pharmacophore models based on conformational ensembles has demonstrated success in mapping protein systems for drug design.9 , 10 MixMD expands the MPS concept to simultaneously allow protein flexibility and competition between probes and water.Several similar efforts have incorporated MSCS concepts into a computational method, but each has notable limitations. FTMap11 is modeled after MSCS, but while it can be used with ensembles like MPS12, neither ligand nor on-the-fly protein flexibility is used during probe mapping. A recent study from Seco et al. utilized MD with mixed water and carlsonh@umich.edu .

Many cyclic peptide natural products are larger and structurally more complex than conventional small molecule drugs. Although some molecules in this class are known to possess favorable pharmacokinetic properties, there have been few reports on the membrane permeabilities of cyclic peptide natural products. Here, we present the passive membrane permeabilities of 39 cyclic peptide natural products, and interpret the results using a computational permeability prediction algorithm based on their known or calculated 3D conformations. We found that the permeabilities of these compounds, measured in a parallel artificial membrane permeability assay, spanned a wide range and demonstrated the important influence of conformation on membrane permeability. These results will aid in the development of these compounds as a viable drug paradigm.

Summary The target range of a bacterial secretion system can be defined by effector substrate specificity or by the efficacy of effector delivery. Here, we report the crystal structure of Tse1, a type VI secretion (T6S) bacteriolytic amidase effector from Pseudomonas aeruginosa. Consistent with its role as a toxin, Tse1 has a more accessible active site than related housekeeping enzymes. The activity of Tse1 against isolated peptidoglycan shows its capacity to act broadly against Gram-negative bacteria, and even certain Gram-positive species. Studies with intact cells indicate that some Gram-positive bacteria remain vulnerable to Tse1 despite cell wall modifications. However, interbacterial competition studies demonstrate that Tse1-dependent lysis is restricted to Gram-negative targets. We propose that the previously observed specificity for T6S against Gram-negative bacteria is a consequence of high local effector concentration achieved by T6S-dependent targeting to its site of action, rather than inherent effector substrate specificity.

Structure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein–ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80–95%. This review examines the current toolbox for flexible protein–ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.

Cyclic peptide natural products contain a variety of conserved, nonproteinogenic structural elements such as d-amino acids and amide N-methylation. In addition, many cyclic peptides incorporate γ-amino acids and other elements derived from polyketide synthases. We hypothesized that the position and orientation of these extended backbone elements impact the ADME properties of these hybrid molecules, especially their ability to cross cell membranes and avoid metabolic degradation. Here we report the synthesis of cyclic hexapeptide diastereomers containing γ-amino acids (e.g., statines) and systematically investigate their structure-permeability relationships. These compounds were much more water-soluble and, in many cases, were both more membrane permeable and more stable to liver microsomes than a similar non-statine-containing derivative. Permeability correlated well with the extent of intramolecular hydrogen bonding observed in the solution structures determined in the low-dielectric solvent CDCl3, and one compound showed an oral bioavailability of 21% in rat. Thus, the incorporation of γ-amino acids offers a route to increase backbone diversity and improve ADME properties in cyclic peptide scaffolds.

Natural product and synthetic macrocycles are chemically and topologically diverse. An efficient, accurate, and general method for generating macrocycle conformations would enable structure-based design of macrocycle drugs or host–guest complexes. Computational sampling also provides insight into transiently populated states, complementing crystallographic and NMR data. Here, we report a new algorithm, BRIKARD, that addresses this challenge through computational algebraic geometry and inverse kinematics together with local energy minimization. BRIKARD is demonstrated on 67 diverse macrocycles with structural data, encompassing various ring topologies. We find this approach enumerates diverse structures with macrocyclic RMSD < 1.0 Å to the experimental conformation for 85% of our data set in contrast to success rates of 67–75% with other approaches, while for the subset of 21 more challenging compounds in the data set, these rates are 57% and 10–29%, respectively. Because the algorithm can be efficiently run in parallel on many processors, exhaustive conformational sampling of complex cycles can be obtained in minutes rather than hours: with a 40 processor implementation protocol, BRIKARD samples the conformational diversity of a potential energy landscape in a median of 1.3 minutes of wallclock time, much faster than 3.1–10.3 hours necessary with current programs. By rigorously testing BRIKARD on a broad range of scaffolds with highly complex ring systems, we push the frontiers of macrocycle sampling to encompass multiring compounds, including those with more than 50 ring atoms and up to seven interlaced flexible rings.

ObjectiveTo determine the cause and course of a novel syndrome with progressive encephalopathy and brain atrophy in children.MethodsClinical whole-exome sequencing was performed for global developmental delay and intellectual disability; some patients also had spastic paraparesis and evidence of clinical regression. Six patients were identified with de novo missense mutations in the kinesin gene KIF1A. The predicted functional disruption of these mutations was assessed in silico to compare the calculated conformational flexibility and estimated efficiency of ATP binding to kinesin motor domains of wild-type (WT) versus mutant alleles. Additionally, an in vitro microtubule gliding assay was performed to assess the effects of de novo dominant, inherited recessive, and polymorphic variants on KIF1A motor function.ResultsAll six subjects had severe developmental delay, hypotonia, and varying degrees of hyperreflexia and spastic paraparesis. Microcephaly, cortical visual impairment, optic neuropathy, peripheral neuropathy, ataxia, epilepsy, and movement disorders were also observed. All six patients had a degenerative neurologic course with progressive cerebral and cerebellar atrophy seen on sequential magnetic resonance imaging scans. Computational modeling of mutant protein structures when compared to WT kinesin showed substantial differences in conformational flexibility and ATP-binding efficiency. The de novo KIF1A mutants were nonmotile in the microtubule gliding assay.InterpretationDe novo mutations in KIF1A cause a degenerative neurologic syndrome with brain atrophy. Computational and in vitro assays differentiate the severity of dominant de novo heterozygous versus inherited recessive KIF1A mutations. The profound effect de novo mutations have on axonal transport is likely related to the cause of progressive neurologic impairment in these patients.