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Identification of cryptic pockets has the potential to open new therapeutic opportunities by discovering ligand binding sites that remain hidden in static apo structures of a target protein. Moreover, allosteric cryptic pockets can become valuable for designing target-selective ligands when the natural ligand binding sites are conserved in variants of a protein. For example, before an allosteric cryptic pocket was discovered, KRAS was considered undruggable due to its smooth surface and conservation of the GDP/GTP binding pocket across the wild type and oncogenic isoforms. Recent identification of the Switch-II cryptic pocket in the KRAS G12C mutant and FDA approval of anticancer drugs targeting this site underscores the importance of cryptic pockets in solving pharmaceutical challenges. Here, we present a newly developed approach for the exploration of cryptic pockets using weighted ensemble molecular dynamics simulations with inherent normal modes as progress coordinates applied to the wild type KRAS and the G12D mutant. We performed extensive all-atomic simulations (>400 μs) with and without several cosolvents (xenon, ethanol, benzene), and analyzed trajectories using three distinct methods to search for potential binding pockets. These methods have been applied as a proof-of-concept to KRAS and have shown they can predict known cryptic binding sites. Furthermore, we performed ligand-binding simulations of a known inhibitor (MRTX1133) to shed light on the nature of cryptic pockets in KRAS G12D and the role of conformational selection vs induced-fit mechanism in the formation of these cryptic pockets.

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol -1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases., (open access.)

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures., (open access.)

Passive permeability of a drug-like molecule is a critical property assayed early in a drug discovery campaign that informs a medicinal chemist how well a compound can traverse biological membranes, such as gastrointestinal epithelial or restrictive organ barriers, so it can perform a specific therapeutic function. However, the challenge that remains is the development of a method, experimental or computational, which can both determine the permeation rate and provide mechanistic insights into the transport process to help with the rational design of any given molecule. Typically, one of the following three methods are used to measure the membrane permeability: (1) experimental permeation assays acting on either artificial or natural membranes; (2) quantitative structure-permeability relationship models that rely on experimental values of permeability or related pharmacokinetic properties of a range of molecules to infer those for new molecules; and (3) estimation of permeability from the Smoluchowski equation, where free energy and diffusion profiles along the membrane normal are taken as input from large-scale molecular dynamics simulations. While all these methods provide estimates of permeation coefficients, they provide very little information for guiding rational drug design. In this study, we employ a highly parallelizable weighted ensemble (WE) path sampling strategy, empowered by cloud computing techniques, to generate unbiased permeation pathways and permeability coefficients for a set of drug-like molecules across a neat 1-palmitoyl-2-oleoyl- sn -glycero-3-phosphatidylcholine membrane bilayer. Our WE method predicts permeability coefficients that compare well to experimental values from an MDCK-LE cell line and PAMPA assays for a set of drug-like amines of varying size, shape, and flexibility. Our method also yields a series of continuous permeation pathways weighted and ranked by their associated probabilities. Taken together, the ensemble of reactive permeation pathways, along with the estimate of the permeability coefficient, provides a clearer picture of the microscopic underpinnings of small-molecule membrane permeation.

A variety of fields would benefit from accurate [Formula: see text] predictions, especially drug design due to the effect a change in ionization state can have on a molecule's physiochemical properties. Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic [Formula: see text]s of 24 drug like small molecules. We recently built a general model for predicting [Formula: see text]s using a Gaussian process regression trained using physical and chemical features of each ionizable group. Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton. These features are fed into a Scikit-learn Gaussian process to predict microscopic [Formula: see text]s which are then used to analytically determine macroscopic [Formula: see text]s. Our Gaussian process is trained on a set of 2700 macroscopic [Formula: see text]s from monoprotic and select diprotic molecules. Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge. Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic. Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy. The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable.

Several submissions for the SAMPL4 hydration free energy set were calculated using OpenEye tools, including many that were among the top performing submissions. All of our best submissions used AM1BCC charges and Poisson-Boltzmann solvation. Three submissions used a single conformer for calculating the hydration free energy and all performed very well with mean unsigned errors ranging from 0.94 to 1.08 kcal/mol. These calculations were very fast, only requiring 0.5-2.0 s per molecule. We observed that our two single-conformer methodologies have different types of failure cases and that these differences could be exploited for determining when the methods are likely to have substantial errors.

A method of rapid entropy estimation for small molecules in vacuum, solution, and inside a protein receptor is proposed. We show that the Hessian matrix of second derivatives built by a quasi-Newton optimizer during geometry optimization of a molecule with a classical molecular potential in these three environments can be used to predict vibrational entropies. We also show that a simple analytical solvation model allows for no less accurate entropy estimation of molecules in solution than a physically rigorous but computationally more expensive model based on Poisson's equation. Our work also suggests that scaled particle theory more precisely estimates the hydrophobic part of solvation entropy than the using a simple surface area term.

Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.

A prospective study of aqueous solvation energies was done using the SM8 and Zap TK models for a variety of geometries. CM4M charges calculated with M06 and M06-2X were found to yield similar results for the SM8 model. Zap TK calculations were primarily done with AM1BCC charges but limited attempts to use charges derived from DFT showed promise. The OMEGA application quickly generated conformations that performed well with both solvation models, while the use of computationally expensive DFT optimized geometries yielded increased RMSE and MSE. It is shown that increasing levels of gas phase geometry optimization yield increasingly unfavorable solvation energy for single conformer models.

The interactions between a molecule and the aqueous environment underpin any process that occurs in solution, from simple chemical reactions to protein-ligand binding to protein aggregation. Fundamental measures of the interaction between molecule and aqueous phase, such as the transfer energy between gas phase and water or the energetic difference between two tautomers of a molecule in solution, remain nontrivial to predict accurately using current computational methods. SAMPL2 represents the third annual blind prediction of transfer energies, and the first time tautomer ratios were included in the challenge. Over 60 sets of predictions were submitted, and each participant also attempted to estimate the error in their predictions, a task that proved difficult for most. The results of this blind assessment of the state of the field for transfer energy and tautomer ratio prediction both indicate where the field is performing well and point out flaws in current methods.

The phosphatidylinositol 3-kinase/AKT signaling pathway plays a critical role in activating survival and antiapoptotic pathways within cancer cells. Several studies have shown that this pathway is constitutively activated in many different cancer types. The goal of this study was to discover novel compounds that bind to the pleckstrin homology (PH) domain of AKT, thereby inhibiting AKT activation. Using proprietary docking software, 22 potential PH domain inhibitors were identified. Surface plasmon resonance spectroscopy was used to measure the binding of the compounds to the expressed PH domain of AKT followed by an in vitro activity screen in Panc-1 and MiaPaCa-2 pancreatic cancer cell lines. We identified a novel chemical scaffold in several of the compounds that binds selectively to the PH domain of AKT, inducing a decrease in AKT activation and causing apoptosis at low micromolar concentrations. Structural modifications of the scaffold led to compounds with enhanced inhibitory activity in cells. One compound, 4-dodecyl-N-(1,3,4-thiadiazol-2-yl)benzenesulfonamide, inhibited AKT and its downstream targets in cells as well as in pancreatic cancer cell xenografts in immunocompromised mice; it also exhibited good antitumor activity. In summary, a pharmacophore for PH domain inhibitors targeting AKT function was developed. Computer-aided modeling, synthesis, and testing produced novel AKT PH domain inhibitors that exhibit promising preclinical properties.

The recent literature is replete with papers evaluating computational tools (often those operating on 3D structures) for their performance in a certain set of tasks. Most commonly these papers compare a number of docking tools for their performance in cognate re-docking (pose prediction) and/or virtual screening. Related papers have been published on ligand-based tools: pose prediction by conformer generators and virtual screening using a variety of ligand-based approaches. The reliability of these comparisons is critically affected by a number of factors usually ignored by the authors, including bias in the datasets used in virtual screening, the metrics used to assess performance in virtual screening and pose prediction and errors in crystal structures used.

Ligand docking is a widely used approach in virtual screening. In recent years a large number of publications have appeared in which docking tools are compared and evaluated for their effectiveness in virtual screening against a wide variety of protein targets. These studies have shown that the effectiveness of docking in virtual screening is highly variable due to a large number of possible confounding factors. Another class of method that has shown promise in virtual screening is the shape-based, ligand-centric approach. Several direct comparisons of docking with the shape-based tool ROCS have been conducted using data sets from some of these recent docking publications. The results show that a shape-based, ligand-centric approach is more consistent than, and often superior to, the protein-centric approach taken by docking.

An automated computational procedure for fitting a ligand into its electron density with the use of the MMFF94 force field and a Gaussian shape description has been developed. It employs a series of adiabatic optimizations of gradually increasing shape potential. Starting from a set of energy-relaxed ligand conformations, the final results are structures realistically strained to fit the crystallographic data.

The human immunodeficiency virus (HIV) epidemic is an important medical problem. Although combination drug regimens have produced dramatic decreases in viral load, current therapies do not provide a cure for HIV infection. We have used structure-based design and combinatorial medicinal chemistry to identify potent and selective HIV-1 reverse transcriptase (RT) inhibitors that may work by a mechanism distinct from that of current HIV drugs. The most potent of these compounds (compound 4, 2-naphthalenesulfonic acid, 4-hydroxy-7-[[[[5-hydroxy-6-[(4-cinnamylphenyl)azo]-7-sulfo-2-naphthalenyl]amino]carbonyl]amino]-3-[(4-cinnamylphenyl)azo], disodium salt) has an IC(50) of 90 nM for inhibition of polymerase chain extension, a K(d) of 40 nM for inhibition of DNA-RT binding, and an IC(50) of 25-100 nM for inhibition of RNaseH cleavage. The parent compound (1) was as effective against 10 nucleoside and non-nucleoside resistant HIV-1 RT mutants as it was against the wild-type enzyme. Compound 4 inhibited HIV-1 RT and murine leukemia virus (MLV) RT, but it did not inhibit T(4) DNA polymerase, T(7) DNA polymerase, or the Klenow fragment at concentrations up to 200 nM. Finally, compound 4 protected cells from HIV-1 infection at a concentration more than 40 times lower than the concentration at which it caused cellular toxicity.

In this paper we describe the search strategies developed for docking flexible molecules to macomolecular sites that are incorporated into the widely distributed DOCK software, version 4.0. The search strategies include incremental construction and random conformation search and utilize the existing Coulombic and Lennard-Jones grid-based scoring function. The incremental construction strategy is tested with a panel of 15 crystallographic testcases, created from 12 unique complexes whose ligands vary in size and flexibility. For all testcases, at least one docked position is generated within 2 A of the crystallographic position. For 7 of 15 testcases, the top scoring position is also within 2 A of the crystallographic position. The algorithm is fast enough to successfully dock a few testcases within seconds and most within 100 s. The incremental construction and the random search strategy are evaluated as database docking techniques with a database of 51 molecules docked to two of the crystallographic testcases. Incremental construction outperforms random search and is fast enough to reliably rank the database of compounds within 15 s per molecule on an SGI R10000 cpu.

We present a general approach to the design, docking, and virtual screening of multiple combinatorial libraries against a family of proteins. The method consists of three main stages: docking the scaffold, selecting the best substituents at each site of diversity, and comparing the resultant molecules within and between the libraries. The core "divide-and-conquer" algorithm for side-chain selection, developed from an earlier version (Sun et al., J Comp Aided Mol Design 1998;12:597-604), provides a way to explore large lists of substituents with linear rather than combinatorial time dependence. We have applied our method to three combinatorial libraries and three serine proteases: trypsin, chymotrypsin, and elastase. We show that the scaffold docking procedure, in conjunction with a novel vector-based orientation filter, reproduces crystallographic binding modes. In addition, the free-energy-based scoring procedure (Zou et al., J Am Chem Soc 1999;121:8033-8043) is able to reproduce experimental binding data for P1 mutants of macromolecular protease inhibitors. Finally, we show that our method discriminates between a peptide library and virtual libraries built on benzodiazepine and tetrahydroisoquinolinone scaffolds. Implications of the docking results for library design are explored.

A virtual library of macrocyclic polyketide molecules was generated and screened to identify novel, conformationally constrained potential motilin receptor agonists ("motilides"). A motilide pharmacophore model was generated from the potent 6,9-enol ether erythromycin and known derivatives from the literature. The pharmacophore for each molecular conformation was a point in a distance-volume space based on presentation of the putative binding moieties. Two methods, one fragment based method and the other reaction based, were explored for constructing the polyketide virtual library. First, a virtual library was assembled from monomeric fragments using the CHORTLES language. Second, the virtual library was assembled by the in silico application of all possible polyketide synthase enzyme reactions to generate the product library. Each library was converted to low-energy 3D conformations by distance geometry and standard minimization methods. The distance-volume metric was calculated for low-energy conformations of the members of the virtual polyketide library and screened against the enol ether pharmacophore. The goal was to identify novel macrocycles that satisfy the pharmacophore. We identified three conformationally constrained, novel polyketide series that have low-energy conformations satisfying the distance-volume constraints of the motilide pharmacophore.

Lysosomal disturbances may be a contributing factor to Alzheimer's disease. We used novel compounds to test if suppression of the lysosomal protease cathepsin D blocks production of known precursors to neurofibrillary tangles. Partial lysosomal dysfunction was induced in cultured hippocampal slices with a selective inhibitor of cathepsins B and L. This led within 48 h to hyperphosphorylated tau protein fragments recognized by antibodies against human tangles. Potent nonpeptidic cathepsin D inhibitors developed using combinatorial chemistry and structure-based design blocked production of the fragments in a dose-dependent fashion. Threshold was in the submicromolar range, with higher concentrations producing complete suppression. The effects were selective and not accompanied by pathophysiology. Comparable results were obtained with three structurally distinct inhibitors. These results support the hypothesis that cathepsin D links lysosomal dysfunction to the etiology of Alzheimer's disease and suggest a new approach to treating the disease.

A number of single-digit nanomolar, low-molecular-weight plasmepsin II aspartyl protease inhibitors have been identified using combinatorial chemistry and structure-based design. By identifying multiple, small-molecule inhibitors using the parallel synthesis of several focused libraries, it was possible to select for compounds with desirable characteristics including enzyme specificity and minimal binding to serum proteins. The best inhibitors identified have Ki's of 2-10 nM, molecular weights between 594 and 650 Da, between 3- and 15-fold selectivity toward plasmepsin II over cathepsin D, the most closely related human protease, good calculated log P values (2.86-4.56), and no apparent binding to human serum albumin at 1 mg/mL in an in vitro assay. These compounds represent the most potent non-peptide plasmepsin II inhibitors reported to date.

We have developed an automatic protein fingerprinting method for the evaluation of protein structural similarities based on secondary structure element compositions, spatial arrangements, lengths, and topologies. This method can rapidly identify proteins sharing structural homologies as we demonstrate with five test cases: the globins, the mammalian trypsinlike serine proteases, the immunoglobulins, the cupredoxins, and the actinlike ATPase domain-containing proteins. Principal components analysis of the similarity distance matrix calculated from an all-by-all comparison of 1,031 unique chains in the Protein Data Bank has produced a distribution of structures within a high-dimensional structural space. Fifty percent of the variance observed for this distribution is bounded by six axes, two of which encode structural variability within two large families, the immunoglobulins and the trypsinlike serine proteases. Many aspects of the spatial distribution remain stable upon reduction of the database to 140 proteins with minimal family overlap. The axes correlated with specific structural families are no longer observed. A clear hierarchy of organization is seen in the arrangement of protein structures in the universe. At the highest level, protein structures populate regions corresponding to the all-alpha, all-beta, and alpha/beta superfamilies. Large protein families are arranged along family-specific axes, forming local densely populated regions within the space. The lowest level of organization is intrafamilial; homologous structures are ordered by variations in peripheral secondary structure elements or by conformational shifts in the tertiary structure.

We have developed a strategy for efficiently docking a large combinatorial library into a target receptor. For each scaffold orientation, all potential fragments are attached to the scaffold, their interactions with the receptor are individually scored and factorial combinations of fragments are constructed. To test its effectiveness, this approach is compared to two simple control algorithms. Our method is more efficient than the controls at selecting best scoring molecules and at selecting fragments for the construction of an exhaustive combinatorial library. We also carried out a retrospective analysis of the experimental results of a 10 x 10 x 10 exhaustive combinatorial library. An enrichment factor of approximately 4 was found for identifying the compounds in the library that are active at 330 nM.

All parasitic protozoa obtain purine nucleotides solely by salvaging purine bases and/or nucleosides from their host. This observation suggests that inhibiting purine salvage may be a good way of killing these organisms. To explore this idea, we attempted to block the purine salvage pathway of the parasitic protozoan Tritrichomonas foetus. T. foetus is a good organism to study because its purine salvage depends primarily on a single enzyme, hypoxanthine-guanine-xanthine phosphoribosyltransferase (HGXPRTase), and could provide a good model for rational drug design through specific enzyme inhibition. Guided by the crystal structure of T. foetus HGXPRTase, we used structure-based drug design to identify several non-purine compounds that inhibited this enzyme without any detectable effect on human HGPRTase. One of these compounds, 4-[N-(3, 4-dichlorophenyl)carbamoyl]phthalic anhydride (referred to as TF1), was selected for further characterization. TF1 was shown to be a competitive inhibitor of T. foetus HGXPRTase with respect to both guanine (in the forward reaction; Ki = 13 microM) and GMP (in the reverse reaction; Ki = 10 microM), but showed no effect on the homologous human enzyme at concentrations of up to 1 mM. TF1 inhibited the in vitro growth of T. foetus with an EC50 of approximately 40 microM. This inhibitory effect was associated with a decrease in the incorporation of exogenous guanine into nucleic acids, and could be reversed by supplementing the growth medium with excess exogenous hypoxanthine or guanine. Thus, rationally targeting an essential enzyme in a parasitic organism has yielded specific enzyme inhibitors capable of suppressing that parasite's growth.

Background: The identification of potent small molecule ligands to receptors and enzymes is one of the major goals of chemical and biological research. Two powerful new tools that can be used in these efforts are combinatorial chemistry and structure-based design. Here we address how to join these methods in a design protocol that produces libraries of compounds that are directed against specific macromolecular targets. The aspartyl class of proteases, which is involved in numerous biological processes, was chosen to demonstrate this effective procedure., Results: Using cathepsin D, a prototypical aspartyl protease, a number of low nanomolar inhibitors were rapidly identified. Although cathepsin D is implicated in a number of therapeutically relevant processes, potent nonpeptide inhibitors have not been reported previously. The libraries, synthesized on solid support, displayed nonpeptide functionality about the (hydroxyethyl)amine isostere. The (hydroxyethyl)amine isostere, which targets the aspartyl protease class, is a stable mimetic of the tetrahedral intermediate of amide hydrolysis. Structure-based design, using the crystal structure of cathepsin D complexed with the peptide-based natural product pepstatin, was used to select the building blocks for the library synthesis. The library yielded a 'hit rate' of 6-7% at 1 microM inhibitor concentrations, with the most potent compound having a Ki value of 73 nM. More potent, nonpeptide inhibitors (Ki = 9-15 nM) of cathepsin D were rapidly identified by synthesizing and screening a small second generation library., Conclusions: The success of these studies clearly demonstrates the power of coupling the complementary methods of combinatorial chemistry and structure-based design. We anticipate that the general approaches described here will be successful for other members of the aspartyl protease class and for many other enzyme classes.