Scaffold-based molecular design with a graph generative model

Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based molecular design. Our model accepts a molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.

We propose a scaffold-based graph generative model for designing novel drug candidates that include the desired scaffold as a substructure.     

Knowledge-based interaction fingerprint scoring: a simple method for improving the effectiveness of fast scoring functions

A new method for the postprocessing of docking outputs has been developed, based on encoding putative 3D binding modes (docking solutions) as ligand-protein interactions into simple bit strings, a method analogous to the structural interaction fingerprint. Instead of employing traditional scoring functions, the method uses a series of new, knowledge-based scores derived from the similarity of the bit strings for each docking solution to that of a known reference binding mode. A GOLD docking study was carried out using the Bissantz estrogen receptor antagonist set along with the new scoring method. Superior recovery rates, with up to 2-fold enrichments, were observed when the new knowledge-based scoring was compared to the GOLD fitness score. In addition, top ranking sets of molecules (actives and potential actives or decoys) were structurally diverse with low molecular weights and structural complexities. Principal component analysis and clustering of the fingerprints permits the easy separation of active from inactive binding modes and the visualization of diverse binding modes.     

Synthesis of Novel Halogenated Heterocycles Based on o-Phenylenediamine and Their Interactions with the Catalytic Subunit of Protein Kinase CK2

Protein kinase CK2 is a highly pleiotropic protein kinase capable of phosphorylating hundreds of protein substrates. It is involved in numerous cellular functions, including cell viability, apoptosis, cell proliferation and survival, angiogenesis, or ER-stress response. As CK2 activity is found perturbed in many pathological states, including cancers, it becomes an attractive target for the pharma. A large number of low-mass ATP-competitive inhibitors have already been developed, the majority of them halogenated. We tested the binding of six series of halogenated heterocyclic ligands derived from the commercially available 4,5-dihalo-benzene-1,2-diamines. These ligand series were selected to enable the separation of the scaffold effect from the hydrophobic interactions attributed directly to the presence of halogen atoms. In silico molecular docking was initially applied to test the capability of each ligand for binding at the ATP-binding site of CK2. HPLC-derived ligand hydrophobicity data are compared with the binding affinity assessed by low-volume differential scanning fluorimetry (nanoDSF). We identified three promising ligand scaffolds, two of which have not yet been described as CK2 inhibitors but may lead to potent CK2 kinase inhibitors. The inhibitory activity against CK2α and toxicity against four reference cell lines have been determined for eight compounds identified as the most promising in nanoDSF assay.     

Optimizing fragment and scaffold docking by use of molecular interaction fingerprints

Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-molecular-weight compounds from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extracted from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given reference was statistically superior to conventional scoring functions in posing low-molecular-weight fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.     

Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists

Identification of novel compound classes for a drug target is a challenging task for cheminformatics and drug design when considerable research has already been undertaken and many potent lead structures have been identified, which leaves limited unclaimed chemical space for innovation. We validated and successfully applied different state-of-the-art techniques for virtual screening (Bayesian machine learning, automated molecular docking, pharmacophore search, pharmacophore QSAR and shape analysis) of 4.6 million unique and readily available chemical structures to identify promising new and competitive antagonists of the strychnine-insensitive Glycine binding site (Glycine_B site) of the NMDA receptor. The novelty of the identified virtual hits was assessed by scaffold analysis, putting a strong emphasis on novelty detection. The resulting hits were tested in vitro and several novel, active compounds were identified. While the majority of the computational methods tested were able to partially discriminate actives from structurally similar decoy molecules, the methods differed substantially in their prospective applicability in terms of novelty detection. The results demonstrate that although there is no single best computational method, it is most worthwhile to follow this concept of focused compound library design and screening, as there still can new bioactive compounds be found that possess hitherto unexplored scaffolds and interesting variations of known chemotypes.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Extension of QM/MM docking and its applications to metalloproteins

To overcome the limitation of conventional docking methods which assume fixed charge model from force field parameters, combined quantum mechanics/molecular mechanics (QM/MM) method has been applied to docking as a variable charge model and shown to exhibit improvement on the docking accuracy over fixed charge based methods. However, it has also been shown that there are a number of examples for which adoption of variable‐charge model fails to reproduce the native binding modes. In particular, for metalloproteins, previously implemented method of QM/MM docking failed most often. This class of proteins has highly polarized binding sites at which high‐coordinate‐numbered metal ions reside. We extend the QM/MM docking method so that protein atoms surrounding the binding site along with metal ions are included as quantum region, as opposed to only ligand atoms. This extension facilitates the required scaling of partial charges on metal ions leading to prediction of correct binding modes in metalloproteins. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009     

Parallelized-over-parts computation of absolute binding free energy with docking and molecular dynamics

We present a technique for biomolecular free energy calculations that exploits highly parallelized sampling to significantly reduce the time to results. The technique combines free energies for multiple, nonoverlapping configurational macrostates and is naturally suited to distributed computing. We describe a methodology that uses this technique with docking, molecular dynamics, and free energy perturbation to compute absolute free energies of binding quickly compared to previous methods. The method does not require a priori knowledge of the binding pose as long as the docking technique used can generate reasonable binding modes. We demonstrate the method on the protein FKBP12 and eight of its inhibitors.     

Evaluating docked complexes with the HINT exponential function and empirical atomic hydrophobicities

Summary Methods that predict geometries of ligands binding to receptor molecules can facilitate ligand discovery and yield information on the factors governing complementarity. Here, the use of atomic hydrophobicities in evaluating binding modes has been examined with four ligand-receptor complexes of known structure. In each system, hundreds of hypothetical binding orientations were generated with DOCK and evaluated using the HINT (Hydropathic INTeractions) exponential function and atomic hydrophobic constants. In three of the four systems, the experimental binding mode received the best HINT score; in the fourth system, the experimental binding mode scored only slightly lower than a similar, apparently reasonable orientation. The HINT function may be generally useful as a scoring method in molecular docking.     

MCDOCK: A Monte Carlo simulation approach to the molecular docking problem

Prediction of the binding mode of a ligand (a drug molecule) to its macromolecular receptor, or molecular docking, is an important problem in rational drug design. We have developed a new docking method in which a non-conventional Monte Carlo (MC) simulation technique is employed. A computer program, MCDOCK, was developed to carry out the molecular docking operation automatically. The current version of the MCDOCK program (version 1.0) allows for the full flexibility of ligands in the docking calculations. The scoring function used in MCDOCK is the sum of the interaction energy between the ligand and its receptor, and the conformational energy of the ligand. To validate the MCDOCK method, 19 small ligands, the binding modes of which had been determined experimentally using X-ray diffraction, were docked into their receptor binding sites. To produce statistically significant results, 20 MCDOCK runs were performed for each protein–ligand complex. It was found that a significant percentage of these MCDOCK runs converge to the experimentally observed binding mode. The root-mean-square (rms) of all non-hydrogen atoms of the ligand between the predicted and experimental binding modes ranges from 0.25 to 1.84 Å for these 19 cases. The computational time for each run on an SGI Indigo2/R10000 varies from less than 1 min to 15 min, depending upon the size and the flexibility of the ligands. Thus MCDOCK may be used to predict the precise binding mode of ligands in lead optimization and to discover novel lead compounds through structure-based database searching.     

Molecular docking using shape descriptors

Molecular docking explores the binding modes of two interacting molecules. The technique is increasingly popular for studying protein-ligand interactions and for drug design. A fundamental problem problem with molecular docking is that orientation space is very large and grows combinatorially with the number of degrees of freedom of the interacting molecules. Here, we describe and evaluate algorithms that improve the efficiency and accuracy of a shape-based docking method. We use molecular organization and sampling techniques to remove the exponential time dependence on molecular size in docking calculations. The new techniques allow us to study systems that were prohibitively large for the original method. The new algorithms are tested in 10 different protein-ligand systems, including 7 systems where the ligand is itself a protein. In all cases, the new algorithms successfully reproduce the experimentally determined configurations of the ligand in the protein.     

Microrobotics and MEMS-based fabrication techniques for scaffold-based tissue engineering

Scaffold based tissue engineering strategies use cells, biomolecules and a scaffold to promote the repair and regeneration of tissues. Although scaffold-based tissue engineering approaches are being actively developed, most are still experimental, and it is not yet clear what defines an ideal scaffold/cell construct. Solid free form fabrication (SFF) techniques can precisely control matrix architecture (size, shape, interconnectivity, branching, geometry and orientation). The SFF methods enable the fabrication of scaffolds with various designs and material compositions, thus providing a control of mechanical properties, biological effects and degradation kinetics. This paper reviews the application of micro-robotics and MEMS-based fabrication techniques for scaffold design and fabrication. It also presents a novel robotic technique to fabricate scaffold/cell constructs for tissue engineering by the assembly of microscopic building blocks.     

Mining for bioactive scaffolds with scaffold networks: improved compound set enrichment from primary screening data

Identification of meaningful chemical patterns in the increasing amounts of high-throughput-generated bioactivity data available today is an increasingly important challenge for successful drug discovery. Herein, we present the scaffold network as a novel approach for mapping and navigation of chemical and biological space. A scaffold network represents the chemical space of a library of molecules consisting of all molecular scaffolds and smaller "parent" scaffolds generated therefrom by the pruning of rings, effectively leading to a network of common scaffold substructure relationships. This algorithm provides an extension of the scaffold tree algorithm that, instead of a network, generates a tree relationship between a heuristically rule-based selected subset of parent scaffolds. The approach was evaluated for the identification of statistically significantly active scaffolds from primary screening data for which the scaffold tree approach has already been shown to be successful. Because of the exhaustive enumeration of smaller scaffolds and the full enumeration of relationships between them, about twice as many statistically significantly active scaffolds were identified compared to the scaffold-tree-based approach. We suggest visualizing scaffold networks as islands of active scaffolds.     

Directed evolution of ATP binding proteins from a zinc finger domain by using mRNA display

Antibodies have traditionally been used for isolating affinity reagents to new molecular targets, but alternative protein scaffolds are increasingly being used for the directed evolution of proteins with novel molecular recognition properties. We have designed a combinatorial library based on the DNA binding domain of the human retinoid-X-receptor (hRXRalpha). We chose this domain because of its small size, stable fold, and two closely juxtaposed recognition loops. We replaced the two loops with segments of random amino acids, and used mRNA display to isolate variants that specifically recognize adenosine triphosphate (ATP), demonstrating a significant alteration of the function of this protein domain from DNA binding to ATP recognition. Many novel independent sequences were recovered with moderate affinity and high specificity for ATP, validating this scaffold for the generation of functional molecules.     

Computational fragment-based design of Wee1 kinase inhibitors with tricyclic core scaffolds

Wee1 is cell cycle protein comprising a kinase domain and is a validated cancer target. We have designed molecules with variable tricyclic core scaffolds [6-6-5] system and extended them based on the chemical space available in the active site of Wee1 kinase using de novo drug design. The core scaffolds and linking fragments were extracted from pharmacophore-based virtual screening of ZINC and PubChem databases and Ludi library. These molecules bind the hinge region of kinase active site and form hydrogen bonds as confirmed from molecular docking, molecular dynamics simulations, and MM_PBSA calculations. When compared with reference inhibitors, AZD1775 and PHA-848125, the de novo designed molecules also show good docking scores and stability, retained non-covalent interactions, and high binding free energies contributed from active site residues.

     

Chiral diaminopyrrolic receptors for selective recognition of mannosides, part 2: a 3D view of the recognition modes by X-ray, NMR spectroscopy, and molecular modeling

The structural features of a representative set of five complexes of octyl α- and β-mannosides with some members of a new generation of chiral tripodal diaminopyrrolic receptors, namely, (R)-5 and (S)- and (R)-7, have been investigated in solution and in the solid state by a combined X-ray, NMR spectroscopy, and molecular modeling approach. In the solid state, the binding arms of the free receptors 7 delimit a cleft in which two solvent molecules are hydrogen bonded to the pyrrolic groups and to the benzenic scaffold. In a polar solvent (CD(3)CN), chemical shift and intermolecular NOE data, assisted by molecular modeling calculations, ascertained the binding modes of the interaction between the receptor and the glycoside for these complexes. Although a single binding mode was found to adequately describe the complex of the acyclic receptor 5 with the α-mannoside, for the complexes of the cyclic receptors 7 two different binding modes were required to simultaneously fit all the experimental data. In all cases, extensive binding through hydrogen bonding and CH-π interactions is responsible for the affinities measured in the same solvent. Furthermore, the binding modes closely account for the recognition preferences observed toward the anomeric glycosides and for the peculiar enantiodiscrimination properties exhibited by the chiral receptors.     

Ligand-protein docking with water molecules

The presence of water molecules plays an important role in the accuracy of ligand-protein docking predictions. Comprehensive docking simulations have been performed on a large set of ligand-protein complexes whose crystal structures contain water molecules in their binding sites. Only those water molecules found in the immediate vicinity of both the ligand and the protein were considered. We have investigated whether prior optimization of the orientation of water molecules in either the presence or absence of the bound ligand has any effect on the accuracy of docking predictions. We have observed a statistically significant overall increase in accuracy when water molecules are included during docking simulations and have found this to be independent of the method of optimization of the orientation of water molecules. These results confirm the importance of including water molecules whenever possible in a ligand-protein docking simulation. Our findings also reveal that prior optimization of the orientation of water molecules, in the absence of any bound ligand, does not have a detrimental effect on the improved accuracy of ligand-protein docking. This is important, given the use of docking simulations to predict the binding modes of new ligands or drug molecules.     

Fingerprint directed scaffold hopping for identification of CCR2 antagonists

Chemokine receptors have evolved as attractive targets for disease conditions which arise due to immunomodulation involving host-defense mechanisms. CCR2, a chemokine receptor, is targeted for diseases like arthritis, multiple sclerosis, vascular disease, obesity, and type 2 diabetes. This study provides a new strategy of a ligand based technique which exploits fingerprint led fragment features in conjunction with structure-guided design for identifying new scaffolds for CCR2. A fragment based mining (FBM) technique was employed on a chemical database to identify novel scaffold hops. The hits were subjected to 3-point pharmacophore fingerprint procedures with Tanimoto similarity metric to compare pharmacophoric fingerprints. The final 66 hits generated by these exercises were predicted by the validated HQSAR model, and the top predicted were suggested as probable scaffolds for CCR2 antagonism. The identified scaffolds were validated through molecular docking studies. The ligands were docked by providing receptor flexibility in the extra cellular domain (1 and 3), N terminal domain, and in the transmembrane (TM1 & TM7) helix region with IFD approach. Some of the scaffolds showed H-bonding potential which was not explored by the data set molecules. All identified scaffolds highlighted a key hydrogen bonding interaction with Thr292 as supported by mutational studies. The observed pi stacking interaction with Tyr188 in data set molecules was also produced by the new scaffolds. Taking the advantage of receptor flexibility the scaffolds explored the hydrophobic binding cleft between helix 1 and 7 occupied by residues Leu44, Leu45, Leu48 and Ile300, Ile303, Ile304, respectively. Two of the identified molecules have promising outcomes and can be considered as novel scaffolds for CCR2 binding.     

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein–ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein–ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein–ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein–ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein–ligand complex structures available to improve predictions on binding.