ParaFrag--an approach for surface-based similarity comparison of molecular fragments

FragExplorer: GRID-Based Fragment Growing and Replacement

Understanding which chemical modifications can be made to known ligands is a key aspect of structure-based drug design and one that was pioneered by the software GRID. We developed FragExplorer with the explicit aim of showing GRID users which fragments would best match the GRID molecular interaction fields in a protein binding site, given a bound ligand as a starting point. Users can grow ligands or replace existing moieties; the R-Group Exploration mode identifies all potential R-Groups and searches for replacements automatically; the Scaffold Exploration mode does the same for all potential scaffolds. For a ligand with three points of variation, R-Group Exploration will typically explore a chemical space of 10¹⁶ potential molecules; including Scaffold Exploration increases this to 10²². FragExplorer was designed to be integrated within an interactive 3D Editor/Designer; therefore, the speed of computation was an important consideration; a typical fragment search takes 20 seconds. In a fragment reprediction test, FragExplorer demonstrates an overall fragment retrieval rate of 55%, increasing to 69% for smaller fragments. At a 90% substructural match, the retrieval rate increases to ∼80%. We also show how the approach could have been used to hop from olmesartan to azilsartan or to optimize a p38 MAP kinase lead to a compound that bears similarity to a known nanomolar inhibitor.

FragRep: A Web Server for Structure-Based Drug Design by Fragment Replacement

The design of efficient computational tools for structure-guided ligand design is essential for the drug discovery process. We hereby present FragRep, a new web server for structure-based ligand design by fragment replacement. The input is a protein and a ligand structure, either from protein data bank or from molecular docking. Users can choose specific substructures they want to modify. The server tries to find suitable fragments that not only meet the geometric requirements of the remaining part of the ligand but also fit well with local protein environments. FragRep is a powerful computational tool for the rapid generation of ligand design ideas; either in scaffold hopping or bioisosteric replacing. The FragRep Server is freely available to researchers and can be accessed at http://xundrug.cn/fragrep.

Scaffold-hopping from xanthines to tricyclic guanines: A case study of dipeptidyl peptidase 4 (DPP4) inhibitors

Molecular modeling of unbound tricyclic guanine scaffolds indicated that they can serve as effective bioisosteric replacements of xanthines. This notion was further confirmed by a combination of X-ray crystallography and SAR studies, indicating that tricyclic guanine DPP4 inhibitors mimic the binding mode of xanthine inhibitors, exemplified by linagliptin. Realization of the bioisosteric relationship between these scaffolds potentially will lead to a wider application of cyclic guanines as xanthine replacements in drug discovery programs for a variety of biological targets. Newly designed DPP4 inhibitors achieved sub-nanomolar potency range and demonstrated oral activity in vivo in mouse glucose tolerance test.

The unrestricted local properties: application in nanoelectronics and for predicting radicals reactivity

The local electron affinity (EA(L)) and the local ionization energy (IE(L)) are successfully used for predicting properties of closed-shell species for drug design and for nanoelectronics. Here the respective unrestricted Hartree-Fock variants of EA(L) and IE(L), i.e., the unrestricted local electron affinity (UHF-EA(L)) and ionization energy (UHF-IE(L)), have been shown to be useful for predicting properties of open-shell species. UHF-EA(L) and UHF-IE(L) have been applied for explaining unique electronic properties of an exemplary nanomaterial carbon peapod. It is also demonstrated that UHF-EA(L) is useful for predicting and better understanding reactivity of radicals related to alkanes activation.

A novel, customizable and optimizable parameter method using spherical harmonics for molecular shape similarity comparisons

A novel molecular shape similarity comparison method, namely SHeMS, derived from spherical harmonic (SH) expansion, is presented in this study. Through weight optimization using genetic algorithms for a customized reference set, the optimal combination of weights for the translationally and rotationally invariant (TRI) SH shape descriptor, which can specifically and effectively distinguish overall and detailed shape features according to the molecular surface, is obtained for each molecule. This method features two key aspects: firstly, the SH expansion coefficients from different bands are weighted to calculate similarity, leading to a distinct contribution of overall and detailed features to the final score, and thus can be better tailored for each specific system under consideration. Secondly, the reference set for optimization can be totally configured by the user, which produces great flexibility, allowing system-specific and customized comparisons. The directory of useful decoys (DUD) database was adopted to validate and test our method, and principal component analysis (PCA) reveals that SH descriptors for shape comparison preserve sufficient information to separate actives from decoys. The results of virtual screening indicate that the proposed method based on optimal SH descriptor weight combinations represents a great improvement in performance over original SH (OSH) and ultra-fast shape recognition (USR) methods, and is comparable to many other popular methods. Through combining efficient shape similarity comparison with SH expansion method, and other aspects such as chemical and pharmacophore features, SHeMS can play a significant role in this field and can be applied practically to virtual screening by means of similarity comparison with 3D shapes of known active compounds or the binding pockets of target proteins.

Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitors

BACKGROUND

Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening.

METHODOLOGY/PRINCIPAL FINDINGS

We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization.

CONCLUSIONS/SIGNIFICANCE

12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort.

Graph-based similarity concepts in virtual screening

Applying similarity for finding new promising compounds is a key issue in drug design. Conversely, quantifying similarity between molecules has remained a difficult task despite the numerous approaches. Here, some general aspects along with recent developments regarding similarity criteria are collected. For the purpose of virtual screening, the compounds have to be encoded into a computer-readable format that permits a comparison, according to given similarity criteria, comprising the use of the 3D structure, fingerprints, graph-based and alignment-based approaches. Whereas finding the most common substructures is the most obvious method, more recent approaches take into account chemical modifications that appear throughout existing drugs, from various therapeutic categories and targets.

Aromatic N versus aromatic F: bioisosterism discovered in RNA base pairing interactions leads to a novel class of universal base analogs

The thermodynamics of base pairing is of fundamental importance. Fluorinated base analogs are valuable tools for investigating pairing interactions. To understand the influence of direct base-base interactions in relation to the role of water, pairing free energies between natural nucleobases and fluorinated analogs are estimated by potential of mean force calculations. Compared to pairing of AU and GC, pairing involving fluorinated analogs is unfavorable by 0.5-1.0 kcal mol(-1). Decomposing the pairing free energies into enthalpic and entropic contributions reveals fundamental differences for Watson-Crick pairs compared to pairs involving fluorinated analogs. These differences originate from direct base-base interactions and contributions of water. Pairing free energies of fluorinated base analogs with natural bases are less unfavorable by 0.5-1.0 kcal mol(-1) compared to non-fluorinated analogs. This is attributed to stabilizing C-F(...)H-N dipolar interactions and stronger N(...)H-C hydrogen bonds, demonstrating direct and indirect influences of fluorine. 7-methyl-7H-purine and its 9-deaza analog (Z) have been suggested as members of a new class of non-fluorinated base analogs. Z is found to be the least destabilizing universal base in the context of RNA known to date. This is the first experimental evidence for nitrogen-containing heterocylces as bioisosteres of aromatic rings bearing fluorine atoms.