Design of multitarget activity landscapes that capture hierarchical activity cliff distributions

An activity landscape model of a compound data set can be rationalized as a graphical representation that integrates molecular similarity and potency relationships. Activity landscape representations of different design are utilized to aid in the analysis of structure-activity relationships and the selection of informative compounds. Activity landscape models reported thus far focus on a single target (i.e., a single biological activity) or at most two targets, giving rise to selectivity landscapes. For compounds active against more than two targets, landscapes representing multitarget activities are difficult to conceptualize and have not yet been reported. Herein, we present a first activity landscape design that integrates compound potency relationships across multiple targets in a formally consistent manner. These multitarget activity landscapes are based on a general activity cliff classification scheme and are visualized in graph representations, where activity cliffs are represented as edges. Furthermore, the contributions of individual compounds to structure-activity relationship discontinuity across multiple targets are monitored. The methodology has been applied to derive multitarget activity landscapes for compound data sets active against different target families. The resulting landscapes identify single-, dual-, and triple-target activity cliffs and reveal the presence of hierarchical cliff distributions. From these multitarget activity landscapes, compounds forming complex activity cliffs can be readily selected.     

Cross-target view to feature selection: identification of molecular interaction features in ligand-target space

There is growing interest in computational chemogenomics, which aims to identify all possible ligands of all target families using in silico prediction models. In particular, kernel methods provide a means of integrating compounds and proteins in a principled manner and enable the exploration of ligand-target binding on a genomic scale. To better understand the link between ligands and targets, it is of fundamental interest to identify molecular interaction features that contribute to prediction of ligand-target binding. To this end, we describe a feature selection approach based on kernel dimensionality reduction (KDR) that works in a ligand-target space defined by kernels. We further propose an efficient algorithm to overcome a computational bottleneck and thereby provide a useful general approach to feature selection for chemogenomics. Our experiment on cytochrome P450 (CYP) enzymes has shown that the algorithm is capable of identifying predictive features, as well as prioritizing features that are indicative of ligand preference for a given target family. We further illustrate its applicability on the mutation data of HIV protease by identifying influential mutated positions within protease variants. These results suggest that our approach has the potential to uncover the molecular basis for ligand selectivity and off-target effects.     

Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes

We systematically compare X-ray structures of inhibitor complexes of four well-known enzymes and correlate two- and three-dimensional (2D and 3D) similarity of inhibitors with their potency. The analysis reveals the presence of unexpected systematic relationships between molecular similarity and potency. These findings explain why apparently inconsistent structure-activity relationships (SARs) can coexist in different targets, and they have general implications for compound screening and optimization. The results suggest that (1) even for active sites with significant binding constraints, there is a high probability that structurally diverse ligands with similar activity can be identified, (2) different types of SARs are not mutually exclusive, and (3) the chemical nature of ligands is of comparable importance for SARs as the features of active sites. These insights aid in the understanding of target-specific SARs and their intrinsic degree of variability.     

SAR monitoring of evolving compound data sets using activity landscapes

In pharmaceutical research, collections of active compounds directed against specific therapeutic targets usually evolve over time. Small molecule discovery is an iterative process. New compounds are discovered, alternative compound series explored, some series discontinued, and others prioritized. The design of new compounds usually takes into consideration prior chemical and structure-activity relationship (SAR) knowledge. Hence, historically grown compound collections represent a viable source of chemical and SAR information that might be utilized to retrospectively analyze roadblocks in compound optimization and further guide discovery projects. However, SAR analysis of large and heterogeneous sets of active compounds is also principally complicated. We have subjected evolving compound data sets to SAR monitoring using activity landscape models in order to evaluate how composition and SAR characteristics might change over time. Chemotype and potency distributions in evolving data sets directed against different therapeutic targets were analyzed and alternative activity landscape representations generated at different points in time to monitor the progression of global and local SAR features. Our results show that the evolving data sets studied here have predominantly grown around seed clusters of active compounds that often emerged early on, while other SAR islands remained largely unexplored. Moreover, increasing scaffold diversity in evolving data sets did not necessarily yield new SAR patterns, indicating a rather significant influence of "me-too-ism" (i.e., introducing new chemotypes that are similar to already known ones) on the composition and SAR information content of the data sets.     

Interaction-dependent PCR: identification of ligand-target pairs from libraries of ligands and libraries of targets in a single solution-phase experiment

Interaction-dependent PCR (IDPCR) is a solution-phase method to identify binding partners from combined libraries of small-molecule ligands and targets in a single experiment. Binding between DNA-linked targets and DNA-linked ligands induces formation of an extendable duplex. Extension links codes that identify the ligand and target into one selectively amplifiable DNA molecule. In a model selection, IDPCR resulted in the enrichment of DNA encoding all five known protein-ligand pairs out of 67?599 possible sequences.     

Navigating the Future of Separation Science Education: A Perspective

Chromatographia - A number of recommendations on how to improve the education and training of separation scientists were recently made by the National Academies of Sciences, Engineering, and...     

A mouse serum two-dimensional gel map: application to profiling burn injury and infection

With the importance of mouse as a model to study human diseases and the human and rat plasma/serum two-dimensional (2-D) maps being extensively annotated, this study was aimed at constructing a detailed mouse serum 2-D map. Serum proteins from two different inbred strains of mice (BALB/cJ and C57BL/6J) and mice subjected to two different inflammatory stimuli (20% burn injury and lipopolysaccharide (LPS) injection) were separated on overlapping gels covering pH 3-8 and stained with SYPRO Ruby dye. The tryptic peptides from the resolved spots were analyzed by mass spectrometry, leading to the identification of 38 different gene products. With the exception of major urinary proteins found in abundance in male C57BL/6J mice, little strain difference of the mouse serum 2-D was observed. Many proteins detected in the mouse serum 2-D map were not reported in human or rat serum 2-D maps including epidermal growth factor receptor. Three major murine acute-phase proteins (APPs), haptoglobin, serum amyloid A, and serum amyloid P, were highly induced by both inflammatory stimuli. Image analysis shows that the variations of APPs between these two inflammatory models were not uniform although LPS (100 microg/animal) in general was more effective than 20% burn injury in inducing APPs. Serum amyloid A, much more sensitive to endotoxin than burn injury, may represent a sensitive marker to differentiate these two different inflammatory states.     

Protein-imprinted materials: rational design, application and challenges

Protein imprinting is a promising tool for generating artificial biomimetic receptors with antibody-like specific recognition sites. Recently, protein-imprinted materials, as potential antibody substitutes, have attracted much attention in many fields, for example chemical sensors, chromatographic stationary phases, and artificial enzymes, owing to their long-term storage stability, potential re-usability, resistance to harsh environment, and low cost. In this critical review, we focus our discussion on the rational preparation of protein-imprinted materials in terms of choice of template, functional monomer, crosslinker, and polymerization format. In addition, several highlighted applications of protein-imprinted materials are emphasized, not only in well-known fields but also in some unique fields, for example proteomics and tissue engineering. Finally, we propose challenges arising from the intrinsic properties of protein imprinting, for example obtaining the template, heterogeneous binding, and extrinsic competition, for example immobilized aptamers.     

Porphyrin-ethynyl arrays: Synthesis, design, and application

The review discusses various models of multiporphyrin arrays with ethyne, diyne, and E- and Z-enediyne linkers. The concept implying multivalence of such systems is considered. Porphyrin-ethynyl arrays are nanosize structures that are promising from the viewpoint of their application in up-to-date fields of medicine and technics, including design of biocomputers.     

Structure-based design technology contour and its application to the design of Renin inhibitors

It is well-known that the structure-based design approach has had a measurable impact on the drug discovery process in identifying novel and efficacious therapeutic agents for a variety of disease targets. The de novo design approach has inherent potential to generate novel molecules that best fit into a protein binding site when compared to all of the computational methods applied to structure-based design. In its initial attempts, this approach did not achieve much success due to technical hurdles. More recently, the algorithmic advancements in the methodologies and clever strategies developed to design drug-like molecules have improved the success rate. We describe a state-of-the-art structure-based design technology called Contour and provide details of the algorithmic enhancements we have implemented. Contour was designed to create novel drug-like molecules by assembling synthetically viable fragments in the protein binding site using a high-resolution crystal structure of the protein. The technology consists of a sophisticated growth algorithm and a novel scoring function based on a directional model. The growth algorithm generates molecules by dynamically selecting only those fragments from the fragment library that are complementary to the binding site, and assembling them by sampling the conformational space for each attached fragment. The scoring function embodying the essential elements of the binding interactions aids in the rank ordering of grown molecules and helps identify those that have high probability of exhibiting activity against the protein target of interest. The application of Contour to identify inhibitors against human renin enzyme eventually leading to the clinical candidate VTP-27,999 will be discussed here.     

Accelerating AIREBO: Navigating the Journey from Legacy to High-Performance Code

Despite initiatives to improve the quality of scientific software, there still is a large presence of legacy code. The focus of such code is usually on domain-science features, rather than maintainability or highest performance. Additionally, architecture specific optimizations often result in less maintainable code. In this article, we focus on the AIREBO potential from LAMMPS, which exhibits large and complex computational kernels, hindering any systematic optimization. We suggest an approach based on complexity-reducing refactoring and hardware abstraction and present the journey from the C++ port of a previous Fortran code to performance-portable, KNC-hybrid, vectorized, scalable, and optimized code supporting full and reduced precision. The journey includes extensive testing that fixed bugs in the original code. Large-scale, full-precision runs sustain speedups of more than 4× (KNL) and 3× (Skylake). © 2019 Wiley Periodicals, Inc.     

Concave reagents — design and application

By incorporating standard organic reagents into a concave environment one can synthesize concave reagents. Their geometry resembles that of a light bulb in a lampshade. In protonations, base catalyses and metal ion catalyzed reactions the concave shielding leads to selectivity enhancements.Presented at the Sixth International Seminar on Inclusion Compounds, Istanbul, Turkey, 27–31 August 1995.     

Wet-adhesive materials of oral and maxillofacial region: From design to application

Oral and maxillofacial diseases are a group of high-incidence disorders that affect people's life quality to a great extent, while the wet and highly movable environment of the related regions brings challenges to traditional therapies. Faced with the obstacles of insufficient adhesive strength and ensuing short drug retention time, conventional oral therapeutic agents often have difficulty in achieving their desired efficacy. Oral and maxillofacial wet-adhesive materials have the advantages of excellent wet environment retention, internal stability, plasticity, and clinical potential, thus have become a significant research direction in the field of oral related disorders healing. In the past decade, the development of oral adhesive materials with good wet adhesion has accelerated based on the chemical molecular interaction, physical interlocking, and biological adhesion mechanisms, including biomimetic-inspired materials, naturally derived polymer–based materials and adhesive electrospun fiber films. These fancy wet-adhesive materials can be used for oral mucosal drug delivery, oral vaccination, wound healing, and bone defects treatments. Despite their numerous novel applications, wet-adhesive materials in stomatology still face unresolved challenges from material and biological aspects. Here, advances in designs of oral and maxillofacial wet-adhesive materials are reviewed in terms of design backgrounds, attachment mechanisms, and common classifications. Recent demonstrations of wet-adhesive materials for oral and maxillofacial region medical applications from drug delivery to multifunctional tissue treatments are presented. To conclude, current challenges and prospects on potential applications of oral and maxillofacial wet-adhesive materials are also briefly discussed.     

Electrochemical sensor for blood deoxyribonucleases: design and application to the diagnosis of autoimmune thyroiditis

We designed an electrochemical sensor based on a carbon nanotube modified electrode (ME) to analyze DNA-cleaving activity. The cleavage of high molecular weight DNA resulted in an increase in the oxidation current from DNA guanine nucleotides due to a change in DNA adsorptive behavior on the surface of the ME. DNA digestion with DNAse I was accompanied by a linear increase in the DNA signal in proportion to the enzyme activity. We then proposed an assay based on the sensor for the direct assessment of the total deoxyribonuclease activity of blood serum as well as the separate detection of DNAse I and DNA abzymes. The assay was applied to analyze deoxyribonucleases in sera from 21 healthy donors and 17 patients with autoimmune thyroiditis. Our results show that the response of the sensor to DNA cleavage by blood deoxyribonucleases is a promising diagnostic criterion for autoimmune thyroiditis. This sensor can be implemented in a disposable screen-printed electrode format for application in clinical laboratories.     

Dispersive transport of biomolecules in periodic energy landscapes with application to nanofilter sieving arrays

We present a theoretical model for describing the electric field-driven migration and dispersion of short anisotropic molecules in nanofluidic filter arrays. The model uses macrotransport theory to derive exact integral-form expressions for the effective mobility and diffusivity of Brownian particles moving in an effective one-dimensional energy landscape. The latter is obtained by modeling the anisotropic molecules as point-sized Brownian particles with their orientational degrees of freedom accounted for by an entropy penalty term, and using a systematic projection procedure for reducing the system dimensionality to the device axial dimension. Our analytical results provide guidance for the design and optimization of nanofluidic separation systems without the need for complex numerical simulations. Comparison with numerical solution of the macrotransport equations in the actual, effectively two-dimensional, geometry shows that the one-dimensional model faithfully describes the field- and size-dependences of mobility and diffusivity, with maximum difference on the order of 10% under the experimentally relevant electric fields.     

Isothermal titration calorimetry: application to structure-based drug design

The road to market for drug compounds is a treacherous one, generally involving a huge temporal and financial investment. The role of structure-based drug design or lead optimisation ranges wildly in importance in different pharmaceutical companies. The adoption of these aids to provide routes to high affinity ligands has not received widespread acceptance. This is based on a number of factors, from the perceived failings of such methods, to the belief that rapid screening of compound libraries alone is the most effective way to discover drugs.

The panacea of being able to take a computer generated representation of the structure of a target site of a given biomolecule and rationally design an high affinity inhibiting compound has proved seemingly unreachable for three major reasons: (1) current capabilities in computing; (2) the requirement for atomic resolution structural detail; and (3) determination of how structural features can be related to the thermodynamics of interactions. It is the last of these points that this review seeks to address. In particular the use of isothermal titration calorimetry is discussed in the light of the accumulation of accurate thermodynamic data and examples are given where this has been applied to understand the structural aspects of formation of drug–biomolecular complexes.