Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials

Toxicity of Three Optical Brighteners: Potential Pharmacological Targets and Effects on Caenorhabditis elegans

Optical brighteners (OBs) have become an integral part of our daily lives and culture, with a growing number of applications in various fields. Most industrially produced OBs are derived from stilbene, which has been found in environmental matrices. The main objectives for this work are as follows: first, to identify protein targets for DAST, FB-28, and FB-71, and second, to assess their effects in some behaviors physiologic of Caenorhabditis elegans. To achieve the first objective, each OB was tested against a total of 844 human proteins through molecular docking using AutoDock Vina, and affinities were employed as the main criteria to identify potential target proteins for the OB. Molecular dynamics simulations took and validated the best 25 docking results from two protein databases. The highest affinity was obtained for the Hsp70-1/DAST, CD40 ligand/FB-71, and CD40 ligand/FB-28 complexes. The possible toxic effects that OBs could cause were evaluated using the nematode C. elegans. The lethality, body length, locomotion, and reproduction were investigated in larval stage L1 or L4 of the wild-type strain N2. In addition, transgenic green fluorescent protein (GFP) strains were employed to estimate changes in relative gene expression. The effects on the inhibition of growth, locomotion, and reproduction of C. elegans nematodes exposed to DAST, FB-71, and FB-28 OBs were more noticeable with respect to lethality. Moreover, an interesting aspect in OB was increased the expression of gpx-4 and sod-4 genes associated with oxidative stress indicating a toxic response related to the generation of reactive oxygen species (ROS). In all cases, a clear concentration-response relationship was observed. It is of special attention that the use of OBs is increasing, and their different sources, such as detergents, textiles, plastics, and paper products, must also be investigated to characterize the primary emissions of OBs to the environment and to develop an adequate regulatory framework.

Binding-Site Match Maker (BSMM): A Computational Method for the Design of Multi-Target Ligands

Multi-target ligand strategies provide a valuable method of drug design. However, to develop a multi-target drug with the desired profile remains a challenge. Herein, we developed a computational method binding-site match maker (BSMM) for the design of multi-target ligands based on binding site matching. BSMM was built based on geometric hashing algorithms and the representation of a binding-site with physicochemical (PC) points. The BSMM software was used to detect proteins with similar binding sites or subsites. In particular, BSMM is independent of protein global folds and sequences and is therefore applicable to the matching of any binding sites. The similar sites between protein pairs with low homology and/or different folds are generally not obvious to the visual inspection. The detection of such similar binding sites by BSMM could be of great value for the design of multi-target ligands.

DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network

Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.

Identification and Visualization of Kinase-Specific Subpockets

The identification and design of selective compounds is important for the reduction of unwanted side effects as well as for the development of tool compounds for target validation studies. This is, in particular, true for therapeutically important protein families that possess conserved folds and have numerous members such as kinases. To support the design of selective kinase inhibitors, we developed a novel approach that allows identification of specificity determining subpockets between closely related kinases solely based on their three-dimensional structures. To account for the intrinsic flexibility of the proteins, multiple X-ray structures of the target protein of interest as well as of unwanted off-target(s) are taken into account. The binding pockets of these protein structures are calculated and fused to a combined target and off-target pocket, respectively. Subsequently, shape differences between these two combined pockets are identified via fusion rules. The approach provides a user-friendly visualization of target-specific areas in a binding pocket which should be explored when designing selective compounds. Furthermore, the approach can be easily combined with in silico alanine mutation studies to identify selectivity determining residues. The potential impact of the approach is demonstrated in four retrospective experiments on closely related kinases, i.e., p38α vs Erk2, PAK1 vs PAK4, ITK vs AurA, and BRAF vs VEGFR2. Overall, the presented approach does not require any profiling data for training purposes, provides an intuitive visualization of a large number of protein structures at once, and could also be applied to other target classes.

Predictive proteochemometric models for kinases derived from 3D protein field-based descriptors

Proteochemometric models of kinases derived from protein fields and ligand 4-point pharmacophoric fingerprints are predictive and visually interpretable.

Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics

Achieving selectivity for small organic molecules toward biological targets is a main focus of drug discovery but has been proven difficult, for example, for kinases because of the high similarity of their ATP binding pockets. To support the design of more selective inhibitors with fewer side effects or with altered target profiles for improved efficacy, we developed a method combining ligand- and receptor-based information. Conventional QSAR models enable one to study the interactions of multiple ligands toward a single protein target, but in order to understand the interactions between multiple ligands and multiple proteins, we have used proteochemometrics, a multivariate statistics method that aims to combine and correlate both ligand and protein descriptions with affinity to receptors. The superimposed binding sites of 50 unique kinases were described by molecular interaction fields derived from knowledge-based potentials and Schrödinger's WaterMap software. Eighty ligands were described by Mold(2), Open Babel, and Volsurf descriptors. Partial least-squares regression including cross-terms, which describe the selectivity, was used for model building. This combination of methods allows interpretation and easy visualization of the models within the context of ligand binding pockets, which can be translated readily into the design of novel inhibitors.

Chemogenomics in drug discovery: computational methods based on the comparison of binding sites

Novel computational methods for understanding relationships between ligands and all possible biological targets have emerged in recent years. Proteins are connected to each other based on the similarity of their ligands or based on the similarity of their binding sites. The assumption is that compounds sharing chemical similarity should share targets and that targets with a similar binding site should also share ligands. A large number of computational techniques have been developed to assess ligand and binding site similarity, which can be used to make quantitative predictions of the most probable biological target of a given compound. This review covers the recent advances in new computational methods for relating biological targets based on the similarity of their binding sites. Binding site comparisons are used for the prediction of their most likely ligands, their possible cross reactivity and selectivity. These comparisons can also be used to infer the function of novel uncharacterized proteins.

The Interweaving of Cheminformatics and HTS

The aim of this chapter is to describe the stages of early drug discovery that can be assisted by techniques commonly used in the field of cheminformatics. In fact, cheminformatics tools can be applied all the way from the design of compound libraries and the analysis of HTS results, to the discovery of functional relationships between compounds and their targets.

Imaging progesterone receptor in breast tumors: synthesis and receptor binding affinity of fluoroalkyl-substituted analogues of tanaproget

The progesterone receptor (PR) is estrogen regulated, and PR levels in breast tumors can be used to predict the success of endocrine therapies targeting the estrogen receptor (ER). Tanaproget is a nonsteroidal progestin agonist with very high PR binding affinity and excellent in vivo potency. When appropriately radiolabeled, it might be used to image PR-positive breast tumors noninvasively by positron emission tomography (PET). We describe the synthesis and PR binding affinities of a series of fluoroalkyl-substituted 6-aryl-1,4-dihydrobenzo[d][1,3]oxazine-2-thiones, analogues of Tanaproget. Some of these compounds have subnanomolar binding affinities, higher than that of either Tanaproget itself or the high affinity PR ligand R5020. Structure-binding affinity relationships can be rationalized by molecular modeling of ligand complexes with PR, and the enantioselectivity of binding has been predicted. These compounds are being further evaluated as potential diagnostic PET imaging agents for breast cancer, and enantiomerically pure materials of defined stereochemistry are being prepared.

Structure-based chemogenomics: analysis of protein family landscapes

Analysis of the three-dimensional structures of protein ligand complexes provides valuable insight into both the common interaction patterns within a target family and the discriminating features between the different members of a target family. Knowledge of the common interaction patterns helps to design target family focused chemical libraries for hit finding, while the discriminating features can be exploited to optimize the selectivity profile of a lead compound against particular member of a target family. Herein, we review the computational tools which have been developed to analyze crystal structures of members of a target family.

Chemogenomic approaches to rational drug design

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.