Computational Studies of Liver Receptor Homolog 1 in the Presence of Small Molecule Agonists: Allosteric Communication and Virtual Screening for New Potential Drug Candidates

Liver Receptor Homolog 1 (LRH-1) is a nuclear receptor whose dysfunction is affiliated with diseases such as diabetes and cancer. Recent investigations demonstrate that higher levels of activation and modulation of its activity can be achieved through its interaction with phospholipids (PLs) and synthetic small molecules. We employed molecular dynamics (MD) simulations to understand more about the structural basis of LRH-1’s activity when bound to small molecule agonist RJW100 as well as the RJW100 derivative 65endo. We find that RJW100 and derivative 65endo can trigger allosteric communication in LRH-1 despite the RJW100 scaffold inducing motions that differ from those induced by PLs. We also provide supporting evidence that a key threonine residue and a water network may be important in RJW100’s ability to activate LRH-1. Finally, in a campaign to identify new LRH-1 lead compounds, virtual screening was performed against RJW100, 65endo, and a second RJW100 derivative, 8AC

Inhibitor binding mode and allosteric regulation of Na+-glucose symporters.

Sodium-dependent glucose transporters (SGLTs) exploit sodium gradients to transport sugars across the plasma membrane. Due to their role in renal sugar reabsorption, SGLTs are targets for the treatment of type 2 diabetes. Current therapeutics are phlorizin derivatives that contain a sugar moiety bound to an aromatic aglycon tail. Here, we develop structural models of human SGLT1/2 in complex with inhibitors by combining computational and functional studies. Inhibitors bind with the sugar moiety in the sugar pocket and the aglycon tail in the extracellular vestibule. The binding poses corroborate mutagenesis studies and suggest a partial closure of the outer gate upon binding. The models also reveal a putative Na+ binding site in hSGLT1 whose disruption reduces the transport stoichiometry to the value observed in hSGLT2 and increases inhibition by aglycon tails. Our work demonstrates that subtype selectivity arises from Na+-regulated outer gate closure and a variable region in extracellular loop EL5

Conformational Heterogeneity of Unbound Proteins Enhances Recognition in Protein–Protein Encounters

To understand cellular processes at the molecular level we need to improve our knowledge of protein−protein interactions, from a structural, mechanistic, and energetic point of view. Current theoretical studies and computational docking simulations show that protein dynamics plays a key role in protein association and support the need for including protein flexibility in modeling protein interactions. Assuming the conformational selection binding mechanism, in which the unbound state can sample bound conformers, one possible strategy to include flexibility in docking predictions would be the use of conformational ensembles originated from unbound protein structures. Here we present an exhaustive computational study about the use of precomputed unbound ensembles in the context of protein docking, performed on a set of 124 cases of the Protein−Protein Docking Benchmark 3.0. Conformational ensembles were generated by conformational optimization and refinement with MODELLER and by short molecular dynamics trajectories with AMBER. We identified those conformers providing optimal binding and investigated the role of protein conformational heterogeneity in protein−protein recognition. Our results show that a restricted conformational refinement can generate conformers with better binding properties and improve docking encounters in medium-flexible cases. For more flexible cases, a more extended conformational sampling based on Normal Mode Analysis was proven helpful. We found that successful conformers provide better energetic complementarity to the docking partners, which is compatible with recent views of binding association. In addition to the mechanistic considerations, these findings could be exploited for practical docking predictions of improved efficiency.Peer ReviewedPostprint (author's final draft

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data

Lectin-Glycan Complexes: A Comprehensive Analysis of Docking Calculations

Lectins are a type of glycan-binding protein that noncovalently bind glycans. Carbohydrates are molecules consisting of sugar units joined together. Glycans are carbohydrates. Hence, glycans are also sugars. Lectins and lectin-glycan complexes have a range of biological roles and can be found in animals (including humans), plants, bacteria, viruses, and yeasts and fungi.1 Many scientists focus on the computational study of these complexes due to their intricate roles in many living organisms. Computational study is important in furthering our knowledge of lectin-glycan complexes and other such protein complexes. However, computational study is not perfect. There are many challenges in computational study, especially in the docking of ligands to receptors. In glycans, there are usually a high number of hydroxyl (OH) groups that affect docking; there could be surrounding ions; the rings in glycans can have CH-π stacking interactions with aromatic residues and cause issues; and glycans larger than one subunit have bonds between subunits that allow them to twist. These represent just a few challenges in docking. It is difficult for the software to accurately dock glycans to corresponding receptors because of these challenges. So, the purpose of this study was to try to evaluate the performance of the docking program Autodock Vina (referred as Vina) and Vina-carb on a large dataset of docking problems and propose a workflow for effective docking of glycan ligands.2 We have looked into the effect of glycan size, seed value (a random starting point for docking calculations), and Carbohydrate Intrinsic (CHI) energy functions in glycosidic linkages.3 We tried using CHI values in Vina-Carb that mimicked Autodock Vina. We saw that in almost all cases Vina-Carb did better, even if it was a marginal difference. Then we tried optimizing the CHI values CHI coefficient and CHI cutoff.3 We did see some patterns emerge for specific values. We also used a random seed for calculations but did not see much of a difference in using a random seed for calculations. There were some improvements and surprises. Overall, we know that optimizing docking software is a challenge, but doing so will improve research for many scientists. More calculations will be done in the future because they will be worthwhile. We originally sought to analyze Vina-Carb to make it better. Such research will help improve future computational study. We have already seen some parameters that have promise for further investigation

Theoretical Model of EphA2-Ephrin A1 Inhibition

This work aims at the theoretical description of EphA2-ephrin A1 inhibition by small molecules. Recently proposed ab initio-based scoring models, comprising long-range components of interaction energy, is tested on lithocholic acid class inhibitors of this protein⁻protein interaction (PPI) against common empirical descriptors. We show that, although limited to compounds with similar solvation energy, the ab initio model is able to rank the set of selected inhibitors more effectively than empirical scoring functions, aiding the design of novel compounds

Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Structural Evidence and Strategies for Finding Small Molecule Modulators

(1) Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose descriptors explaining the nature of different protein-protein complexes are desirable. In this work, we introduce Epic Protein Interface Classification (EPIC) as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines (SVM), C4.5 Decision Trees, K Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms (GA) to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, we represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors (ACVs), DrugScore pair potential vectors (DPV) and SFCscore descriptor vectors (SDV). We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (2) Since protein-protein interactions play pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost interest. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere the complex formation of proteins. On the other hand, there a several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, e.g. as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligand accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable targets. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules. (3) AffinDB is a database of affinity data for structurally resolved protein-ligand complexes from the PDB. It is freely accessible at http://www.agklebe.de/affinity. Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein-ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code, and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH-value of the measurement, ligand molecular weight, and publication data (author, journal, year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design

Fragment-based lead discovery on G-protein-coupled receptors

Introduction: G-protein-coupled receptors (GPCRs) form one of the largest groups of potential targets for novel medications. Low druggability of many GPCR targets and inefficient sampling of chemical space in high-throughput screening expertise however often hinder discovery of drug discovery leads for GPCRs. Fragment-based drug discovery is an alternative approach to the conventional strategy and has proven its efficiency on several enzyme targets. Based on developments in biophysical screening techniques, receptor stabilization and in vitro assays, virtual and experimental fragment screening and fragment-based lead discovery recently became applicable for GPCR targets. Areas covered: This article provides a review of the biophysical as well as biological detection techniques suitable to study GPCRs together with their applications to screen fragment libraries and identify fragment-size ligands of cell surface receptors. The article presents several recent examples including both virtual and experimental protocols for fragment hit discovery and early hit to lead progress. Expert opinion: With the recent progress in biophysical detection techniques, the advantages of fragment-based drug discovery could be exploited for GPCR targets. Structural information on GPCRs will be more abundantly available for early stages of drug discovery projects, providing information on the binding process and efficiently supporting the progression of fragment hit to lead. In silico approaches in combination with biological assays can be used to address structurally challenging GPCRs and confirm biological relevance of interaction early in the drug discovery project