Towards Precision Oncology: The Role of Smoothened and Its Variants in Cancer.

The G protein-coupled receptor Smoothened (Smo) is a central signal transducer of the Hedgehog (Hh) pathway which has been linked to diverse forms of tumours. Stimulated by advancements in structural and functional characterisation, the Smo receptor has been recognised as an important therapeutic target in Hh-driven cancers, and several Smo inhibitors have now been approved for cancer therapy. This receptor is also known to be an oncoprotein itself and its gain-of-function variants have been associated with skin, brain, and liver cancers. According to the COSMIC database, oncogenic mutations of Smo have been identified in various other tumours, although their oncogenic effect remains unknown in these tissues. Drug resistance is a common challenge in cancer therapies targeting Smo, and data analysis shows that healthy individuals also harbour resistance mutations. Based on the importance of Smo in cancer progression and the high incidence of resistance towards Smo inhibitors, this review suggests that detection of Smo variants through tumour profiling could lead to increased precision and improved outcomes of anti-cancer treatments.

Teaching genomics to life science undergraduates using cloud computing platforms with open datasets.

The final year of a biochemistry degree is usually a time to experience research. However, laboratory-based research projects were not possible during COVID-19. Instead, we used open datasets to provide computational research projects in metagenomics to biochemistry undergraduates (80 students with limited computing experience). We aimed to give the students a chance to explore any dataset, rather than use a small number of artificial datasets (~60 published datasets were used). To achieve this, we utilized Google Colaboratory (Colab), a virtual computing environment. Colab was used as a framework to retrieve raw sequencing data (analyzed with QIIME2) and generate visualizations. Setting up the environment requires no prior experience; all students have the same drive structure and notebooks can be shared (for synchronous sessions). We also used the platform to combine multiple datasets, perform a meta-analysis, and allowed the students to analyze large datasets with 1000s of subjects and factors. Projects that required increased computational resources were integrated with Google Cloud Compute. In future, all research projects can include some aspects of reanalyzing public data, providing students with data science experience. Colab is also an excellent environment in which to develop data skills in multiple languages (e.g., Perl, Python, Julia).

Hotspot Identification and Drug Design of Protein-Protein Interaction Modulators Using the Fragment Molecular Orbital Method.

Protein-protein interactions (PPIs) are essential for the function of many proteins. Aberrant PPIs have the potential to lead to disease, making PPIs promising targets for drug discovery. There are over 64,000 PPIs in the human interactome reference database; however, to date, very few PPI modulators have been approved for clinical use. Further development of PPI-specific therapeutics is highly dependent on the availability of structural data and the existence of reliable computational tools to explore the interface between two interacting proteins. The fragment molecular orbital (FMO) quantum mechanics method offers comprehensive and computationally inexpensive means of identifying the strength (in kcal/mol) and the chemical nature (electrostatic or hydrophobic) of the molecular interactions taking place at the protein-protein interface. We have integrated FMO and PPI exploration (FMO-PPI) to identify the residues that are critical for protein-protein binding (hotspots). To validate this approach, we have applied FMO-PPI to a dataset of protein-protein complexes representing several different protein subfamilies and obtained FMO-PPI results that are in agreement with published mutagenesis data. We observed that critical PPIs can be divided into three major categories: interactions between residues of two proteins (intermolecular), interactions between residues within the same protein (intramolecular), and interactions between residues of two proteins that are mediated by water molecules (water bridges). We extended our findings by demonstrating how this information obtained by FMO-PPI can be utilized to support the structure-based drug design of PPI modulators (SBDD-PPI).

Predicting Residence Time of GPCR Ligands with Machine Learning.

Drug-target residence time, the duration of binding at a given protein target, has been shown in some protein families to be more significant for conferring efficacy than binding affinity. To carry out efficient optimization of residence time in drug discovery, machine learning models that can predict that value need to be developed. One of the main challenges with predicting residence time is the paucity of data. This chapter outlines all of the currently available ligand kinetic data, providing a repository that contains the largest publicly available source of GPCR-ligand kinetic data to date. To help decipher the features of kinetic data that might be beneficial to include in computational models for the prediction of residence time, the experimental evidence for properties that influence residence time are summarized. Finally, two different workflows for predicting residence time with machine learning are outlined. The first is a single-target model trained on ligand features; the second is a multi-target model trained on features generated from molecular dynamics simulations.

Educating and engaging new communities of practice with high performance computing through the integration of teaching and research.

The identification of strategies by which to increase the representation of women and increase diversity in STEM fields (science, technology, engineering and mathematics), including medicine, has been a pressing matter for global agencies including the European Commission, UNESCO and numerous international scientific societies. In my role as UCL training lead for CompBioMed, a European Commission Horizon 2020-funded Centre of Excellence in Computational Biomedicine (compbiomed.eu), and as Head of Teaching for Molecular Biosciences at UCL from 2010 to 2019, I have integrated research and teaching to lead the development of high-performance computing (HPC)-based education targeting medical students and undergraduate students studying biosciences in a way that is explicitly integrated into the existing university curriculum as a credit-bearing module. One version of the credit-bearing module has been specifically designed for medical students in their pre-clinical years of study and one of the unique features of the course is the integration of clinical and computational aspects, with students obtaining and processing clinical samples and then interrogating the results computationally using code that was ported to HPC at CompBioMed's HPC Facility core partners (EPCC (UK), SURFsara (The Netherlands) and the Barcelona Supercomputing Centre (Spain)). Another version of the credit-bearing module has, over the course of this project, evolved into a replacement for the third year research project course for undergraduate biochemistry, biotechnology and molecular biology students, providing students with the opportunity to design and complete an entire specialist research project from the formulation of experimental hypotheses to the investigation of these hypotheses in a way that involves the integration of experimental and HPC-based computational methodologies. Since 2017-2018, these UCL modules have been successfully delivered to over 350 students-a cohort with a demographic of greater than 50% female. CompBioMed's experience with these two university modules has enabled us to distil our methodology into an educational template that can be delivered at other universities in Europe and worldwide. This educational approach to training enables new communities of practice to effectively engage with HPC and reveals a means by which to improve the underrepresentation of women in supercomputing.

Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors.

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol-1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule-receptor protein systems.

Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method.

G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information. Using the fragment molecular orbital (FMO) quantum mechanics method to analyze 35 crystal structures representing different branches of the class A GPCR family, we have identified 69 topologically equivalent TM residues that form a consensus network of 51 inter-TM interactions, providing novel results that are consistent with and help to rationalize experimental data. This discovery establishes a comprehensive picture of how defined molecular forces govern specific interhelical interactions which, in turn, support the structural stability, ligand binding, and activation of GPCRs.

Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method.

G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanics (QM) approaches are often too computationally expensive to be of practical use in time-sensitive situations, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed, and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule toward ligand binding, including an analysis of their chemical nature. Such information is essential for an efficient structure-based drug design (SBDD) process. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions.

Characterizing Rhodopsin-Arrestin Interactions with the Fragment Molecular Orbital (FMO) Method.

Arrestin binding to G protein-coupled receptors (GPCRs) plays a vital role in receptor signaling. Recently, the crystal structure of rhodopsin bound to activated visual arrestin was resolved using XFEL (X-ray free electron laser). However, even with the crystal structure in hand, our ability to understand GPCR-arrestin binding is limited by the availability of accurate tools to explore receptor-arrestin interactions. We applied fragment molecular orbital (FMO) method to explore the interactions formed between the residues of rhodopsin and arrestin. FMO enables ab initio approaches to be applied to systems that conventional quantum mechanical (QM) methods would be too compute-expensive. The FMO calculations detected 35 significant interactions involved in rhodopsin-arrestin binding formed by 25 residues of rhodopsin and 28 residues of arrestin. Two major regions of interaction were identified: at the C-terminal tail of rhodopsin (D330-S343) and where the "finger loop" (G69-T79) of arrestin directly inserts into rhodopsin active core. Out of these 35 interactions, 23 were mainly electrostatic and 12 hydrophobic in nature.

Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method.

Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes.

Computational prediction of GPCR oligomerization.

There has been a recent and prolific expansion in the number of GPCR crystal structures being solved: in both active and inactive forms and in complex with ligand, with G protein and with each other. Despite this, there is relatively little experimental information about the precise configuration of GPCR oligomers during these different biologically relevant states. While it may be possible to identify the experimental conditions necessary to crystallize a GPCR preferentially in a specific structural conformation, computational approaches afford a potentially more tractable means of describing the probability of formation of receptor dimers and higher order oligomers. Ensemble-based computational methods based on structurally determined dimers, coupled with a computational workflow that uses quantum mechanical methods to analyze the chemical nature of the molecular interactions at a GPCR dimer interface, will generate the reproducible and accurate predictions needed to predict previously unidentified GPCR dimers and to inform future advances in our ability to understand and begin to precisely manipulate GPCR oligomers in biological systems. It may also provide information needed to achieve an increase in the number of experimentally determined oligomeric GPCR structures.

Characterising GPCR-ligand interactions using a fragment molecular orbital-based approach.

There has been fantastic progress in solving GPCR crystal structures. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanical approaches (QM) are often too computationally expensive, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule towards ligand binding, including an analysis of their chemical nature.

Ensemble-Based Steered Molecular Dynamics Predicts Relative Residence Time of A2A Receptor Binders.

Drug-target residence time, the length of time for which a small molecule stays bound to its receptor target, has increasingly become a key property for optimization in drug discovery programs. However, its in silico prediction has proven difficult. Here we describe a method, using atomistic ensemble-based steered molecular dynamics (SMD), to observe the dissociation of ligands from their target G protein-coupled receptor in a time scale suitable for drug discovery. These dissociation simulations accurately, precisely, and reproducibly identify ligand-residue interactions and quantify the change in ligand energy values for both protein and water. The method has been applied to 17 ligands of the A2A adenosine receptor, all with published experimental kinetic binding data. The residues that interact with the ligand as it dissociates are known experimentally to have an effect on binding affinities and residence times. There is a good correlation ( R2 = 0.79) between the computationally calculated change in water-ligand interaction energy and experimentally determined residence time. Our results indicate that ensemble-based SMD is a rapid, novel, and accurate semi-empirical method for the determination of drug-target relative residence time.

Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding.

There is a substantial amount of historical ligand binding data available from site-directed mutagenesis (SDM) studies of many different GPCR subtypes. This information was generated prior to the wave of GPCR crystal structure, in an effort to understand ligand binding with a view to drug discovery. Concerted efforts to determine the atomic structure of GPCRs have proven extremely successful and there are now more than 80 GPCR crystal structure in the PDB database, many of which have been obtained in the presence of receptor ligands and associated G proteins. These structural data enable the generation of computational model structures for all GPCRs, including those for which crystal structures do not yet exist. The power of these models in designing novel ligands, especially those with improved residence times, and for better understanding receptor function can be enhanced tremendously by combining them synergistically with historic SDM ligand binding data. Here, we describe a protocol by which historic SDM binding data and receptor models may be used together to identify novel key residues for mutagenesis studies.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

The biological impact of blood pressure-associated genetic variants in the natriuretic peptide receptor C gene on human vascular smooth muscle.

Elevated blood pressure (BP) is a major global risk factor for cardiovascular disease. Genome-wide association studies have identified several genetic variants at the NPR3 locus associated with BP, but the functional impact of these variants remains to be determined. Here we confirmed, by a genome-wide association study within UK Biobank, the existence of two independent BP-related signals within NPR3 locus. Using human primary vascular smooth muscle cells (VSMCs) and endothelial cells (ECs) from different individuals, we found that the BP-elevating alleles within one linkage disequilibrium block identified by the sentinel variant rs1173771 was associated with lower endogenous NPR3 mRNA and protein levels in VSMCs, together with reduced levels in open chromatin and nuclear protein binding. The BP-elevating alleles also increased VSMC proliferation, angiotensin II-induced calcium flux and cell contraction. However, an analogous genotype-dependent association was not observed in vascular ECs. Our study identifies novel, putative mechanisms for BP-associated variants at the NPR3 locus to elevate BP, further strengthening the case for targeting NPR-C as a therapeutic approach for hypertension and cardiovascular disease prevention.

Rapid and accurate assessment of GPCR-ligand interactions Using the fragment molecular orbital-based density-functional tight-binding method.

The reliable and precise evaluation of receptor-ligand interactions and pair-interaction energy is an essential element of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been used to accelerate QM calculations, and by combining FMO with the density-functional tight-binding (DFTB) method we are able to decrease computational cost 1000 times, achieving results in seconds, instead of hours. We have applied FMO-DFTB to three different GPCR-ligand systems. Our results correlate well with site directed mutagenesis data and findings presented in the published literature, demonstrating that FMO-DFTB is a rapid and accurate means of GPCR-ligand interactions. © 2017 Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

An Ensemble-Based Protocol for the Computational Prediction of Helix-Helix Interactions in G Protein-Coupled Receptors using Coarse-Grained Molecular Dynamics.

The accurate identification of the specific points of interaction between G protein-coupled receptor (GPCR) oligomers is essential for the design of receptor ligands targeting oligomeric receptor targets. A coarse-grained molecular dynamics computer simulation approach would provide a compelling means of identifying these specific protein-protein interactions and could be applied both for known oligomers of interest and as a high-throughput screen to identify novel oligomeric targets. However, to be effective, this in silico modeling must provide accurate, precise, and reproducible information. This has been achieved recently in numerous biological systems using an ensemble-based all-atom molecular dynamics approach. In this study, we describe an equivalent methodology for ensemble-based coarse-grained simulations. We report the performance of this method when applied to four different GPCRs known to oligomerize using error analysis to determine the ensemble size and individual replica simulation time required. Our measurements of distance between residues shown to be involved in oligomerization of the fifth transmembrane domain from the adenosine A2A receptor are in very good agreement with the existing biophysical data and provide information about the nature of the contact interface that cannot be determined experimentally. Calculations of distance between rhodopsin, CXCR4, and ß1AR transmembrane domains reported to form contact points in homodimers correlate well with the corresponding measurements obtained from experimental structural data, providing an ability to predict contact interfaces computationally. Interestingly, error analysis enables identification of noninteracting regions. Our results confirm that GPCR interactions can be reliably predicted using this novel methodology.

An Immunological Approach to Increase the Brain's Resilience to Insults.

We have previously demonstrated the therapeutic potential of inducing a humoral response with autoantibodies to the N-methyl D-aspartate (NMDA) receptor using a genetic approach. In this study, we generated three recombinant proteins to different functional domains of the NMDA receptor, which is implicated in mediating brain tolerance, specifically NR1[21-375], NR1[313-619], NR1[654-800], and an intracellular scaffolding protein, Homer1a, with a similar anatomical expression pattern. All peptides showed similar antigenicity and antibody titers following systemic vaccination, and all animals thrived. Two months following vaccination, rats were administered the potent neurotoxin, kainic acid. NR1[21-375] animals showed an antiepileptic phenotype but no neuroprotection. Remarkably, despite ineffective antiepileptic activity, 6 of 7 seizing NR1[654-800] rats showed absolutely no injury with only minimal changes in the remaining animal, whereas the majority of persistently seizing rats in the other groups showed moderate to severe hippocampal injury. CREB, BDNF, and HSP70, proteins associated with preconditioning, were selectively upregulated in the hippocampus of NR1[654-800] animals, consistent with the observed neuroprotective phenotype. These results identify NR1 epitopes important in conferring anticonvulsive and neuroprotective effects and support the concept of an immunological strategy to induce a chronic state of tolerance in the brain.

Molecular characterisation of post-bio-electrosprayed human brain astrocytoma cells.

Bio-electrospraying (BES) is a method for directly jetting living cells under conditions that allow their distribution in the x, y, and z axes. Previous work has been focused on achieving jetting in stable cone-jet mode, which is required for precision placement, and these studies have demonstrated that there are no significant effects of bio-electrospraying on cell morphology or viability. In this work, we examine the biological properties of bio-electrosprayed cells using assays of cellular function that range from the molecular level through to integrated cellular systems, and include proteomics, signal transduction, cell growth and proliferation, and the characterisation of apoptotic blebs. From these molecular methods, we have determined that bio-electrospraying, under the electric field conditions used to achieve stable cone-jet mode, causes no alterations to the biological properties and function of the cells being jetted. Bio-electrosprayed and control cells had similar viability, proliferation properties and virtually indistinguishable cell cycle profiles. The biophysical properties of large conducting (BK) potassium channels were unchanged, as were the pharmacological responses of the endogenous muscarinic and exogenous P2Y(11) receptors, both of which are cell surface receptors of the 7TM superfamily. Proteomic analyses revealed that although three proteins had subtle differences in expression level between bio-electrosprayed and control cells, none of these fold differences was above the 1.5-fold cut-off threshold required for further analyses. These findings support the further development of bio-electrosprays as a viable technology for a wide diversity of tissue engineering, regenerative biology, advanced cellular therapeutics and medicinal applications, having significance in the clinic.