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).

Comparing Perceptions and Decisional Conflict Towards Participation in Cancer Clinical Trials Among African American Patients Who Have and Have Not Participated.

Despite efforts to increase the diversity of cancer clinical trial participants, African Americans are still underrepresented. While perceptions of participation have been studied, the objective of this study was to compare perceptions and decisional conflict towards clinical trials among African American cancer patients who have and have not participated in clinical trials to identify key areas for intervention. Post hoc analysis also looked at whether they had been asked to participate and how that group differed from those who did. Forty-one African American cancer patients were surveyed at two urban cancer centers and asked to agree/disagree to statements related to clinical trials perceptions (facilitators, barriers, beliefs, values, support, and helpfulness), and complete the O'Connor Decisional Conflict Scale. Independent-samples t tests compared participants by clinical trials participation status; 41% had participated in a clinical trial. Results revealed significant perceptual differences among the groups in three main areas: helpfulness of clinical trials, facilitators to participate in clinical trials, and barriers to participating in clinical trials. Post hoc analysis indicated that those who were not asked about clinical trials and had not participated differed significantly in all areas compared with participants. Additionally, clinical trial participants reported significantly lower decisional conflict in most items compared with both those who had and had not be asked to participate. These differences can give practitioners clues as to how to bridge the gap from non-participator to participator. Messages could then be infused in the clinician-patient dyad when introducing and discussing clinical trials, potentially providing a more effective strategy for communicating with African American patients.

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.

Using theory and user-centered design in digital health: The development of the mychoice communication tool to prepare patients and improve informed decision making regarding clinical trial participation.

OBJECTIVE: Designing salient digital health interventions requires theoretically-based formative research and user-center design with stakeholder input throughout impacting content and technology design. mychoice is a theory-based, stakeholder-guided digital health tool to improve clinical trial informed decision making, particularly among African American patients. METHODS: mychoice was developed by (1) mixed-methods formative research, including in-depth interviews (n=16) and surveys (N=41) with African American cancer patients who had and had not participated in a clinical trial; (2) e-tool design process including perceptual mapping analysis to prioritize messages, multi-disciplinary team and stakeholder input; and (3) iterative production and user testing. RESULTS: Interview findings showed that clinical trial participants expressed more positive attributes about and an openness to consider clinical trials, even though they expressed common concerns such as "fear of being a guinea pig". Survey results indicated that clinical trial participants expressed they had been given information to make the decision (P = .001), while those who had not more frequently reported (P > .001) that no one had talked to them about trials. Perceptual mapping indicated that values such as "helping find a cure" or "value to society" had little resonance to those who had not participated, providing message strategy for prototype development. User testing of the tool resulted in modifications; the most significant was the adaptation to a multi-cultural version. CONCLUSIONS: With the promise of digital health interventions, theory-guided, user-centered and best practice development is critical and mychoice serves as an example of the application of these principles.

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.

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.

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.

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).

A Plan-Do-Study-Act Approach to the Development, Implementation and Evaluation of a Patient Navigation Program to Reduce Breast Cancer Screening Disparities in Un- and Under-Insured, Racially and Ethnically Diverse Urban Women.

Introduction: For the over 28 million Americans without health insurance, there is a great need to develop programs that help meet the health needs of the uninsured population. Materials and Methods: We applied the Plan-Do-Study-Act (PDSA) quality improvement framework to the development, implementation, and evaluation of a breast cancer screening navigation program for un- and under-insured women. Results: Six critical steps emerged: (1) obtain program funding; (2) navigator training; (3) establish a referral base network of community partners that serve the un- and under-insured women; (4) implement a process to address the barriers to accessing mammography; (5) develop a language- and culturally-tailored messaging and media campaign; and (6) develop measures and process evaluation to optimize and expand the program's reach. Discussion: A Plan-Do-Study-Act approach allowed identification of the key elements for successful development, implementation and optimization of a breast cancer screening navigation program aimed at reaching and screening un- and underinsured women.

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.

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.

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.

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.

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 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.

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.

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.

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.

Involvement of P2Y1 and P2Y11 purinoceptors in parasympathetic inhibition of colonic smooth muscle.

Purinergic signaling was first recognized in the guinea pig (Cavia porcellus) taenia coli, where relaxation of smooth muscle by nerve-released ATP may involve the activation of P2Y(1) and P2Y(11) receptors, and where transcripts for both genes have been found. A partial sequence for P2Y(11) protein was identified; the full-length P2Y(1) sequence has already been described. P2Y(1) and P2Y(11) proteins were localized by immunohistochemistry in smooth muscle cells. P2X(2) and P2X(3) proteins were also localized in motoneurons of the myenteric plexus. alphabeta-Methylene-ATP (alphabetameATP) and dibenzoyl-ATP (BzATP) evoked fast relaxations in the taenia, and they were inhibited by the P2Y(1) receptor antagonist 2'-deoxy-N(6)-methyladenosine 3',5'-bisphosphate (MRS2179). However, alphabetameATP and BzATP may stimulate neuronal P2X receptors to release ATP, which then acts on P2Y(1) receptors. In accordance, fast relaxations evoked by alphabetameATP and BzATP were inhibited by the P2X(3) and P2X(2/3) receptor antagonist 5-({[3-phenoxybenzyl][(1S)-1,2,3,4-tetrahydro-1-naphthalenyl] amino} carbonyl)-1,2,4-benzene-tricarboxylic acid (A317491). When P2Y(1), P2X(3), and P2X(2/3) receptors were blocked and adenosine was removed enzymatically, alphabetameATP and BzATP evoked slow relaxations that were inhibited by Reactive Red. Fast and slow relaxations involve small and large conductance calcium-activated potassium channels; the latter are dependent on intracellular cyclic AMP levels, which altered the duration and amplitude of relaxations. alphabetameATP and BzATP were confirmed as agonists, and Reactive Red as an antagonist, of human P2Y(11) receptors. In summary, G(q)-coupled P2Y(1) receptors are involved mainly in fast relaxations, whereas G(q)and G(s)-coupled P2Y(11) receptors are involved in both fast and slow relaxations. These P2Y receptor subtypes, plus neuronal P2X receptors, may explain the phenomenon of parasympathetic inhibition first described by Langley (1898).