Validation of Coevolving Residue Algorithms via Pipeline Sensitivity Analysis: ELSC and OMES and ZNMI, Oh My!

Correlated amino acid substitution algorithms attempt to discover groups of residues that co-fluctuate due to either structural or functional constraints. Although these algorithms could inform both ab initio protein folding calculations and evolutionary studies, their utility for these purposes has been hindered by a lack of confidence in their predictions due to hard to control sources of error. To complicate matters further, naive users are confronted with a multitude of methods to choose from, in addition to the mechanics of assembling and pruning a dataset. We first introduce a new pair scoring method, called ZNMI (Z-scored-product Normalized Mutual Information), which drastically improves the performance of mutual information for co-fluctuating residue prediction. Second and more important, we recast the process of finding coevolving residues in proteins as a data-processing pipeline inspired by the medical imaging literature. We construct an ensemble of alignment partitions that can be used in a cross-validation scheme to assess the effects of choices made during the procedure on the resulting predictions. This pipeline sensitivity study gives a measure of reproducibility (how similar are the predictions given perturbations to the pipeline?) and accuracy (are residue pairs with large couplings on average close in tertiary structure?). We choose a handful of published methods, along with ZNMI, and compare their reproducibility and accuracy on three diverse protein families. We find that (i) of the algorithms tested, while none appear to be both highly reproducible and accurate, ZNMI is one of the most accurate by far and (ii) while users should be wary of predictions drawn from a single alignment, considering an ensemble of sub-alignments can help to determine both highly accurate and reproducible couplings. Our cross-validation approach should be of interest both to developers and end users of algorithms that try to detect correlated amino acid substitutions

COMPUTATIONAL AND EXPERIMENTAL APPROACHES TO OVERCOME THE G PROTEIN-COUPLED RECEPTOR STRUCTURAL KNOWLEDGE GAP

COMPUTATIONAL AND EXPERIMENTAL APPROACHES TO OVERCOME THE G PROTEIN-COUPLED RECEPTOR STRUCTURAL KNOWLEDGE GA

Membrane and soluble protein structure determination by cryo-TEM

Proteins are biological polymers ubiquitous through all forms of life. Essential processes such as ion conduction, enzymatic catalysis, signal detection and transduction rely on proteins. Structural aspects of the cell such as the cell shape or the compact packing of DNA in a chromosome also require proteins. While DNA carries the genetic information, which ultimately defines the response of a cell to any given event, virtually all processes in the cell depend on proteins to occur. In this thesis cryogenic electron microscopy and single particle analysis workflow are used to determine electron density maps of a set of both soluble and membrane proteins. The obtained structural information is used to elucidate biological processes of the analysed proteins therefore linking structure to function

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Computational Approaches for the Characterization of the Structure and Dynamics of G Protein-Coupled Receptors: Applications to Drug Design

G Protein-Coupled Receptors (GPCRs) constitute the most pharmacologically relevant superfamily of proteins. In this thesis, a computational pipeline for modelling the structure and dynamics of GPCRs is presented, properly combined with experimental collaborations for GPCR drug design. These include the discovery of novel scaffolds as potential antipsychotics, and the design of a new series of A3 adenosine receptor antagonists, employing successful combinations of structure- and ligand-based approaches. Additionally, the structure of Adenosine Receptors (ARs) was computationally assessed, with implications in ligand affinity and selectivity. The employed protocol for Molecular Dynamics simulations has allowed the characterization of structural determinants of the activation of ARs, and the evaluation of the stability of GPCR dimers of CXCR4 receptor. Finally, the computational pipeline here developed has been integrated into the web server GPCR-ModSim (http://gpcr.usc.es), contributing to its application in biochemical and pharmacological studies on GPCRs

Understanding Allosteric Modulation of G-Protein Coupled Receptors

G protein-coupled receptors (GPCRs) which are seven-transmembrane allosteric machine constitutes largest and diverse family of membrane proteins. GPCR participate in activating a diverse range of signaling pathways, in response to ligand perturbation which ranges from neurotransmitters, hormones to photons. The role of GPCRs in a wide range of key physiological processes and their ubiquity in mammalian genome makes them attractive pharmaceutical targets. Signal transduction in GPCR occur mainly, via G-proteins and leads to a cascade of signaling. In addition to the orthosteric site, GPCRs also possesses a topographically distinct allosteric site which contributes to allosteric modulation, i.e long distant ligand binding for activating G proteins and trigger GDP release. The mechanism that governs allosteric activation triggering GDP release is yet uncertain. Differential ligands bind to GPCR\u27s orthosteric sites and can modulate allosteric signaling. Ligands that increase or decrease the GPCR signaling are classified as agonists and antagonists respectively. Compared to orthosteric ligand allosteric modulator through electrostatic repulsion, steric hindrance or conformational stability can select subsets of signaling responses. We in this study are trying to understand the basis of ligand-biased signaling or functional selectivity that leads to long-distance signaling in a receptor. Using the information from crystal structures of the receptor, combined with molecular dynamics simulations, we performed a systematic analysis to identify the basis of conformational selectivity for allosteric bias in GPCRs. Our study explores the conformational landscape of GPCRs as a function of the activity of the receptor. Normal modes analysis (NMA) was used to identify low-frequency modes that describe conformational changes due to large-scale domain motions in the receptor. NMA characterized changes in correlated motions of residues in the rest six global modes and revealed conformation shift starting from the inactive structure. We used MD simulations coupled with network analysis to reveal correlated motion between G-protein Coupling site and ligand binding site. Changes in dynamically correlated residue motion in allosteric networks reveals the characteristic feature of receptor activity in GPCRs. Single point mutations studies were aimed to analyze the changes in the structural scaffold of GPCRs as a result of mutations. Mutational studies facilitated in determining the basis of functional selectivity and changes in the allosteric communication as a result of allosteric binding to the receptor. Single point mutations also revealed residues critical for functional activity of GPCRs. Inter residue contact network responsible for biased signaling using microsecond atomic level simulations reveals differential allosteric modulation. Finally, comparative analysis using mutual information in the internal coordinates of mutants and wild types helped to quantify the allosteric modulation and long-range cooperativity between binding sites in GPCRs

Dynamics of protein-drug interactions inferred from structural ensembles and physics-based models

The conformational flexibility of target proteins is a major challenge in understanding and modeling protein-drug interactions. A fundamental issue, yet to be clarified, is whether the observed conformational changes are controlled by the protein, or induced by the inhibitor. While the concept of induced fit has been widely adopted for describing the structural changes that accompany ligand binding, there is growing evidence in support of the dominance of proteins' intrinsic dynamics, which has been evolutionarily optimized to accommodate its functional interactions. The wealth of structural data for target proteins in the presence of different ligands now permits us to make a critical assessment of the balance between these two effects in selecting the bound forms. We focused on three widely studied drug targets, HIV-1 reverse transcriptase, p38 MAP kinase, and cyclin-dependent kinase 2. A total of 292 structures determined for these enzymes in the presence of different inhibitors as well as unbound form permitted us to perform an extensive comparative analysis of the conformational space accessed upon ligand binding, and its relation to the intrinsic dynamics prior to ligand binding as predicted by elastic network model analysis. Further, we analyzed NMR ensembles of ubiquitin and calmodulin representing their microseconds range solution dynamics. Our results show that the ligand selects the conformer that best matches its structural and dynamic properties amongst the conformers intrinsically accessible to the protein in the unliganded form. The results suggest that simple but robust rules encoded in the protein structure play a dominant role in pre-defining the mechanisms of ligand binding, which may be advantageously exploited in designing inhibitors. We apply these lessons to the study of MAP kinase phosphatases (MKPs), which are therapeutically relevant but challenging signaling enzymes. Our study provides insights into the interactions and selectivity of MKP inhibitors and shows how an allosteric inhibition mechanism holds for a recently discovered inhibitor of MKP-3. We also provide evidence for the functional significance of the structure-encoded dynamics of rhodopsin and nicotinic acetylcholine receptor, members of two membrane proteins classes serving as targets for more than 40% of all current FDA approved drugs

COMPUTATIONAL ANALYSIS OF G-PROTEIN COUPLED RECEPTOR SCREENING, DIMERIZATION, AND DESENSITIZATION

Mechanistic models of G-protein coupled receptor (GPCR) signaling are used to gain insight into how changes in drug properties affect cellular response. Broadly, this work is divided in to three areas focusing on drug screening, desensitization, and receptor dimerization. First, ordinary differential equation models are used to examine biases in drug screening assays such as those used in drug discovery. It is shown that some screens should be innately biased against detecting inverse agonists and as such may miss pharmaceutically valuable drug leads. However, the results also suggest ways in which the screening assay can be modified to correct this bias. Second, Monte Carlo simulations of protein diffusion and reaction are used to determine the effects of drug properties on GPCR activation and desensitization. For most GPCRs, drugs cause an initial burst of activity (activation) followed by an attenuation of the signal over long times (desensitization). Simulations of this activation and desensitization process show that the mean drug-receptor lifetime can affect desensitization in a way that allows receptor activation and desensitization to be partially decoupled. Third, Monte Carlo simulations of receptor dimerization and diffusion are used to show how dimerization can affect membrane organization. Many membrane bound proteins, including GPCRs, form transient dimers, but the physiological reason for dimerization is not clear. The simulations show that dimerization under diffusion limited conditions can lead to the formation of extended clusters. These clusters, in turn, can alter the receptor internalization rate and the degree of cross-talk among receptors, in agreement with experimental findings. Overall, this work has a variety of implications. Pharmacologically, this work presents a new way of making drug discovery a more rational process by focusing assays toward drugs with desirable efficacies and improved desensitization profiles. Similarly, receptor dimerization could also provide a novel mechanism for affecting drug signaling. For basic biology, the modeling work presented here suggests that dimerization could provide a new way to control protein organization within the cell membrane. Together this work helps us to provide us with a more mechanistic understanding of how cells communicate via GPCRs.http://deepblue.lib.umich.edu/bitstream/2027.42/133962/1/woolf.thesis.pdfDescription of woolf.thesis.pdf : Peter Woolf Thesis Documen

신뢰도가 낮은 구조 환경에서의 단백질 모델 정밀화 방법 개발

학위논문 (박사)-- 서울대학교 대학원 : 화학부 물리화학 전공, 2017. 2. 석차옥.The number of experimentally determined protein structures is increasing exponentially. Based on this abundant structural information, homology modeling is now the most popular method for protein structure prediction. Still however, knowledge of high resolution structures is critical for applications using the protein structure such as drug discovery and protein design. By realizing this, protein structure refinement methods have been developed to improve the structure quality of low resolution experimental structures or model structures. Another realm of protein structure refinement is to predict the protein structure in the environment of interest, such as binding to a specific partner, when only structures resolved in different conformational states or model structures are provided. In this thesis, four modeling methods (GalaxyLoop-PS2, GalaxyRefine2, GalaxyVoyage, and Galaxy7TM) developed in the scope of refining predicted protein structures are introduced. The methods were evolved by either extending the range of structure targeted for refinement or considering the interaction with a particular binding partner. The shared problem of these methods was that the environment of modeling was unreliable due to errors embedded in model structures. Commonly, two approaches were taken to tackle this problem. These were initially searching the conformational space in low resolution and developing a hybrid energy function less sensitive to environmental error. The development and application results of the approaches taken for each modeling method will be addressed in detail.1. Introduction 1 2. Protein loop modeling in unreliable structural environments 4 2.1. Introduction 4 2.2. Methods 5 2.2.1. Development of a new hybrid energy function 5 2.2.2. Initial loop conformation sampling 6 2.2.3. Global optimization using conformational space annealing 7 2.2.4. Generation of test sets with various range of structural error 9 2.3. Results and discussion 10 2.3.1. Energy function for protein loop modeling 10 2.3.2. Environmental error of the test set 14 2.3.3. Loop reconstruction in the crystal structure framework 16 2.3.4. Loop modeling in sidechain perturbed environment 19 2.3.5. Loop modeling in backbone perturbed environment 20 2.3.6. Loop modeling on template-based models 23 2.3.7. Comparison of using hybrid energy, physics-based energy, and knowledge-based energy for loop modeling 26 2.4. Conclusion 29 3. Global refinement of protein model structures 30 3.1. Introduction 30 3.1.1. Extension of the range of structure targeted for refinement 30 3.1.2. Conformational search methods and energy functions for global refinement 32 3.2. Global refinement based on loop modeling and overall relaxation 34 3.2.1. Methods 34 3.2.2. Results and discussion 44 3.3. Global refinement with diverse modeling methods applied on unreliable local regions 63 3.3.1. Methods 63 3.3.2. Results and discussion 68 3.4. Conclusion 82 4. Flexible docking of ligands to G-protein-coupled receptors based on structure refinement 83 4.1. Introduction 83 4.1.1. Ligand docking to model structures 83 4.1.2. Predicting the ligand-bound G-protein-coupled receptor structure 84 4.2. Methods 86 4.2.1. Initial docking of ligand to receptor conformations generated by ANM 86 4.2.2. Energy function for complex structure refinement 87 4.2.3. Complex structure refinement and final model selection 89 4.2.4. Benchmark test set construction 90 4.3. Results and discussion 92 4.3.1. Energy function for complex structure refinement 92 4.3.2. Overall performance of Galaxy7TM 96 4.3.3. The relation between the docking accuracy of Galaxy7TM predictions and the receptor model quality 103 4.4. Conclusion 109 5. Conclusion 110 Bibliography 111 국문초록 121Docto