Understanding and Exploiting Protein Allostery and Dynamics Using Molecular Simulations

Protein conformational landscapes contain much of the functionally relevant information that is useful for understanding biological processes at the chemical scale. Understanding and mapping out these conformational landscapescan provide valuable insight into protein behaviors and biological phenomena, and has relevance to the process of therapeutic design. While structural biology methods have been transformative in studying protein dynamics, they are limited by technicallimitations and have inherent resolution limits. Molecular dynamics (MD) simulations are a powerful tool for exploring conformational landscapes, and provide atomic-scale information that is useful in understanding protein behaviors. With recent advances in generating datasets of large timescale simulations (using Folding@home) and powerful methods to interpret conformational landscapes such as Markov State Models (MSMs), it is now possible to study complex biological phenomena and long-timescale processes. However, inferring communication between residues across long distances, referred to as allosteric communication, remains a challenge. Allostery is a ubiquitious biological phenomena by which two distant regions of a protein are coupled to one anotherover large distances. Allosteric coupling is the mechanism through which events in one region (such as ligand binding) alter the conformation or dynamics of another region (ie. large conformational domain motions). For example, allostery plays a critical role in cellular signaling, such as in the transfer of a signal from outside the cell to cytosolic proteins for generating a cellular response. While many methods have made tremendous progress in inferring and measuring allosteric communication usingstructures or molecular simulations, they rely on a structural view of allostery and do not account for the role of conformational entropy. Furthermore, it remains a challenge to interpret allosteric coupling in large, complex biomolecules relevant to physiology and disease. In this thesis, I present a method to measure the Correlation of All Rotameric and Dynamical States (CARDS) whichis used to construct and interpret allosteric networks in biological systems. CARDS allows us to infer allostery both via concerted changes in protein structure and in correlated changes in conformational entropy (dynamic allostery). CARDS does so by parsing trajectories into dynamical states which reflect whether a residue is locally ordered (ie. stable in a single rotameric basin) or disordered (ie. rapidly hopping between rotamers). Here I explain the CARDS methodology (chapter 2) and demonstrate applications to a variety of disease-relevantsystems. In particular, I apply CARDS and other sophisticated computational methods to understand the process of G protein activation (chapter 3), a protein whose mutations are linked to cancers such as uveal melanoma. I further demonstrate the utility of CARDS in the study a potentially druggable pocket in the ebolavirus protein VP35 (chapter 4). The analyses and models constructed in this work are supported by experimental testing. Lastly, I demonstrate how integrating MD with experiments, sometimes with the help of citizen-scientists around the world, can provide unique insight into biological systems and identify potentially useful targets. In particular, I highlight our recent effort converting Folding@home into an exascale computer platform to hunt for potentially druggable pockets in the proteome of SARS-CoV-2 (chapter 7) (the cause of the COVID19 pandemic)

Visualizing cell surface interactions using cryogenic electron microscopy

The study of the three-dimensional structures of biological macromolecules has given us significant insight into life and its mechanisms. Understanding these structures in their native contexts, a challenging but important goal, came closer to reality with the development of electron microscopy. After many years of technological development, we are now starting to understand previously intractable biological phenomena at an unprecedented resolution. One such phenomenon is how neighboring cells interact, both to communicate and send signals, and to adhere and form complex tissue structures. While the molecules that mediate such processes have long been studied in isolation, electron microscopy allows us to examine them in a more native biophysical environment; as hydrated, dynamic molecules tethered to opposed cellular membranes.Imaging unadulterated biological material using electron microscopy requires that the sample be embedded in a thin layer of vitreous ice to immobilize the molecules and protect them from the vacuum of the microscope, and thus is generally referred to as cryogenic electron microscopy (cryo-EM). Samples can be imaged using two common cryo-EM modalities: single particle analysis (SPA), where many two-dimensional projection images of molecules in solution are collected, and cryo-electron tomography (cryo-ET), where the sample is tilted as it is imaged at multiple angles to reconstruct a three-dimensional volume. In this work, I will describe how I have used both SPA and cryo-ET to understand cell surface interactions involving a variety of proteins. The first chapter will look at the cell surface molecules known as the Toll receptors, a family of molecules found in Drosophila melanogaster, with orthologs in mammals known as the Toll-like receptors (TLRs). I will focus on their role in the development of the Drosophila embryo during germ band extension, a kind of convergent extension that is a conserved process through all metazoans. Biophysical assays of the three implicated Toll receptors, Toll-2, -6, and -8, revealed both homophilic and heterophilic interactions. SPA was used to determine the structure of monomeric Toll-2 which closely resembles the overall fold of Toll, whose structure was previously solved by x-ray crystallography. Surface plasmon resonance (SPR) spectroscopy and analytical ultracentrifugation (AUC) showed Toll-6 is a dimer in solution, which I visualized using cryo-EM. The Toll-6 homodimer is a novel dimer interface for Tolls and TLRs, where molecules on the same cell surface have been shown to dimerize in the presence of a wide variety of ligands. In contrast, the Toll-6 dimer is formed in the absence of any ligand and exists in an antiparallel arrangement that could be formed by molecules on opposing cell surfaces. Together, these results provide a biochemical basis for germ band extension which may be further explored through the study of structure-based mutations. While cryo-EM SPA is a powerful tool, cryo-ET allows one to reconstruct three dimensional volumes of highly heterogeneous samples, such as the interior of cells, where molecules of interest may not exist in enough copies to facilitate averaging. This technique, where the sample is imaged multiple times as it is tilted to obtain three-dimensional information of a region of interest, was used to study cell adhesion of a different type: that mediated by the classical cadherins. These calcium-dependent adhesion molecules cluster into adherens junctions, spot-like protein densities found in a wide variety of tissues. In the second chapter, these junctions are recapitulated between synthetic liposome membranes by tethering the adherent cadherin molecules to chemically functionalized lipids. They are then imaged using cryo-ET to reveal higher-order structural details. First, this method is applied to the clustered protocadherins, a family of cadherins that mediate neuronal self-avoidance in mammals. Cryo-ET in combination with x-ray crystallography revealed that clustered protocadherins form extended one-dimensional zippers between membranes, which are a combination of strictly homophilic trans interactions coupled with promiscuous cis interactions. Neurons express unique subsets of the ~50-60 possible isoforms, and when two neuronal processes express identical subsets, which happens only when those processes are a part of the same cell, these linear chains grow and initiate a repulsive signal. If the subsets are different, the chains terminate and no repulsive signal is generated. The same technique has been used previously to study the type I classical cadherins, perhaps the most well-studied members of the cadherin superfamily. In the second half of this chapter, we extend our analysis to include the type II classical cadherins, which possess more complex expression patterns and binding specificities. Cryo-ET of type II cadherin ectodomains tethered to synthetic liposomes revealed that several representative members of this family form only moderately ordered arrays between liposomes, a finding in agreement with their role in cell sorting and migration. However, VE-cadherin, an outlier type II expressed in vascular endothelial cells where it withstands blood pressure, forms extraordinarily ordered junctions. Subtomogram averaging reveals the regularity of this two-dimensional array. In the final chapter, I describe my work on a membrane surface molecule of a different kind, one not involved in cell adhesion but viral infection. The global COVID-19 pandemic gave me the opportunity to contribute to our understanding of SARS-CoV-2 by studying the structure of neutralizing antibodies bound to the viral spike protein, perhaps the most infamous membrane surface protein. The first subchapter describes the initial isolation, neutralization, and structural analysis of antibodies isolated from convalescent COVID-19 patients. This work revealed that patients with severe COVID-19 produce potently neutralizing antibodies that target two spike protein domains: the receptor binding domain (RBD) and the N-terminal domain (NTD). RBD-directed antibodies occlude binding to ACE2, the human receptor that mediates viral fusion, but the neutralization mechanism of NTD-directed antibodies is unknown. The following two subchapters are more detailed structural studies of two specific types of antibodies. The first looks at a class of RBD-directed antibodies derived from the VH1-2 gene, which are some of the most potent and common antibodies against SARS-CoV-2. The heavy chains of these antibodies recognize almost identical epitopes, but the antibodies employ a modular approach to recognize the RBD in either of its possible conformations. The second class are antibodies that target the NTD, which our work revealed all bind to a single antigenic supersite. The final subchapter focuses on emerging SARS-CoV-2 variants and includes the structures of two antibodies that are still capable of neutralizing these new variants. They are also infrequent in the human antibody response to SARS-CoV-2, meaning they put little selective pressure on the virus to produce escape mutations, making them good candidates for monoclonal antibody therapies. Though Drosophila embryogenesis, adherens junction formation, and SARS-CoV-2 neutralization are seemingly unrelated systems, they are united by the incredible flexibility of cryo-EM to visualize biological molecules in more native environments. Whether it is the ability to study multiprotein complexes or assemblies formed between membranes, cryo-EM is a powerful technique that promises to help bridge the divide between structure and function

2021 GREAT Day Program

SUNY Geneseo’s Fifteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1015/thumbnail.jp

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

Topological Deep Learning: Going Beyond Graph Data

Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes, cell complexes, and hypergraphs, which generalize many domains encountered in scientific computations. In this paper, we present a unifying deep learning framework built upon a richer data structure that includes widely adopted topological domains. Specifically, we first introduce combinatorial complexes, a novel type of topological domain. Combinatorial complexes can be seen as generalizations of graphs that maintain certain desirable properties. Similar to hypergraphs, combinatorial complexes impose no constraints on the set of relations. In addition, combinatorial complexes permit the construction of hierarchical higher-order relations, analogous to those found in simplicial and cell complexes. Thus, combinatorial complexes generalize and combine useful traits of both hypergraphs and cell complexes, which have emerged as two promising abstractions that facilitate the generalization of graph neural networks to topological spaces. Second, building upon combinatorial complexes and their rich combinatorial and algebraic structure, we develop a general class of message-passing combinatorial complex neural networks (CCNNs), focusing primarily on attention-based CCNNs. We characterize permutation and orientation equivariances of CCNNs, and discuss pooling and unpooling operations within CCNNs in detail. Third, we evaluate the performance of CCNNs on tasks related to mesh shape analysis and graph learning. Our experiments demonstrate that CCNNs have competitive performance as compared to state-of-the-art deep learning models specifically tailored to the same tasks. Our findings demonstrate the advantages of incorporating higher-order relations into deep learning models in different applications

Advances in Molecular Simulation

Molecular simulations are commonly used in physics, chemistry, biology, material science, engineering, and even medicine. This book provides a wide range of molecular simulation methods and their applications in various fields. It reflects the power of molecular simulation as an effective research tool. We hope that the presented results can provide an impetus for further fruitful studies

XSEDE: The Extreme Science and Engineering Discovery Environment (OAC 15-48562) Interim Project Report 13: Report Year 5, Reporting Period 2 August 1, 2020 – October 31, 2020

This is the Interim Project Report 13 (IPR13) for the NSF XSEDE project. It includes Key Performance Indicator data and project highlights for Reporting Year 5, Report Period 2 (August 1-October 31, 2020).NSF OAC 15-48562Ope

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Modelling the interplay between human behaviour and the spread of infectious diseases: From toy models to quantitative approaches

Prevenir la propagació de malalties infeccioses és un dels reptes més grans de la humanitat. Moltes malalties es transmeten per contacte, per la qual cosa la xarxa d'interaccions humanes actua com a substrat per a la propagació. Per aquest motiu, els models epidèmics sempre inclouen, ja sigui implícita o explícitament, una descripció de com els éssers humans interactuen entre ells. Malgrat això, actualment no es disposa d’una teoria general de la interacció entre el comportament humà i la propagació d'agents. L’objectiu d’aquesta tesi és contribuir a la descripció matemàtica del comportament humà en el context de les malalties infeccioses, treballant tant amb models quantitatius com qualitatius. En el primer capítol es desenvolupen dos models qualitatius per entendre com l’adopció de mesures profilàctiques de manera dinàmica basada en el risc pot causar cicles epidèmics. En el segon capítol, considerem aspectes estàtics específics del comportament humà -homofília i patrons de contacte heterogenis- i n'analitzem les implicacions en el control d'epidèmies. En contrast amb el què es creia anteriorment, demostrem que l'homofília en l'adopció d’eines profilàctiques no sempre resulta perjudicial. A més a més, qüestionem el paradigma actual de les estratègies d'immunització basades en el risc. L'últim capítol d'aquesta tesi se centra en enfocs quantitatius per modelitzar la propagació del SARS-CoV-2, en particular la primera onada i la propagació de la variant Delta. A més dels avenços metodològics, mostrem com l’adaptació voluntària del comportament va determinar el curs de l’epidèmia més enllà de les intervencions no farmacèutiques. En conjunt, aquesta tesi revela una nova fenomenologia, afegeix proves empíriques addicionals i proporciona noves eines per analitzar com evolucionen el comportament humà i les epidèmies. La combinació d'enfocaments quantitatius i qualitatius també proporciona una via per analitzar i interpretar l’enorme quantitat de dades recopilades durant la pandèmia de SARS-CoV-2.Prevenir la propagación de enfermedades infecciosas es uno de los mayores retos de la humanidad. Muchas enfermedades se transmiten por contacto, por lo que la red de interacciones humanas actúa como sustrato para su propagación. Por esta razón, los modelos epidémicos siempre incluyen una descripción de cómo interactúan los seres humanos entre ellos. Sin embargo, actualmente no existe una teoría general de la interacción entre el comportamiento humano y la propagación de agentes. El objetivo de esta tesis es contribuir a la descripción matemática del comportamiento humano en el contexto de las enfermedades infecciosas, trabajando tanto con modelos cuantitativos como cualitativos. El primer capítulo desarrolla dos modelos cualitativos para esbozar cómo la profilaxis dinámica basada en el riesgo puede sostener ciclos epidémicos. En el segundo capítulo, consideramos aspectos estáticos específicos del comportamiento humano -homofilia y patrones de contacto heterogéneos- y analizamos sus implicaciones en el control de epidemias. En contraste con resultados anteriores, demostramos que la homofilia en la adopción de herramientas profilácticas no siempre es perjudicial. Además, cuestionamos el paradigma actual de las estrategias de inmunización basadas en el riesgo. El último capítulo de esta tesis se centra en enfoques cuantitativos para modelizar la propagación del SARS-CoV-2, en particular, la primera oleada y la propagación de la variante Delta. Además de los avances metodológicos, mostramos cómo la adaptación voluntaria del comportamiento fue capaz de determinar el curso de la epidemia más allá de las intervenciones no farmacéuticas. En conjunto, esta tesis desvela una nueva fenomenología, añade pruebas empíricas adicionales y proporciona nuevas herramientas para analizar cómo evolucionan el comportamiento humano y las epidemias. La combinación de enfoques cuantitativos y cualitativos proporciona una vía muy útil para analizar e interpretar la gran cantidad de datos recopilados durante la pandemia de SARS-CoV-2. Preventing the spread of infectious diseases is one of the greatest challenges of humanity's past, present, and foreseeable future. Many infectious diseases are transmitted upon contact, and hence the complex web of human interactions acts as a substrate for their propagation. For this reason, epidemic models always comprise, either explicitly or implicitly, a description of how humans interact. However, the quest for a general theory of the interplay between human behaviour and the spread of pathogens is far from complete. The aim of this thesis is to contribute to the mathematical description of human behaviour in the context of infectious diseases, working with both quantitative and qualitative models. The first chapter develops two qualitative toy models to outline how dynamical risk-based prophylaxis can sustain epidemic cycles. In the second chapter, we consider specific static aspects of human behaviour -- homophily and heterogeneous contact patterns -- and analyse their implications on epidemic control. In contrast to previous belief, we show that homophily in the adoption of many prophylactic tools is not always detrimental. Furthermore, we question the current paradigm of risk-based immunisation strategies and show that targeting hubs is only optimal for protection with high efficacy. The last chapter of this thesis focuses on quantitative approaches to model the spread of SARS-CoV-2, in particular, the first wave and the spread of the Delta variant. Besides the methodological advances, we add evidence of how voluntary behavioural adaptation shaped the course of the epidemic beyond non-pharmaceutical interventions. Overall, this thesis unveils new phenomenology, adds additional empirical evidence, and provides new tools to analyse how human behaviour and epidemics coevolve. The flexible blend of quantitative and qualitative approaches may also provide a pathway to analyse and interpret the vast amount of data currently collected during the SARS-CoV-2 pandemic

Dynamic hypoxic pre-conditioning of cells seeded in tissue-engineered scaffold to improve neovascularisation

Introduction: Tissue engineering (TE) is the potential solution to the global shortage of tissue and organs. However, the lack of adequate angiogenesis to TE scaffolds during the initial stages of implantation has hindered its success in vivo. Mesenchymal stem cells (MSC) have the most established track record for translational regenerative therapy and have been widely used in combination with TE scaffolds. Hypoxia is one of the main potentiators for upregulating angiogenic factors in MSC. However, fine-tuning their cellular function and behaviour is still not fully understood. This study aims to help increase the understanding of this process by determining the effects of in vitro hypoxic conditioning on enhancement of angiogenesis of MSC for the purpose of pre-clinical translational for TE application. Methods: The angiogenic potential of 3 different tissue sources (bone marrow, umbilical cord and adipose) MSC were initially determined for downstream pre-clinical application. We established the appropriate regime for in vitro dynamic hypoxia conditions in 2D and 3D hydrogel to enhance MSC angiogenic pathway using real-time continuous oxygen sensors and angiogenic cytokine profiling. Cell metabolism and proliferation effects were also evaluated using intravital Realtime-glo, D-luciferin (on transduced MSC) and microscopic Live-Dead stain techniques. We optimised seeding of cells on the tissue engineered dermal (INTEGRA®) for in vivo translational purpose and used targeted in vitro and ex vivo angiogenesis assays, which helped to determine aspects of the MSC conditioned media on endothelial migration, proliferation, morphogenesis and matrix degradation. Finally, the functional reproducibility of the in vitro angiogenic response was assessed using in vivo angiogenesis CAM assay and murine diabetic wound healing models. Results: Adipose derived MSC (adMSC) were found to have the most angiogenic potential in response to hypoxic conditioning. Dynamic hypoxia (DH) regime of changing oxygen levels from 21% to 1% when transitioning from T-flask subculture to multiwell plate seeding was most effective at eliciting pro-angiogenic response from adMSC for both in vitro 2D and 3D models compared to controls using static normoxia (21% oxygen) and static hypoxia (1%). Low seeding density of adMSC was found to be the most appropriate to ensure optimised cell adherence and survival post-seeding on TE dermal scaffold (INTEGRA®). It also minimised on localised hypoxic gradient induced oxidative stress by the seeded cells when compared to high seeding density techniques found on non-invasive oxygen monitoring. Conditioned media from DH seeded adMSC was shown to have enhanced angiogenic proteomic profile compared to the controls. In vitro angiogenesis assays showed better human endothelial cell migration and morphogenesis in scratch assay and tubular formation assay compared to controls. Preliminary ex vivo organ assay results using novel human umbilical arterial rings showed better endothelial out-sprouting and migration through embedded matrix compared to controls. Results from in vivo transplantation of adMSC seeded INTEGRA® scaffold showed a mixed response in the CAM assay, highlighting an unaccounted scaffold effect from INTEGRA® from the host. Histological sections showed increased vascular and host tissue infiltration into the scaffold. When evaluating the functional angiogenesis in murine wound healing models, although DH adMSC seeded scaffolds showed non-statistically significant increased rate of wound closure, there was significantly greater vessel density within the scaffold on histological evaluation in this group compared to controls. Conclusion: The results provide a better comprehension of how cells behave in 2D and 3D environments when cultured in dynamically changing oxygen environments. The study addresses important issues, such as the effects of chronic hypoxia on MSC, and how dynamic hypoxia can enhance angiogenic signalling. It also offers a crucial understanding of the in vitro oxygen culture environments for future research applications. Further insight into cell-scaffold interaction during in vivo transplantation was also established. The importance of having an appropriate in vivo model to determine if such in vitro angiogenic enhancement would translate to functionally improving neoangiogenesis and subsequent tissue regeneration in vivo was also highlighted in this study. Improving and advancing research into optimising and evaluating the in vitro environment for clinical application will undoubtedly have a huge impact on the future of cell therapy for regenerative medicine purposes