Efficient estimation of binding free energies between peptides and an MHC class II molecule using coarse‐grained molecular dynamics simulations with a weighted histogram analysis method

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137767/1/jcc24845.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137767/2/jcc24845_am.pd

Integrating Experiment and Theory to Understand TCR-pMHC Dynamics

The conformational dynamism of proteins is well established. Rather than having a single structure, proteins are more accurately described as a conformational ensemble that exists across a rugged energy landscape, where different conformational sub-states interconvert. The interaction between αβ T cell receptors (TCR) and cognate peptide-MHC (pMHC) is no exception, and is a dynamic process that involves substantial conformational change. This review focuses on technological advances that have begun to establish the role of conformational dynamics and dynamic allostery in TCR recognition of the pMHC and the early stages of signaling. We discuss how the marriage of molecular dynamics (MD) simulations with experimental techniques provides us with new ways to dissect and interpret the process of TCR ligation. Notably, application of simulation techniques lags behind other fields, but is predicted to make substantial contributions. Finally, we highlight integrated approaches that are being used to shed light on some of the key outstanding questions in the early events leading to TCR signaling

Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders

The prediction of peptide binders to Major Histocompatibility Complex (MHC) class II receptors is of great interest to study autoimmune diseases and for vaccine development. Most approaches predict the affinities using sequence-based models trained on experimental data and multiple alignments from known peptide substrates. However, detecting activity differences caused by single-point mutations is a challenging task. In this work, we used interactions calculated from simulations to build scoring matrices for quickly estimating binding differences by single-point mutations. We modelled a set of 837 peptides bound to an MHC class II allele, and optimized the sampling of the conformations using the Rosetta backrub method by comparing the results to molecular dynamics simulations. From the dynamic trajectories of each complex, we averaged and compared structural observables for each amino acid at each position of the 9°mer peptide core region. With this information, we generated the scoring-matrices to predict the sign of the binding differences. We then compared the performance of the best scoring-matrix to different computational methodologies that range in computational costs. Overall, the prediction of the activity differences caused by single mutated peptides was lower than 60% for all the methods. However, the developed scoring-matrix in combination with existing methods reports an increase in the performance, up to 86% with a scoring method that uses molecular dynamics

Rapid microsphere‐assisted peptide screening (MAPS) of promiscuous MHCII‐binding peptides in Zika virus envelope protein

Despite promising developments in computational tools, peptide‐class II MHC (MHCII) binding predictors continue to lag behind their peptide‐class I MHC counterparts. Consequently, peptide–MHCII binding is often evaluated experimentally using competitive binding assays, which tend to sacrifice throughput for quantitative binding detail. Here, we developed a high‐throughput semiquantitative peptide–MHCII screening strategy termed microsphere‐assisted peptide screening (MAPS) that aims to balance the accuracy of competitive binding assays with the throughput of computational tools. Using MAPS, we screened a peptide library from Zika virus envelope (E) protein for binding to four common MHCII alleles (DR1, DR4, DR7, DR15). Interestingly, MAPS revealed a significant overlap between peptides that promiscuously bind multiple MHCII alleles and antibody neutralization sites. This overlap was also observed for rotavirus outer capsid glycoprotein VP7, suggesting a deeper relationship between B cell and CD4+ T cell specificity which can facilitate the design of broadly protective vaccines to Zika and other viruses.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154342/1/aic16697.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154342/2/aic16697_am.pd

IN SILICO STUDY OF PROTEIN PROTEIN INTERACTION STABILIZATION AND MECHANICAL FORCE APPLICATION ON BIOMOLECULES

Targeting protein-protein interactions is a challenging task in drug discovery process. Despite the challenges, several studies have provided evidences for the development of small molecules modulating protein-protein interactions. In Part I, it is demonstrated that how a small molecule can induce the formation of an otherwise unstable protein-protein complex. A study of the stabilization of a FKBP12-FRB complex by a small molecule rapamycin is presented. The stability of the complex is analyzed and its interactions are characterized at the atomic level by performing free energy calculations and computational alanine scanning. It is shown that rapamycin stabilizes the complex by acting as a bridge between the two proteins; and the complex is stable only in the presence of rapamycin. The reported results and the good performance of standard molecular modeling techniques in describing the model system can be interesting not only in the design and development of improved molecules acting as FKBP12\u2013FRB protein interaction stabilizers, but also in the somehow neglected study of protein-protein interactions stabilizers in general. In Part II, studies regarding computational modeling of the application of mechanical force to biomolecules is presented. This part is further divided into two chapters since the investigations have been performed on two biological systems. In the first chapter of Part II (chapter 6), it is described that how the osmolyte molecules affect the mechanical unfolding of a peptide. The mechanical unfolding of peptide has been performed by using Steered Molecular Dynamics. In this study, the effect of four different osmolytes on the free energy difference between the folded and the denatured state have been calculated. The observed trend mirrors the expected behavior of the studied osmolytes and unfolding pathways analysis allows an insight into the mechanism of action of osmolytes. After the successful application of Steered molecular dynamics technique on the \u3b2-hairpin peptide, the same is applied on tubulin heterodimers for the in-depth study of the lateral and longitudinal interactions which are responsible for the stability and dynamics of the microtubules. In the other chapter of Part II (chapter 7), these interactions are studied with the help of mechanical dissociation of the tubulin heterodimers. These studies have allowed the identification of the critical interactions responsible for the binding of tubulin heterodimers laterally as well as longitudinally. The observations obtained could be important for the design of compounds that target these interactions and acts as microtubule inhibitors or stabilizers

Understanding the "rules of engagement" for membrane protein folding : chemical biology and computational approaches for determination of structure and dynamics

Approximately one third of genes in the human genome (1) encode transmembrane (TM) proteins and form more than half of all drug targets (2). However, our understanding of how these proteins fold into their functional form, as well as how they may misfold into a disease-associated form, remains a difficult area of study. By observing the effects of single point mutations in the context of a native sequence, in addition to adding and mutating interhelical interaction motifs on a low complexity sequence background, we aim to elicit ‘rules’ of TM protein domain association. For the single point mutation in the context of a native sequence, the TM domain of the sequence Neu, along with its oncogenic substitution V664E form Neu*, were selected. Using molecular dynamics (MD) a united atom model of each dimer in a model bilayer system was subjected to umbrella sampling along an interhelical reaction coordinate to yield a free energy profile of self-association. The lipid order, bilayer thickness, and peptide tilt angle were calculated from trajectories taken from three points along the reaction coordinate. Helical composition, solvent accessible surface area, and hydrogen bond analysis (for the V664E substitution) were performed at the free energy minimum. Low complexity sequences of polyleucine and polyleucine-alanine heptad repeat sequences, with and without interaction motifs similar to those present in the Neu model, were ligated into PBLM100 plasmids. Transformed E. coli cells were subjected to semi-quantitative homo-interaction analysis using the GALLEX assay. The same TM sequences were modelled using a coarse grained (CG) forcefield. Umbrella sampling along an interhelical reaction coordinate was performed to yield a free energy profile of self-association. Single-linkage cluster analysis of peptides was performed at the global free energy minimum. A representative structure from each set was compared to an averaged structure from the clusters of an atomistic conformational search. The results presented in this study, could contribute to what in theory would be a large database of motif-driven rules for TM helical domain oligomerisation. This may encourage further investigation into TM protein design for novel application

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

Molecular Dynamics simulations of amyloidogenic proteins. Unfolding, misfolding and aggregation.

Proteins are the main bulding blocks of biological systems. Their structure and function have been extensively studied so far both by experiments (Nuclear Magnetic Resonance, X-ray crystallography, Mass Spectrometry, etc.) and modeling strategies (Molecular Dynamics and Monte Carlo simulations, Density Functional Theory. etc.). In vivo in general and in solution in particular, they mostly adopt different and unique secondary and tertiary configurations, owing to their conformational freedom. The route and mechanism by which a specific shape is formed, i.e. the folding, which is not reversible in many cases, is not fully understood for several protein models, nothwithstanding the fulgurant advances achieved in experimental and in silico techniques in the last decades. Under specific conditions (pH, temperature, concentration, etc.), such three-dimensional arrangement as well as the intra/inter-chains interactions can be lost, and species such as disordered or fibrilar aggregates involved in several known human pathologies may develop. In this thesis we probe the atomistic scale conformational dynamics of two amyloidogenic proteins, transthyretin and \u3b22-microglobulin, using molecular dynamics simulations. We aim at understanding the major factors driving the misfolding and/or (un)folding of the latter specified proteins, which play a precursor and prominent role in neurodegrative deseases. To this end the dynamics and dissociation of wild-type and mutant transthyretin is simulated. In particular the behaviour of a triple mutant (designed by Prof. R. Berni and coworkers to be monomeric) is studied. It comes out that the mutation considerably shifts the tetramer-folded monomer equilibrium towards the monomer, making this triple mutant a useful tool for structural and dynamical studies. The interaction of \u3b22-microglobulin with hydrophobic surfaces is studied by molecular dynamics and the thermodynamics of the process is addressed using end-point free energy calculations. The results rationalize experimental observation reported in the literature. Protein conformational dynamics and thermodynamics are currently experimentally probed by the backbone amide hydrogen exchange experiment (HDX). The Bluu-Tramp experiment developed by prof. Esposito and coworkers allows the measurement of free energy, enthalpy and entropy of exchange in a single experiment. A proper comparison between experimental and simulation data require modeling of the process at atomic detail. Hence, we analyze also this aspect and try to relate the amide hydrogen protection observed in NMR experiments to various microscopic properties of the protein structure computed in the simulations. Using free energy calculations we aim at reproducing also the temperature dependence of the process. Given the predominant role of protein association in most biological functions, we introduce a modeling approach to estimate the entropy loss upon complex formation, a contribution which is almost always neglected in many free energy calculation methodologies due to the high dimensionality of the degrees of freedom, and adequate theoretical methods. The approach is applied to the case proteins considered in this thesis and an exact and approximate estimation of the full rotational-translational entropy are obtained in the context of nearest neighbor-based entropy formulation. Overall, this thesis explores various aspects favouring the formation of misfolded and/or (un)folded protein species, ranging from dissociation of an homotetramer of transthyretin engineered in silico, through the interaction of \u3b22-microglobulin with an hydrophobic surface model, to the backbone amide hydrogen exchange pattern of protection of the latter. Lastly and not the least, the thesis presents a computational methodology to address the roto-translational entropy loss upon complex formation of biomolecules

Molecular Dynamics for Synthetic Biology

Synthetic biology is the field concerned with the design, engineering, and construction of organisms and biomolecules. Biomolecules such as proteins are nature's nano-bots, and provide both a shortcut to the construction of nano-scale tools and insight into the design of abiotic nanotechnology. A fundamental technique in protein engineering is protein fusion, the concatenation of two proteins so that they form domains of a new protein. The resulting fusion protein generally retains both functions, especially when a linker sequence is introduced between the two domains to allow them to fold independently. Fusion proteins can have features absent from all of their components; for example, FRET biosensors are fusion proteins of two fluorescent proteins with a binding domain. When the binding domain forms a complex with a ligand, its dynamics translate the concentration of the ligand to the ratio of fluorescence intensities via FRET. Despite these successes, protein engineering remains laborious and expensive. Computer modelling has the potential to improve the situation by enabling some design work to occur virtually. Synthetic biologists commonly use fast, heuristic structure prediction tools like ROSETTA, I-TASSER and FoldX, despite their inaccuracy. By contrast, molecular dynamics with modern force fields has proven itself accurate, but sampling sufficiently to solve problems accurately and quickly enough to be relevant to experimenters remains challenging. In this thesis, I introduce molecular dynamics to a structural biology audience, and discuss the challenges and theory behind the technique. With this knowledge, I introduce synthetic biology through a review of fluorescent sensors. I then develop a simple computational tool, Rangefinder, for the design of one variety of these sensors, and demonstrate its ability to predict sensor performance experimentally. I demonstrate the importance of the choice of linker with yet another sensor whose performance depends critically thereon. In chapter 6, I investigate the structure of a conserved, repeating linker sequence connecting two domains of the malaria circumsporozoite protein. Finally, I develop a multi-scale enhanced sampling molecular dynamics approach to predicting the structure and dynamics of fusion proteins. It is my hope that this work contributes to the structural biology community's understanding of molecular dynamics and inspires new techniques developed for protein engineering