Molecular Basis of Bcl-XL-p53 Interaction: Insights from Molecular Dynamics Simulations

Bcl-XL, an antiapoptotic Bcl-2 family protein, plays a central role in the regulation of the apoptotic pathway. Heterodimerization of the antiapoptotic Bcl-2 family proteins with the proapoptotic family members such as Bad, Bak, Bim and Bid is a crucial step in the apoptotic regulation. In addition to these conventional binding partners, recent evidences reveal that the Bcl-2 family proteins also interact with noncanonical binding partners such as p53. Our previous NMR studies showed that Bcl-XL: BH3 peptide and Bcl-XL: SN15 peptide (a peptide derived from residues S15-N29 of p53) complex structures share similar modes of bindings. To further elucidate the molecular basis of the interactions, here we have employed molecular dynamics simulations coupled with MM/PBSA approach. Bcl-XL and other Bcl-2 family proteins have 4 hydrophobic pockets (p1–p4), which are occupied by four systematically spaced hydrophobic residues (h1–h4) of the proapoptotic Bad and Bak BH3 peptides. We observed that three conserved hydrophobic residues (F19, W23 and L26) of p53 (SN15) peptide anchor into three hydrophobic pockets (p2–p4) of Bcl-XL in a similar manner as BH3 peptide. Our results provide insights into the novel molecular recognition by Bcl-XL with p53

Multi-scale Simulations of Dynamic Protein Structures and Interactions

Intrinsically disordered proteins (IDPs) are functional proteins that lack stable tertiary structures in the unbound state. They frequently remain dynamic even within specific complexes and assemblies. IDPs are major components of cellular regulatory networks and have been associated with cancers, diabetes, neurodegenerative diseases, and other human diseases. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for mechanistic understanding of IDPs in biology, diseases, and therapeutics. However, accurate simulation of the heterogeneous ensembles and dynamic interactions of IDPs is extremely challenging because of both the prohibitive computational cost and demanding force field accuracy. In this dissertation, we developed a set of enhanced sampling and multi-scale simulation methods to overcome these limitations, and successfully applied them to study the structure, interaction and phase separation of IDPs. We have first applied the state-of-the-art explicit solvent atomistic simulations to study the inhibitory mechanism of the disordered N-terminal domain of Staphylococcal peroxidase inhibitor (SPIN). We performed high-temperature simulations to study the coupled binding and folding process during SPIN inhibition of the host myeloperoxidase (MPO) enzyme. The results showed that differences in dynamics may provide a physical basis of the ability of different SPIN homologs to inhibit innate immunity. Recognizing the need for enhanced sampling methods for IDP simulation, we have developed a new replica-exchange with solute tempering (REST) protocol to achieve more efficient explicit solvent sampling of disordered protein ensembles. We proposed that the scaling of protein-water interactions in REST is a free parameter that could be optimized to better control how the protein conformational properties (e.g., chain expansion) at different effective temperatures and achieve more effective sampling. Specifically, we developed a REST3 protocol that rebalances the protein-protein and protein-water interactions and eliminates the unanticipated chain collapse at high temperature conditions in the previous REST2 protocol. Application to model IDPs demonstrated that REST3 prevented the conformational segregation during exchanges, leading to an effective temperature random walk across all conditions and accelerating the simulation of the protein conformational space. Even with enhanced sampling, accurate description of disordered conformations at atomistic level remains extremely challenging for complex IDPs. Alternatively, coarse-grained simulations can provide an effective strategy for overcoming the length- and time-scale limitations. Here, we refined a hybrid-resolution coarse-grained model (HyRes) for accurate simulation of disordered protein ensembles and dynamic protein interactions. HyRes contains atomistic backbone and coarse-grained sidechain beads, to provide semi-quantitative description of residual secondary structures and long-range interactions. Specifically, we introduced a surface area-based implicit solvation energy term, and iteratively re-optimized the effective interaction strength potentials. The new model, referred as HyRes II, provides near quantitative descriptions of IDP long-range non-specific interactions and secondary structures, at a level comparable to the latest atomistic protein force fields. Applied to the disordered N-terminal transactivation domain (TAD) of tumor suppressor p53, HyRes II faithfully recapitulates various nontrivial structural properties to a level of accuracy that is comparable to a99SB-disp, one of the best atomistic protein force fields. Moreover, we demonstrate HyRes II’s efficacy in accurately simulating the dynamic interaction between TAD and the DNA-binding domain of p53, generating structural ensembles that align closely with existing NMR data. Encouraged by successes of HyRes II for probing dynamic interactions of IDPs, we further investigated its suitability for simulating IDP-mediated phase separation, which underlies the formation of biomolecular condensates and has attracted intensive interests. Compared to the popular single-bead models, HyRes has the potential to describe backbone-mediated interactions and capture the role of residual structures in phase separation. Reimplemented on GPU, our simulations showed that HyRes is efficient enough to directly simulate the spontaneous phase separation of IDPs and at the time balanced enough to capture the effects of mutational and structural perturbations. For peptide GY-23, HyRes simulations reveal increased ��-structures in condensates, which are consistent with experimental observations. For the conserved region (CR) of TDP-43, HyRes simulations successfully recapitulate the apparent correlation between helical propensities and condensate stability. In depth analysis, however, revealed that residual helices did not directly mediate interpeptide interactions to stabilize the condensed phase. Instead, it is the balance between backbone and sidechain-mediated interactions, as modulated by residual structures, that actually determines phase separation propensity. Finally, we have applied the HyRes II model to study the dynamic interaction of West Nile virus (WNV) NS2B/NS3 proteases with the ClyA protein nanopore. Nanopore tweezers provide a powerful approach for label-free detection of protein dynamics at the single-molecule level, by capturing the protein analyte in the lumen of the nanopore. From the steered-MD and standard MD simulations, we discovered that the protease could bind dynamically to a middle region of the ClyA nanopore, mediated mainly by electrostatically interactions. In particular, we identified a key Glu residue within the ClyA lumen, mutation of which to Ala or Lys could further stabilize the protease/nanopore interaction. This led to the design a modified ClyA nanopore tweezer that can stably capture the protease and resolve the dynamics between NS2B/NS3 open and closed conformations

Intrinsically Disordered Proteins and Chronic Diseases

This book is an embodiment of a series of articles that were published as part of a Special Issue of Biomolecules. It is dedicated to exploring the role of intrinsically disordered proteins (IDPs) in various chronic diseases. The main goal of the articles is to describe recent progress in elucidating the mechanisms by which IDPs cause various human diseases, such as cancer, cardiovascular disease, amyloidosis, neurodegenerative diseases, diabetes, and genetic diseases, to name a few. Contributed by leading investigators in the field, this compendium serves as a valuable resource for researchers, clinicians as well as postdoctoral fellows and graduate student

IMPROVING RATIONAL DRUG DESIGN BY INCORPORATING NOVEL BIOPHYSICAL INSIGHT

Computer-aided drug design is a valuable and effective complement to conventional experimental drug discovery methods. In this thesis, we will discuss our contributions to advancing a number of outstanding challenges in computational drug discovery: understanding protein flexibility and dynamics, the role of water in small molecule binding and using and understanding large amounts of data. First, we describe the molecular steps involved in the induced-fit binding mechanism of p53 and MDM2. We use molecular dynamics simulations to understand the key chemistry responsible for the dynamic transition between the apo and holo structures of MDM2. This chemistry involves not only the indole side chain of the anchor residue of p53, Trp23, but surprisingly, the beta-carbon as well. We demonstrate that this chemistry plays a key role in opening the binding site by coordinating the position and orientation of MDM2 residues, Val93 and His96, through a previously undescribed transition state. We confirm these findings by observing that this chemistry is preserved in all available inhibitor-bound MDM2 co-crystal structures. Second, we discuss our advances in understanding water molecules in ligand binding sites by data mining the structural information of water molecules found in X-ray crystal structures. We examine a large set of paired bound and unbound proteins and compare the water molecules found in the binding site of the unbound structure to the functional groups on the ligand that displace them upon binding. We identify a number of generalized functional groups that are associated with characteristic clusters of water molecules. This information has been utilized in several successful and ongoing virtual screens. Third, we discuss software that we have developed that allows for very efficient exploration and selection of virtual screening results. Implemented as a PyMOL plugin, ClusterMols clusters compounds based on a user-defined level of chemical similarity. The software also provides advanced visualization tools and a number of controls for quickly navigating and selecting compounds of interest, as well as the ability to check online for available vendors. Finally, we present several published examples of modeling protein-lipid and protein-small molecules interactions for a number of important targets including ABL, c-Src and 5-LOX

THE HDAC INHIBITOR ITF2357 (GIVINOSTAT) AS A KEY PLAYER IN EPIGENETIC TARGETING OF MELANOMA AND COLON CANCER CELLS

Histone deacetylase inhibitors (HDACIs) are epigenetic compounds that have been recently considered for their promising anti-tumor activity. The aim of this PhD thesis was to elucidate and characterize the anti-tumor effect of the HDAC inhibitor ITF2357 (Givinostat) in melanoma and colon cancer cells that are characterized by oncogenic BRAF mutations. Interestingly, data reported in this thesis demonstrate that ITF2357 exerts a remarkable anti-tumor effect in melanoma cells by inducing a switch from a pro-survival autophagy to caspase-dependent apoptosis. The thesis provides the first evidences that ITF2357 is able to target oncogenic BRAF and oncogenic p53. The ITF2357 decreasing effect on BRAF was due to both reduced expression and increased proteolysis, while the effect on p53 was mainly due to proteasome-mediated degradation. Moreover, the thesis demonstrates for the first time an interplay between oncogenic BRAF and oncogenic p53 in melanoma cells, which is reduced by ITF2357, thus supporting a possible use of this compound in melanoma targeted therapy. The anti-tumor effect of ITF2357 was also evaluated in colon cancer cells. In these models, the HDAC inhibitor was combined with other epigenetic drugs, including DNA methyltransferase (DNMT) inhibitors. The results indicated that ITF2357 potentiates the effects of both general and selective DNMT inhibitors in HCT116 cells. Furthermore, the compound was able to increase the immunogenic response induced by these other epi-drugs. Taken together, the results shown in this thesis pave the way for a possible use of ITF2357 for anti-tumor targeted therapy, either alone or in combination with other epigenetic compounds

It\u27s getting complicated-A fresh look at p53-MDM2-ARF triangle in tumorigenesis and cancer therapy

Anti-tumorigenic mechanisms mediated by the tumor suppressor p53, upon oncogenic stresses, are our bodies\u27 greatest weapons to battle against cancer onset and development. Consequently, factors that possess significant p53-regulating activities have been subjects of serious interest from the cancer research community. Among them, MDM2 and ARF are considered the most influential p53 regulators due to their abilities to inhibit and activate p53 functions, respectively. MDM2 inhibits p53 by promoting ubiquitination and proteasome-mediated degradation of p53, while ARF activates p53 by physically interacting with MDM2 to block its access to p53. This conventional understanding of p53-MDM2-ARF functional triangle have guided the direction of p53 research, as well as the development of p53-based therapeutic strategies for the last 30 years. Our increasing knowledge of this triangle during this time, especially through identification of p53-independent functions of MDM2 and ARF, have uncovered many under-appreciated molecular mechanisms connecting these three proteins. Through recognizing both antagonizing and synergizing relationships among them, our consideration for harnessing these relationships to develop effective cancer therapies needs an update accordingly. In this review, we will re-visit the conventional wisdom regarding p53-MDM2-ARF tumor-regulating mechanisms, highlight impactful studies contributing to the modern look of their relationships, and summarize ongoing efforts to target this pathway for effective cancer treatments. A refreshed appreciation of p53-MDM2-ARF network can bring innovative approaches to develop new generations of genetically-informed and clinically-effective cancer therapies

Molecular mechanisms and targets of new anticancer treatments

The work presented in this thesis is an effort to decipher and understand the mechanism of action (MOA) of anticancer agents by building on and complementing chemical proteomics methods. The backbone of the thesis relies on a recent method called Functional Identification of Target by Expression Proteomics (FITExP) developed in Zubarev lab, where drug induced proteomic signatures are analyzed in various cell lines and top differentially regulated proteins with consistent behavior are determined, among which the drug target and mechanistic proteins are usually present. FITExP relies on the assumption that proteins most affected with a perturbation have a higher probability of being involved in that process. In this regard, Paper I aimed to enhance the performance of FITExP analysis by merging proteomic data from drug-treated matrix attached and detached cells. This is while the majority if not all proteomics and molecular biology experiments are performed in matrix attached cells, as the general belief is that detached cells lose their structural integrity and do not harbor valuable information. However, detached cells are those that are more sensitive to chemotherapeutics and might reflect the proteome changes better. The comparative proteomics of living and dying cells improved FITExP performance with regards to identification of targets and provided insight about proteins involved in cellular life and death decisions. Furthermore, the orthogonal partial least squares-discriminant analysis (OPLS-DA) paradigm presented in this study, was used throughout the thesis for contrasting and visualizing the proteomic signature of a molecule against others, to reveal targets and specific proteins changing in response to the molecule of interest. In Paper II, as a further development of FITExP and to demonstrate its applicability in a broader context, we built a proteome signature library of 56 clinical and experimental anticancer agents in A549 lung adenocarcinoma cell line. This resource called ProTargetMiner can be used for different purposes. The proximity of compounds in hierarchical clustering or t-SNE could be used for prediction of the mechanism of new compounds. Contrasting each molecule against other treatments using the OPLS-DA scheme presented in Paper I, revealed drug targets, mechanistic proteins, resistance factors, drug metabolizing enzymes and effects on protein complexes. Representative examples were used to demonstrate that the specificity factors extracted from the OPLS-DA models can help identify subtle but biologically significant processes, even when such an effect is as low as 15% fold change. Furthermore, we showed that the inclusion of 8-10 contrasting molecules in the OPLS-DA models can produce enough specificity for drug target deconvolution, which offered a miniaturization opportunity. Therefore, we built three deeper datasets using 9 compounds that showed the most diverse proteome changes in the orthogonal space in three cell lines from major cancer types: A549 lung, MCF-7 breast and RKO colon cancers. These datasets provide a unique depth of 7398, 8735 and 8551 respectively, with no missing values. Subsequently, a Shiny package was created in R, which can employ these datasets as a resource and merge it with user data and provide OPLS-DA output and target deconvolution opportunity for new compounds. Finally, using the original ProTargetMiner data, we also built a first of its kind proteomic correlation database which can find applications in deciphering the function of uncharacterized proteins. Moreover, the resource helped to identify a set of core or untouchable proteins with stable expression across all the treatments, revealing essential functions within the cells. Such proteins could be used as house-keeping controls in molecular biology experiments. In paper III, we combined FITExP with other chemical proteomics tools Thermal Proteome Profiling (TPP) and multiplexed redox proteomics, to study the target and mechanism space of auranofin. This would also allow to assess the power, orthogonality and complementarity of these techniques in the realm of chemical proteomics. TPP is a recently developed technique that can monitor changes in the stability of proteins upon binding to small molecules. Redox proteomics is a method by which the oxidation level of protein cysteinome can be quantitatively analyzed. Auranofin is an FDA-approved anti-inflammatory drug for treatment of rheumatoid arthritis, but due to its potent antitumor activity, it is currently in clinical trials against cancer. Although several MOAs have been suggested for auranofin, uncertainties exist regarding its cellular targets; therefore, this molecule was chosen as a challenging candidate to test the chemical proteomics tools. A combination of the above mentioned tools confirmed thioredoxin reductase 1 (TXNRD1) (ranking 3rd) as the cognate target of auranofin and demonstrated that perturbation of oxidoreductase pathway is the main route of auranofin cytotoxicity. We next showed that changes in the redox state of specific cysteines can be linked to protein stability in TPP. Some of these cysteines were mapped to the active sites of redox-active enzymes. In Paper IV, using quantitative multiplexed proteomics, we helped to show that b-AP15, a bis-benzylidine piperidone compound inhibiting deubiquitinases USP14 and UCHL5, produces a similar perturbation signature as bortezomib in colon cancer cells. However, in comparison with bortezomib, b-AP15 induces chaperone expression to a significantly higher level and leads to a more extensive accumulation of polyubiqutinated proteins. The polyubiqutinated proteins co-localize with mitochondrial membrane and subsequently reduce oxidative phosphorylation. These results help define the atypical cell death induced by b-AP15 and describe why this molecule is effective against apoptosis resistant cells in variety of tumor models. Finally, in Paper V, we extended the applications of TPP and combined it with specificity concept for proteome-wide discovery of specific protein substrates for enzymes. We developed a universal method called System-wide Identification of Enzyme Substrates by Thermal Analysis (SIESTA) that relies on the hypothesis that enzymatic post-translational modification of substrate proteins can potentially change their stability against thermal denaturation. Furthermore, we applied the concept of specificity similar to the above papers, to reveal potential substrates using OPLS-DA. SIESTA was applied to two enzyme systems, namely TXNRD1 and poly-(ADP-ribose) polymerase-10 (PARP10), identifying known and putative candidate substrates. A number of these candidate proteins were validated as PARP10 substrates by targeted mass spectrometry, chemiluminescence and other assays. SIESTA is an unbiased and system wide approach and its broad application can improve our understanding of enzyme function in homeostasis and disease. In turn, specific protein substrates can serve as readouts in high throughput screening and facilitate drug discovery. Taken together, in this thesis, FITExP methodology was improved in two directions. In paper I, we improved the performance of FITExP by combining the proteomics data from detached and attached cells. In Paper II, we demonstrated how the proteomics data on a multitude of drugs in a single cell line enables the discovery of compound targets and MOA. Furthermore, we built an R Shiny package which can serve as a resource for the cancer community in target and MOA deconvolution. In Papers III and IV, we applied an arsenal of chemical proteomics tools for characterization of two anticancer compounds. In Paper V, we expanded the applications of TPP to identification of specific protein substrates for enzymes in a system-wide manner

Absolute binding enthalpy calculations using molecular dynamics simulations

Computers play an essential role in drug discovery as advancements in technology, hardware, and algorithms have allowed for improved simulations of biomolecules. The field of drug discovery stands to benefit significantly from these developments. Currently, many innovative approaches to studying drug binding and predicting binding affinity are being explored. Using computational methods to predict thermodynamic components in drug design has become routine. While progress has been made in calculating free energy, the prediction of enthalpy and entropy remains an area that requires further investigation. These components reflect the interactions and dynamics between the ligand and protein. However, despite years of research, our understanding of these components still needs to be improved. Computing the enthalpy is particularly challenging, and even the achievable accuracy of these predictions is still not precise despite the apparent simplicity of the calculations per se. In my thesis, I conduct a series of studies to examine the potential utility of absolute binding enthalpy calculations using the direct method based on molecular dynamics simulations. In Chapter 3, I first assess the accuracy of water models and the host-guest force field in calculating the absolute binding enthalpy for 25 host-guest pairs. While actual protein-ligand or protein-protein data would be ideal for evaluating force fields, using very simplified test systems can be helpful for preliminary exploration of parameters. Then, in Chapter 4, I focus on predicting the binding enthalpies of small molecules to bromodomains, which are small protein modules involved in gene regulation linked to many diseases, such as cancer and inflammation. I evaluated the direct method for calculating absolute binding enthalpies by testing its ability to predict the binding enthalpies of 10 different ligands to BRD4-1. The results showed a strong correlation between the behaviour of the ZA loop and the predicted enthalpy. In Chapter 5, I extended the study by evaluating the method to include multiple protein-protein complexes essential in all cellular processes, ranging from signal transmission to enzyme activity. Understanding the thermodynamics of protein-peptide binding events is a significant challenge in computational chemistry. The complexity of both components having many degrees of freedom presents a substantial challenge for methods attempting to directly compute the enthalpic contribution to binding. Despite this, the method produced highly accurate and well-converged binding enthalpies for small protein-protein systems. Perhaps unsurprisingly, most inaccuracies can be attributed to poor conformational sampling. Nevertheless, I have shown that this can actually be used to highlight the possibility of hidden states. Overall, my work has shown that absolute enthalpy calculations using the direct method can be performed on protein-ligand and protein-protein systems with reasonable accuracy and that this is a useful contribution to computational drug design

Efficient Computer Simulations of Protein-Peptide Binding Using Weighted Ensemble Sampling

Molecular dynamics simulations can, in principle, provide detailed views of protein-protein association processes. However, these processes frequently occur on timescales inaccessible on current computing resources. These are not particularly slow processes, but rather they are rare — fast but infrequent. The weighted ensemble (WE) sampling approach provides a way to exploit this separation of timescales and focus computing power efficiently on rare events. In this work, it is demonstrated that WE sampling can be used to efficiently obtain kinetic rate constants, pathways, and energy landscapes of molecular association processes. Chapter 1 of this dissertation further discusses the need for enhanced sampling techniques like the WE approach. In Chapter 2, WE sampling is used to study the kinetics of association of four model molecular recognition systems (methane/methane, Na+/Cl–, methane/benzene, and K+/18-crown-6 ether) using molecular dynamics (MD) simulations in explicit water. WE sampling reproduces straightforward “brute force” results while increasing the efficiency of sampling by up to three orders of magnitude. Importantly, the efficiency of WE simulation increases with increasing complexity of the systems under consideration. In Chapter 3, weighted ensemble Brownian dynamics (BD) simulations are used to explore the association between a 13-residue fragment of the p53 tumor suppressor and the MDM2 oncoprotein. The association rates obtained compare favorably with experiment. By directly comparing both flexible and pre-organized variants of p53, it is shown that the “fly-casting” effect, by which natively unstructured proteins may increase their association rates, is not significant in MDM2-p53 peptide binding. Including hydrodynamic interactions in the simulation model dramatically alters the association rate, indicating that the detailed motion of solvent may have substantial effects on the kinetics of protein-protein association. In Chapter 4, an all-atom molecular dynamics simulation of p53-MDM2 binding is described. We obtain an association rate that agrees with the experimental value. The free energy landscape of binding is “funnel-like”, downhill after the initial encounter between p53 and MDM2. Together, the studies described here establish that WE sampling is highly effective in simulating rare molecular association events

The Conformational Universe of Proteins and Peptides: Tales of Order and Disorder

Proteins represent one of the most abundant classes of biological macromolecules and play crucial roles in a vast array of physiological and pathological processes. The knowledge of the 3D structure of a protein, as well as the possible conformational transitions occurring upon interaction with diverse ligands, are essential to fully comprehend its biological function.In addition to globular, well-folded proteins, over the past few years, intrinsically disordered proteins (IDPs) have received a lot of attention. IDPs are usually aggregation-prone and may form toxic amyloid fibers and oligomers associated with several human pathologies. Peptides are smaller in size than proteins but similarly represent key elements of cells. A few peptides are able to work as tumor markers and find applications in the diagnostic and therapeutic fields. The conformational analysis of bioactive peptides is important to design novel potential drugs acting as selective modulators of specific receptors or enzymes. Nevertheless, synthetic peptides reproducing different protein fragments have frequently been implemented as model systems in folding studies relying on structural investigations in water and/or other environments.This book contains contributions (seven original research articles and five reviews published in the journal Molecules) on the above-described topics and, in detail, it includes structural studies on globular folded proteins, IDPs and bioactive peptides. These works were conducted usingdifferent experimental methods