Hierarchical Modeling of Activation Mechanisms in the ABL and EGFR Kinase Domains: Thermodynamic and Mechanistic Catalysts of Kinase Activation by Cancer Mutations

Structural and functional studies of the ABL and EGFR kinase domains have recently suggested a common mechanism of activation by cancer-causing mutations. However, dynamics and mechanistic aspects of kinase activation by cancer mutations that stimulate conformational transitions and thermodynamic stabilization of the constitutively active kinase form remain elusive. We present a large-scale computational investigation of activation mechanisms in the ABL and EGFR kinase domains by a panel of clinically important cancer mutants ABL-T315I, ABL-L387M, EGFR-T790M, and EGFR-L858R. We have also simulated the activating effect of the gatekeeper mutation on conformational dynamics and allosteric interactions in functional states of the ABL-SH2-SH3 regulatory complexes. A comprehensive analysis was conducted using a hierarchy of computational approaches that included homology modeling, molecular dynamics simulations, protein stability analysis, targeted molecular dynamics, and molecular docking. Collectively, the results of this study have revealed thermodynamic and mechanistic catalysts of kinase activation by major cancer-causing mutations in the ABL and EGFR kinase domains. By using multiple crystallographic states of ABL and EGFR, computer simulations have allowed one to map dynamics of conformational fluctuations and transitions in the normal (wild-type) and oncogenic kinase forms. A proposed multi-stage mechanistic model of activation involves a series of cooperative transitions between different conformational states, including assembly of the hydrophobic spine, the formation of the Src-like intermediate structure, and a cooperative breakage and formation of characteristic salt bridges, which signify transition to the active kinase form. We suggest that molecular mechanisms of activation by cancer mutations could mimic the activation process of the normal kinase, yet exploiting conserved structural catalysts to accelerate a conformational transition and the enhanced stabilization of the active kinase form. The results of this study reconcile current experimental data with insights from theoretical approaches, pointing to general mechanistic aspects of activating transitions in protein kinases

Quantifying Intramolecular Binding in Multivalent Interactions: A Structure-Based Synergistic Study on Grb2-Sos1 Complex

Numerous signaling proteins use multivalent binding to increase the specificity and affinity of their interactions within the cell. Enhancement arises because the effective binding constant for multivalent binding is larger than the binding constants for each individual interaction. We seek to gain both qualitative and quantitative understanding of the multivalent interactions of an adaptor protein, growth factor receptor bound protein-2 (Grb2), containing two SH3 domains interacting with the nucleotide exchange factor son-of-sevenless 1 (Sos1) containing multiple polyproline motifs separated by flexible unstructured regions. Grb2 mediates the recruitment of Sos1 from the cytosol to the plasma membrane where it activates Ras by inducing the exchange of GDP for GTP. First, using a combination of evolutionary information and binding energy calculations, we predict an additional polyproline motif in Sos1 that binds to the SH3 domains of Grb2. This gives rise to a total of five polyproline motifs in Sos1 that are capable of binding to the two SH3 domains of Grb2. Then, using a hybrid method combining molecular dynamics simulations and polymer models, we estimate the enhancement in local concentration of a polyproline motif on Sos1 near an unbound SH3 domain of Grb2 when its other SH3 domain is bound to a different polyproline motif on Sos1. We show that the local concentration of the Sos1 motifs that a Grb2 SH3 domain experiences is approximately 1000 times greater than the cellular concentration of Sos1. Finally, we calculate the intramolecular equilibrium constants for the crosslinking of Grb2 on Sos1 and use thermodynamic modeling to calculate the stoichiometry. With these equilibrium constants, we are able to predict the distribution of complexes that form at physiological concentrations. We believe this is the first systematic analysis that combines sequence, structure, and thermodynamic analyses to determine the stoichiometry of the complexes that are dominant in the cellular environment

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.Comment: 64 pages, 11 figure

Computational Analysis and Prediction of the Binding Motif and Protein Interacting Partners of the Abl SH3 Domain

Protein-protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains, such as Src Homology 2 and 3 (SH2 and SH3) domains, which bind to specific sequence and structural motifs. It is important but challenging to determine the binding specificity of these domains accurately and to predict their physiological interacting partners. In this study, the interactions between 35 peptide ligands (15 binders and 20 non-binders) and the Abl SH3 domain were analyzed using molecular dynamics simulation and the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. The calculated binding free energies correlated well with the rank order of the binding peptides and clearly distinguished binders from non-binders. Free energy component analysis revealed that the van der Waals interactions dictate the binding strength of peptides, whereas the binding specificity is determined by the electrostatic interaction and the polar contribution of desolvation. The binding motif of the Abl SH3 domain was then determined by a virtual mutagenesis method, which mutates the residue at each position of the template peptide relative to all other 19 amino acids and calculates the binding free energy difference between the template and the mutated peptides using the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. A single position mutation free energy profile was thus established and used as a scoring matrix to search peptides recognized by the Abl SH3 domain in the human genome. Our approach successfully picked ten out of 13 experimentally determined binding partners of the Abl SH3 domain among the top 600 candidates from the 218,540 decapeptides with the PXXP motif in the SWISS-PROT database. We expect that this physical-principle based method can be applied to other protein domains as well

計算機支援によるペプチド設計の理論と応用

学位の種別: 課程博士審査委員会委員 : (主査)東京大学客員准教授 富井 健太郎, 東京大学教授 菅野 純夫, 東京大学教授 浅井 潔, 東京大学准教授 木立 尚孝, 東京大学客員准教授 KamY. Zhang, 東京大学客員教授 泰地 真弘人University of Tokyo(東京大学

In silico studies of nucleic acid complexes with proteins, and therapeutic small molecules.

In silico approaches to nucleic acid targeted drug discovery have been used in order to study duplex DNA, in complexes with proteins as well as more unusual form of G-rich DNA folded into higher-order structures termed as G-quadruplexes, in complexes with therapeutic small molecules. The overall aim of this work has been to provide insight into the stability, recognition, energetics of binding and dynamic behavior of these DNAs in complexes with the STAT3βtc homodimer:DNA complex and with therapeutic small molecules in G-quadruplex/pyridostatin and G-quadruplex/fragment complexes by means of combined in silico approaches. The techniques of explicit solvent molecular dynamics (MD) simulations, and subsequent calculations of the free energies of binding, molecular docking, and 3D-pharmacophore modeling have been applied to study STAT3 and G-quadruplex DNA, promising targets for anticancer therapeutic intervention. Analysis of the data obtained from multiple 50-ns MD simulations of the STAT3-DNA complexes has suggested how the transcription factor STAT3 interacts with duplex DNA, the nature of the conformational changes, and ways in which func- tion may be affected. A majority of known pathologic mutations affecting the DNA-biding region of the STAT3 have been found at the protein-DNA interface, and they have been mapped in detail. The STAT3 conformations obtained from these MD simulations have been subsequently used as a basis for a comparative multiple-target molecular docking study with an in-house library of potential STAT3 inhibitors, providing a rational of their binding in the absence of structural data. A novel “dynamic docking” approach (robust platform of numerous MD simulations) has been developed to address the G-quadruplex receptor and ligand flexibility issue, and subsequent conformational change upon binding. The strength of binding at different regions and both sites of the G-quadruplex were then closely examined. An in silico study of a fragment-based approach towards G-quadruplex stabilizing ligands has also been explored, in parallel with experimental studies, to assess whether this could provide a reliable rapid approach to finding hit fragments in the case of the c-MYC promoter quadruplex

In silico identification and assessment of novel allosteric protein binding sites to expand the “druggable” human proteome

Ph. D. Thesis.Throughout the last years there has been a considerable number of drugs that were discovered thanks to computer aided drug design (CADD) techniques. Using the 3D information, such as protein structures obtained by X-ray crystallography or nuclear magnetic resonance (NMR), it is possible to identify the binding sites and to design molecules that may specifically target these sites. This approach saves a lot of time and money, as the lead search is more accurate: less compounds need to be synthesised and tested. Although a great number of proteins have been successfully targeted with this structure-based approach, there are a lot of disease-linked proteins that have been considered “undruggable” by conventional structure-based techniques. This is mainly due to failure in detection of potential binding sites, which precludes the structure-guided design of suitable ligands. There is the presumption that the “druggable” human proteome may be larger than previously expected. Protein structures may present multiple binding sites (allosteric and/or cryptic) that cannot be targeted by the means of conventional CADD techniques. In the past years, several novel methods have been developed to identify and/or unveil these binding hotspots. Amongst them cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterisation of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. The present study focused on the development of the novel cosolvent analysis toolkit (denoted as CAT), as an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with popular molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranked the dynamic interactions between the macromolecular target and cosolvent molecular probes. Alongside the development of CAT, this work investigated the signal transducer activator of transcription 3 (STAT3) as the case study. STAT3 is among the most investigated oncogenic transcription factors, as it is highly associated with cancer initiation, progression, metastasis, chemoresistance, and immune evasion. Constitutive activation of STAT3 by mutations occurs frequently in tumour cells, and directly contributes to many malignant phenotypes. The evidence from both preclinical and clinical studies have demonstrated that STAT3 plays a critical role in several malignancies associated with poor prognosis such as glioblastoma and triple-negative breast cancer (TNBC), and STAT3 inhibitors have shown efficacy in inhibiting cancer growth and metastasis. Unfortunately, detailed structural biology studies on STAT3 as well as target-based drug discovery efforts have been hampered by difficulties in the expression and purification of the full length STAT3 and a lack of ligand-bound crystal structures. Considering these, computational methods offer an attractive strategy for the assessment of “druggability” of STAT3 dimers and allow investigations of reported activating and inhibiting STAT3 mutants at the atomistic level of detail. This work studied effects exerted by reported STAT3 mutations on the protein structure, dynamics, DNA binding and dimerisation, thus linking structure, dynamics, energetics, and the biological function. By employing a combination of equilibrium molecular dynamics (MD) and umbrella sampling (US) simulations to a series of human STAT3 dimers, which comprised wild-type protein and four mutations; the work presented herein explains the modulation of STAT3 activity by these mutations. The binding sites were mapped by the combination of MD simulations, molecular docking, and CAT analysis, and the binding mode of a clinical candidate napabucasin/BBI-608 at STAT3, which resembles the effect of D570K mutation, has been characterised. Collectively the results of this study demonstrate the robustness of the newly developed CAT methodology and its applicability in computational studies aiming at identification of protein “hotspots” in a wide range of protein targets, including the challenging ones. This work contributes to understanding the activation/inhibition mechanism of STAT3, and it explains the molecular mechanism of STAT3 inhibition by BBI-608. Alongside the characterisation of the BBI-608 binding mode, a novel binding site amenable to bind small molecule v ligands has been discovered in this work, which may pave the way to design novel STAT3 inhibitors and to suggest new strategies for pharmacological intervention to combat cancers associated with poor prognosis. It is expected that the results presented in this dissertation will contribute to an increase of the size of the potentially “druggable” human proteome

Computation of Conformational Coupling in Allosteric Proteins

In allosteric regulation, an effector molecule binding a protein at one site induces conformational changes, which alter structure and function at a distant active site. Two key challenges in the computational modeling of allostery are the prediction of the structure of one allosteric state starting from the structure of the other, and elucidating the mechanisms underlying the conformational coupling of the effector and active sites. Here we approach these two challenges using the Rosetta high-resolution structure prediction methodology. We find that the method can recapitulate the relaxation of effector-bound forms of single domain allosteric proteins into the corresponding ligand-free states, particularly when sampling is focused on regions known to change conformation most significantly. Analysis of the coupling between contacting pairs of residues in large ensembles of conformations spread throughout the landscape between and around the two allosteric states suggests that the transitions are built up from blocks of tightly coupled interacting sets of residues that are more loosely coupled to one another

Molecular modeling of proteins and peptides related to cell attachment in vivo and in vitro

Polypeptides constitute half of the dry mass of the cell, they form the bulk of the extracellular matrix (ECM), and they are a common element of extra- and intracellular signaling pathways. There is increasing interest in the development of computational methods in polypeptide and protein engineering on all length scales. This research concerns the development of computational methods for study of polypeptide interactions related to cell attachment in vivo and in vitro. Polypeptides are inherently biocompatible, and an astronomical range of unique sequences can be designed and realized in massive quantities by modern methods of synthesis and purification. These macromolecules therefore constitute an intriguing class of polyelectrolyte for biomedically-oriented multilayer film engineering (Haynie et al., 2005), Applications of such films include artificial cells, drug delivery systems, and implant device coatings, cell/tissue scaffolds (ECM mimics). The plasma membrane-associated cytoplasmic protein tensin is involved in cell attachment, cell migration, embryogenesis, and wound healing. The tensin polypeptide comprises several modular domains implicated in signal transduction. It has been shown that the N-terminal region of tensin is a close homolog of a tumor suppressor that is highly mutated in glioblastomas, breast cancer, and other cancers. There are two related areas of development in this work: Polypeptide multilayer films, a type of ECM mimics, and the molecular physiology of tensin. Two studies have been carried out on polypeptide multilayer films: aggregates of the model polypeptides poly(L-lysine) (PLL) and poly(L-glutamic acid) (PLGA), and interpolyelectrolytes complexes (IPECs) of designed peptides. Molecular models of all known domain of tensin have been developed by homology modeling. The binding properties of the two domain of tensin have been studied. Molecular dynamics (MD) simulations of PLL/PLGA aggregates suggest that both hydrophobic interactions and electrostatics interactions play a significant role in stabilizing polypeptide multilayer structures. The approach provides a general means to determine how non-covalent interactions contribute to the structure and stability of polypeptide multilayer films. MD simulations of designed polypeptide complexes have been carried out in vacuum and in implicit solvent. The simulation results correlate with experimental data on the same peptides. Energy minimization and MD study of tensin domain-peptide complexes has provided insight on biofunctionality of the tensin molecule and thereby its role in cell adhesion. Such knowledge will be important for determining the molecular basis of cell adhesion in health and disease and engineering treatments of abnormalities involving cell attachment