Multi-Scaled Explorations of Binding-Induced Folding of Intrinsically Disordered Protein Inhibitor IA3 to its Target Enzyme

Biomolecular function is realized by recognition, and increasing evidence shows that recognition is determined not only by structure but also by flexibility and dynamics. We explored a biomolecular recognition process that involves a major conformational change – protein folding. In particular, we explore the binding-induced folding of IA3, an intrinsically disordered protein that blocks the active site cleft of the yeast aspartic proteinase saccharopepsin (YPrA) by folding its own N-terminal residues into an amphipathic alpha helix. We developed a multi-scaled approach that explores the underlying mechanism by combining structure-based molecular dynamics simulations at the residue level with a stochastic path method at the atomic level. Both the free energy profile and the associated kinetic paths reveal a common scheme whereby IA3 binds to its target enzyme prior to folding itself into a helix. This theoretical result is consistent with recent time-resolved experiments. Furthermore, exploration of the detailed trajectories reveals the important roles of non-native interactions in the initial binding that occurs prior to IA3 folding. In contrast to the common view that non-native interactions contribute only to the roughness of landscapes and impede binding, the non-native interactions here facilitate binding by reducing significantly the entropic search space in the landscape. The information gained from multi-scaled simulations of the folding of this intrinsically disordered protein in the presence of its binding target may prove useful in the design of novel inhibitors of aspartic proteinases

Structural Biology: Modeling applications and techniques at a glance

As recent advancements in biology shows, the molecular machines specially proteins, RNA and complex molecules play the main role of the so called cell functionality. It means a very big part of the system biology is concerned with the interactions of such molecular components. Drug industries and research institutes are trying hard to better understand the concepts underlying these interactions and are highly dependent on the issues regarding these molecular elements. However the costs for such projects are so high and in many cases these projects will be funded by governments or profit making companies. With this in mind it has to be said that the techniques like stimulation are always a very good candidate to decrease such costs and to provide scientists with a bright future of the project results before undergoing costly experiments. However the costs involved projects that determine an approximation for the problem is not that much high but they are also costly. So it is of utmost importance to invent special techniques for the concept of stimulation that can also decrease the project costs and also predict much accurately. Since the system biology and proteomics as the study of the proteins and their functions are in the center of consideration for the purpose of drug discovery, understanding the cell functionalities and the underlying causes behind diseases; so we need advance software and algorithms that can predict the structure of the molecular components and to provide researchers with the computational tools to analyze such models. In this paper we make review of the importance of molecular modeling, its limitations and applications

Binding Free Energy Landscape of Domain-Peptide Interactions

Peptide recognition domains (PRDs) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell. How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood. We explore the peptide binding process of PDZ domains, a large PRD family, from an equilibrium perspective using an all-atom Monte Carlo (MC) approach. Our focus is two different PDZ domains representing two major PDZ classes, I and II. For both domains, a binding free energy surface with a strong bias toward the native bound state is found. Moreover, both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface. Consistent with this, various binding observables show a temperature dependence well described by a simple two-state model. We also find important differences in the details between the two domains. While both domains exhibit well-defined binding free energy barriers, the class I barrier is significantly weaker than the one for class II. To probe this issue further, we apply our method to a PDZ domain with dual specificity for class I and II peptides, and find an analogous difference in their binding free energy barriers. Lastly, we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions. These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I. Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial, rate-limiting step

Exploration of Multi-State Conformational Dynamics and Underlying Global Functional Landscape of Maltose Binding Protein

An increasing number of biological machines have been revealed to have more than two macroscopic states. Quantifying the underlying multiple-basin functional landscape is essential for understanding their functions. However, the present models seem to be insufficient to describe such multiple-state systems. To meet this challenge, we have developed a coarse grained triple-basin structure-based model with implicit ligand. Based on our model, the constructed functional landscape is sufficiently sampled by the brute-force molecular dynamics simulation. We explored maltose-binding protein (MBP) which undergoes large-scale domain motion between open, apo-closed (partially closed) and holo-closed (fully closed) states responding to ligand binding. We revealed an underlying mechanism whereby major induced fit and minor population shift pathways co-exist by quantitative flux analysis. We found that the hinge regions play an important role in the functional dynamics as well as that increases in its flexibility promote population shifts. This finding provides a theoretical explanation of the mechanistic discrepancies in PBP protein family. We also found a functional “backtracking” behavior that favors conformational change. We further explored the underlying folding landscape in response to ligand binding. Consistent with earlier experimental findings, the presence of ligand increases the cooperativity and stability of MBP. This work provides the first study to explore the folding dynamics and functional dynamics under the same theoretical framework using our triple-basin functional model

Mapping transiently formed and sparsely populated conformations on a complex energy landscape

Determining the structures, kinetics, thermodynamics and mechanisms that underlie conformational exchange processes in proteins remains extremely difficult. Only in favourable cases is it possible to provide atomic-level descriptions of sparsely populated and transiently formed alternative conformations. Here we benchmark the ability of enhanced-sampling molecular dynamics simulations to determine the free energy landscape of the L99A cavity mutant of T4 lysozyme. We find that the simulations capture key properties previously measured by NMR relaxation dispersion methods including the structure of a minor conformation, the kinetics and thermodynamics of conformational exchange, and the effect of mutations. We discover a new tunnel that involves the transient exposure towards the solvent of an internal cavity, and show it to be relevant for ligand escape. Together, our results provide a comprehensive view of the structural landscape of a protein, and point forward to studies of conformational exchange in systems that are less characterized experimentally. DOI: http://dx.doi.org/10.7554/eLife.17505.00

Mapping transiently formed and sparsely populated conformations on a complex energy landscape

Abstract Determining the structures, kinetics, thermodynamics and mechanisms that underlie conformational exchange processes in proteins remains extremely difficult. Only in favourable cases is it possible to provide atomic-level descriptions of sparsely populated and transiently formed alternative conformations. Here we benchmark the ability of enhanced-sampling molecular dynamics simulations to determine the free energy landscape of the L99A cavity mutant of T4 lysozyme. We find that the simulations capture key properties previously measured by NMR relaxation dispersion methods including the structure of a minor conformation, the kinetics and thermodynamics of conformational exchange, and the effect of mutations. We discover a new tunnel that involves the transient exposure towards the solvent of an internal cavity, and show it to be relevant for ligand escape. Together, our results provide a comprehensive view of the structural landscape of a protein, and point forward to studies of conformational exchange in systems that are less characterized experimentally

Multiscale Simulations of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) lack stable secondary and/or tertiary structures under physiological conditions. The have now been recognized to play important roles in numerous biological processes, particularly cellular signaling and regulation. Mutation of IDPs are frequently associated with human diseases, such as cancers and neuron degenerative diseases. Therefore, it is important to understand the structure, dynamics, and interactions of IDPs, so as to establish the mechanistic basis of how intrinsic disorder mediates versatile functions and how such mechanisms may fail in human diseases. However, the heterogeneous structural ensembles of IDPs are not amenable to high resolution characterization solely through experimental measurements, and molecular modelling and simulation are required to study IDP structures, dynamics, and interactions at the atomistic levels. Here, we first applied the state-of-the-art explicit solvent atomistic simulations to an anti-apoptotic protein Bcl-xL and demonstrated how inherent structural disorder may provide a physical basis of protein regulated unfolding in signaling transduction. We have also constructed a series of efficient coarse-grained models to directly simulate the interactions between IDPs and unveiled how the preexisting structural elements accelerate binding of ACTR to NCBD by promoting efficient folding upon encounter. These studies shed important light on how IDPs perform functions in the cellular regulatory network, but also reveal the necessity of new sampling techniques for more efficient simulations of IDPs. We have thus developed a novel sampling technique, called multiscale enhanced sampling (MSES). MSES couples the atomistic model with coarse-grained ones, to accelerate the sampling of atomistic conformational space. Bias from coupling to a coarse-grained model can be removed using Hamiltonian replica exchange. To achieve the best possible efficiency of MSES simulations, we have developed a new hybrid resolution protein model that could capture the essential features of IDP structures, so as to generate local and long-range fluctuations that are largely consistent with those at the atomistic level. We have also developed an advanced replica exchange protocol, to allow the fast conformational transitions observed in the coupled conditions to be rapidly exchanged to the unbiased limit. Application of these strategies to characterize the structural ensembles of a few non-trivial IDPs shows that faster convergence rate can be achieved, demonstrating the great potential of MSES for atomistic simulations of larger and more complex IDPs

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