Multiscale approaches for studying energy transduction in dynein

Computational modeling of dynein motor proteins at work

Along with various experimental methods, a combination of theoretical and computational methods is essential to explore different length-scale and time-scale processes in the biological system. The functional mechanism of a dynein, an ATP-fueled motor protein, working in a multiprotein complex, involves a wide range of length/time-scale events. It generates mechanical force from chemical energy and moves on microtubules towards the minus end direction while performing a large number of biological processes including ciliary beating, intracellular material transport, and cell division. Like in the cases of other conventional motor proteins, a combination of experimental techniques including X-crystallography, cryo-electron microscopy, and single molecular assay have provided a wealth of information about the mechanochemical cycle of a dynein. Dyneins have a large and complex structural architecture and therefore, computational modeling of different aspects of a dynein is extremely challenging. As the process of dynein movement involves varying length and timescales, it demands, like in experiments, a combination of computational methods covering such a wide range of processes for the comprehensive investigation of the mechanochemical cycle. In this review article, we will summarize how the use of state-of-the-art computational methods can provide a detailed molecular understanding of the mechanochemical cycle of the dynein. We implemented all-atom molecular dynamics simulations and hybrid quantum-mechanics/molecular-mechanics simulations to explore the ATP hydrolysis mechanisms at the primary ATPase site (AAA1) of dynein. To investigate the large-scale conformational changes we employed coarse-grained structure-based molecular dynamics simulations to capture the domain motions. Here we explored the conformational changes upon binding of ATP at AAA1, nucleotide state-dependent regulation of the mechanochemical cycle, and inter-head coordination by inter-head tension. Additionally, implementing a phenomenological theoretical model we explore the force-dependent detachment rate of a motorhead from the microtubule and the principle of multi-dynein cooperation during cargo transport.

Free energy-based model of CTCF-mediated chromatin looping in the human genome

In recent years, high-throughput techniques have revealed considerable structural organization of the human genome with diverse regions of the chromatin interacting with each other in the form of loops. Some of these loops are quite complex and may encompass regions comprised of many interacting chain segments around a central locus. Popular techniques for extracting this information are chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture (Hi-C). Here, we introduce a physics-based method to predict the three-dimensional structure of chromatin from population-averaged ChIA-PET data. The approach uses experimentally-validated data from human B-lymphoblastoid cells to generate 2D meta-structures of chromatin using a dynamic programming algorithm that explores the chromatin free energy landscape. By generating both optimal and suboptimal meta-structures we can calculate both the free energy and additionally the relative thermodynamic probability. A 3D structure prediction program with applied restraints then can be used to generate the tertiary structures. The main advantage of this approach for population-averaged experimental data is that it provides a way to distinguish between the principal and the spurious contacts. This study also finds that euchromatin appear to have rather precisely regulated 2D meta-structures compared to heterochromatin. The program source-code is available at https://github.com/plewczynski/looper.

Experimentally-driven protein structure modeling

Revolutions in natural and exact sciences started at the dawn of last century have led to the explosion of theoretical, experimental, and computational approaches to determine structures of molecules, complexes, as well as their rich conformational dynamics. Since different experimental methods produce information that is attributed to specific time and length scales, corresponding computational methods have to be tailored to these scales and experiments. These methods can be then combined and integrated in scales, hence producing a fuller picture of molecular structure and motion from the "puzzle pieces" offered by various experiments. Here, we describe a number of computational approaches to utilize experimental data to glance into structure of proteins and understand their dynamics. We will also discuss the limitations and the resolution of the constraints-based modeling approaches. SIGNIFICANCE: Experimentally-driven computational structure modeling and determination is a rapidly evolving alternative to traditional approaches for molecular structure determination. These new hybrid experimental-computational approaches are proving to be a powerful microscope to glance into the structural features of intrinsically or partially disordered proteins, dynamics of molecules and complexes. In this review, we describe various approaches in the field of experimentally-driven computational structure modeling.

Coarse-grained modeling of RNA 3D structure

Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result.

Exploring the mechanochemical cycle of dynein motor proteins: structural evidence of crucial intermediates

Exploration of the biologically relevant pathways of dynein's mechanochemical cycle using structure based models.

Model for bidirectional movement of cytoplasmic dynein

Cytoplasmic dynein exhibits a directional processive movement on microtubule filaments and is known to move in steps of varying length based on the number of ATP molecules bound to it and the load that it carries. It is experimentally observed that dynein takes occasional backward steps and the frequency of such backward steps increases as the load approaches the stall force. Using a stochastic process model, we investigate the bidirectional movement of single head of a dynein motor. The probability for backward step is implemented based on fluctuation theorem of non-equilibrium statistical mechanics. We find that the movement of dynein motor is characterized with negative velocity implying backward motion beyond stall force. We observe that the motor moves backward for super stall forces by hydrolyzing the ATP exactly the same way as it does while moving forward for sub-stall forces. Movement of dynein is also simulated using a kinetic Monte Carlo method and the simulated velocities are in good agreement with velocities obtained using a stochastic rate equation model.

Coarse-grained modeling of the structural states and transition underlying the powerstroke of dynein motor domain

This study aims to model a minimal dynein motor domain capable of motor function, which consists of the linker domain, six AAA+ modules (AAA1-AAA6), coiled coil stalk, and C-terminus domain. To this end, we have used the newly solved X-ray structures of dynein motor domain to perform a coarse-grained modeling of dynein's post- and pre-powerstroke conformation and the conformational transition between them. First, we have used normal mode analysis to identify a single normal mode that captures the coupled motions of AAA1-AAA2 closing and linker domain rotation, which enables the ATP-driven recovery stroke of dynein. Second, based on the post-powerstroke conformation solved crystallographically, we have modeled dynein's pre-powerstroke conformation by computationally inducing AAA1-AAA2 closing and sliding of coiled coil stalk, and the resulting model features a linker domain near the pre-powerstroke position and a slightly tilted stalk. Third, we have modeled the conformational transition from pre- to post-powerstroke conformation, which predicts a clear sequence of structural events that couple microtubule binding, powerstroke and product release, and supports a signaling path from stalk to AAA1 via AAA3 and AAA4. Finally, we have found that a closed AAA3-AAA4 interface (compatible with nucleotide binding) is essential to the mechano-chemical coupling in dynein. Our modeling not only offers unprecedented structural insights to the motor function of dynein as described by past single-molecule, fluorescence resonance energy transfer, and electron microscopy studies, but also provides new predictions for future experiments to test.

Physical microscopic model of proteins under force

Nature has evolved proteins to counteract forces applied on living cells, and has designed proteins that can sense forces. One can appreciate Nature's ingenuity in evolving these proteins to be highly sensitive to force and to have a high dynamic force range at which they operate. To achieve this level of sensitivity, many of these proteins are composed of multiple domains and linking peptides connecting these domains, each of them having their own force response regimes. Here, using a simple model of a protein, we address the question of how each individual domain responds to force. We also ask how multidomain proteins respond to forces. We find that the end-to-end distance of individual domains under force scales linearly with force. In multidomain proteins, we find that the force response has a rich range: at low force, extension is predominantly governed by "weaker" linking peptides or domain intermediates, while at higher force, the extension is governed by unfolding of individual domains. Overall, the force extension curve comprises multiple sigmoidal transitions governed by unfolding of linking peptides and domains. Our study provides a basic framework for the understanding of protein response to force, and allows for interpretation experiments in which force is used to study the mechanical properties of multidomain proteins.

A physical model reveals the mechanochemistry responsible for dynein's processive motion

The molecular motor dynein is associated with various cellular activities, such as directed transport along microtubules and the rhythmic beating of the axoneme. Because of the size and complexity of the protein, a detailed understanding of the mechanochemistry that drives dynein's processive motion is lacking. To overcome this deficiency, we developed the first (to our knowledge) computational model for two-headed dynein that couples conformational changes of the motor's subunits to the biochemical steps involved in ATP hydrolysis. Analysis of the model provides what we believe are several novel insights into how the protein functions: 1), structural constraints limit the motion of the free microtubule binding domain to one dimension, increasing the efficiency with which this domain finds a binding site; 2), in addition to the power stroke of the bound head, recovery of the free head to a pre-power-stroke conformation is required for this head to reach a forward binding site; 3), the order in which the power stroke and recovery transitions occur affects the probability of back-stepping; and 4), the existence of multiple equilibria in the motor's bending energy provides a mechanism for processive back-stepping. To the best of our knowledge, our computational model provides the first complete mechanochemical description of the motor protein dynein, and the findings presented here should motivate new experimental investigations to test its predictions.

Macromolecular crowding induces polypeptide compaction and decreases folding cooperativity

A cell's interior is comprised of macromolecules that can occupy up to 40% of its available volume. Such crowded environments can influence the stability of proteins and their rates of reaction. Using discrete molecular dynamics simulations, we investigate how both the size and number of neighboring crowding reagents affect the thermodynamic and folding properties of structurally diverse proteins. We find that crowding induces higher compaction of proteins. We also find that folding becomes less cooperative with the introduction of crowders into the system. The crowders may induce alternative non-native protein conformations, thus creating barriers for protein folding in highly crowded media.