Mechanical unfolding of RNA: From hairpins to structures with internal multiloops

Mechanical unfolding of RNA structures, ranging from hairpins to ribozymes, using laser optical tweezer (LOT) experiments have begun to reveal the features of the energy landscape that cannot be easily explored using conventional experiments. Upon application of constant force ($f$), RNA hairpins undergo cooperative transitions from folded to unfolded states whereas subdomains of ribozymes unravel one at a time. Here, we use a self-organized polymer (SOP) model and Brownian dynamics simulations to probe mechanical unfolding at constant force and constant-loading rate of four RNA structures of varying complexity. Our work shows (i) the response of RNA to force is largely determined by the native structure; (ii) only by probing mechanical unfolding over a wide range of forces can the underlying energy landscape be fully explored.Comment: 26 pages, 6 figures, Biophys. J. (in press

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

Equi-energy sampler with applications in statistical inference and statistical mechanics

We introduce a new sampling algorithm, the equi-energy sampler, for efficient statistical sampling and estimation. Complementary to the widely used temperature-domain methods, the equi-energy sampler, utilizing the temperature--energy duality, targets the energy directly. The focus on the energy function not only facilitates efficient sampling, but also provides a powerful means for statistical estimation, for example, the calculation of the density of states and microcanonical averages in statistical mechanics. The equi-energy sampler is applied to a variety of problems, including exponential regression in statistics, motif sampling in computational biology and protein folding in biophysics.Comment: This paper discussed in: [math.ST/0611217], [math.ST/0611219], [math.ST/0611221], [math.ST/0611222]. Rejoinder in [math.ST/0611224]. Published at http://dx.doi.org/10.1214/009053606000000515 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction

Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding.Comment: 13 pages, 7 Postscript figure

Molecular dynamics-based approaches for enhanced sampling of long-time, large-scale conformational changes in biomolecules

The rugged energy landscape of biomolecules together with shortcomings of traditional molecular dynamics (MD) simulations require specialized methods for capturing large-scale, long-time configurational changes along with chemical dynamics behavior. In this report, MD-based methods for biomolecules are surveyed, involving modification of the potential, simulation protocol, or algorithm as well as global reformulations. While many of these methods are successful at probing the thermally accessible configuration space at the expense of altered kinetics, more sophisticated approaches like transition path sampling or Markov chain models are required to obtain mechanistic information, reaction pathways, and/or reaction rates. Divide-and-conquer methods for sampling and for piecing together reaction rate information are especially suitable for readily available computer cluster networks. Successful applications to biomolecules remain a challenge

The Role of High-Dimensional Diffusive Search, Stabilization, and Frustration in Protein Folding

Proteins are polymeric molecules with many degrees of conformational freedom whose internal energetic interactions are typically screened to small distances. Therefore, in the high-dimensional conformation space of a protein, the energy landscape is locally relatively flat, in contrast to low-dimensional representations, where, because of the induced entropic contribution to the full free energy, it appears funnel-like. Proteins explore the conformation space by searching these flat subspaces to find a narrow energetic alley that we call a hypergutter and then explore the next, lower-dimensional, subspace. Such a framework provides an effective representation of the energy landscape and folding kinetics that does justice to the essential characteristic of high-dimensionality of the search-space. It also illuminates the important role of nonnative interactions in defining folding pathways. This principle is here illustrated using a coarse-grained model of a family of three-helix bundle proteins whose conformations, once secondary structure has formed, can be defined by six rotational degrees of freedom. Two folding mechanisms are possible, one of which involves an intermediate. The stabilization of intermediate subspaces (or states in low-dimensional projection) in protein folding can either speed up or slow down the folding rate depending on the amount of native and nonnative contacts made in those subspaces. The folding rate increases due to reduced-dimension pathways arising from the mere presence of intermediate states, but decreases if the contacts in the intermediate are very stable and introduce sizeable topological or energetic frustration that needs to be overcome. Remarkably, the hypergutter framework, although depending on just a few physically meaningful parameters, can reproduce all the types of experimentally observed curvature in chevron plots for realizations of this fold

On the development of a GroEL based platform for identifying pharmacological chaperones

Many protein misfolding diseases are due to changes in protein homeostasis. This might lead to protein misfolding and possibly intra- or extracellular aggregation, causing loss of protein function or gain of toxic function. In several cases, the cellular clearance mechanism is sometimes inhibited and unable to degrade the aggregated forms leading to cell injury and death. This is frequently observed in misfolding diseases like Parkinson's disease, Cystic fibrosis, Alzheimer's, etc. These diseases account for nearly 30-50 % of all known human diseases afflicting millions and have a significant economic impact. However, there are currently few treatments to counteract these diseases. Thus, there is a pressing need to develop strategies to treat these diseases. One strategy is to develop small molecule ligand drugs that prevent the initial protein misfolding reaction. Proteins are somewhat metastable and naturally exist in dynamic equilibria between native fold and an ensemble of partially unfolded forms. This makes the misfolded forms moving targets and thus difficult to stabilize. This difficulty is compounded while developing high throughput assays to screen for stabilizing ligands for these moving protein targets. Consequently, these assays depend on detecting secondary misfolding events such as aggregation or removal of misfolded species, thus increasing the duration of the assays (hours-days). Additionally, in most instances specific cell-based assay systems have to be developed for each misfolding protein. This inhibits the development of broad based assays and complicates rapid screening of the huge compound libraries developed by rational drug design and combinatorial chemistry. In this dissertation, the development of a broad based high throughput assay for identifying novel stabilizers for protein misfolding diseases has been presented. The bacterial chaperonin GroEL binds to proteins that are partially unfolded or exist in a folded to partially folded dynamic equilibrium. Based on this property, the development of a generic broad based assay to probe a multitude of protein substrates based on changes in hydrophobic character was hypothesized and carried out. Using the chaperonin as a detection platform will enable the extension of this detection platform to identify potential stabilizers of the native fold that prevent or inhibit protein misfolding

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft