The Interplay between Chemistry and Mechanics in the Transduction of a Mechanical Signal into a Biochemical Function

There are many processes in biology in which mechanical forces are generated. Force-bearing networks can transduce locally developed mechanical signals very extensively over different parts of the cell or tissues. In this article we conduct an overview of this kind of mechanical transduction, focusing in particular on the multiple layers of complexity displayed by the mechanisms that control and trigger the conversion of a mechanical signal into a biochemical function. Single molecule methodologies, through their capability to introduce the force in studies of biological processes in which mechanical stresses are developed, are unveiling subtle intertwining mechanisms between chemistry and mechanics and in particular are revealing how chemistry can control mechanics. The possibility that chemistry interplays with mechanics should be always considered in biochemical studies.Comment: 50 pages, 18 figure

Single-molecule experiments in biological physics: methods and applications

I review single-molecule experiments (SME) in biological physics. Recent technological developments have provided the tools to design and build scientific instruments of high enough sensitivity and precision to manipulate and visualize individual molecules and measure microscopic forces. Using SME it is possible to: manipulate molecules one at a time and measure distributions describing molecular properties; characterize the kinetics of biomolecular reactions and; detect molecular intermediates. SME provide the additional information about thermodynamics and kinetics of biomolecular processes. This complements information obtained in traditional bulk assays. In SME it is also possible to measure small energies and detect large Brownian deviations in biomolecular reactions, thereby offering new methods and systems to scrutinize the basic foundations of statistical mechanics. This review is written at a very introductory level emphasizing the importance of SME to scientists interested in knowing the common playground of ideas and the interdisciplinary topics accessible by these techniques. The review discusses SME from an experimental perspective, first exposing the most common experimental methodologies and later presenting various molecular systems where such techniques have been applied. I briefly discuss experimental techniques such as atomic-force microscopy (AFM), laser optical tweezers (LOT), magnetic tweezers (MT), biomembrane force probe (BFP) and single-molecule fluorescence (SMF). I then present several applications of SME to the study of nucleic acids (DNA, RNA and DNA condensation), proteins (protein-protein interactions, protein folding and molecular motors). Finally, I discuss applications of SME to the study of the nonequilibrium thermodynamics of small systems and the experimental verification of fluctuation theorems. I conclude with a discussion of open questions and future perspectives.Comment: Latex, 60 pages, 12 figures, Topical Review for J. Phys. C (Cond. Matt

TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table

Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks

Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design

Capturing the essence of folding and functions of biomolecules using Coarse-Grained Models

The distances over which biological molecules and their complexes can function range from a few nanometres, in the case of folded structures, to millimetres, for example during chromosome organization. Describing phenomena that cover such diverse length, and also time scales, requires models that capture the underlying physics for the particular length scale of interest. Theoretical ideas, in particular, concepts from polymer physics, have guided the development of coarse-grained models to study folding of DNA, RNA, and proteins. More recently, such models and their variants have been applied to the functions of biological nanomachines. Simulations using coarse-grained models are now poised to address a wide range of problems in biology.Comment: 37 pages, 8 figure

Ion Mobility-Mass Spectrometry and Collision Induced Unfolding of Multi-Protein Ligand Complexes.

Mass spectrometry (MS) serves as an indispensable technology for modern pharmaceutical drug discovery and development processes, where it is used to assess ligand binding to target proteins and to search for biomarkers that can be used to gauge disease progression and drug action. However, MS is rarely treated as a screening technology for the structural consequences of drug binding. Instead, more time-consuming technologies capable of projecting atomic models of protein-drug interactions are utilized. In this thesis, ion mobility-mass spectrometry (IM-MS) methods are developed in order to fill these technology gaps. Principle among these is collision induced unfolding (CIU), which leverages the ability of IM to separate ions according to their size and charge, in order to fingerprint gas-phase unfolding pathways for non-covalent protein complexes. Following a comprehensive introductory chapter, we demonstrate the consequences of sugar binding on the CIU of Concanavalin A (Con A) in Chapter 2. Our CIU assay reveals cooperative stabilization upon small molecule binding, and such effect cannot be easily detected by solution phase assays, or by MS alone. In Chapter 3, the underlying mechanism of multi-protein unfolding is systematically investigated by IM-MS and molecular modeling approaches. Our results show a strong positive correlation between monomeric Coulombic unfolding and the tetrameric CIU process. This provides strong evidence that multi-protein unfolding events are initiated primarily by charge migration from the complex to a single monomer. In Chapter 4, the interactions between human histone deacetylase 8 (HDAC8) and poly-r(C)-binding protein 1 (PCBP1) are investigated by IM-MS. Our data suggest that these proteins interact with each other in a specific manner, a fact revealed by our optimized ESI-MS workflow for quantifying binding affinity (KD) for weakly-associated hetero-protein complexes. In Chapter 5, the translocator protein (TSPO) dimer from Rhodobacter sphaeroides, as well as its disease-associated variant forms, is analyzed by IM-MS and CIU assays. By utilizing a combination of CIU and collision induced dissociation (CID) stability data, an unknown endogenous ligand bound to TSPO is detected and identified.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116693/1/shuainiu_1.pd

Methods and Informatics for Gas-Phase Structural Biology and Drug Discovery

Methods for rapid interrogation of structure and stability attributes of proteins and protein complexes are becoming increasingly important for developing our understanding of biology and the development of pharmaceuticals. Gas-phase technologies such as mass spectrometry and ion mobility spectrometry have proven valuable in these endeavors, as they provide unique perspectives on the solution-phase equilibrium of protein complexes and their conformations. Before fully harnessing the information derived from these gas-phase techniques, new approaches for data analysis and mechanistic understanding of gas-phase protein structure are necessary. In this dissertation, we develop ion mobility mass spectrometry methods and informatics for the study of gas-phase proteins, multiprotein complexes, and protein-small molecule complexes. In the first half of the dissertation, novel data analysis tools and experimental methodologies are outlined for the study of gas-phase protein unfolding. After providing the software tools necessary for robust analysis of gas-phase unfolding trajectories in Chapter 2, we turned our attention to understanding the mechanism of unfolding for large multidomain proteins. In Chapter 3, we focus on the factors driving changes in unfolding trajectories for a variety of serum albumin homologues, and through the use of novel unfolding experiments utilizing chemical probes and non-covalent protein constructs, a detailed mechanism for solvent-free protein unfolding is provided. Subsequent chapters in the dissertation focus on the characterization of multiprotein complexes, especially through the use of ion mobility-mass spectrometry and coarse-grained modeling. In chapter 4, we develop and benchmark new algorithms for translating ion mobility and mass spectrometry datasets into coarse grained models. These studies outline the limits in current coarse-graining methodologies, and define the minimum restraint sets necessary to generate high confidence multiprotein models. Additionally, best practices for dealing with ambiguous models resulting from sparse datasets are described. In chapter 5, the tools developed in the previous chapter are applied to structurally characterize the urease pre-activation complex, a transient 18-subunit complex that is a target for inhibition of urease-related pathology. When our ion mobility-mass spectrometry datasets are combined with previously published chemical crosslinking and x-ray scattering data, a discrete population of conformations for the urease pre-activation complex emerges which compares favorably to previous models generated using computational techniques. In Chapter 6, I highlight more applications of ion mobility-mass spectrometry to engineered and naturally occurring protein complexes. These applications highlight the power of ion mobility mass-mass spectrometry datasets for rapid analysis of protein oligomerization state and structure, providing a basis for further integration of the technology into pharmaceutical and structural biology workflows.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138785/1/joeesch_1.pd

Role of Resultant Dipole Moment in Mechanical Dissociation of Biological Complexes

Protein-peptide interactions play essential roles in many cellular processes and their structural characterization is the major focus of current experimental and theoretical research. Two decades ago, it was proposed to employ the steered molecular dynamics to assess the strength of protein-peptide interactions. The idea behind using steered molecular dynamics simulations is that the mechanical stability can be used as a promising and an efficient alternative to computationally highly demanding estimation of binding affinity. However, mechanical stability defined as a peak in force-extension profile depends on the choice of the pulling direction. Here we propose an uncommon choice of the pulling direction along resultant dipole moment vector, which has not been explored in simulations so far. Using explicit solvent all-atom MD simulations, we apply steered molecular dynamics technique to probe mechanical resistance of protein-peptide system pulled along two different vectors. A novel pulling direction, along the resultant dipole moment vector, results in stronger forces compared to commonly used peptide unbinding along center of masses vector. Our results demonstrate that resultant dipole moment is one of the factors influencing the mechanical stability of protein-peptide complex.Comment: 11 pages, 4 figures, 2 table