Comment on ``Experimental Free Energy Reconstruction From Single-Molecule Force Spectroscopy Using Jarzynski's Equality''

Harris, Song and Kiang [1] (HSK) describe their results on reconstructing the free energy profiles for both the stretch of the titin polymer, and the unfolding of an individual I27 domain. The new finding reported in [1] is the measurement of the free energy barrier (or activation energy) to unfolding the I27 domain. Due to a misinterpretation of the mechanics involved, the free energy surface (and thus the energy barrier) to unfolding the I27 domain was not measured

Unified Model of Dynamic Forced Barrier Crossing in Single Molecules

Thermally activated barrier crossing in the presence of an increasing load can reveal kinetic rate constants and energy barrier parameters when repeated over a range of loading rates. Here we derive a model of the mean escape force for all relevant loading rates--the complete force spectrum. Two well-known approximations emerge as limiting cases; one of which confirms predictions that single-barrier spectra should converge to a phenomenological description in the slow loading limit

Quantifying the free energy landscape between polymers and minerals

Higher organisms as well as medical and technological materials exploit mineral-polymer interactions, however, mechanistic understanding of these interactions is poorly constrained. Dynamic force spectroscopy can probe the free energy landscape of interacting bonds, but interpretations are challenged by the complex mechanical behavior of polymers. Here we restate the difficulties inherent to applying DFS to polymer-linked adhesion and present an approach to gain quantitative insight into polymer-mineral bindingpublishersversionPeer reviewe

Mechanistic insight into biopolymer induced iron oxide mineralization through quantification of molecular bonding

Microbial production of iron (oxyhydr)oxides on polysaccharide rich biopolymers occurs on such a vast scale that it impacts the global iron cycle and has been responsible for major biogeochemical events. Yet the physiochemical controls these biopolymers exert on iron (oxyhydr)oxide formation are poorly understood. Here we used dynamic force spectroscopy to directly probe binding between complex, model and natural microbial polysaccharides and common iron (oxyhydr)oxides. Applying nucleation theory to our results demonstrates that if there is a strong attractive interaction between biopolymers and iron (oxyhydr)oxides, the biopolymers decrease the nucleation barriers, thus promoting mineral nucleation. These results are also supported by nucleation studies and density functional theory. Spectroscopic and thermogravimetric data provide insight into the subsequent growth dynamics and show that the degree and strength of water association with the polymers can explain the influence on iron (oxyhydr)oxide transformation rates. Combined, our results provide a mechanistic basis for understanding how polymer-mineral-water interactions alter iron (oxyhydr)oxides nucleation and growth dynamics and pave the way for an improved understanding of the consequences of polymer induced mineralization in natural systems

Dynamic force spectroscopy of parallel individual mucin1-antibody bonds

We used atomic force microscopy (AFM) to measure the binding forces between Mucin1 (MUC1) peptide and a single chain antibody fragment (scFv) selected from a scFv library screened against MUC1. This binding interaction is central to the design of the molecules for targeted delivery of radioimmunotherapeutic agents for prostate and breast cancer treatment. Our experiments separated the specific binding interaction from non-specific interactions by tethering the antibody and MUC1 molecules to the AFM tip and sample surface with flexible polymer spacers. Rupture force magnitude and elastic characteristics of the spacers allowed identification of the bond rupture events corresponding to different number of interacting proteins. We used dynamic force spectroscopy to estimate the intermolecular potential widths and equivalent thermodynamic off rates for mono-, bi-, and tri-valent interactions. Measured interaction potential parameters agree with the results of molecular docking simulation. Our results demonstrate that an increase of the interaction valency leads to a precipitous decline in the dissociation rate. Binding forces measured for mono and multivalent interactions match the predictions of a Markovian model for the strength of multiple uncorrelated bonds in parallel configuration. Our approach is promising for comparison of the specific effects of molecular modifications as well as for determination of the best configuration of antibody-based multivalent targeting agents

Single-Molecule Force Spectroscopy: Experiments, Analysis, and Simulations

International audienceThe mechanical properties of cells and of subcellular components are important to obtain a mechanistic molecular understanding of biological processes. The quantification of mechanical resistance of cells and biomolecules using biophysical methods matured thanks to the development of nanotechnologies such as optical and magnetic tweezers, the biomembrane force probe and atomic force microscopy (AFM). The quantitative nature of force spectroscopy measurements has converted AFM into a valuable tool in biophysics. Force spectroscopy allows the determination of the forces required to unfold protein domains and to disrupt individual receptor/ligand bonds. Molecular simulation as a computational microscope allows investigation of similar biological processes with an atomistic detail. In this chapter, we first provide a step-by-step protocol of force spectroscopy including sample preparation, measurement and analysis of force spectroscopy using AFM and its interpretation in terms of available theories. Next, we present the background for molecular dynamics (MD) simulations focusing on steered molecular dynamics (SMD) and the importance of bridging of computational tools with experimental technique

Cementomimetics—constructing a cementum-like biomineralized microlayer via amelogenin-derived peptides

This is the published version. Copyright 2012 Nature Publishing GroupCementum is the outer-, mineralized-tissue covering the tooth root and an essential part of the system of periodontal tissue that anchors the tooth to the bone. Periodontal disease results from the destructive behavior of the host elicited by an infectious biofilm adhering to the tooth root and left untreated, may lead to tooth loss. We describe a novel protocol for identifying peptide sequences from native proteins with the potential to repair damaged dental tissues by controlling hydroxyapatite biomineralization. Using amelogenin as a case study and a bioinformatics scoring matrix, we identified regions within amelogenin that are shared with a set of hydroxyapatite-binding peptides (HABPs) previously selected by phage display. One 22-amino acid long peptide regions referred to as amelogenin-derived peptide 5 (ADP5) was shown to facilitate cell-free formation of a cementum-like hydroxyapatite mineral layer on demineralized human root dentin that, in turn, supported attachment of periodontal ligament cells in vitro. Our findings have several implications in peptide-assisted mineral formation that mimic biomineralization. By further elaborating the mechanism for protein control over the biomineral formed, we afford new insights into the evolution of protein–mineral interactions. By exploiting small peptide domains of native proteins, our understanding of structure–function relationships of biomineralizing proteins can be extended and these peptides can be utilized to engineer mineral formation. Finally, the cementomimetic layer formed by ADP5 has the potential clinical application to repair diseased root surfaces so as to promote the regeneration of periodontal tissues and thereby reduce the morbidity associated with tooth loss

Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era