Force Distribution in Macromolecules

All living organisms utilize thousands of molecular building blocks to perform mechanical tasks. These building blocks are mostly proteins, and their mechanical properties define the way they can be utilized by the cell. The spectrum ranges from rope like structures that give hold and stability to our bodies to microscopic engines helping us to perform or sense mechanical work. An increasing number of biological processes are revealed to be driven by force and well-directed distribution of strain is the very base of many of these mechanisms. We need to be able to observe the distribution of strain within bio-molecules if we want to gain detailed insight into the function of these highly complex nano-machines. Only by theoretical understanding and prediction of mechanical processes on the molecular level will we be able to rationally tailor proteins to mimic specific biological functions. This thesis aims at understanding the molecular mechanics of a wide range of biological molecules, such as the muscle protein titin or silk fibers. We introduce Force Distribution Analysis (FDA), a new approach to directly study the forces driving molecular processes, instead of indirectly observing them by means of coordinate changes

Implementation of force distribution analysis for molecular dynamics simulations

<p>Abstract</p> <p>Background</p> <p>The way mechanical stress is distributed inside and propagated by proteins and other biopolymers largely defines their function. Yet, determining the network of interactions propagating internal strain remains a challenge for both, experiment and theory. Based on molecular dynamics simulations, we developed force distribution analysis (FDA), a method that allows visualizing strain propagation in macromolecules.</p> <p>Results</p> <p>To be immediately applicable to a wide range of systems, FDA was implemented as an extension to Gromacs, a commonly used package for molecular simulations. The FDA code comes with an easy-to-use command line interface and can directly be applied to every system built using Gromacs. We provide an additional R-package providing functions for advanced statistical analysis and presentation of the FDA data.</p> <p>Conclusions</p> <p>Using FDA, we were able to explain the origin of mechanical robustness in immunoglobulin domains and silk fibers. By elucidating propagation of internal strain upon ligand binding, we previously also successfully revealed the functionality of a stiff allosteric protein. FDA thus has the potential to be a valuable tool in the investigation and rational design of mechanical properties in proteins and nano-materials.</p

Dynamic Allostery in the Methionine Repressor Revealed by Force Distribution Analysis

Many fundamental cellular processes such as gene expression are tightly regulated by protein allostery. Allosteric signal propagation from the regulatory to the active site requires long-range communication, the molecular mechanism of which remains a matter of debate. A classical example for long-range allostery is the activation of the methionine repressor MetJ, a transcription factor. Binding of its co-repressor SAM increases its affinity for DNA several-fold, but has no visible conformational effect on its DNA binding interface. Our molecular dynamics simulations indicate correlated domain motions within MetJ, and quenching of these dynamics upon SAM binding entropically favors DNA binding. From monitoring conformational fluctuations alone, it is not obvious how the presence of SAM is communicated through the largely rigid core of MetJ and how SAM thereby is able to regulate MetJ dynamics. We here directly monitored the propagation of internal forces through the MetJ structure, instead of relying on conformational changes as conventionally done. Our force distribution analysis successfully revealed the molecular network for strain propagation, which connects collective domain motions through the protein core. Parts of the network are directly affected by SAM binding, giving rise to the observed quenching of fluctuations. Our results are in good agreement with experimental data. The force distribution analysis suggests itself as a valuable tool to gain insight into the molecular function of a whole class of allosteric proteins

Force Distribution in Macromolecules

All living organisms utilize thousands of molecular building blocks to perform mechanical tasks. These building blocks are mostly proteins, and their mechanical properties define the way they can be utilized by the cell. The spectrum ranges from rope like structures that give hold and stability to our bodies to microscopic engines helping us to perform or sense mechanical work. An increasing number of biological processes are revealed to be driven by force and well-directed distribution of strain is the very base of many of these mechanisms. We need to be able to observe the distribution of strain within bio-molecules if we want to gain detailed insight into the function of these highly complex nano-machines. Only by theoretical understanding and prediction of mechanical processes on the molecular level will we be able to rationally tailor proteins to mimic specific biological functions. This thesis aims at understanding the molecular mechanics of a wide range of biological molecules, such as the muscle protein titin or silk fibers. We introduce Force Distribution Analysis (FDA), a new approach to directly study the forces driving molecular processes, instead of indirectly observing them by means of coordinate changes

Force Distribution in Macromolecules

All living organisms utilize thousands of molecular building blocks to perform mechanical tasks. These building blocks are mostly proteins, and their mechanical properties define the way they can be utilized by the cell. The spectrum ranges from rope like structures that give hold and stability to our bodies to microscopic engines helping us to perform or sense mechanical work. An increasing number of biological processes are revealed to be driven by force and well-directed distribution of strain is the very base of many of these mechanisms. We need to be able to observe the distribution of strain within bio-molecules if we want to gain detailed insight into the function of these highly complex nano-machines. Only by theoretical understanding and prediction of mechanical processes on the molecular level will we be able to rationally tailor proteins to mimic specific biological functions. This thesis aims at understanding the molecular mechanics of a wide range of biological molecules, such as the muscle protein titin or silk fibers. We introduce Force Distribution Analysis (FDA), a new approach to directly study the forces driving molecular processes, instead of indirectly observing them by means of coordinate changes

Handling of data containing outliers

1 PCA robust to outliers Away from often showing missing values, Microarray or Metabolite data are often corrupted with extreme values (outliers). Standard SVD is highly susceptible to outliers. In the extreme case, an individual data point, if sufficiently outlying, can draw even the leading principal component toward itself. This problem can be addressed by using a robust analysis method. Hereto we provide robustSvd, a singular value decomposition robust to outliers. robustPca is a PCA implementation that resembles the original R prcomp method, with the difference that it uses robustSvd instead of the standard svd function. Robust SVD and its application to microarray data were proposed in [1] and [2]. The algorithm is based on the idea to use a sequential estimation of the eigenvalues and left and right eigenvectors that ignores missing values and is resistant to outliers. The robustSvd script included here was contributed by Kevin Wright. Thanks a lot to him