The strength or weakness of an algorithm is ultimately governed by the confidence of its result. When the domain of the problem is large (e.g. traversal of a high-dimensional space), a perfect solution cannot be obtained, so approximations must be made. These approximations often lead to a reported quantity of interest (QOI) which varies between runs, decreasing the confidence of any single run. When the algorithm further computes this final QOI based on uncertain or noisy data, the variability (or lack of confidence) of the final QOI increases. Unbounded, these two sources of uncertainty (algorithmic approximations and uncertainty in input data) can result in a reported statistic that has low correlation with ground truth. In biological applications, this is especially applicable, as the search space is generally approximated at least to some degree (e.g. a high percentage of protein structures are invalid or energetically unfavorable) and the explicit conversion from continuous to discrete space for protein representation implies some uncertainty in the input data. This research applies uncertainty quantification techniques to the difficult protein-protein docking problem, first showing the variability that exists in existing software, and then providing a method for computing probabilistic certificates in the form of Chernoff-like bounds. Finally, this paper leverages these probabilistic certificates to accurately bound the uncertainty in docking from two docking algorithms, providing a QOI that is both robust and statistically meaningful.
Amadei al @cite_18 suggested it was possible to separate the protein configurational space into two subspaces, the essential'' subspace (only a few degrees of freedom that give rise to the large-scale motions of the protein) and the physically constrained'' subspace, consisting of largely Gaussian motion. Thus, identifying a small number of hinge'' residues that give rise to the 'essential' subspace has been the focus of another group of work. Shamsuddin al @cite_17 constructed @math basis vectors to define the motion of rigid bodies joined by hinge residues, HingeProt @cite_1 is a popular method for identifying hinges using cross-correlation of movements from a normal modes analysis (NMA).
@cite_18 @cite_1 @cite_17
2016280956 2106301834
Analysis of extended molecular dynamics (MD) simulations of several proteins in aquous solutions reveals that it is possible to separate the configurational space into two subspaces: (1) an ‘essential’ subspace containing only a few degrees of freedom in which anharmonic motion occurs that comprises most of the positional fluctuations; and (2) the remaining space in which the motion has a narrow Gaussian distribution and which can be considered as ‘physically constrained’. Proteins play a significant role in virtually all biological processes, with motions of interest occurring on the timescale of pico- to nano-seconds. Existing experimental techniques are not able to reliably provide the level of detail required for studying the exact mechanisms of the molecular motion and the underlying structure-function relationship, essential for effective drug screening and design. Theoretical models and computational tools are instrumental for gaining better mechanistic understanding and predictive power. We focus on “hinge” proteins, which exhibit a rotational movement of one region of the protein relative to another; this motion is similar to that allowed by revolute joints. By applying rigidity theoretic techniques that analyze the protein's geometric structure, we predict a relative axis of motion between a given pair of domains. To the best of our knowledge, this is the first computational method to predict such an axis based on a single conformational state. The most closely related approaches for predicting hinges either use a single conformation to identify the residues permitting the motion (Stonehinge, HingeProt), or require two conformations as input (DynDom). Our results show that rigidity theory can be applied to analyze proteins and accurately predict information that may elucidate conformational changes tied to protein function.