260,789 research outputs found
Polycation-Ï€ Interactions Are a Driving Force for Molecular Recognition by an Intrinsically Disordered Oncoprotein Family
Molecular recognition by intrinsically disordered proteins (IDPs) commonly involves specific localized contacts and target-induced disorder to order transitions. However, some IDPs remain disordered in the bound state, a phenomenon coined "fuzziness", often characterized by IDP polyvalency, sequence-insensitivity and a dynamic ensemble of disordered bound-state conformations. Besides the above general features, specific biophysical models for fuzzy interactions are mostly lacking. The transcriptional activation domain of the Ewing's Sarcoma oncoprotein family (EAD) is an IDP that exhibits many features of fuzziness, with multiple EAD aromatic side chains driving molecular recognition. Considering the prevalent role of cation-π interactions at various protein-protein interfaces, we hypothesized that EAD-target binding involves polycation- π contacts between a disordered EAD and basic residues on the target. Herein we evaluated the polycation-π hypothesis via functional and theoretical interrogation of EAD variants. The experimental effects of a range of EAD sequence variations, including aromatic number, aromatic density and charge perturbations, all support the cation-π model. Moreover, the activity trends observed are well captured by a coarse-grained EAD chain model and a corresponding analytical model based on interaction between EAD aromatics and surface cations of a generic globular target. EAD-target binding, in the context of pathological Ewing's Sarcoma oncoproteins, is thus seen to be driven by a balance between EAD conformational entropy and favorable EAD-target cation-π contacts. Such a highly versatile mode of molecular recognition offers a general conceptual framework for promiscuous target recognition by polyvalent IDPs. © 2013 Song et al
Molecular Recognition as an Information Channel: The Role of Conformational Changes
Molecular recognition, which is essential in processing information in
biological systems, takes place in a crowded noisy biochemical environment and
requires the recognition of a specific target within a background of various
similar competing molecules. We consider molecular recognition as a
transmission of information via a noisy channel and use this analogy to gain
insights on the optimal, or fittest, molecular recognizer. We focus on the
optimal structural properties of the molecules such as flexibility and
conformation. We show that conformational changes upon binding, which often
occur during molecular recognition, may optimize the detection performance of
the recognizer. We thus suggest a generic design principle termed
'conformational proofreading' in which deformation enhances detection. We
evaluate the optimal flexibility of the molecular recognizer, which is
analogous to the stochasticity in a decision unit. In some scenarios, a
flexible recognizer, i.e., a stochastic decision unit, performs better than a
rigid, deterministic one. As a biological example, we discuss conformational
changes during homologous recombination, the process of genetic exchange
between two DNA strands.Comment: Keywords--Molecular information channels, molecular recognition,
conformational proofreading.
http://www.weizmann.ac.il/complex/tlusty/papers/IEEE2009b.pd
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Deep convolutional neural networks (CNN) have shown their promise as a
universal representation for recognition. However, global CNN activations lack
geometric invariance, which limits their robustness for classification and
matching of highly variable scenes. To improve the invariance of CNN
activations without degrading their discriminative power, this paper presents a
simple but effective scheme called multi-scale orderless pooling (MOP-CNN).
This scheme extracts CNN activations for local patches at multiple scale
levels, performs orderless VLAD pooling of these activations at each level
separately, and concatenates the result. The resulting MOP-CNN representation
can be used as a generic feature for either supervised or unsupervised
recognition tasks, from image classification to instance-level retrieval; it
consistently outperforms global CNN activations without requiring any joint
training of prediction layers for a particular target dataset. In absolute
terms, it achieves state-of-the-art results on the challenging SUN397 and MIT
Indoor Scenes classification datasets, and competitive results on
ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets
Learning Finer-class Networks for Universal Representations
Many real-world visual recognition use-cases can not directly benefit from
state-of-the-art CNN-based approaches because of the lack of many annotated
data. The usual approach to deal with this is to transfer a representation
pre-learned on a large annotated source-task onto a target-task of interest.
This raises the question of how well the original representation is
"universal", that is to say directly adapted to many different target-tasks. To
improve such universality, the state-of-the-art consists in training networks
on a diversified source problem, that is modified either by adding generic or
specific categories to the initial set of categories. In this vein, we proposed
a method that exploits finer-classes than the most specific ones existing, for
which no annotation is available. We rely on unsupervised learning and a
bottom-up split and merge strategy. We show that our method learns more
universal representations than state-of-the-art, leading to significantly
better results on 10 target-tasks from multiple domains, using several network
architectures, either alone or combined with networks learned at a coarser
semantic level.Comment: British Machine Vision Conference (BMVC) 201
Antibody fragments as probe in biosensor development
Today's proteomic analyses are generating increasing numbers of biomarkers, making it essential to possess highly specific probes able to recognize those targets. Antibodies are considered to be the first choice as molecular recognition units due to their target specificity and affinity, which make them excellent probes in biosensor development. However several problems such as difficult directional immobilization, unstable behavior, loss of specificity and steric hindrance, may arise from using these large molecules. Luckily, protein engineering techniques offer designed antibody formats suitable for biomarker analysis. Minimization strategies of antibodies into Fab fragments, scFv or even single-domain antibody fragments like VH, VL or VHHs are reviewed. Not only the size of the probe but also other issues like choice of immobilization tag, type of solid support and probe stability are of critical importance in assay development for biosensing. In this respect, multiple approaches to specifically orient and couple antibody fragments in a generic one-step procedure directly on a biosensor substrate are discussed
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