Showing papers by "Juan Cortés published in 2017"
TL;DR: Recent advances in the application of SAS to disordered proteins and highly flexible complexes are described and current challenges are discussed.
78 citations
TL;DR: The unique capabilities of SAS enable its application to extremely challenging disordered systems such as low-complexity regions, amyloidogenic proteins and transient biomolecular complexes, which reinforces the fundamental role of SAS in the structural and dynamic characterization of this elusive family of proteins.
Abstract: Intrinsically Disordered Proteins (IDPs) are fundamental actors of biological processes. Their inherent plasticity facilitates very specialized tasks in cell regulation and signalling, and their malfunction is linked to severe pathologies. Understanding the functional role of disorder requires the structural characterization of IDPs and the complexes they form. Small-angle Scattering of X-rays (SAXS) and Neutrons (SANS) have notably contributed to this structural understanding. In this review we summarize the most relevant developments in the field of SAS studies of disordered proteins. Emphasis is given to ensemble methods and how SAS data can be combined with computational approaches or other biophysical information such as NMR. The unique capabilities of SAS enable its application to extremely challenging disordered systems such as low-complexity regions, amyloidogenic proteins and transient biomolecular complexes. This reinforces the fundamental role of SAS in the structural and dynamic characterization of this elusive family of proteins.
34 citations
01 Dec 2017
TL;DR: This work combines the GCM theory with randomized path-planning approaches, and local path optimization techniques to derive a tool that can provide global, good-quality paths.
Abstract: This paper addresses the problem of generating a path for a fleet of robots navigating in a cluttered environment, while maintaining the so called generalized connectivity. The main challenge in the management of a group of robots is to ensure the coordination between them, taking into account limitations in communication range and sensors, possible obstacles, inter-robot avoidance and other constraints. The Generalized Connectivity Maintenance (GCM) theory already provides a way to represent and consider the aforementioned constraints, but previous works only find solutions via locally-steering functions that do not provide global and optimal solutions. In this work, we merge the GCM theory with randomized path-planning approaches, and local path optimization techniques to derive a tool that can provide global, good-quality paths. The proposed approach has been intensively tested and verified by mean of numerical simulations.
10 citations