Ratcheted molecular-dynamics simulations identify efficiently the transition state of protein folding

The atomistic characterization of the transition state is a fundamental step to improve the understanding of the folding mechanism and the function of proteins. From a computational point of view, the identification of the conformations that build out the transition state is particularly cumbersome, mainly because of the large computational cost of generating a statistically-sound set of folding trajectories. Here we show that a biasing algorithm, based on the physics of the ratchet-and-pawl, can be used to identify efficiently the transition state. The basic idea is that the algorithmic ratchet exerts a force on the protein when it is climbing the free-energy barrier, while it is inactive when it is descending. The transition state can be identified as the point of the trajectory where the ratchet changes regime. Besides discussing this strategy in general terms, we test it within a protein model whose transition state can be studied independently by plain molecular dynamics simulations. Finally, we show its power in explicit-solvent simulations, obtaining and characterizing a set of transition--state conformations for ACBP and CI2

An implementation of the maximum-caliber principle by replica-averaged time-resolved restrained simulations

Inferential methods can be used to integrate experimental informations and molecular simulations. The maximum entropy principle provides a framework for using equilibrium experimental data and it has been shown that replica-averaged simulations, restrained using a static potential, are a practical and powerful implementation of such principle. Here we show that replica-averaged simulations restrained using a time-dependent potential are equivalent to the principle of maximum caliber, the dynamic version of the principle of maximum entropy, and thus may allow to integrate time-resolved data in molecular dynamics simulations. We provide an analytical proof of the equivalence as well as a computational validation making use of simple models and synthetic data. Some limitations and possible solutions are also discussed

Sensing Requirements and Vision-Aided Navigation Algorithms for Vertical Landing in Good and Low Visibility UAM Scenarios

To support the rapid development of the Urban Air Mobility framework, safe navigation must be ensured to Vertical Take-Off and Landing aircraft, especially in the approach and landing phases. Visual sensors have the potential of providing accurate measurements with reduced budgets, although integrity issues, as well as performance degradation in low visibility and highly dynamic environments, may pose challenges. In this context, this paper focuses on autonomous navigation during vertical approach and landing procedures and provides three main contributions. First, visual sensing requirements relevant to Urban Air Mobility scenarios are defined considering realistic landing trajectories, landing pad dimensions, and wind effects. Second, a multi-sensor-based navigation architecture based on an Extended Kalman Filter is presented which integrates visual estimates with inertial and GNSS measurements and includes different operating modes and ad hoc integrity checks. The presented processing pipeline is built to provide the required navigation performance in different conditions including day/night flight, atmospheric disturbances, low visibility, and can support the autonomous initialization of a missed approach procedure. Third, performance assessment of the proposed architecture is conducted within a highly realistic simulation environment which reproduces real world scenarios and includes variable weather and illumination conditions. Results show that the proposed architecture is robust with respect to dynamic and environmental challenges, providing cm-level positioning uncertainty in the final landing phase. Furthermore, autonomous initialization of a Missed Approach Procedure is demonstrated in case of loss of visual contact with the landing pad and consequent increase of the self-estimated navigation uncertainty

Sampling the Denatured State of Polypeptides in Water, Urea, and Guanidine Chloride to Strict Equilibrium Conditions with the Help of Massively Parallel Computers

The denatured state of polypeptides and proteins, stabilized by chemical denaturants like urea and guanidine chloride, displays residual secondary structure when studied by nuclear-magnetic-resonance spectroscopy. However, these experimental techniques are weakly sensitive, and thus molecular-dynamics simulations can be useful to complement the experimental findings. To sample the denatured state, we made use of massively-parallel computers and of a variant of the replica exchange algorithm, in which the different branches, connected with unbiased replicas, favor the formation and disruption of local secondary structure. The algorithm is applied to the second hairpin of GB1 in water, in urea, and in guanidine chloride. We show with the help of different criteria that the simulations converge to equilibrium. It results that urea and guanidine chloride, besides inducing some polyproline-II structure, have different effect on the hairpin. Urea disrupts completely the native region and stabilizes a state which resembles a random coil, while guanidine chloride has a milder effect

Analysis of ground infrastructure and sensing strategies for all-weather approach and landing in Urban Air Mobility

As a key requirement for the development of a safe Urban Air Mobility (UAM) framework, all-time/all-weather navigation and perception capabilities must be ensured to Vertical Take-Off and Landing (VTOL) aircraft, especially in the approach and landing phases. In these scenarios, the conventional landing systems are expected to be complemented or replaced by technological solutions tailored to UAM, including the use of multiple exteroceptive sensors and, consequently, of multi-sensor navigation algorithms. Furthermore, fully autonomous landing capabilities represent a prerequisite to gradually remove the necessity for onboard pilots while maintaining safe operations. Within this framework, after a brief overview of the possible navigation solutions both in terms of required onboard sensors and ground infrastructure, this paper focuses on the integration of Artificial Intelligence (AI) techniques in a vision-based navigation architecture. A Deep Learning based object detector is trained to recognize conventional landing pads and integrated within a pose determination pipeline to generate aiding measurements for a multi-sensor state estimation filter. The performance of the implemented architecture is assessed in simulated scenarios. Finally, initial research efforts relevant to the integration of Frequency Modulated Continuous Wave (FMCW) radars within the onboard sensing suite are presented, which deal with the generation of simulated radar data in customizable urban scenarios

Extending Enhanced Visual Operations to Urban Air Mobility: Requirements and Approaches

This paper addresses the possibility to exploit Enhanced Vision Systems (EVS) for landing operations within the Urban Air Mobility (UAM) framework. First, an analysis of sensing requirements is carried out in terms of sensors’ Field of View (FOV), operational range, resolution, and frame rate. These requirements are estimated considering different factors such as airspace structure and possible landing trajectories, navigation performance, and wind conditions. A preliminary assessment of the level of maturity of the available technologies is obtained as a result of the comparison of the EVS state of the art and the estimated visual requirements for the UAM scenarios. Finally, the performance of sensing systems fulfilling the previously defined requirements is assessed within a numerical simulation framework able to realistically reproduce UAM landing scenarios including trajectory/attitude disturbances. Specifically, visual-based techniques for pose estimation with respect to a vertiport are developed and then integrated within a multi-sensor fusion architecture combining other sources such as inertial sensors

Vision-aided approach and landing through AI-based vertiport recognition

This paper presents a vision-aided navigation pipeline to support the approach and landing phase of autonomous Vertical Take-Off and Landing aircraft in Urban Air Mobility scenarios. The proposed filtering scheme is fed by measurements provided by an Inertial Measurement Unit and a GNSS receiver, as well as by pose estimates computed from images collected by onboard cameras. Specifically, the camera frames are processed by a Convolutional Neural Network (CNN) trained to detect the vertiport landing marking in urban scenarios. Subsequently, the relevant 2D features of the pattern inside the resulting bounding box are extracted, recognized and used to solve the Perspective-n-Point problem. The performance of the implemented navigation filter is first analyzed using synthetic data collected simulating realistic landing trajectories. Then, two different training strategies are compared to verify the contribution of real data to the detection performance and to check the capability of the CNN to correctly identify the pattern in the tested scenarios. In addition, the entire pipeline for landing pad detection and pose estimation is tested on real images under various pose, illumination and background conditions

A highly compliant protein native state with a spontaneous-like mechanical unfolding pathway

The mechanical properties of proteins and their force-induced structural changes play key roles in many biological processes. Previous studies have shown that natively folded proteins are brittle under tension, unfolding after small mechanical deformations, while partially folded intermediate states, such as molten globules, are compliant and can deform elastically a great amount before crossing the transition state barrier. Moreover, under tension proteins appear to unfold through a different sequence of events than during spontaneous unfolding. Here, we describe the response to force of the four-a-helix acyl-CoA binding protein (ACBP) in the low-force regime using optical tweezers and ratcheted molecular dynamics simulations. The results of our studies reveal an unprecedented mechanical behavior of a natively folded protein. ACBP displays an atypical compliance along two nearly orthogonal pulling axes, with transition states located almost halfway between the unfolded and folded states. Surprisingly, the deformability of ACBP is greater than that observed for the highly pliant molten globule intermediate states. Furthermore, when manipulated from the N- and C-termini, ACBP unfolds by populating a transition state that resembles that observed during chemical denaturation, both for structure and position along the reaction coordinate. Our data provide the first experimental evidence of a spontaneous-like mechanical unfolding pathway of a protein. The mechanical behavior of ACBP is discussed in terms of topology and helix propensity