Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

A statistical framework to evaluate virtual screening

<p>Abstract</p> <p>Background</p> <p>Receiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails to address the "early recognition" problem specific to VS. Although many other metrics, such as RIE, BEDROC, and pROC that emphasize "early recognition" have been proposed, there are no rigorous statistical guidelines for determining the thresholds and performing significance tests. Also no comparisons have been made between these metrics under a statistical framework to better understand their performances.</p> <p>Results</p> <p>We have proposed a statistical framework to evaluate VS studies by which the threshold to determine whether a ranking method is better than random ranking can be derived by bootstrap simulations and 2 ranking methods can be compared by permutation test. We found that different metrics emphasize "early recognition" differently. BEDROC and RIE are 2 statistically equivalent metrics. Our newly proposed metric SLR is superior to pROC. Through extensive simulations, we observed a "seesaw effect" – overemphasizing early recognition reduces the statistical power of a metric to detect true early recognitions.</p> <p>Conclusion</p> <p>The statistical framework developed and tested by us is applicable to any other metric as well, even if their exact distribution is unknown. Under this framework, a threshold can be easily selected according to a pre-specified type I error rate and statistical comparisons between 2 ranking methods becomes possible. The theoretical null distribution of SLR metric is available so that the threshold of SLR can be exactly determined without resorting to bootstrap simulations, which makes it easy to use in practical virtual screening studies.</p

Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1)

BACKGROUND: In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1. METHOD: Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test. RESULTS: For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay. CONCLUSION: The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project) and from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1)

Sequential decision making based on emergent emotion for a humanoid robot

Certain emotions and moods can be manifestations of complex and costly neural computations that our brain wants to avoid. Instead of reaching an optimal decision based on the facts, we find it often easier and sometimes more useful to rely on hunches. In this work, we extend a previously developed model for such a mechanism where a simple neural associative memory was used to implement a visual recall system for a humanoid robot. In the model, the changes in the neural state consume (neural) energy, and to minimize the total cost and the time to recall a memory pattern, the robot should take the action that will lead to minimal neural state change. To do so, the robot needs to learn to act rationally, and for this, it has to explore and find out the cost of its actions in the long run. In this study, a humanoid robot (iCub) is used to act in this scenario. The robot is given the sole action of changing his gaze direction. By reinforcement learning (RL) the robot learns which state-action pair sequences lead to minimal energy consumption. More importantly, the reward signal for RL is not given by the environment but obtained internally, as the actual neural cost of processing an incoming visual stimuli. The results indicate that reinforcement learning with the internally generated reward signal leads to non-trivial behaviours of the robot which might be interpreted by external observers as the robot's `liking' of a specific visual pattern, which in fact emerged solely based on the neural cost minimization principle.European Commission ; Italian Ministry of Foreign Affairs, General Directorate for the Promotion of the "Country System", Bilateral and Multilateral Scientific and Technological Cooperation Uni

Multimodal sensory representation for object classification via neocortically-inspired algorithm

This study reports our initial work on multimodal sensory representation for object classification. To form a sensory representation we used the spatial pooling phase of the Hierarchical Temporal Memory - a Neocortically-inspired algorithm. The classification task was carried out on the Washington RGB-D dataset in which the employed method provides extraction of non-hand engineered representations (or features) from different modalities which are pixel values (RGB) and depth (D) information. These representations, both early and lately fused, were used as inputs to a machine learning algorithm to perform object classification. The obtained results show that using multimodal representations significantly improve (by 5 %) the classification performance compared to a when a single modality is used. The results also indicate that the performed method is effective for multimodal learning and different sensory modalities are complementary for the object classification. Therefore, we envision that this method can be employed for object concept formation that requires multiple sensory information to execute cognitive tasks

Spatial pooling as feature selection method for object recognition

This paper reports our work on object recognition by using the spatial pooler of Hierarchical Temporal Memory (HTM) as a method for feature selection. To perform the recognition task, we employed this pooling method to select features from COIL-100 dataset. We bench-marked the results with state-of-the-art feature extraction methods while using different amounts of training data (from 5% to 45%). The results indicate that the performed method is effective for object recognition with a low amount of training data in which state-of-the-art feature extraction methods show limitations

Knowledge based potentials: the reverse Boltzmann methodology, virtual screening and molecular weight dependence

We discuss the rationale for using the reverse Boltzmann methodology to convert atom atom distance distributions from a knowledge base of protein-ligand complexes into energy-like functions. We also generate an updated version of the BLEEP statistical potential, using a dataset of 196 complexes. This performs similarly to the existing BLEEP. An algorithm is implemented to allow the automatic calculation of bond orders, and hence of the appropriate numbers of hydrogen atoms present. An attempt is made to generate a potential specific to strongly bound complexes; however, we find no evidence that this improves the prediction of binding affinities. We also discuss the range of binding energies available as a function of ligand molecular weight and derive some simple functions describing this behaviour.</p

Challenges in Producing Reliable Tensile Properties by SIMA 7075

Aluminium alloys are one of the preferred materials especially for land and air transportation because of their high strength and low-density properties. Although production using casting method is economical yet it has some disadvantages. Shrinkage which is occurred due to the density difference between the solid and liquid metal is prevented by feeders which need to be calculated. Liquid metal should be transferred to the mould without any turbulence. As a result, sprues are needed to be designed precisely. On the other hand, aluminium alloys can also be shaped by forging at semi-solid temperatures. There are some advantages compared to the traditional forging methods of improving die life due to the lower tonnage values. In this study, semi-solid produced 7075 aluminium alloy die filling capabilities were investigated. To achieve semisolid structure strain induced melt activated method (SIMA) was used. The desired structure was achieved at 635 degrees C and 30 minutes of duration of heat treatment. After determining the optimum parameters, metallographic analysis, density calculations, porosity distribution and tensile tests were carried out. It was found that the reproducibility of SIMA produced 7075 alloy was quite low. A proper tensile test result was achieved only 7 of the total 15 tests and the mean value was 386 MPa. The main reason for this scattered in mechanical properties could be the chemical composition of the alloy and the rapid solidification of the liquid eutectic phases. It is important to define the best fitting process parameters and controlling them precisely will be the most important factors for future studies