Docking Success Rate Research Articles

Design of A Self-Correctable Docking Mechanism in Disturbed Water Surface Environment

The docking mechanism is critical for unmanned surface vehicle (USV) applications, such as traction, dragging, charging, data transmission, building pontoons, etc. These applications are significant for observation, exploration, data acquisition, patrol, rescuing, etc., in aquatic environments. In this paper, a novel autonomous docking mechanism for USVs is proposed. It is capable of (1) docking the USV subject to disturbance efficiently, (2) latching without power consumption after the docking is completed, and (3) guiding the docking head to three specific angles at the end of the docking process. This paper proved the feasibility and rationality of the docking mechanism through theoretical force analysis. Experiments with three different scenarios are implemented to test the mechanism's performance. The experimental results show that the docking success rate is greater than or equal to 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on the disturbed water surface.

Dynamic Docking Anti‐Disturbance Control of Overactuated AUV: System, Method, and Lake Trails

ABSTRACTDynamic docking control technology is crucial for autonomous underwater vehicles (AUV) to perform tasks underwater. To enhance the docking success rate of AUVs during dynamic docking, this paper presents a robust anti‐disturbance control algorithm specifically designed for overactuated AUV dynamic docking scenarios. During a dynamic docking mission, the AUV's depth control is adversely affected by the complex flow field generated by the underwater recovery device. To address this issue, this research proposes an AUV control scheme that combines an extended state observer (ESO) with a combined disturbance rejection method of the elevator‐vertical tunnel controller. First, an ESO is constructed to estimate and compensate for complicated disturbances such as model uncertainty and environmental disturbances. These estimations are then incorporated into the control law to mitigate the effects of the complicated flow field interference experienced during the AUV's dynamic docking process. Second, as turbulence intensifies at the end of the docking stage, the vertical thrust allocation is achieved using a hyperbolic tangent transition function. This ensures the stability of the AUV's attitude and depth, thereby enabling precise docking. Finally, the effectiveness of the proposed control algorithm is verified through lake trials and compared against the classic proportional‐integral‐differential (PID) and active disturbance rejection control (ADRC) methods. The trial results indicate that the proposed control algorithm significantly reduces the pitch and depth errors of the AUV, resulting in a remarkable 91% success rate for dynamic docking (based on 45 tests). The lake trials demonstrate that the proposed control algorithm is highly precise and robust.

Research on Automatic Recharging Technology for Automated Guided Vehicles Based on Multi-Sensor Fusion

Automated guided vehicles (AGVs) play a critical role in indoor environments, where battery endurance and reliable recharging are essential. This study proposes a multi-sensor fusion approach that integrates LiDAR, depth cameras, and infrared sensors to address challenges in autonomous navigation and automatic recharging. The proposed system overcomes the limitations of LiDAR’s blind spots in near-field detection and the restricted range of vision-based navigation. By combining LiDAR for precise long-distance measurements, depth cameras for enhanced close-range visual positioning, and infrared sensors for accurate docking, the AGV’s ability to locate and autonomously connect to charging stations is significantly improved. Experimental results show a 25% increase in docking success rate (from 70% with LiDAR-only to 95%) and a 70% decrease in docking error (from 10 cm to 3 cm). These improvements demonstrate the effectiveness of the proposed sensor fusion method, ensuring more reliable, efficient, and precise operations for AGVs in complex indoor environments.

Fast automatic docking control for electric tractors

In this study, we investigate the automatic docking of an electric tractor with a semi-trailer in narrow and long-distance scenarios. We propose an automatic docking method based on a multi-sensor fusion high-precision positioning algorithm and a multi-strategy docking control algorithm. To solve the heading-angle error of the involved camera due to mechanical errors, a compensation value in the form of angle deviation is introduced. To alleviate the increase in the camera detection error with longitudinal distance, a piecewise coordinate compensation function is proposed. Repositioning is performed using light detection and ranging (Lidar) to improve the pose-perception accuracy to the millimeter level. To avoid the interference of point clouds on the relocation accuracy of Lidar, a search-and-location method that combines both small and large neighborhoods is proposed. To achieve the desired docking success rate, a multi-strategy-adjustment control algorithm is used to enable comprehensive adjustment, including approximate control of the maximum angle based on lateral deviation and fine proportional-integral-differential (PID) control based on lateral deviation and longitudinal distance, security check cycle and premature warning mechanism, and risk avoidance control. Finally, the reliability and stability of the design scheme are verified by conducting multiple experiments involving actual vehicles.

Quantum Chemistry-Based Protein-Protein Docking without Empirical Parameters.

This study developed a novel protein-protein docking approach based on quantum chemistry. To judge the appropriateness of complex structures, we introduced two criterion values, EV1 and EV2, computed using the fragment molecular orbital method without any empirical parameters. These criterion values enable us to search complex structures in which patterns of the electrostatic potential of the two proteins are optimally aligned at their interface. The performance of our method was validated using 53 complexes in a benchmark set provided for protein-protein docking. When employing bound state structures, docking success rates reached 64% for EV1 and 76% for EV2. On the other hand, when employing unbound state structures, docking success rates reached 13% for EV1 and 17% for EV2.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Flexible protein-protein docking with a multitrack iterative transformer.

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody-antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.

Molecular docking with conformer-dependent charges.

Determination of protein-ligand interactions is crucial for structure-based drug design. But, accurate prediction of the binding structures of protein-ligand is still a major challenge for molecular docking methods. Herein, we developed molecular docking with conformer-dependent charges (MDCC), a docking method to combine conformational search with RESP charges. Compared to the conventional Glide SP method (51.9%) and Glide XP method (52.6%), the MDCC method (60%) exhibited a higher docking success rate based on 285 protein-small molecule ligand complexes from the PDBbind core set. And when the ligand met one of the conditions (the total hydrophobic surface area > 438, the number of hydrophobic atoms > 10, and molecular weight > 235), the docking success rate of the MDCC method (>90%) was higher than that of the Glide SP method (∼72%). Furthermore, we also applied the MDCC method to predict the protein-ligand interactions in GPCR Dock 2021 competition, with our prediction models ranking 2nd out of all 202 participating models for APJ and 5th out of all 193 participating models for GPR139, demonstrating the relatively high docking accuracy of the MDCC method. In addition, we also found that the MDCC method combined with molecular dynamics simulations could facilitate the application of AlphaFold 2 in drug discovery. The above results all set the stage for the application of the MDCC method in future practical drug design.

Comparing the Performance of Molecular Docking Tools for HIV-1 Protease Inhibitors

Human immunodeficiency virus (HIV) continues to pose a significant public health threat worldwide, disproportionately affecting marginalized and vulnerable populations despite advancements in prevention and treatment. One promising approach in the development of antiretroviral therapies is the inhibition of HIV-1 protease, a key enzyme in the virus life cycle. To this end, we evaluated 23 known inhibitors of HIV-1 protease using three docking methods - Autodock 4.2 (AD4), AutoDockVina (Vina), and a modified Vina (mVina)- and found that all three methods produced reasonable binding affinities for these ligands, but Vina performed best in terms of precision and docking success rates. The results provide important guidance for the selection of appropriate support tools for screening potential HIV-1 protease inhibitors in treating HIV/AIDS. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Learning-Based Compound Docking Control for UAV Aerial Recovery: Methodology and Implementation

This article focuses on unmanned aerial vehicle (UAV) docking control, which is one of the most difficult and significant issues during UAV aerial recovery. The UAV probe, which is affected by the barycenter translational and attitude rotational motion, needs to be controlled precisely to track and dock with a dynamic drogue towed by a flexible cable under multiple wind perturbations. Thus, a learning-based docking control method compounded of a barycenter translation antidisturbance controller, which has good antidisturbance ability and can ensure the control stability, and an attitude rotation learning controller, which can utilize the effect of rotational motion on the probe motion to further improve the docking precision and is trained by a deep reinforcement learning algorithm using the proposed experience-based value expansion mechanism to improve the training efficiency, is proposed. Then, a distributed mechatronics docking experimental system scheme with the characteristics of accurate simulation, realistic system, and intuitive display is proposed to comprehensively verify the proposed docking control method. The results show that the proposed method can reduce the tracking error by 20% and improve the docking success rate by 10%.

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking.

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

FitDock: protein-ligand docking by template fitting.

Protein-ligand docking is an essential method in computer-aided drug design and structural bioinformatics. It can be used to identify active compounds and reveal molecular mechanisms of biological processes. A successful docking usually requires thorough conformation sampling and scoring, which are computationally expensive and difficult. Recent studies demonstrated that it can be beneficial to docking with the guidance of existing similar co-crystal structures. In this work, we developed a protein-ligand docking method, named FitDock, which fits initial conformation to the given template using a hierarchical multi-feature alignment approach, subsequently explores the possible conformations and finally outputs refined docking poses. In our comprehensive benchmark tests, FitDock showed 40%-60% improvement in terms of docking success rate and an order of magnitude faster over popular docking methods, if template structures exist (> 0.5 ligand similarity). FitDock has been implemented in a user-friendly program, which could serve as a convenient tool for drug design and molecular mechanism exploration. It is now freely available for academic users at http://cao.labshare.cn/fitdock/.

Computer Aided Drug Design Based on Artificial Intelligence Algorithm

The problems such as high cost and long development time in drug design and development have an important impact on its development, which makes many scholars devote themselves to looking for the auxiliary model of drug design. With the rapid development of computer technology, computer-aided drug molecular research model is more and more mature. This paper aims to study the computer-aided drug system based on artificial intelligence algorithm, so that researchers can speed up the process and reduce the cost when searching for specific protein molecules. In this paper, the principle of complementary matching in the docking process of target molecules and ligands, which is commonly used in drug design, is described, and the functional expression mode and various docking methods of molecular docking are studied. Finally, the research hotspots of molecular docking technology are analyzed, including scoring function, search strategy and flexible protein docking. Ant colony algorithm is introduced into molecular docking platform as a variant of conformation search algorithm, and a new plants algorithm is developed. Finally, the implementation of plants algorithm is analyzed in detail, and the optimized plants system and gold system based on genetic algorithm are simulated, and the relevant experimental data are counted. The simulation results show that the new drug design method based on ant colony algorithm has advantages in docking success rate, docking speed and docking accuracy. The success rate of plants is higher than that of gold, and the docking time is only 1/6 of that of gold.

InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps.

The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.

Application of Photoelectric Sensor in Control of Industrial Robot

Indoor positioning and automatic charging technology of the robot is an important indicator to test intelligent control of robot. The surrounding condition is collected by acousto-optic sensor and photoelectric sensor, and the closed-loop control of DC motor is realized based on Fuzzy PID. The system integrates multiple photoelectric sensors and uses high-performance processor, S3C6410 ARM11, as the core instead of PC, reducing the power consumption and cost, so as to complete the acquisition of ranging information. And the environment image is obtained for image processing. The infrared docking method is adopted in the docking process, that is, three infrared transmitting tubes in the charging dock emit different positions, and the position relative to the charging dock is judged according to the number of received signals. In the experiment, the positioning error of industrial robot is about 1%, which can meet the requirements of indoor positioning. The docking success rate of automatic charging is more than 98%, and the time spent is less than 65 seconds, which satisfies with the requirements of automatic charging.

Reachability analysis on optimal trim state for aerial docking

Aerial refueling is an important capability to increase the endurance and flight range of aircraft, but it often suffers from a low success rate. The altitude and speed of the tanker aircraft in the docking phase play a great role in the docking success rate. According to this, the optimal trim state, namely the optimal speed and altitude of the tanker aircraft, is investigated through the reachability analysis method in this paper. The optimal problem is transformed to find the trim state corresponding to the maximum volume of the reachable set. First, a relative motion model of the receiver aircraft with respect to the drogue is proposed. Then, based on reachability analysis, an optimization problem is formulated and a solution procedure is given in detail. In the simulation, the volumes of reachable sets are plotted with respect to the given discrete speeds and altitudes, based on which the optimal trim state of the docking phase is determined. Finally, the determined optimal trim state is verified by using numerous docking control simulations and the degree of controllability from another aspect. The effectiveness of the proposed method is demonstrated.

Docking Safety Assessment and Optimization for Autonomous Aerial Refueling: A Data-Driven Method

Docking safety is a key issue in the autonomous aerial refueling (AAR), but it has not been sufficiently studied. Because the docking system is complicated with multiple disturbances and uncertainties, docking safety is difficult to analyze theoretically. Therefore, a data-driven docking safety assessment and optimization method is proposed in this article to improve the AAR docking safety and success rate. First, a comprehensive AAR docking system is established to generate abundant realistic simulation data for the data-driven framework. Then, a deep learning method is used to extract useful information from the docking data. A safety assessment network (SAN) is proposed to predict the final docking success rate according to the current docking state. A motion prediction network (MPN) is proposed to establish the mapping relationship between the expected probe position and the docking state. Furthermore, a novel probe trajectory optimization method is proposed based on the gradient of the MPN and SAN to improve the docking success rate and improve docking safety. Finally, based on the SAN, a novel safety-oriented docking strategy that can predict failed docking attempts and retreat in advance is proposed to further improve docking safety. The effectiveness of the proposed method is demonstrated by simulations.

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking.

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.

Application of Docking to Protein Models

Modeling is essential for structural characterization of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. To test the ability of docking procedures to predict the structure of protein model-model complexes, we built docking benchmark sets of protein models generated by the Phyre modeling pipeline [1], with accuracy assessed by the Phyre ranks, for protein complexes from the Dockground docking benchmark [2] and GWIDD database [3]. The sets are different from previously generated ones, in which arrays of models were generated according to RMSD from the native structure. The current sets rather reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, with no reference to the native structure, which would be unknown in practical applications. The sets are available at http://dockground.compbio.ku.edu and http://www.gwyre.org. The docking success rate was correlated with the Phyre modeling parameters for individual proteins. For the first time, the exhaustive benchmarking was done for protein pairs in which proteins are modeled at different levels of accuracy (e.g. low accuracy “receptor” and high accuracy “ligand”), again reflecting the real-case scenario of practical applications. The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models. 1. Kelley & Sternberg, Nat Protoc. 2009;4:363-371 2. Kundrotas et al., Protein Sci. 2018;27:172-181 3. Kundrotas et al., Human Genomics. 2012;6:7

Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.