Predicting Multi-Component Protein Assemblies Using an Ant Colony Approach

Monoclonal antibodies have recently started to deliver on their promise as highly specific and active drugs; however, a more effective, knowledge-based approach to the selection, design, and optimization of potential therapeutic antibodies is currently limited by the surprising lack of detailed structural information for complexes formed with target proteins. Here we show that complexes formed with minimal antigen binding single chain variable fragments (scFv) reliably reflect all the features of the binding interface present in larger Fab fragments, which are commonly used as therapeutics, and report the development of a robust, reliable, and relatively rapid approach to the determination of high resolution models for scFv-target protein complexes. This NMR spectroscopy-based approach combines experimental determination of the interaction surfaces and relative orientations of the scFv and target protein, with NMR restraint-driven, semiflexible docking of the proteins to produce a reliable and highly informative model of the complex. Experience with scFvs and Fabs targeted at a number of secreted regulatory proteins suggests that the approach will be applicable to many therapeutic antibodies targeted at proteins, and its application is illustrated for a potential therapeutic antibody targeted at the cytokine IL-1beta. The detailed structural information that can be obtained by this approach has the potential to have a major impact on the rational design and development of an increasingly important class of biological pharmaceuticals.

This paper describes and uses an ant colony meta-heuristic optimization method to solve the redundancy optimization problem. This problem is known as total investment-cost minimization of series-parallel power system configuration. Redundant components are included to achieve a desired level of availability. System availability is represented by a multi-state availability function. The power systems components are characterized by their performance (capacity), availability and cost. These components are chosen among a list of products available on the market. The proposed meta-heuristic seeks to the best minimal cost power system configuration with desired availability. To estimate the series-parallel power system availability, a fast method based on universal moment generating function (UMGF) is suggested. The ant colony approach is used as an optimization technique. An example of electrical power system is presented.

In this paper, an algorithm based ant colony approach (ACA) is designed to find the optimal configuration for redundancy apportionment problem (RAP) of series-parallel multi-state system (MSS). In order to achieve maximum system reliability under cost and performance constraints, the algorithm takes advantage of ACA’s combinatorial optimization, uses heuristic information about reliability to arrange components from available choice. Different from traditional nominal performance and reliability system, universal generating function (UGF) is introduced and used to estimate the multi-state system reliability. Finally the experiment has shown that the presented algorithm can provide optimal and near optimal solutions, and have efficient and convenient calculation performance.

AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb.

Advances in template-based protein docking by utilizing interfaces towards completing structural interactome

Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures

Preparation of DNA Substrates and Functionalized Glass Surfaces for Correlative Nanomanipulation and Colocalization (NanoCOSM) of Single Molecules.

Cryo-electron tomography (cryo-ET) has emerged as a powerful technique for elucidating the structures of cellular organelles and large protein complexes in their native environments. Unlike conventional room-temperature electron microscopy, cryo-ET minimizes radiation-induced and preparation-induced damage to internal structures. Moreover, in contrast to single-particle analysis (SPA), cryo-ET reduces the need for repetitive purification, thereby preserving the most authentic structural information of the samples. To achieve high-resolution imaging, cryo-ET also imposes stringent requirements on the imaging conditions of the TEM. Despite the continuous improvement in cryo-ET data acquisition efficiency and the emergence of numerous software platforms, most solutions are proprietary, vendor-specific, and tightly coupled to certain electron microscope models, leading to high implementation costs. In this study, we installed the open-source software SerialEM on a 200 kV electron microscope (Glacios 2 cryo-TEM) with a dual condenser lens system and performed comprehensive calibrations to enable high-quality tomographic data acquisition. Using apoferritin as a model specimen, we describe the entire workflow-from cryo-sample preparation to automated cryo-ET data collection, followed by tomogram reconstruction and sub-tomogram analysis. This method offers a cost-effective and accessible solution for cryo-ET data collection, particularly for laboratories using Glacios microscopes without access to proprietary software.

The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.

Scheduling is regarded as one of the vital decision-making processes used frequently in many real-time cases. It manages everything from resource allocation to the task completion, with the goal to optimize the desired objectives. Subject to the problem, the resources, tasks, and goals can differ. The aim is to design a corporative multiagent system for optimal scheduling. Many of the scheduling available algorithms calculate optimality based on different perspectives. The proposal is to create the dataset using multiple algorithms with different performance metrics to find an optimal one. This data can be imported into machine learning tools for training and predicting, based on the selected performance metrics. The algorithm considered in the empirical analysis includes first come first serve, Round robin, and Ant colony approach. The major finding shows that scheduling using Ant colony is an optimal algorithm, which is based on speed and velocity. The future extension would be to check the correctness of optimality using machine learning tools.

In this letter, we address the problem of minimizing the energy consumption of disseminating a library of files to a set of Internet-of-Things (IoT) devices using an unmanned aerial vehicle (UAV). A framework is provided, in which a subset of devices are selected to receive data from a UAV and then forward the required data to other devices. Furthermore, optimal energy-efficient path selection is considered in order to realize efficient data dissemination. Specifically, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to disseminate the required files to the former. An ant colony optimization (ACO) algorithm is developed to solve the optimization problem. Simulation results show that the proposed framework is more energy-efficient compared to a baseline approach, where the UAV hovers above each device to deliver the data. Results also illustrate that the proposed ACO algorithm provides performance close to the optimal solution, which is obtained through exhaustive search.

As a reaction to the volatile market demands with regards to the number and variants of products offered, ever more complex procedures for manufacturing control are being developed. Most recently, self-organising procedures, which often mimic the behaviour of natural systems, have arisen. The method of ant colony optimisation (ACO), which was inspired by ants, can provide the necessary fundamentals in order to realise self-organising manufacturing control. In this context, the ifab-Institute has developed the AntControl tool for self-organising manufacturing control based on ACO. In order to investigate the potential of ACO, several concepts have been developed and integrated into the existing OSim simulation tool to create the new OSim-Ant tool. An exemplary simulation study within a manufacturing system has been carried out to evaluate the behaviour of AntControl. This paper presents this tool as well as the results of the simulation study.

The ant systems optimization approach is a new method of solving combinatorial optimization problems. It was originally introduced as a metaheuristic approach for the well-known traveling salesman problem. But it was subsequently shown to be an equally effective algorithm for solving other optimization problems. This article supplies more results for an extended ant colony algorithm, which can be used to compute a minimum cost Steiner tree from a graph. In each pass of the proposed algorithm, ants are placed at the terminal nodes of the Steiner tree to be computed. They are then allowed to move towards one another, along the edges of the graph, until they merge into a single entity. In this process, the paths taken by the ants define a Steiner tree. Edges receive reinforcement in the form of pheromone deposits along the paths taken by the ant, that is based on the quality of the Steiner tree produced. Pheromones eventually accrue most along better edges. As a result, after several iterations, a good Steiner tree can be extracted from the deposit. The algorithm can easily be used in several practical applications.

Structures assisting radical movements revealed

Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.