Accurate and automated high-coverage identification of chemically cross-linked peptides with MaxLynx

Cross-linking combined with mass spectrometry (XL-MS) provides a wealth of information about the 3D structure of proteins and their interactions. We introduce MaxLynx, a novel computational proteomics workflow for XL-MS integrated into the MaxQuant environment. It is applicable to non-cleavable and MS-cleavable cross linkers. For both we have generalized the Andromeda peptide database search engine to efficiently identify cross-linked peptides. For non-cleavable peptides, we implemented a novel di-peptide Andromeda score, which is the basis for a computationally efficient N-squared search engine. Additionally, partial scores summarize the evidence for the two constituents of the di-peptide individually. A posterior error probability based on total and partial scores is used to control false discovery rates. For MS-cleavable cross linkers a scoring of signature peaks is combined with the conventional Andromeda score on the cleavage products. The MaxQuant 3D-peak detection was improved to ensure more accurate determination of the monoisotopic peak of isotope patterns for heavy molecules, which cross-linked peptides typically are. A wide selection of filtering parameters can replace manual filtering of identifications, which is often necessary when using other pipelines. On benchmark datasets of synthetic peptides, MaxLynx outperforms all other tested software on data for both types of cross linkers as well as on a proteome-wide dataset of cross-linked D. melanogaster cell lysate. The workflow also supports ion-mobility enhanced MS data. MaxLynx runs on Windows and Linux, contains an interactive viewer for displaying annotated cross-linked spectra and is freely available at https://www.maxquant.org/.

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(Hyper)Graph Embedding and Classification via Simplicial Complexes

Algorithms ◽

10.3390/a12110223 ◽

2019 ◽

Vol 12 (11) ◽

pp. 223 ◽

Cited By ~ 10

Author(s):

Alessio Martino ◽

Alessandro Giuliani ◽

Antonello Rizzi

Keyword(s):

Algebraic Topology ◽

3D Structure ◽

Graph Embedding ◽

Recognition System ◽

Simplicial Complexes ◽

Learning System ◽

Graph Classification ◽

Enzymatic Function ◽

Benchmark Datasets ◽

Embedding Strategy

This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols. The selected symbols can be analysed by field-experts in order to extract further knowledge about the process to be modelled by the learning system, hence the proposed modelling strategy can be considered as a grey-box. The proposed embedding has been tested on thirty benchmark datasets for graph classification and, further, we propose two real-world applications, namely predicting proteins’ enzymatic function and solubility propensity starting from their 3D structure in order to give an example of the knowledge discovery phase which can be carried out starting from the proposed embedding strategy.

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An effective methodology for drop test analysis and optimization of aerospace composite structures

10.31219/osf.io/qb7hj ◽

2019 ◽

Author(s):

DC Pham

Keyword(s):

Composite Structures ◽

Impact Analysis ◽

3D Structure ◽

Structural Response ◽

Drop Test ◽

Computationally Efficient ◽

Test Analysis ◽

Design And Optimization ◽

Structural Configurations ◽

Modeling Strategy

An effective modeling strategy for drop test analysis of composite structures is proposed which allows engineers to generate feasible designs of complex structures undergoing impact. Traditional drop test analysis of 3D structural structures requires careful and detailed modeling and simulation, especially for composite materials where structural response may significantly vary upon changing the composite material properties, layup stacking or ply orientations. To account for various structural configurations, an optimization scheme that is computationally efficient is highly desired for composite design and analysis. This work illustrates a robust modeling strategy for drop test design and optimization of general composite structures by combining an explicit impact analysis of a 2D structural profile and subsequently a quasi-static analysis of the relevant 3D structure. This novel methodology is validated and shows good correlation with experimental results

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APPTEST is an innovative new method for the automatic prediction of peptide tertiary structures

10.1101/2021.03.09.434600 ◽

2021 ◽

Cited By ~ 1

Author(s):

Patrick Brendan Timmons ◽

Chandralal M. Hewage

Keyword(s):

Network Architecture ◽

Structure Prediction ◽

Cyclic Peptides ◽

Tertiary Structure ◽

Prediction Method ◽

Computational Method ◽

Computationally Efficient ◽

Biological Targets ◽

Benchmark Datasets ◽

Natural Amino Acids

AbstractGood knowledge of a peptide’s tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational method that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5-40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and Peplook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more-native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

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An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document

Wireless Communications and Mobile Computing ◽

10.1155/2020/8879054 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

G. Siva Shankar ◽

P. Ashokkumar ◽

R. Vinayakumar ◽

Uttam Ghosh ◽

Wathiq Mansoor ◽

...

Keyword(s):

Search Engine ◽

Side Information ◽

Classification Model ◽

Web Pages ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Web Document ◽

Exponential Increase ◽

Benchmark Datasets ◽

Optimal Classification

With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.

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Semantics-Aligned Representation Learning for Person Re-Identification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6775 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11173-11180 ◽

Cited By ~ 3

Author(s):

Xin Jin ◽

Cuiling Lan ◽

Wenjun Zeng ◽

Guoqiang Wei ◽

Zhibo Chen

Keyword(s):

State Of The Art ◽

Representation Learning ◽

The State ◽

Feature Representation ◽

Texture Image ◽

Computationally Efficient ◽

Feature Maps ◽

Benchmark Datasets ◽

Texture Generation ◽

Base Network

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID.

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Foam-like phantoms for comparing tomography algorithms

Journal of Synchrotron Radiation ◽

10.1107/s1600577521011322 ◽

2022 ◽

Vol 29 (1) ◽

Author(s):

Daniël M. Pelt ◽

Allard A. Hendriksen ◽

Kees Joost Batenburg

Keyword(s):

Real World ◽

Data Driven ◽

Computationally Efficient ◽

Experimental Conditions ◽

Mathematical Definition ◽

Fair Comparison ◽

Acquisition Mode ◽

Benchmark Datasets ◽

Real World Datasets ◽

Definition Of

Tomographic algorithms are often compared by evaluating them on certain benchmark datasets. For fair comparison, these datasets should ideally (i) be challenging to reconstruct, (ii) be representative of typical tomographic experiments, (iii) be flexible to allow for different acquisition modes, and (iv) include enough samples to allow for comparison of data-driven algorithms. Current approaches often satisfy only some of these requirements, but not all. For example, real-world datasets are typically challenging and representative of a category of experimental examples, but are restricted to the acquisition mode that was used in the experiment and are often limited in the number of samples. Mathematical phantoms are often flexible and can sometimes produce enough samples for data-driven approaches, but can be relatively easy to reconstruct and are often not representative of typical scanned objects. In this paper, we present a family of foam-like mathematical phantoms that aims to satisfy all four requirements simultaneously. The phantoms consist of foam-like structures with more than 100000 features, making them challenging to reconstruct and representative of common tomography samples. Because the phantoms are computer-generated, varying acquisition modes and experimental conditions can be simulated. An effectively unlimited number of random variations of the phantoms can be generated, making them suitable for data-driven approaches. We give a formal mathematical definition of the foam-like phantoms, and explain how they can be generated and used in virtual tomographic experiments in a computationally efficient way. In addition, several 4D extensions of the 3D phantoms are given, enabling comparisons of algorithms for dynamic tomography. Finally, example phantoms and tomographic datasets are given, showing that the phantoms can be effectively used to make fair and informative comparisons between tomography algorithms.

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A mathematical representation of protein binding sites using structural dispersion of atoms from principal axes for classification of binding ligands

PLoS ONE ◽

10.1371/journal.pone.0244905 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0244905

Author(s):

Galkande Iresha Premarathna ◽

Leif Ellingson

Keyword(s):

Coordinate System ◽

Binding Sites ◽

Three Dimensional ◽

Structural Features ◽

Mathematical Representation ◽

Computationally Efficient ◽

Probability Models ◽

Principal Axes ◽

Structural Dispersion ◽

Benchmark Datasets

Many researchers have studied the relationship between the biological functions of proteins and the structures of both their overall backbones of amino acids and their binding sites. A large amount of the work has focused on summarizing structural features of binding sites as scalar quantities, which can result in a great deal of information loss since the structures are three-dimensional. Additionally, a common way of comparing binding sites is via aligning their atoms, which is a computationally intensive procedure that substantially limits the types of analysis and modeling that can be done. In this work, we develop a novel encoding of binding sites as covariance matrices of the distances of atoms to the principal axes of the structures. This representation is invariant to the chosen coordinate system for the atoms in the binding sites, which removes the need to align the sites to a common coordinate system, is computationally efficient, and permits the development of probability models. These can then be used to both better understand groups of binding sites that bind to the same ligand and perform classification for these ligand groups. We demonstrate the utility of our method for discrimination of binding ligand through classification studies with two benchmark datasets using nearest mean and polytomous logistic regression classifiers.

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DETECTION OF TEXTURE-LESS OBJECTS BY LINE-BASED APPROACH

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr1902079c ◽

2020 ◽

Vol 18 (2) ◽

pp. 079

Author(s):

Stevica Cvetković ◽

Nemanja Grujić ◽

Slobodan Ilić ◽

Goran Stančić

Keyword(s):

Object Detection ◽

Affine Invariant ◽

Computationally Efficient ◽

Rotation Invariant ◽

Shape Information ◽

Art Object ◽

Extensive Evaluation ◽

Benchmark Datasets ◽

On Line ◽

Hypotheses Generation

This paper proposes a method for tackling the problem of scalable object instance detection in the presence of clutter and occlusions. It gathers together advantages in respect of the state-of-the-art object detection approaches, being at the same time able to scale favorably with the number of models, computationally efficient and suited to texture-less objects as well. The proposed method has the following advantages: a) generality – it works for both texture-less and textured objects, b) scalability – it scales sub-linearly with the number of objects stored in the object database, and c) computational efficiency – it runs in near real-time. In contrast to the traditional affine-invariant detectors/descriptors which are local and not discriminative for texture-less objects, our method is based on line segments around which it computes semi-global descriptor by encoding gradient information in scale and rotation invariant manner. It relies on both texture and shape information and is, therefore, suited for both textured and texture-less objects. The descriptor is integrated into efficient object detection procedure which exploits the fact that the line segment determines scale, orientation and position of an object, by its two endpoints. This is used to construct several effective techniques for object hypotheses generation, scoring and multiple object reasoning; which are integrated in the proposed object detection procedure. Thanks to its ability to detect objects even if only one correct line match is found, our method allows detection of the objects under heavy clutter and occlusions. Extensive evaluation on several public benchmark datasets for texture-less and textured object detection, demonstrates its scalability and high effectiveness.

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Parallel heuristic search in forward partial-order planning

The Knowledge Engineering Review ◽

10.1017/s0269888916000230 ◽

2016 ◽

Vol 31 (5) ◽

pp. 417-428

Author(s):

Oscar Sapena ◽

Alejandro Torreño ◽

Eva Onaindía

Keyword(s):

Partial Order ◽

Heuristic Search ◽

State Of The Art ◽

Causal Link ◽

Computational Effort ◽

Total Order ◽

Computationally Efficient ◽

High Coverage ◽

Parallel Processes ◽

Multi Agent

AbstractMost of the current top-performing planners are sequential planners that only handle total-order plans. Although this is a computationally efficient approach, the management of total-order plans restrict the choices of reasoning and thus the generation of flexible plans. In this paper, we present FLAP2, a forward-chaining planner that follows the principles of the classical POCL (Partial-Order Causal-Link Planning) paradigm. Working with partial-order plans allows FLAP2 to easily manage the parallelism of the plans, which brings several advantages: more flexible executions, shorter plan durations (makespan) and an easy adaptation to support new features like temporal or multi-agent planning. However, one of the limitations of POCL planners is that they require far more computational effort to deal with the interactions that arise among actions. FLAP2 minimizes this overhead by applying several techniques that improve its performance: the combination of different state-based heuristics and the use of parallel processes to diversify the search in different directions when a plateau is found. To evaluate the performance of FLAP2, we have made a comparison with four state-of-the-art planners: SGPlan, YAHSP2, Temporal Fast Downward and OPTIC. Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks.

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3D Ligand-Based Virtual Screening with Support Vector Machines

Chemoinformatics and Advanced Machine Learning Perspectives ◽

10.4018/978-1-61520-911-8.ch003 ◽

2011 ◽

pp. 35-45

Author(s):

Jean-Philippe Vert

Keyword(s):

Support Vector Machines ◽

Virtual Screening ◽

3D Structure ◽

Support Vector ◽

Vector Machines ◽

Discretization Step ◽

Benchmark Datasets

The author reviews an approach, proposed recently by Mahé, Ralaivola, Stoven, and Vert (2006), for ligand-based virtual screening with support vector machines using a kernel based on the 3D structure of the molecules. The kernel detects putative 3-point pharmacophores, and generalizes previous approaches based on 3-point pharmacophore fingerprints. It overcomes the categorization issue associated with the discretization step usually required for the construction of fingerprints, and leads to promising results on several benchmark datasets.

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