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Article ; Online: Structure, function, and inhibition of catalytically asymmetric ABC transporters: Lessons from the PDR subfamily.

Banerjee, Atanu / Pata, Jorgaq / Chaptal, Vincent / Boumendjel, Ahcène / Falson, Pierre / Prasad, Rajendra

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy

2023 Volume 71, Page(s) 100992

Abstract: ATP-binding cassette (ABC) superfamily comprises a large group of ubiquitous transmembrane proteins that play a crucial role in transporting a diverse spectrum of substrates across cellular membranes. They participate in a wide array of physiological and ...

Abstract	ATP-binding cassette (ABC) superfamily comprises a large group of ubiquitous transmembrane proteins that play a crucial role in transporting a diverse spectrum of substrates across cellular membranes. They participate in a wide array of physiological and pathological processes including nutrient uptake, antigen presentation, toxin elimination, and drug resistance in cancer and microbial cells. ABC transporters couple ATP binding and hydrolysis to undergo conformational changes allowing substrate translocation. Within this superfamily, a set of ABC transporters has lost the capacity to hydrolyze ATP at one of their nucleotide-binding sites (NBS), called the non-catalytic NBS, whose importance became evident with extensive biochemistry carried out on yeast pleiotropic drug resistance (PDR) transporters. Recent single-particle cryogenic electron microscopy (cryo-EM) advances have further catapulted our understanding of the architecture of these pumps. We provide here a comprehensive overview of the structural and functional aspects of catalytically asymmetric ABC pumps with an emphasis on the PDR subfamily. Furthermore, given the increasing evidence of efflux-mediated antifungal resistance in clinical settings, we also discuss potential grounds to explore PDR transporters as therapeutic targets.
MeSH term(s)	Humans ; ATP-Binding Cassette Transporters/genetics ; ATP-Binding Cassette Transporters/metabolism ; Membrane Transport Proteins ; Saccharomyces cerevisiae ; Drug Resistance, Fungal ; Adenosine Triphosphate/metabolism
Chemical Substances	ATP-Binding Cassette Transporters ; Membrane Transport Proteins ; Adenosine Triphosphate (8L70Q75FXE)
Language	English
Publishing date	2023-08-05
Publishing country	Scotland
Document type	Review ; Journal Article
ZDB-ID	1474513-6
ISSN	1532-2084 ; 1368-7646
ISSN (online)	1532-2084
ISSN	1368-7646
DOI	10.1016/j.drup.2023.100992
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Book ; Online: Hyperparameter optimization of data-driven AI models on HPC systems

Wulff, Eric / Girone, Maria / Pata, Joosep

2022

Abstract: In the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Performance Computing ... ...

Abstract	In the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Performance Computing resources to perform large-scale hyperparameter optimization using distributed training on multiple compute nodes. This is part of RAISE's work on data-driven use cases which leverages AI- and HPC cross-methods developed within the project. In response to the demand for parallelizable and resource efficient hyperparameter optimization methods, advanced hyperparameter search algorithms are benchmarked and compared. The evaluated algorithms, including Random Search, Hyperband and ASHA, are tested and compared in terms of both accuracy and accuracy per compute resources spent. As an example use case, a graph neural network model known as MLPF, developed for the task of Machine-Learned Particle-Flow reconstruction in High Energy Physics, acts as the base model for optimization. Results show that hyperparameter optimization significantly increased the performance of MLPF and that this would not have been possible without access to large-scale High Performance Computing resources. It is also shown that, in the case of MLPF, the ASHA algorithm in combination with Bayesian optimization gives the largest performance increase per compute resources spent out of the investigated algorithms. Comment: Submitted to the proceedings of the ACAT 2021 conference and is to be published in the Journal Of Physics: Conference Series
Keywords	Physics - Data Analysis ; Statistics and Probability ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-03-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Production and Purification of a GFP-Tagged ABC Transporter CaCdr1p.

Pata, Jorgaq / Moreno, Alexis / Magnard, Sandrine / Banerjee, Atanu / Prasad, Rajendra / Falson, Pierre

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2507, Page(s) 175–185

Abstract: The production and purification are the first steps required in any functional or structural study of a protein of interest. In the case of membrane proteins, these tasks can be difficult due to low expression levels and the necessity to extract them ... ...

Abstract	The production and purification are the first steps required in any functional or structural study of a protein of interest. In the case of membrane proteins, these tasks can be difficult due to low expression levels and the necessity to extract them from their membrane environment. This chapter describes a convenient method based on GFP tagged to the membrane protein to facilitates these steps. Production is carried out in the yeast S. cerevisiae and purification steps are carried out and monitored taking advantage of an anti-GFP nanobody. We show how GFP can be a very helpful tool for controlling the correct addressing of the protein and for probing and optimizing purification. These methods are described here for producing and purifying CaCdr1p, an ABC exporter conferring multiantifungal resistance to C. albicans. This purification method can be amenable to any other GFP-tagged protein.
MeSH term(s)	ATP-Binding Cassette Transporters/genetics ; ATP-Binding Cassette Transporters/metabolism ; Candida albicans/metabolism ; Green Fluorescent Proteins/genetics ; Green Fluorescent Proteins/metabolism ; Membrane Proteins/metabolism ; Saccharomyces cerevisiae/genetics ; Saccharomyces cerevisiae/metabolism
Chemical Substances	ATP-Binding Cassette Transporters ; Membrane Proteins ; Green Fluorescent Proteins (147336-22-9)
Language	English
Publishing date	2022-06-30
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2368-8_9
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Book ; Online: Processing Columnar Collider Data withGPU-Accelerated Kernels

Pata, Joosep / Spiropulu, Maria

2019

Abstract: At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query languages allow, such ... ...

Abstract	At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query languages allow, such that custom numerical codes are used. At present, these codes mostly operate on individual event records and are parallelized in multi-step data reduction workflows using batch jobs across CPU farms. Based on a simplified top quark pair analysis with CMS Open Data, we demonstrate that it is possible to carry out significant parts of a collider analysis at a rate of around a million events per second on a single multicore server with optional GPU acceleration. This is achieved by representing HEP event data as memory-mappable sparse arrays of columns, and by expressing common analysis operations as kernels that can be used to process the event data in parallel. We find that only a small number of relatively simple functional kernels are needed for a generic HEP analysis. The approach based on columnar processing of data could speed up and simplify the cycle for delivering physics results at HEP experiments. We release the \texttt{hepaccelerate} prototype library as a demonstrator of such methods.
Keywords	Physics - Data Analysis ; Statistics and Probability ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Physics - Computational Physics
Subject code	006
Publishing date	2019-06-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Progress towards an improved particle flow algorithm at CMS with machine learning

Mokhtar, Farouk / Pata, Joosep / Duarte, Javier / Wulff, Eric / Pierini, Maurizio / Vlimant, Jean-Roch

2023

Abstract: The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 ... ...

Abstract	The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity. In recent years, the machine learned particle-flow (MLPF) algorithm, a graph neural network that performs PF reconstruction, has been explored in CMS, with the possible advantages of directly optimizing for the physical quantities of interest, being highly reconfigurable to new conditions, and being a natural fit for deployment to heterogeneous accelerators. We discuss progress in CMS towards an improved implementation of the MLPF reconstruction, now optimized using generator/simulation-level particle information as the target for the first time. This paves the way to potentially improving the detector response in terms of physical quantities of interest. We describe the simulation-based training target, progress and studies on event-based loss terms, details on the model hyperparameter tuning, as well as physics validation with respect to the current PF algorithm in terms of high-level physical quantities such as the jet and missing transverse momentum resolutions. We find that the MLPF algorithm, trained on a generator/simulator level particle information for the first time, results in broadly compatible particle and jet reconstruction performance with the baseline PF, setting the stage for improving the physics performance by additional training statistics and model tuning. Comment: 7 pages, 4 Figures, 1 Table
Keywords	Physics - Data Analysis ; Statistics and Probability ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Instrumentation and Detectors
Subject code	621
Publishing date	2023-03-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

Pata, Joosep / Wulff, Eric / Mokhtar, Farouk / Southwick, David / Zhang, Mengke / Girone, Maria / Duarte, Javier

2023

Abstract: Experiments at the High-Luminosity LHC and the Future Circular Collider need efficient algorithms to reconstruct granular events expected at such detectors with high fidelity. We study scalable machine learning models for event reconstruction in electron- ...

Abstract	Experiments at the High-Luminosity LHC and the Future Circular Collider need efficient algorithms to reconstruct granular events expected at such detectors with high fidelity. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. Accurate reconstruction can significantly improve future measurements at colliders. The resulting model is portable across Nvidia, AMD and Habana hardware. Our datasets and software are published following the findable, accessible, interoperable, and reusable principles. Comment: 20 pages, 11 figures
Keywords	Physics - Data Analysis ; Statistics and Probability ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Instrumentation and Detectors ; Statistics - Machine Learning
Subject code	006
Publishing date	2023-09-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: ABCG: a new fold of ABC exporters and a whole new bag of riddles!

Banerjee, Atanu / Moreno, Alexis / Pata, Jorgaq / Falson, Pierre / Prasad, Rajendra

Advances in protein chemistry and structural biology

2020 Volume 123, Page(s) 163–191

Abstract: ATP-binding cassette (ABC) superfamily comprises membrane transporters that power the active transport of substrates across biological membranes. These proteins harness the energy of nucleotide binding and hydrolysis to fuel substrate translocation via ... ...

Abstract	ATP-binding cassette (ABC) superfamily comprises membrane transporters that power the active transport of substrates across biological membranes. These proteins harness the energy of nucleotide binding and hydrolysis to fuel substrate translocation via an alternating-access mechanism. The primary structural blueprint is relatively conserved in all ABC transporters. A transport-competent ABC transporter is essentially made up of two nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs). While the NBDs are conserved in their primary sequence and form at their interface two nucleotide-binding sites (NBSs) for ATP binding and hydrolysis, the TMDs are variable among different families and form the translocation channel. Transporters catalyzing the efflux of substrates from the cells are called exporters. In humans, they range from A to G subfamilies, with the B, C and G subfamilies being involved in chemoresistance. The recently elucidated structures of ABCG5/G8 followed by those of ABCG2 highlighted a novel structural fold that triggered extensive research. Notably, suppressor genetics in the orthologous yeast Pleiotropic Drug Resistance (PDR) subfamily proteins have pointed to a crosstalk between TMDs and NBDs modulating substrate export. Considering the structural information provided by their neighbors from the G subfamily, these studies provide mechanistic keys and posit a functional role for the non-hydrolytic NBS found in several ABC exporters. The present chapter provides an overview of structural and functional aspects of ABCG proteins with a special emphasis on the yeast PDR systems.
MeSH term(s)	ATP-Binding Cassette Transporters/genetics ; ATP-Binding Cassette Transporters/metabolism ; Animals ; Binding Sites ; Cell Membrane/genetics ; Cell Membrane/metabolism ; Humans ; Models, Molecular
Chemical Substances	ATP-Binding Cassette Transporters
Language	English
Publishing date	2020-12-04
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ISSN	1876-1631 ; 1876-1623
ISSN (online)	1876-1631
ISSN	1876-1623
DOI	10.1016/bs.apcsb.2020.09.006
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Article ; Online: Directed Mutational Strategies Reveal Drug Binding and Transport by the MDR Transporters of

Banerjee, Atanu / Pata, Jorgaq / Sharma, Suman / Monk, Brian C / Falson, Pierre / Prasad, Rajendra

Journal of fungi (Basel, Switzerland)

2021 Volume 7, Issue 2

Abstract: Multidrug resistance (MDR) transporters belonging to either the ATP-Binding Cassette (ABC) or Major Facilitator Superfamily (MFS) groups are major determinants of clinical drug resistance in fungi. The overproduction of these proteins enables the ... ...

Abstract	Multidrug resistance (MDR) transporters belonging to either the ATP-Binding Cassette (ABC) or Major Facilitator Superfamily (MFS) groups are major determinants of clinical drug resistance in fungi. The overproduction of these proteins enables the extrusion of incoming drugs at rates that prevent lethal effects. The promiscuity of these proteins is intriguing because they export a wide range of structurally unrelated molecules. Research in the last two decades has used multiple approaches to dissect the molecular basis of the polyspecificity of multidrug transporters. With large numbers of drug transporters potentially involved in clinical drug resistance in pathogenic yeasts, this review focuses on the drug transporters of the important pathogen
Language	English
Publishing date	2021-01-20
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2784229-0
ISSN	2309-608X ; 2309-608X
ISSN (online)	2309-608X
ISSN	2309-608X
DOI	10.3390/jof7020068
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Article ; Online: A tale of three SARS-CoV-2 variants with independently acquired P681H mutations in New York State

Lasek-Nesselquist, Erica / Pata, Janice / Schneider, Erasmus / St. George, Kirsten

medRxiv

Abstract: Several SARS-CoV-2 variants of concern have independently acquired some of the same Spike protein mutations, notably E484K, N501Y, S477N, and K417T, associated with increased viral transmission and/or reduced sensitivity to neutralization by antibodies. ... ...

Abstract	Several SARS-CoV-2 variants of concern have independently acquired some of the same Spike protein mutations, notably E484K, N501Y, S477N, and K417T, associated with increased viral transmission and/or reduced sensitivity to neutralization by antibodies. Repeated evolution of the same mutations, particularly in variants that are now rapidly spreading in various regions of the world, suggests a fitness advantage. Mutations at position P681 in Spike, possibly affecting viral transmission, have also evolved multiple times, including in two variants of concern. Here, we describe three variants circulating in New York State that have independently acquired a P681H mutation and the different trajectories they have taken. While one variant rose to high prevalence since later summer 2020 it appears to be in decline. The other two variants were more recently detected in New York and harbor additional Spike mutations that might be cause for continued monitoring. The latter two P681H variants have shown moderate increases in prevalence but ultimately all might be subject to the same fate as more competitive variants come to dominate the scene
Keywords	covid19
Language	English
Publishing date	2021-03-12
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.03.10.21253285
Database	COVID19

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Book ; Online: Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning

Bazarov, Abdullah / Benito, María / Hütsi, Gert / Kipper, Rain / Pata, Joosep / Põder, Sven

2022

Abstract: The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM. In this paper, we investigate the use of machine learning-based tools to ... ...

Abstract	The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM. In this paper, we investigate the use of machine learning-based tools to quantify the magnitude of phase-space perturbations caused by the passage of DM subhalos. A simple binary classifier and an anomaly detection model are proposed to estimate if stars or star particles close to DM subhalos are statistically detectable in simulations. The simulated datasets are three Milky Way-like galaxies and nine synthetic Gaia DR2 surveys derived from these. Firstly, we find that the anomaly detection algorithm, trained on a simulated galaxy with full 6D kinematic observables and applied on another galaxy, is nontrivially sensitive to the DM subhalo population. On the other hand, the classification-based approach is not sufficiently sensitive due to the extremely low statistics of signal stars for supervised training. Finally, the sensitivity of both algorithms in the Gaia-like surveys is negligible. The enormous size of the Gaia dataset motivates the further development of scalable and accurate data analysis methods that could be used to select potential regions of interest for DM searches to ultimately constrain the Milky Way's subhalo mass function, as well as simulations where to study the sensitivity of such methods under different signal hypotheses. Comment: 13 pages, 8 figures, 1 table. Accepted for publication in Astronomy and Computing
Keywords	Astrophysics - Astrophysics of Galaxies ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning ; Physics - Data Analysis ; Statistics and Probability ; Statistics - Machine Learning
Subject code	520
Publishing date	2022-03-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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