The mechanism of Pseudomonas aeruginosa outer membrane vesicle biogenesis determines their protein composition

Proteomics (Wiley)

Wiley: PROTEOMICS: Table of Contents

Table of Contents for PROTEOMICS. List of articles from both the latest and EarlyView issues.

The mechanism of Pseudomonas aeruginosa outer membrane vesicle biogenesis determines their protein composition

Abstract

Gram-negative bacteria produce outer membrane vesicles (OMVs) and contain bacterial cargo including nucleic acids and proteins. The proteome of OMVs can be altered by various factors including bacterial growth stage, growth conditions, and environmental factors. However, it is currently unknown if the mechanism of OMV biogenesis can determine their proteome. In this study, we examined whether the mechanisms of OMV biogenesis influenced the production and protein composition of Pseudomonas aeruginosa OMVs. OMVs were isolated from three P. aeruginosa strains that produced OMVs either by budding alone, by explosive cell lysis, or by both budding and explosive cell lysis. We identified that the mechanism of OMV biogenesis dictated OMV quantity. Furthermore, a global proteomic analysis comparing the proteome of OMVs to their parent bacteria showed significant differences in the identification of proteins in bacteria and OMVs. Finally, we determined that the mechanism of OMV biogenesis influenced the protein composition of OMVs, as OMVs released by distinct mechanisms of biogenesis differed significantly from one another in their proteome and functional enrichment analysis. Overall, our findings reveal that the mechanism of OMV biogenesis is a main factor that determines the OMV proteome which may affect their subsequent biological functions.

Lauren Zavan,
Haoyun Fang,
Ella L. Johnston,
Cynthia Whitchurch,
David W. Greening,
Andrew F. Hill,
Maria Kaparakis‐Liaskos
March 18, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202200464?af=R

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry‐based proteomics

Proteomics (Wiley)

Wiley: PROTEOMICS: Table of Contents

Table of Contents for PROTEOMICS. List of articles from both the latest and EarlyView issues.

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry‐based proteomics

Abstract

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.

Thang V. Pham,
Vinh V. Nguyen,
Duong Vu,
Alex A. Henneman,
Robin A. Richardson,
Sander R. Piersma,
Connie R. Jimenez
March 18, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202200041?af=R

Exploring the noncanonical translatome using massively integrated coexpression analysis

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

Exploring the noncanonical translatome using massively integrated coexpression analysis

Cells transcribe and translate thousands of noncanonical open reading frames (nORFs) whose impacts on cellular phenotypes are unknown. Here, we investigated nORF transcription, evolution, and potential cellular roles using a coexpression approach. We measured coexpression between ~6,000 nORFs and ~6000 canonical ORFs (cORFs) in the Saccharomyces cerevisiae genome by massively integrating thousands of RNA sequencing samples and developing a dedicated computational framework that accounts for low expression levels. Our findings reveal that almost all cORFs are strongly coexpressed with at least one nORF. However, almost half of nORFs are not strongly coexpressed with any cORFs and form entirely new transcription modules. Many nORFs recently evolved de novo in genomic regions that were non-coding in the Saccharomyces ancestor. Coexpression profiles suggest that half of de novo nORFs are functionally associated with conserved genes involved in cellular transport or homeostasis. Furthermore, we discovered that de novo ORFs located downstream of conserved genes leverage their neighbors’ transcripts resulting in high expression levels. Where a de novo nORF emerges could be just as important as its sequence for shaping how it can influence cellular phenotype. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes and evolve.
Rich, A., Acar, O., Carvunis, A.-R.
March 18, 2023
http://biorxiv.org/cgi/content/short/2023.03.16.533058v1?rss=1

[ASAP] Using an In-Sample Addition of Medronic Acid for the Analysis of Purine- and Pyrimidine-Related Derivatives and Its Application in the Study of Lung Adenocarcinoma A549 Cell Lines by LC–MS/MS

Journal of Proteome Research

Journal of Proteome Research: Latest Articles (ACS Publications)

latest articles published in Journal of Proteome Research

[ASAP] Using an In-Sample Addition of Medronic Acid for the Analysis of Purine- and Pyrimidine-Related Derivatives and Its Application in the Study of Lung Adenocarcinoma A549 Cell Lines by LC–MS/MS

TOC Graphic

Journal of Proteome Research
DOI: 10.1021/acs.jproteome.2c00736

Ya-Ting Lin, Shan-An Chan, Yi-Jung Chen, Kuei-Pin Chung, and Ching-Hua Kuo
March 18, 2023
http://dx.doi.org/10.1021/acs.jproteome.2c00736

[ASAP] Spatial Proteome Reorganization of a Photosynthetic Model Cyanobacterium in Response to Abiotic Stresses

Journal of Proteome Research

Journal of Proteome Research: Latest Articles (ACS Publications)

latest articles published in Journal of Proteome Research

[ASAP] Spatial Proteome Reorganization of a Photosynthetic Model Cyanobacterium in Response to Abiotic Stresses

TOC Graphic

Journal of Proteome Research
DOI: 10.1021/acs.jproteome.2c00759

Yan Wang, Haitao Ge, Zhen Xiao, Chengcheng Huang, Gaojie Wang, Xiaoxiao Duan, Limin Zheng, Jinghui Dong, Xiahe Huang, Yuanya Zhang, Hongyu An, Wu Xu, and Yingchun Wang
March 17, 2023
http://dx.doi.org/10.1021/acs.jproteome.2c00759

A fast, accurate and comprehensive LC-MS/MS method validation for the sensitive quantification of water-soluble vitamins in walnut, almond, hazelnut and pistachio fruits

International Journal of Mass Spectrometry

ScienceDirect Publication: International Journal of Mass Spectrometry

ScienceDirect RSS

A fast, accurate and comprehensive LC-MS/MS method validation for the sensitive quantification of water-soluble vitamins in walnut, almond, hazelnut and pistachio fruits

Publication date: June 2023

Source: International Journal of Mass Spectrometry, Volume 488

Author(s): Mustafa Abdullah Yilmaz, Oguz Cakir, Ismail Yener

March 17, 2023
https://www.sciencedirect.com/science/article/pii/S1387380623000313?dgcid=rss_sd_all

Proteomic analysis reveals the non-coding small RNA Qrr5 influences autoaggregation and growth competition in Vibrio parahaemolyticus

Journal of Proteomics

ScienceDirect Publication: Journal of Proteomics

ScienceDirect RSS

Proteomic analysis reveals the non-coding small RNA Qrr5 influences autoaggregation and growth competition in Vibrio parahaemolyticus

Publication date: 15 May 2023

Source: Journal of Proteomics, Volume 279

Author(s): Fei Zha, Rui Pang, Shixuan Huang, Jumei Zhang, Juan Wang, Moutong Chen, Liang Xue, Qinghua Ye, Shi Wu, Meiyan Yang, Qihui Gu, Yu Ding, Qingping Wu, Hao Zhang

March 17, 2023
https://www.sciencedirect.com/science/article/pii/S1874391923000556?dgcid=rss_sd_all

[ASAP] Ultra-Accurate Correlation between Precursor and Fragment Ions in Two-Dimensional Mass Spectrometry: Acetylated vs Trimethylated Histone Peptides

Journal of The American Society for Mass Spectrometry

Journal of the American Society for Mass Spectrometry: Latest Articles (ACS Publications)

latest articles published in Journal of the American Society for Mass Spectrometry

[ASAP] Ultra-Accurate Correlation between Precursor and Fragment Ions in Two-Dimensional Mass Spectrometry: Acetylated vs Trimethylated Histone Peptides

TOC Graphic

Journal of the American Society for Mass Spectrometry
DOI: 10.1021/jasms.2c00319

Michael Palasser, Sarah V. Heel, Marc-André Delsuc, Kathrin Breuker, and Maria A. van Agthoven
March 17, 2023
http://dx.doi.org/10.1021/jasms.2c00319

Measurement and utilization of the proteomic reactivity by mass spectrometry

Mass Spectrometry Reviews

Wiley: Mass Spectrometry Reviews: Table of Contents

Table of Contents for Mass Spectrometry Reviews. List of articles from both the latest and EarlyView issues.

Measurement and utilization of the proteomic reactivity by mass spectrometry

Abstract

Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.

Clodette Punzalan,
Lei Wang,
Bekim Bajrami,
Xudong Yao
March 17, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/mas.21837?af=R

Guide-specific loss of efficiency and off-target reduction with Cas9 variants

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

Guide-specific loss of efficiency and off-target reduction with Cas9 variants

High-fidelity Cas9 variants have been developed to reduce the off-target effects of CRISPR systems at a cost of efficiency loss. To systematically evaluate the efficiency and off-target tolerance of Cas9 variants in complex with different single guide RNAs (sgRNAs), we applied high-throughput viability screens and a synthetic paired sgRNA-target system to assess thousands of sgRNAs in combination with two high-fidelity Cas9 variants HiFi and LZ3. Comparing these variants against WT SpCas9, we found that ~20% of sgRNAs are associated with a significant loss of efficiency when complexed with either HiFi or LZ3. The loss of efficiency is dependent on the sequence context in the seed region of sgRNAs, as well as at positions 15-18 in the non-seed region that interacts with the REC3 domain of Cas9, suggesting that the variant-specific mutations in REC3 domain account for the loss of efficiency. We also observed various degrees of sequence-dependent off-target reduction when different sgRNAs are used in combination with the variants. Given these observations, we developed GuideVar, a transfer-learning-based computational framework for the prediction of on-target efficiency and off-target effect with high-fidelity variants. GuideVar facilitates the prioritization of sgRNAs in the applications with HiFi and LZ3, as demonstrated by the improvement of signal-to-noise ratios in high-throughput viability screens using these high-fidelity variants.
Zhang, L., He, W., Fu, R., Xu, H.
March 17, 2023
http://biorxiv.org/cgi/content/short/2023.03.16.532856v1?rss=1

Identifying drug effects in a cardiac model of electrophysiology using kernel-based parameter estimation methods

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

Identifying drug effects in a cardiac model of electrophysiology using kernel-based parameter estimation methods

Computational models of cardiac electrophysiology hold great potential in the drug discovery process to bridge the gap between in vitro and in vivo preclinical trials. Current methods for solving inverse problems in cardiac electrophysiology are limited by their accuracy, scalability, practicality, or a combination of these. We investigate the feasibility of using kernel methods to solve the inverse problem of estimating the parameters of ionic membrane currents from observations of corresponding action potential (AP) traces. In particular, we consider AP traces generated by a cardiac cell action potential model, which mimics those obtained experimentally in measurable in vitro cardiac systems. This proof-of-concept study aims to improve existing pipelines for identifying drug effects in "heart-on-a-chip" systems by introducing a new approach to solving the inverse problem. Using synthetic training data from the 1977 Beeler-Reuter AP model of mammalian ventricular cardiomyocytes, we demonstrate our recently proposed boosted KRR solver StreaMRAK, which is particularly robust and well-adapted for high-complexity functions. We show that this method is less memory demanding, estimates the model parameters with higher accuracy, and is less sensitive to parameter identifiability problems than existing methods, such as standard KRR solvers and loss-minimization schemes based on nearest neighbor heuristics.
Oslandsbotn, A., Forsch, N., Cloninger, A.
March 17, 2023
http://biorxiv.org/cgi/content/short/2023.03.15.532862v1?rss=1

The influence of matrix concentration and solvent composition on the results of MALDI‐MSI, with the aid of wet‐interface matrix deposition

Journal of Mass Spectrometry

Wiley: Journal of Mass Spectrometry: Table of Contents

Table of Contents for Journal of Mass Spectrometry. List of articles from both the latest and EarlyView issues.

The influence of matrix concentration and solvent composition on the results of MALDI‐MSI, with the aid of wet‐interface matrix deposition

Abstract

Imaging mass spectrometry is a powerful technique for the molecular analysis of tissue sections. As in many analytical methods, sample preparation is one of the main and most important steps to obtain results of good quality. Usually, the matrix concentration and solvent composition in different studies are taken for granted without any further consideration. In our studies, we aimed to find how matrix concentration and a type of solvent influence the signal. Moreover, what is the relationship between these parameters, and how do they influence the spectra, and obtained ion maps. In our experiments, we used SunCollectÂŽ, which is a commercially available wet-interface system for matrix deposition. We decided to choose two matrix concentrations (DHB: 15 and 25 mg/ml; 9AA: 7 and 5 mg/ml) and two different water solutions of solvents in two different percentages for the matrices (DHB: 50% and 70% of MeOH and acetonitrile (ACN); 9AA 70% and 50% of EtOH and MeOH). In the end, the influence of these parameters on obtained spectra and ion maps was assessed.

Przemysław Mielczarek,
Piotr Suder,
Igor Kotsan,
Anna Bodzon‐Kulakowska
March 17, 2023
https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/jms.4916?af=R

[ASAP] Microprotein Dysregulation in the Serum of Patients with Atrial Fibrillation

Journal of Proteome Research

Journal of Proteome Research: Latest Articles (ACS Publications)

latest articles published in Journal of Proteome Research

[ASAP] Microprotein Dysregulation in the Serum of Patients with Atrial Fibrillation

TOC Graphic

Journal of Proteome Research
DOI: 10.1021/acs.jproteome.2c00622

Zheng Zhang, Tao Tian, Ni Pan, Yi Wang, Mingbo Peng, Xinbo Zhao, Zhenwei Pan, and Cuihong Wan
March 16, 2023
http://dx.doi.org/10.1021/acs.jproteome.2c00622

[ASAP] Detection and Isolation of Circulating Tumor Cells from Breast Cancer Patients Using CUB Domain-Containing Protein 1

Journal of Proteome Research

Journal of Proteome Research: Latest Articles (ACS Publications)

latest articles published in Journal of Proteome Research

[ASAP] Detection and Isolation of Circulating Tumor Cells from Breast Cancer Patients Using CUB Domain-Containing Protein 1

TOC Graphic

Journal of Proteome Research
DOI: 10.1021/acs.jproteome.2c00739

Kai Bartkowiak, Parinaz Mossahebi Mohammadi, Sebastian Gärtner, Marcel Kwiatkowski, Antje Andreas, Maria Geffken, Sven Peine, Karl Verpoort, Ursula Scholz, Thomas M. Deutsch, Laura L. Michel, Andreas Schneeweiss, Verena Thewes, Andreas Trumpp, Volkmar Müller, Sabine Riethdorf, Hartmut Schlüter&, and Klaus Pantel
March 16, 2023
http://dx.doi.org/10.1021/acs.jproteome.2c00739

Corrigendum to “The explorations of dynamic interactions of paxillin at the focal adhesions” [Biochimica et Biophysica Acta(BBA) – Proteins and Proteomics 1870/10 (2022) 140825]

Biochimica et Biophysica Acta – Proteins and Proteomics

ScienceDirect Publication: Biochimica et Biophysica Acta (BBA) – Proteins and Proteomics

ScienceDirect RSS

Corrigendum to “The explorations of dynamic interactions of paxillin at the focal adhesions” [Biochimica et Biophysica Acta(BBA) – Proteins and Proteomics 1870/10 (2022) 140825]

Publication date: 1 July 2023

Source: Biochimica et Biophysica Acta (BBA) – Proteins and Proteomics, Volume 1871, Issue 4

Author(s): Aziz ur Rehman Aziz, Sha Deng, Yuhang Jin, Na Li, Zhengyao Zhang, Xiaohui Yu, Bo Liu

March 16, 2023
https://www.sciencedirect.com/science/article/pii/S1570963923000195?dgcid=rss_sd_all

Leveraging gene correlations in single cell transcriptomic data

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

Leveraging gene correlations in single cell transcriptomic data

Many approaches have been developed to overcome technical noise in single cell (and single nucleus) RNA-sequencing (scRNAseq). As researchers dig deeper into data–looking for rare cell types, subtleties of cell states, and details of gene regulatory networks–there is a growing need for algorithms with controllable accuracy and a minimum of ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in the event that ground truth about biological variation is unknown (i.e., most of the time). Here we approach this problem analytically, based on the assumption that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We then analyze scRNAseq data without normalization–a step that can skew distributions, particular for sparse data–and calculate p-values associated with key statistics. We develop an improved method for the selection of features for cell clustering and the identification of gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), accurately captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify tens of thousands of correlations that, when clustered without supervision into gene communities, both align with cellular components and biological processes, and point toward potentially novel cell biological relationships.
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S., Nie, Q., Lander, A. D.
March 16, 2023
http://biorxiv.org/cgi/content/short/2023.03.14.532643v1?rss=1

Comparative analysis between RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

Comparative analysis between RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability, but the physiological roles of noise have remained difficult to determine in the absence of generalized noise-modulation approaches. Previous single-cell RNA sequencing (scRNA-seq) suggested that the pyrimidine-base analog (5′-iodo-2′ deoxyuridine, IdU) could generally amplify noise without substantially altering mean expression levels but scRNA-seq technical drawbacks potentially obscured the penetrance of IdU-induced transcriptional noise amplification. Here we quantify global-vs.-partial penetrance of IdU induced noise amplification by assessing scRNAseq data using numerous normalization algorithms and directly quantifying noise using single-molecule RNA FISH (smFISH) for a panel of genes from across the transcriptome. Alternate scRNA-seq analyses indicate IdU-induced noise amplification for ~90% of genes, and smFISH data verified noise amplification for ~90% of tested genes. Collectively, this analysis indicates which scRNA-seq algorithms are appropriate for quantifying noise and argues that IdU is a globally penetrant noise enhancer molecule that could enable investigations of the physiological impacts of transcriptional noise.
Calia, G. P., Chen, X., Zuckerman, B., Weinberger, L.
March 16, 2023
http://biorxiv.org/cgi/content/short/2023.03.14.532632v1?rss=1

A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion

T cells play a key role in a variety of immune responses, including infection and cancer. Upon stimulation, naive CD8+ T cells proliferate and differentiate into a variety of memory and effector cell types; however, failure to clear antigens causes prolonged stimulation of CD8+ T cells, ultimately leading to T cell exhaustion (TCE). The functional and phenotypic changes that occur during CD8+ T cell differentiation are well characterized, but the underlying gene expression state changes are not completely understood. Here, we utilize a previously published data-driven Boolean model of gene regulatory interactions shown to mediate TCE. Our network analysis and modeling reveal the final gene expression states that correspond to TCE, along with the sequence of gene expression patterns that give rise to those final states. With a model that predicts the changes in gene expression that lead to TCE, we could evaluate strategies to inhibit the exhausted state. Overall, we demonstrate that a common pathway model of CD8+ T cell gene regulatory interactions can provide insights into the transcriptional changes underlying the evolution of cell states in TCE.
Ildefonso, G. V., Finley, S. D.
March 16, 2023
http://biorxiv.org/cgi/content/short/2023.03.13.532500v1?rss=1

Leukemia core transcriptional circuitry is a sparsely interconnected hierarchy stabilized by incoherent feed-forward loops

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

Leukemia core transcriptional circuitry is a sparsely interconnected hierarchy stabilized by incoherent feed-forward loops

Lineage-defining transcription factors form densely interconnected circuits in chromatin occupancy assays, but the functional significance of these networks remains underexplored. We reconstructed the functional topology of a leukemia cell transcription network from the direct gene-regulatory programs of eight core transcriptional regulators established in pre-steady state assays coupling targeted protein degradation with nascent transcriptomics. The core regulators displayed narrow, largely non-overlapping direct transcriptional programs, forming a sparsely interconnected functional hierarchy stabilized by incoherent feed-forward loops. BET bromodomain and CDK7 inhibitors disrupted the core regulators’ direct programs, acting as mixed agonists/antagonists. The network is predictive of dynamic gene expression behaviors in time-resolved assays and clinically relevant pathway activity in patient populations.
Harada, T., Kalfon, J., Perez, M. W., Eagle, K., Braes, F. D., Batley, R., Heshmati, Y., Ferrucio, J. X., Ewers, J., Mehta, S., Kossenkov, A., Ellegast, J. M., Bowker, A., Wickramasinghe, J., Nabet, B., Paralkar, V. R., Dharia, N. V., Stegmaier, K., Orkin, S. H., Pimkin, M.
March 16, 2023
http://biorxiv.org/cgi/content/short/2023.03.13.532438v1?rss=1

MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters

BioRxiv

bioRxiv Subject Collection: Systems Biology
This feed contains articles for bioRxiv Subject Collection "Systems Biology"

MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters

SUMMARY: Low-affinity interactions among multivalent biomolecules may lead to the formation of molecular complexes that undergo phase transitions to become extra-large clusters. Characterizing the physical properties of these clusters is important in recent biophysical research. Due to weak interactions such clusters are highly stochastic, demonstrating a wide range of sizes and compositions. We have developed a Python package to perform multiple stochastic simulation runs using NFsim (Network-Free stochastic simulator), characterize and visualize the distribution of cluster sizes, molecular composition, and bonds across molecular clusters and individual molecules of different types. AVAILABILITY AND IMPLEMENTATION: The software is implemented in Python. A detailed Jupyter notebook is provided to enable convenient running. Code, user guide and examples are freely available at https://molclustpy.github.io/
Chattaraj, A., Nalagandla, I., Loew, L. M., Blinov, M. L.
March 16, 2023
http://biorxiv.org/cgi/content/short/2023.03.14.532640v1?rss=1