Joan Planas-Iglesias

Scopus Publications

Taurine inhibits apolipoprotein E4 aggregation
Anthony Legrand, Katerina Amruz Cerna, Sérgio M. Marques, Naina Verma, Jakub Kopko, Tereza Vanova, Madhumalar Subramanian, Aliaksandra Kursit, Jaroslav Bendl, Tomas Henek, Pavel Vanacek, Josef Kucera, Joan Planas-Iglesias, Jiri Sedmik, Veronika Pospisilova, Petr Kouba, Aneta Vaskova, Marketa Nezvedova, Jiri Sedlar, Jiri Damborsky, Stanislav Mazurenko, Martin Marek, Josef Sivic, Lenka Hernychova, David Bednar, Dasa Bohaciakova, Zbynek Prokop
Biomedicine and Pharmacotherapy, 2026
Apolipoprotein E4 (ApoE4) is a major genetic risk factor in many neurodegenerative diseases, yet effective therapeutic strategies targeting its associated pathologies remain unresolved. The aggregation of ApoE4, a key pathological feature, is modulated by tramiprosate and its metabolite 3-sulfopropanoic acid. In this study, we provide mechanistic insights into how taurine, a close chemical analogue of tramiprosate, interacts with ApoE4 and may similarly modulate its aggregation behavior. Using an integrated approach, which included molecular dynamics simulations, static light scattering, mass spectrometry, and cerebral organoid models, we investigated the effects of taurine on ApoE4 aggregation. Our results indicate that taurine effectively prevents ApoE4 aggregation and exerts a partial disaggregating effect on pre-formed aggregates. Notably, taurine modulates molecular and cellular features associated with the ApoE4 isoform, shifting them toward patterns observed in the more benign ApoE3 isoform. These observations are consistent with effects similar to those reported for tramiprosate and 3-sulfopropanoic acid and suggest that taurine influences ApoE4-related molecular mechanisms, particularly in the context of the high-risk ApoE4/E4 genotype.
Mobilizing the Biocatalysis Community for Reproducible and Reusable Data Collection
Sérgio M. Marques, Joan Planas-Iglesias, Jan Velecký, Milos Musil, Yasuhisa Asano, Tomasz Borowski, Vânia Brissos, Marco Cespugli, Koar Chorozian, Mohammad Dadashipour, Elif Erdem, Erica Elisa Ferrandi, Konstantinos Grigorakis, Anna Kluza, Janina Lawniczek, Konstantinos Makryniotis, Daniela Monti, Bettina Nestl, Anna C. Ngo, Efstratios Nikolaivits, Stefania Patti, Christina Pentari, Carolina F. Rodrigues, Tobias Schopper, Karolina Seweryn-Ożóg, Maciej Szaleniec, André Taborda, Mateusz Tataruch, Dirk Tischler, Evangelos Topakas, Jingyu Wang, Patrycja Wójcik, Agnieszka M. Wojtkiewicz, John M. Woodley, Olga Zastawny, Lígia O. Martins, Marco Fraaije, Jürgen Pleiss, Santiago Schnell, Jiri Damborsky, Stanislav Mazurenko, David Bednar
ACS Catalysis, 2026
cience is an ever-evolving endeavor, with all new research grounded in knowledge gained in previous studies and publications. This applies not only at the level of theory and fundamental knowledge, but also at the level of specific data. In the context of enzyme research, that includes information on properties such as protein production and folding, protein solubility, stability, catalytic activity, together with specificity and stereoselectivity, as well as regulatory effects as activation and inhibition, and kinetics, which are crucial for multiple practical reasons. In the fields of biology and biochemistry, the availability of<br/>high-quality experimental data has already contributed to several breakthroughs over time.
FireProtDB 2.0: large-scale manually curated database of the protein stability data
Milos Musil, Simeon Borko, Joan Planas-Iglesias, David Lacko, Monika Rosinska, Petr Kabourek, Lígia O Martins, Mateusz Tataruch, Jiri Damborsky, Stanislav Mazurenko, David Bednar
Nucleic Acids Research, 2026
Thermostable proteins are crucial in numerous biomedical and biotechnological applications. However, naturally occurring proteins have evolved to function in mild conditions, and laboratory experiments aiming at improving protein stability have proven laborious and expensive. Computational methods overcome this issue by providing a cheap and scalable alternative. Despite significant progress, their reliability is still hindered by the availability of high-quality data. FireProtDB 2.0 (http://loschmidt.chemi.muni.cz/fireprotdb) is a large-scale database aggregating stability data from multiple sources. The second version builds upon its predecessor, retaining its original functionality while introducing a new approach to data storage and maintenance. The new scheme enables the introduction of both absolute and relative data types connected with measurements of wild-types, mutants, protein domains, and de novo designed proteins. Furthermore, while the original database was limited to single-point mutations, more complex data such as insertions, deletions, and multiple-point mutations are now available. As a result, the inclusion of large-scale mutagenesis has increased the size of the database from 16 000 to almost 5 500 000 experiments. Moreover, the updated abstract scheme is fully expandable with any new measurements and annotations without the need for any restructuring. Finally, the tracking of history together with fixed identifiers is in accordance with the FAIR principles.
Automated Engineering Protein Dynamics via Loop Grafting: Improving Renilla Luciferase Catalysis
Joan Planas-Iglesias, Marika Majerova, Daniel Pluskal, Michal Vasina, Jiri Damborsky, Zbynek Prokop, Martin Marek, David Bednar
ACS Catalysis, 2025
Engineering protein dynamics is a challenging and unsolved problem in protein design. Loop transplantation or loop grafting has been previously employed to transfer dynamic properties between proteins. We recently released a LoopGrafter Web server to execute the loop grafting task, employing eight computational tools and one database. The LoopGrafter method relies on the prediction of the local dynamic behavior of the elements to be transplanted and has successfully reconstructed previously engineered sequences. However, it was unclear whether catalytically competitive previously uncharacterized designs could be obtained by this method. Here, we address this question, showing how LoopGrafter generates viable loop-grafted chimeras of luciferases, how these chimeras encompass the activity of interest and unique kinetic properties, and how all this process is done fully automatically and agnostic of any previous knowledge. All constructed designs proved to be catalytically active, and the most active one improved the activity of the template enzyme by 4 orders of magnitude. The computational details and parameter optimization of the sequence pairing step of the LoopGrafter workflow are revealed. The optimized and experimentally validated loop grafting workflow available as a fully automated Web server represents a powerful approach for engineering catalytically efficient enzymes by modification of protein dynamics.
LEARNING TO ENGINEER PROTEIN FLEXIBILITY
13th International Conference on Learning Representations Iclr 2025, 2025
Visual Support for the Loop Grafting Workflow on Proteins
Filip Opálený, Pavol Ulbrich, Joan Planas-Iglesias, Jan Byška, Jan Štourač, David Bednář, Katarína Furmanová, Barbora Kozlíková
IEEE Transactions on Visualization and Computer Graphics, 2025
In understanding and redesigning the function of proteins in modern biochemistry, protein engineers are increasingly focusing on exploring regions in proteins called loops. Analyzing various characteristics of these regions helps the experts design the transfer of the desired function from one protein to another. This process is denoted as loop grafting. We designed a set of interactive visualizations that provide experts with visual support through all the loop grafting pipeline steps. The workflow is divided into several phases, reflecting the steps of the pipeline. Each phase is supported by a specific set of abstracted 2D visual representations of proteins and their loops that are interactively linked with the 3D View of proteins. By sequentially passing through the individual phases, the user shapes the list of loops that are potential candidates for loop grafting. Finally, the actual in-silico insertion of the loop candidates from one protein to the other is performed, and the results are visually presented to the user. In this way, the fully computational rational design of proteins and their loops results in newly designed protein structures that can be further assembled and tested through in-vitro experiments. We showcase the contribution of our visual support design on a real case scenario changing the enantiomer selectivity of the engineered enzyme. Moreover, we provide the readers with the experts' feedback.
Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO
Rayyan Tariq Khan, Petra Pokorna, Jan Stourac, Simeon Borko, Adam Dobias, Joan Planas-Iglesias, Stanislav Mazurenko, Ihor Arefiev, Gaspar Pinto, Veronika Szotkowska, Jaroslav Sterba, Jiri Damborsky, Ondrej Slaby, David Bednar
Computational and Structural Biotechnology Journal, 2024
Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder non-specialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.97 and 0.94, and the average precision from the precision-recall curve of 0.99 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/.
A computational workflow for analysis of missense mutations in precision oncology
Rayyan Tariq Khan, Petra Pokorna, Jan Stourac, Simeon Borko, Ihor Arefiev, Joan Planas-Iglesias, Adam Dobias, Gaspar Pinto, Veronika Szotkowska, Jaroslav Sterba, Ondrej Slaby, Jiri Damborsky, Stanislav Mazurenko, David Bednar
Journal of Cheminformatics, 2024
Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation’s effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ . Scientific contribution This work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.
AggreProt: A web server for predicting and engineering aggregation prone regions in proteins
Joan Planas-Iglesias, Simeon Borko, Jan Swiatkowski, Matej Elias, Martin Havlasek, Ondrej Salamon, Ekaterina Grakova, Antonín Kunka, Tomas Martinovic, Jiri Damborsky, Jan Martinovic, David Bednar
Nucleic Acids Research, 2024
Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.
CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules
Sérgio M. Marques, Petr Kouba, Anthony Legrand, Jiri Sedlar, Lucas Disson, Joan Planas-Iglesias, Zainab Sanusi, Antonin Kunka, Jiri Damborsky, Tomas Pajdla, Zbynek Prokop, Stanislav Mazurenko, Josef Sivic, David Bednar
Jacs Au, 2024
Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here, we developed a comparative Markov state analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machine learning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered Aβ42 peptide involved in Alzheimer's disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of Aβ42 by interacting nonspecifically with charged residues. SPA impacted Aβ42 more than TMP, protecting α-helices and suppressing the formation of aggregation-prone β-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on Aβ42 and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target biomolecules other than Aβ peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules.
PredictONCO: A web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning
Jan Stourac, Simeon Borko, Rayyan T Khan, Petra Pokorna, Adam Dobias, Joan Planas-Iglesias, Stanislav Mazurenko, Gaspar Pinto, Veronika Szotkowska, Jaroslav Sterba, Ondrej Slaby, Jiri Damborsky, David Bednar
Briefings in Bioinformatics, 2024
Domino-like effect of C112R mutation on ApoE4 aggregation and its reduction by Alzheimer’s Disease drug candidate
Michal Nemergut, Sérgio M. Marques, Lukas Uhrik, Tereza Vanova, Marketa Nezvedova, Darshak Chandulal Gadara, Durga Jha, Jan Tulis, Veronika Novakova, Joan Planas-Iglesias, Antonin Kunka, Anthony Legrand, Hana Hribkova, Veronika Pospisilova, Jiri Sedmik, Jan Raska, Zbynek Prokop, Jiri Damborsky, Dasa Bohaciakova, Zdenek Spacil, Lenka Hernychova, David Bednar, Martin Marek
Molecular Neurodegeneration, 2023
SBILib: a handle for protein modeling and engineering
Patrick Gohl, Jaume Bonet, Oriol Fornes, Joan Planas-Iglesias, Narcís Fernandez-Fuentes, Baldo Oliva
Bioinformatics, 2023
Study of Protein Conformational Dynamics Using Hydrogen/Deuterium Exchange Mass Spectrometry
Lukas Uhrik, Tomas Henek, Joan Planas-Iglesias, Josef Kucera, Jiri Damborsky, Martin Marek, Lenka Hernychova
Methods in Molecular Biology, 2023
LoopGrafter: a web tool for transplanting dynamical loops for protein engineering
Joan Planas-Iglesias, Filip Opaleny, Pavol Ulbrich, Jan Stourac, Zainab Sanusi, Gaspar P Pinto, Andrea Schenkmayerova, Jan Byska, Jiri Damborsky, Barbora Kozlikova, David Bednar
Nucleic Acids Research, 2022
Tools for computational design and high-throughput screening of therapeutic enzymes
Michal Vasina, Jan Velecký, Joan Planas-Iglesias, Sergio M. Marques, Jana Skarupova, Jiri Damborsky, David Bednar, Stanislav Mazurenko, Zbynek Prokop
Advanced Drug Delivery Reviews, 2022
Characterization of the AGR2 Interactome Uncovers New Players of Protein Disulfide Isomerase Network in Cancer Cells
Pavla Bouchalova, Lucia Sommerova, David Potesil, Andrea Martisova, Petr Lapcik, Veronika Koci, Alex Scherl, Petr Vonka, Joan Planas-Iglesias, Eric Chevet, Pavel Bouchal, Roman Hrstka
Molecular and Cellular Proteomics, 2022
Engineering the protein dynamics of an ancestral luciferase
Andrea Schenkmayerova, Gaspar P. Pinto, Martin Toul, Martin Marek, Lenka Hernychova, Joan Planas-Iglesias, Veronika Daniel Liskova, Daniel Pluskal, Michal Vasina, Stephane Emond, Mark Dörr, Radka Chaloupkova, David Bednar, Zbynek Prokop, Florian Hollfelder, Uwe T. Bornscheuer, Jiri Damborsky
Nature Communications, 2021
Computational Enzyme Stabilization Can Affect Folding Energy Landscapes and Lead to Catalytically Enhanced Domain-Swapped Dimers
Klara Markova, Antonin Kunka, Klaudia Chmelova, Martin Havlasek, Petra Babkova, Sérgio M. Marques, Michal Vasina, Joan Planas-Iglesias, Radka Chaloupkova, David Bednar, Zbynek Prokop, Jiri Damborsky, Martin Marek
ACS Catalysis, 2021
Web-based tools for computational enzyme design
Sérgio M Marques, Joan Planas-Iglesias, Jiri Damborsky
Current Opinion in Structural Biology, 2021
Galaxy InteractoMIX: An Integrated Computational Platform for the Study of Protein–Protein Interaction Data
Patricia Mirela-Bota, Joaquim Aguirre-Plans, Alberto Meseguer, Cristiano Galletti, Joan Segura, Joan Planas-Iglesias, Javi Garcia-Garcia, Emre Guney, Baldo Oliva, Narcis Fernandez-Fuentes
Journal of Molecular Biology, 2021
Computational design of enzymes for biotechnological applications
Joan Planas-Iglesias, Sérgio M. Marques, Gaspar P. Pinto, Milos Musil, Jan Stourac, Jiri Damborsky, David Bednar
Biotechnology Advances, 2021
LoopGrafter: Visual Support for the Grafting Workflow of Protein Loops
Filip Opaleny, Pavol Ulbrich, Joan Planas-Iglesias, Jan Byska, Gaspar P. Pinto, David Bednar, Katarina FurmanovA, Barbora KozlikovA
IEEE Transactions on Visualization and Computer Graphics, 2021
Activity-dependent interdomain dynamics of matrix metalloprotease-1 on fibrin
Lokender Kumar, Joan Planas-Iglesias, Chase Harms, Sumaer Kamboj, Derek Wright, Judith Klein-Seetharaman, Susanta K. Sarkar
Scientific Reports, 2020
Allosteric Communications between Domains Modulate the Activity of Matrix Metalloprotease-1
Lokender Kumar, Anthony Nash, Chase Harms, Joan Planas-Iglesias, Derek Wright, Judith Klein-Seetharaman, Susanta K. Sarkar
Biophysical Journal, 2020
Angiotensin converting enzyme (ACE): A marker for personalized feedback on dieting
Shilpa Tejpal, Narinder Sanghera, Vijayalaxmi Manoharan, Joan Planas-Iglesias, Claire C Bastie, Judith Klein-Seetharaman
Nutrients, 2020
Deciphering the structural basis of high thermostability of dehalogenase from psychrophilic bacterium marinobacter sp. ELB17
Lukas Chrast, Katsiaryna Tratsiak, Joan Planas-Iglesias, Lukas Daniel, Tatyana Prudnikova, Jan Brezovsky, David Bednar, Ivana Kuta Smatanova, Radka Chaloupkova, Jiri Damborsky
Microorganisms, 2019
Correction: Comparison of the molecular properties of retinitis pigmentosa P23H and N15S amino acid replacements in rhodopsin (PLoS ONE (2019) 14: 5 (e0214639) DOI: 10.1371/journal.pone.0214639)
James Mitchell, Fernanda Balem, Kalyan Tirupula, David Man, Harpreet Kaur Dhiman, Naveena Yanamala, Julian Ollesch, Joan Planas-Iglesias, Barbara J. Jennings, Klaus Gerwert, Alessandro Iannaccone, Judith Klein-Seetharaman
Plos One, 2019
Towards personalised molecular feedback for weight loss
Shilpa Tejpal, Narinder Sanghera, Vijayalaxmi Manoharan, Joan Planas-Iglesias, Kate Myler, Judith Klein-Seetharaman
BMC Obesity, 2019
Comparison of the molecular properties of retinitis pigmentosa P23H and N15S amino acid replacements in rhodopsin
James Mitchell, Fernanda Balem, Kalyan Tirupula, David Man, Harpreet Kaur Dhiman, Naveena Yanamala, Julian Ollesch, Joan Planas-Iglesias, Barbara J. Jennings, Klaus Gerwert, Alessandro Iannaccone, Judith Klein-Seetharaman
Plos One, 2019
Structural characterization of cardiolipin-driven activation of cytochrome c into a peroxidase and membrane perturbation
Dariush Mohammadyani, Naveena Yanamala, Alejandro K. Samhan-Arias, Alexander A. Kapralov, German Stepanov, Nick Nuar, Joan Planas-Iglesias, Narinder Sanghera, Valerian E. Kagan, Judith Klein-Seetharaman
Biochimica Et Biophysica Acta Biomembranes, 2018
On the mechanisms of protein interactions: Predicting their affinity from unbound tertiary structures
Manuel Alejandro Marín-López, Joan Planas-Iglesias, Joaquim Aguirre-Plans, Jaume Bonet, Javier Garcia-Garcia, Narcis Fernandez-Fuentes, Baldo Oliva
Bioinformatics, 2018
Known unknowns of cardiolipin signaling: The best is yet to come
John J. Maguire, Yulia Y. Tyurina, Dariush Mohammadyani, Aleksandr A. Kapralov, Tamil S. Anthonymuthu, Feng Qu, Andrew A. Amoscato, Louis J. Sparvero, Vladimir A. Tyurin, Joan Planas-Iglesias, Rong-Rong He, Judith Klein-Seetharaman, Hülya Bayır, Valerian E. Kagan
Biochimica Et Biophysica Acta Molecular and Cell Biology of Lipids, 2017
Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
Solveig K. Sieberts, Fan Zhu, Javier García-García, Eli Stahl, Abhishek Pratap, Gaurav Pandey, Dimitrios Pappas, Daniel Aguilar, Bernat Anton, Jaume Bonet, Ridvan Eksi, Oriol Fornés, Emre Guney, Hongdong Li, Manuel Alejandro Marín, Bharat Panwar, Joan Planas-Iglesias, Daniel Poglayen, Jing Cui, Andre O. Falcao, Christine Suver, Bruce Hoff, Venkat S. K. Balagurusamy, Donna Dillenberger, Elias Chaibub Neto, Thea Norman, Tero Aittokallio, Muhammad Ammad-ud-din, Chloe-Agathe Azencott, Víctor Bellón, Valentina Boeva, Kerstin Bunte, Himanshu Chheda, Lu Cheng, Jukka Corander, Michel Dumontier, Anna Goldenberg, Peddinti Gopalacharyulu, Mohsen Hajiloo, Daniel Hidru, Alok Jaiswal, Samuel Kaski, Beyrem Khalfaoui, Suleiman Ali Khan, Eric R. Kramer, Pekka Marttinen, Aziz M. Mezlini, Bhuvan Molparia, Matti Pirinen, Janna Saarela, Matthias Samwald, Véronique Stoven, Hao Tang, Jing Tang, Ali Torkamani, Jean-Phillipe Vert, Bo Wang, Tao Wang, Krister Wennerberg, Nathan E. Wineinger, Guanghua Xiao, Yang Xie, Rae Yeung, Xiaowei Zhan, Cheng Zhao, Members of the Rheumatoid Arthritis Challenge Consortium, Manuel Calaza, Haitham Elmarakeby, Lenwood S. Heath, Quan Long, Jonathan D. Moore, Stephen Obol Opiyo, Richard S. Savage, Jun Zhu, Jeff Greenberg, Joel Kremer, Kaleb Michaud, Anne Barton, Marieke Coenen, Xavier Mariette, Corinne Miceli, Nancy Shadick, Michael Weinblatt, Niek de Vries, Paul P. Tak, Danielle Gerlag, Tom W. J. Huizinga, Fina Kurreeman, Cornelia F. Allaart, S. Louis Bridges, Lindsey Criswell, Larry Moreland, Lars Klareskog, Saedis Saevarsdottir, Leonid Padyukov, Peter K. Gregersen, Stephen Friend, Robert Plenge, Gustavo Stolovitzky, Baldo Oliva, Yuanfang Guan, Lara M. Mangravite
Nature Communications, 2016
Inferring causal molecular networks: Empirical assessment through a community-based effort
The HPN-DREAM Consortium, Steven M Hill, Laura M Heiser, Thomas Cokelaer, Michael Unger, Nicole K Nesser, Daniel E Carlin, Yang Zhang, Artem Sokolov, Evan O Paull, Chris K Wong, Kiley Graim, Adrian Bivol, Haizhou Wang, Fan Zhu, Bahman Afsari, Ludmila V Danilova, Alexander V Favorov, Wai Shing Lee, Dane Taylor, Chenyue W Hu, Byron L Long, David P Noren, Alexander J Bisberg, Gordon B Mills, Joe W Gray, Michael Kellen, Thea Norman, Stephen Friend, Amina A Qutub, Elana J Fertig, Yuanfang Guan, Mingzhou Song, Joshua M Stuart, Paul T Spellman, Heinz Koeppl, Gustavo Stolovitzky, Julio Saez-Rodriguez, Sach Mukherjee
Nature Methods, 2016
Mitochondrial Redox Opto-Lipidomics Reveals Mono-Oxygenated Cardiolipins as Pro-Apoptotic Death Signals
Gaowei Mao, Feng Qu, Claudette M. St. Croix, Yulia Y. Tyurina, Joan Planas-Iglesias, Jianfei Jiang, Zhentai Huang, Andrew A. Amoscato, Vladimir A. Tyurin, Alexandr A. Kapralov, Amin Cheikhi, John Maguire, Judith Klein-Seetharaman, Hülya Bayır, Valerian E. Kagan
ACS Chemical Biology, 2016
The brown adipocyte protein CIDEA promotes lipid droplet fusion via a phosphatidic acid-binding amphipathic helix
David Barneda, Joan Planas-Iglesias, Maria L Gaspar, Dariush Mohammadyani, Sunil Prasannan, Dirk Dormann, Gil-Soo Han, Stephen A Jesch, George M Carman, Valerian Kagan, Malcolm G Parker, Nicholas T Ktistakis, Judith Klein-Seetharaman, Ann M Dixon, Susan A Henry, Mark Christian
Elife, 2015
Cardiolipin Interactions with Proteins
Joan Planas-Iglesias, Himal Dwarakanath, Dariush Mohammadyani, Naveena Yanamala, Valerian E. Kagan, Judith Klein-Seetharaman
Biophysical Journal, 2015
Frag'r'Us: Knowledge-based sampling of protein backbone conformations for de novo structure-based protein design
Jaume Bonet, Joan Segura, Joan Planas-Iglesias, Baldomero Oliva, Narcis Fernandez-Fuentes
Bioinformatics, 2014
ArchDB 2014: Structural classification of loops in proteins
Jaume Bonet, Joan Planas-Iglesias, Javier Garcia-Garcia, Manuel A. Marín-López, Narcis Fernandez-Fuentes, Baldo Oliva
Nucleic Acids Research, 2014
ILoops: A protein-protein interaction prediction server based on structural features
Joan Planas-Iglesias, Manuel A. Marin-Lopez, Jaume Bonet, Javier Garcia-Garcia, Baldo Oliva
Bioinformatics, 2013
Understanding protein-protein interactions using local structural features
Joan Planas-Iglesias, Jaume Bonet, Javier García-García, Manuel A. Marín-López, Elisenda Feliu, Baldo Oliva
Journal of Molecular Biology, 2013
Extending signaling pathways with protein-interaction networks. Application to apoptosis
Joan Planas-Iglesias, Emre Guney, Javier García-García, Kevin A Robertson, Sobia Raza, Tom C. Freeman, Peter Ghazal, Baldo Oliva
OMICS A Journal of Integrative Biology, 2012
Biana: A software framework for compiling biological interactions and analyzing networks
Javier Garcia-Garcia, Emre Guney, Ramon Aragues, Joan Planas-Iglesias, Baldo Oliva
BMC Bioinformatics, 2010
Comparative modelling of protein structure and its impact on microbial cell factories
Nuria B Centeno, Joan Planas-Iglesias, Baldomero Oliva
Microbial Cell Factories, 2005

Joan Planas-Iglesias

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS