KAUST Visualization Laboratory (KVL)

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The Visualization Laboratory at KAUST is a fully staffed state-of-the-art facility that offers students, faculty, researchers and university collaborators a unique opportunity to utilize one-of-a-kind visualization, interaction, and computational resources for the exploration and presentation of scientific data. 2D and 3D display environments, highly spatialized and immersive audio, monoscopic and stereoscopic displays, wireless interaction devices, and fully integrated and portable desktop applications are some of the services the laboratory offers. All spaces are fully interconnected with a 10Gb link, and can also be utilized for academic events and research meetings. Audio/video streaming, recording, and playback of research presentations/seminars are available throughout the facility. The facility is available for use by any KAUST member, and the laboratory staff can provide assistance in creating new customized applications with their expertise in computer graphics, human-computer interaction, virtual reality, scientific visualization and sonification. A Special Research Partnership has also been established between KAUST and the CalIT2 Institute at University of California San Diego. The design and implementation of the lab facilities was done in collaboration with UCSD and new research projects can avail the full benefit of this ongoing partnership. Students from KAUST academic programs spend summer internships at UCSD, collaborating with research scientists there and coming back to the lab to apply and implement their newly learned skills. Contact us at vislab@kaust.edu.sa

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Recent Submissions

Now showing 1 - 5 of 27
  • Software

    vibbits/Elixir-3DBioInfo-Benchmark-Protein-Interfaces:

    (Github, 2023-01-28) Schweke, Hugo; Xu, Qifang; Tauriello, Gerardo; Pantolini, Lorenzo; Schwede, Torsten; Cazals, Frédéric; Lhéritier, Alix; Fernandez-Recio, Juan; Rodríguez-Lumbreras, Luis Angel; Schueler-Furman, Ora; Varga, Julia K.; Jiménez-García, Brian; Réau, Manon F.; Bonvin, Alexandre M. J. J.; Savojardo, Castrense; Martelli, Pier-Luigi; Casadio, Rita; Tubiana, Jérôme; Wolfson, Haim J.; Oliva, Romina; Barradas Bautista, Didier; Ricciardelli, Tiziana; Cavallo, Luigi; Venclovas, Česlovas; Olechnovič, Kliment; Guerois, Raphael; Andreani, Jessica; Martin, Juliette; Wang, Xiao; Terashi, Genki; Sarkar, Daipayan; Christoffer, Charles; Aderinwale, Tunde; Verburgt, Jacob; Kihara, Daisuke; Marchand, Anthony; Correia, Bruno E.; Duan, Rui; Qiu, Liming; Xu, Xianjin; Zhang, Shuang; Zou, Xiaoqin; Dey, Sucharita; Dunbrack, Roland L.; Levy, Emmanuel D.; Wodak, Shoshana J; Kaust Visualization Lab Core lab Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia; Physical Sciences and Engineering Division Kaust Catalysis Center King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia; Visualization; Bioengineering Program; Biological and Environmental Science and Engineering (BESE) Division; Chemical Science Program; KAUST Catalysis Center (KCC); Physical Science and Engineering (PSE) Division; KAUST Visualization Laboratory (KVL); Department of Chemical and Structural Biology Weizmann Institute of Science Rehovot Israel; Institute for Cancer Research Fox Chase Cancer Center Philadelphia Pennsylvania USA; Biozentrum University of Basel & SIB Swiss Institute of Bioinformatics Basel Switzerland; Centre Inria d'Université Côte d'Azur Sophia-Antipolis France; Amadeus SAS Sophia-Antipolis France; Instituto de Ciencias de la Vid y del Vino (ICVV) CSIC-UR-Gobierno de La Rioja Logroño Spain; Department of Microbiology and Molecular Genetics The Institute for Medical Research Israel-Canada Hebrew University-Hadassah Medical School Jerusalem Israel; Computational Structural Biology Group Department of Chemistry Bijvoet Centre Faculty of Science Utrecht University Utrecht The Netherlands; Zymvol Biomodeling SL Barcelona Spain; Biocomputing Group Department of Pharmacy and Biotechnology University of Bologna Bologna Italy; Blavatnik School of Computer Science Tel Aviv University Tel Aviv Israel; Department of Sciences and Technologies University of Naples “Parthenope” Naples Italy; Institute of Biotechnology Life Sciences Center Vilnius University Vilnius Lithuania; Institute for Integrative Biology of the Cell (I2BC) Commissariat à l'Energie Atomique CNRS Université Paris-Sud Université Paris-Saclay Gif-sur-Yvette France; Univ Lyon Université Claude Bernard Lyon 1 CNRS, UMR 5086 MMSB Lyon France; Department of Computer Science Purdue University West Lafayette Indiana USA; Department of Biological Sciences Purdue University West Lafayette Indiana USA; Laboratory of Protein Design and Immunoengineering Ecole polytechnique fédérale de Lausanne (EPFL) Lausanne Switzerland; Department of Physics and Astronomy Department of Biochemistry Dalton Cardiovascular Research Center Institute for Data Science and Informatics University of Missouri Columbia Missouri USA; Department of Bioscience and Bioengineering Indian Institute of Technology Jodhpur Karwar Rajasthan India; VIB-VUB Center for Structural Biology Brussels Belgium
  • Article

    Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

    (Wiley, 2023-06-27) Schweke, Hugo; Xu, Qifang; Tauriello, Gerardo; Pantolini, Lorenzo; Schwede, Torsten; Cazals, Frédéric; Lhéritier, Alix; Fernandez-Recio, Juan; Rodríguez-Lumbreras, Luis Angel; Schueler-Furman, Ora; Varga, Julia K.; Jiménez-García, Brian; Réau, Manon F.; Bonvin, Alexandre M. J. J.; Savojardo, Castrense; Martelli, Pier-Luigi; Casadio, Rita; Tubiana, Jérôme; Wolfson, Haim J.; Oliva, Romina; Barradas-Bautista, Didier; Ricciardelli, Tiziana; Cavallo, Luigi; Venclovas, Česlovas; Olechnovič, Kliment; Guerois, Raphael; Andreani, Jessica; Martin, Juliette; Wang, Xiao; Terashi, Genki; Sarkar, Daipayan; Christoffer, Charles; Aderinwale, Tunde; Verburgt, Jacob; Kihara, Daisuke; Marchand, Anthony; Correia, Bruno E.; Duan, Rui; Qiu, Liming; Xu, Xianjin; Zhang, Shuang; Zou, Xiaoqin; Dey, Sucharita; Dunbrack, Roland L.; Levy, Emmanuel D.; Wodak, Shoshana J; Kaust Visualization Lab Core lab Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia; Physical Sciences and Engineering Division Kaust Catalysis Center King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia; Bioengineering Program; Biological and Environmental Science and Engineering (BESE) Division; Chemical Science Program; KAUST Catalysis Center (KCC); Physical Science and Engineering (PSE) Division; KAUST Visualization Laboratory (KVL); Department of Chemical and Structural Biology Weizmann Institute of Science Rehovot Israel; Institute for Cancer Research Fox Chase Cancer Center Philadelphia Pennsylvania USA; Biozentrum University of Basel & SIB Swiss Institute of Bioinformatics Basel Switzerland; Centre Inria d'Université Côte d'Azur Sophia-Antipolis France; Amadeus SAS Sophia-Antipolis France; Instituto de Ciencias de la Vid y del Vino (ICVV) CSIC-UR-Gobierno de La Rioja Logroño Spain; Department of Microbiology and Molecular Genetics The Institute for Medical Research Israel-Canada Hebrew University-Hadassah Medical School Jerusalem Israel; Computational Structural Biology Group Department of Chemistry Bijvoet Centre Faculty of Science Utrecht University Utrecht The Netherlands; Zymvol Biomodeling SL Barcelona Spain; Biocomputing Group Department of Pharmacy and Biotechnology University of Bologna Bologna Italy; Blavatnik School of Computer Science Tel Aviv University Tel Aviv Israel; Department of Sciences and Technologies University of Naples “Parthenope” Naples Italy; Institute of Biotechnology Life Sciences Center Vilnius University Vilnius Lithuania; Institute for Integrative Biology of the Cell (I2BC) Commissariat à l'Energie Atomique CNRS Université Paris-Sud Université Paris-Saclay Gif-sur-Yvette France; Univ Lyon Université Claude Bernard Lyon 1 CNRS, UMR 5086 MMSB Lyon France; Department of Computer Science Purdue University West Lafayette Indiana USA; Department of Biological Sciences Purdue University West Lafayette Indiana USA; Laboratory of Protein Design and Immunoengineering Ecole polytechnique fédérale de Lausanne (EPFL) Lausanne Switzerland; Department of Physics and Astronomy Department of Biochemistry Dalton Cardiovascular Research Center Institute for Data Science and Informatics University of Missouri Columbia Missouri USA; Department of Bioscience and Bioengineering Indian Institute of Technology Jodhpur Karwar Rajasthan India; VIB-VUB Center for Structural Biology Brussels Belgium

    Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

  • Article

    The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions.

    (Oxford University Press (OUP), 2023-05-04) Jiménez-García, Brian; Roel-Touris, Jorge; Barradas Bautista, Didier; Kaust Visualization Lab, Core lab Division, King Abdullah University of Science and Technology (KAUST), 23955-6900, Thuwal, Saudi Arabia.; Visualization; KAUST Visualization Laboratory (KVL); Zymvol Biomodeling, Pau Claris 94 3B, 08010, Barcelona, Spain.; Protein Design and Modeling Lab, Department of Structural Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), Baldiri Reixac 15, 08028Barcelona, Spain.

    Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies.

  • Conference Paper

    Imaging Oil Recovery from Mixed-Wet Microporous Carbonates

    (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) Hassan, Ahmed; Kaprielova, Ksenia; Saad, Ahmed; Yutkin, Maxim; Patzek, Tadeusz; Earth Science and Engineering Program; Physical Science and Engineering (PSE) Division; Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC); KAUST Visualization Laboratory (KVL); Energy Resources and Petroleum Engineering Program

    This paper summarizes some of our efforts in comprehensive imaging of a complex limestone-water-asphaltenic crude oil reservoir system at scales ranging from a fraction of a micron to centimeters. These types of imaging and analyses that follow are necessary if one were to understand fully the fundamentals of improved oil recovery in mixed wet rock. Perhaps as much as 50% of the oil-in-place in carbonate formations around the world is locked away in the easy to bypass microporosity. If some of this oil is unlocked by the improved recovery processes designed speci cally for tight carbonate formations, the world may gain a major source of lower-rate power over several decades. Here, we overview our work on the Arab D limestones and Indiana limestones. We investigate the occurrence of microporosity of different origins and sizes using scanning electron microscopy (SEM) and pore casting techniques. We show that large portions of the micropores in Arab D formation would have been bypassed during primary drainage unless the invading crude oil ganglia were su ciently long. We also show that, under prevailing conditions of primary drainage of the strongly water-wet Arab formations in the Ghawar, the microporosity there was invaded and the porosity-weighted initial oil saturations of 60-85% are expected. Considering the asphaltenic nature of crude oil in the Ghawar, we expect the invaded portions of the pores to turn mixed-wet, thus becoming inaccessible to water ooding until further measures are taken to modify the system's surface chemistry and/or create substantial local pore pressure gradients. All types of imaging and experiments described in this paper guide our spontaneous counter-current imbibition in Amott cell experiments, a convenient laboratory method of studying oil recovery from oil-saturated rock samples in secondary or tertiary oil recovery by water ood of tunable composition. Classical Amott cell experiment estimates ultimate oil recovery. It is not designed, however, for studying the dynamics of oil recovery. In this work we identify and x a aw in the classical Amott cell imbibition experiments that hinders the development of predictive recovery models for mixed-wet carbonates. We then follow with a statistical analysis and scaling of the imbibition. We apply Generalized Extreme Value distribution to model the cumulative oil production. Here, we start with the Amott imbibition experiments and scaling analysis for Indiana limestone core plugs saturated with mineral oil. The knowledge gained from this study will allow us to develop a predictive model of water-oil displacement for reservoir carbonate rock and crude oil recovery systems.

  • Article

    Improving classification of correct and incorrect protein-protein docking models by augmenting the training set

    (Oxford University Press (OUP), 2023-02-02) Barradas Bautista, Didier; Almajed, Ali; Oliva, Romina; Kalnis, Panos; Cavallo, Luigi; Kaust Visualization Lab, Core lab Division, King Abdullah University of Science and Technology (KAUST) , 23955-6900, Thuwal, Saudi Arabia; King Abdullah University of Science and Technology (KAUST) Kaust Extreme Computing Center, Computer, Electrical and Mathematical Science and Engineering Division, , 23955-6900, Thuwal, Saudi Arabia; King Abdullah University of Science and Technology (KAUST) Kaust Catalysis Center, Physical Sciences and Engineering Division, , 23955-6900, Thuwal, Saudi Arabia; Visualization; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Extreme Computing Research Center; Chemical Science Program; KAUST Catalysis Center (KCC); Physical Science and Engineering (PSE) Division; KAUST Visualization Laboratory (KVL); University of Naples “Parthenope” Department of Sciences and Technologies, , I-80143, Naples, Italy

    Motivation: Protein-protein interactions drive many relevant biological events, such as infection, replication, and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling, like protein-protein docking, can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling is that it generates a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers. Results: Using weak supervision, we developed a data augmentation method that we named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 MCC on the test set, surpassing the state-of-the-art scoring functions.