Article
Biochemistry & Molecular Biology
Damiano Clementel, Alessio Del Conte, Alexander Miguel Monzon, Giorgia F. Camagni, Giovanni Minervini, Damiano Piovesan, Silvio C. E. Tosatto
Summary: Residue interaction networks (RINs) have been proven effective in analyzing complex systems as an alternative to coordinate data. The new RING 3.0 version extends the previous functionality by supporting mmCIF format, providing typed interactions for the entire PDB chemical component dictionary, and employing probabilistic graphs for analyzing structural data. The web interface has also been expanded to enhance visualization and interactivity.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Biochemical Research Methods
Laurel F. Kinman, Barrett M. Powell, Ellen D. Zhong, Bonnie Berger, Joseph H. Davis
Summary: This paper introduces a machine learning system called CryoDRGN for reconstructing proteins and protein complexes from single-particle cryo-EM data. The system utilizes a deep generative model to generate 3D density maps and provides methods for analyzing and interpreting the resulting ensemble. However, interpreting the ensemble is still a challenge.
Article
Engineering, Geological
Akshay Kumar, Gaurav Tiwari
Summary: This study presents a Bayesian multi-model inference methodology to accurately characterize probabilistic models and model parameters of rock properties, considering the issue of insufficient data. The methodology estimates the reliability of rock slopes and tunnels and includes prior information for analysis, making it superior to other methods.
Article
Engineering, Geological
Wenmin Yao, Changdong Li, Changbin Yan, Hongbin Zhan
Summary: The study proposes a hybrid framework for slope reliability based on Bayesian sequential updating technology, integrating prior knowledge, multiple estimation methods, and model uncertainties to estimate slope reliability with limited geotechnical data. Through experiments with three slope examples, the framework is shown to provide reliable and accurate estimations of slope reliability.
Article
Biochemistry & Molecular Biology
Z. Faidon Brotzakis
Summary: Metadynamics electron microscopy metaInference (MEMMI) is an integrative structural biology method that combines cryo-electron microscopy electron density maps with metadynamic-enhanced-sampling molecular dynamics to characterize protein structural dynamics and errors in cryo-EM experimental data. It can accurately determine atomistic structural ensembles and errors, even in cases where conformations are separated by high energy barriers. In this study, MEMMI is used to analyze the fuzzy coat of IAPP, a fibril associated with type II diabetes.
Article
Biochemical Research Methods
Xavier Didelot, David Helekal, Michelle Kendall, Paolo Ribeca
Summary: The ability to distinguish imported cases from locally acquired cases is important for selecting public health control strategies. This study proposes an alternative approach using genomic data from a specific location to detect imported cases by comparing them with previous cases from the same location.
Article
Mathematics, Applied
Josie Koenig, Melina A. Freitag
Summary: This paper discusses the application of balanced truncation to linear Gaussian Bayesian inference, particularly the 4D-Var method, and strengthens the connection between systems theory and data assimilation. The similarities between both types of data assimilation problems allow for the generalization of the state-of-the-art approach, proposing an enhanced method to balance Bayesian inference for unstable systems and improve numerical results for short observation periods.
JOURNAL OF SCIENTIFIC COMPUTING
(2023)
Article
Physics, Fluids & Plasmas
Kai Shimagaki, John P. Barton
Summary: This article proposes a framework for accurately estimating time-integrated quantities using Bezier interpolation and applies it to two dynamical inference problems. The results show that Bezier interpolation reduces estimation bias, especially for data sets with limited time resolution.
Article
Physics, Multidisciplinary
Ginestra Bianconi
Summary: Maximum entropy network ensembles have been effective in modeling and solving inference problems in sparse network topologies. However, existing models have limitations which have been addressed by proposing hierarchical models for exchangeable networks that can handle fixed or arbitrary number of nodes in a grand canonical approach.
Article
Physics, Multidisciplinary
Vijay Varma, Sylvia Biscoveanu, Tousif Islam, Feroz H. Shaik, Carl-Johan Haster, Maximiliano Isi, Will M. Farr, Scott E. Field, Salvatore Vitale
Summary: The final black hole formed after a binary black hole merger can have a significant recoil velocity, which has important implications for gravitational wave astronomy, black hole formation scenarios, testing general relativity, and galaxy evolution. This study analyzes the gravitational wave signal from the GW200129 binary black hole merger and provides the first identification of a large kick velocity for an individual event. The study also estimates the probability of retaining the remnant black hole after the merger and discusses the potential impact of kick effects on ringdown tests of general relativity.
PHYSICAL REVIEW LETTERS
(2022)
Article
Astronomy & Astrophysics
Oliver Edy, Andrew Lundgren, Laura K. Nuttall
Summary: This study focuses on using Bayesian inference to extract unknown parameters from gravitational wave signals. The research finds that the posterior of estimated waveform parameters is no longer valid under the assumption of stationary noise, leading to under- or overestimated errors compared to the true posterior. While nonstationarity in short signals has minimal impact on parameter estimation, nonstationary data containing signals lasting tens of seconds or longer will result in significantly worse errors than stationary noise.
Article
Chemistry, Physical
Oufan Zhang, Mojtaba Haghighatlari, Jie Li, Zi Hao Liu, Ashley Namini, Joao M. C. Teixeira, Julie D. Forman-Kay, Teresa Head-Gordon
Summary: The computational approach combined with experiments is necessary to characterize the structural diversity and dynamics of proteins with a disorder. The selection of conformations consistent with solution experiments depends on the initial pool of conformers, and current tools are limited by sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions based on experimental data, allowing the model to physically change conformations to better match experiments.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Multidisciplinary Sciences
Antoine Baker, Indaco Biazzo, Alfredo Braunstein, Giovanni Catania, Luca Dall'Asta, Alessandro Ingrosso, Florent Krzakala, Fabio Mazza, Marc Mezard, Anna Paola Muntoni, Maria Refinetti, Stefano Sarao Mannelli, Lenka Zdeborova
Summary: Research suggests that probabilistic risk estimation can enhance the performance of digital contact tracing, aiding in mitigating the impact of epidemics.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Multidisciplinary Sciences
Nicola Dietler, Umberto Lupo, Anne-Florence Bitbol
Summary: This study investigates the impact of phylogenetic correlations on contact prediction from protein sequences. The results show that global inference methods are more resilient to these correlations than local methods, which explains their success.
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2023)
Article
Astronomy & Astrophysics
Cailin Plunkett, Sophie Hourihane, Katerina Chatziioannou
Summary: This article investigates the parameter estimation for compact binary signals in gravitational waves, comparing traditional sequential estimation method and new full marginalization method. The study finds that, at current detector sensitivities, uncertainty about the noise power spectral density has a minor impact on the parameter estimation.
Correction
Multidisciplinary Sciences
Paulina Regenthal, Jesper S. Hansen, Ingemar Andre, Karin Lindkvist-Petersson
Article
Chemistry, Multidisciplinary
Robert Lizatovic, Marvin Assent, Arjan Barendregt, Jonathan Dahlin, Anna Bille, Katharina Satzinger, Dagnija Tupina, Albert J. R. Heck, Stefan Wennmalm, Ingemar Andre
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2018)
Article
Biochemistry & Molecular Biology
Ingemar Andre, Sinisa Bjelic
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2018)
Article
Chemistry, Multidisciplinary
Ryan C. Oliver, Wojciech Potrzebowski, Seyed Morteza Najibi, Martin Nors Pedersen, Lise Arleth, Najet Mahmoudi, Ingemar Andre
Article
Biochemical Research Methods
Filip Ljung, Ingemar Andre
Summary: The study introduces ZEAL, an interactive tool for superimposing global and local protein structures based on their shape resemblance. ZEAL outperforms other methods for shape-based superposition and is particularly effective for comparing proteins with limited sequence and backbone-fold similarity. The tool can be used to study relationships between shape and protein function, with particularly common global surface shape similarity found among DNA binding proteins.
Article
Biochemistry & Molecular Biology
Diego A. Leonardo, Italo A. Cavini, Fernanda A. Sala, Deborah C. Mendonca, Higor V. D. Rosa, Patricia S. Kumagai, Edson Crusca Jr, Napoleao F. Valadares, Ivo A. Marques, Jose Brandao-Neto, Claudia E. Munte, Hans R. Kalbitzer, Nicolas Soler, Isabel Uson, Ingemar Andre, Ana P. U. Araujo, Humberto D'Muniz Pereira, Richard C. Garratt
Summary: Septins are composed of different paralogues that must be correctly assembled into functional filaments important for essential cellular events. Most septins possess C-terminal domains capable of forming coils, and the study reveals dimeric structures with both parallel and antiparallel arrangements. Both arrangements are energetically accessible, with antiparallel structures presenting a mixed coiled-coil interface.
JOURNAL OF MOLECULAR BIOLOGY
(2021)
Article
Biochemistry & Molecular Biology
Marie Sofie Moller, Sita Vaag Olesen, Ingemar Andre
Summary: This study investigates the stability of ultra-high affinity in the LD-LDI complex, demonstrating that high affinity of LD-LDI requires interactions of several residues at the rim of the protein interface. The mutational analysis reveals that ultra-high binding affinity can be conferred without hotspot residues.
Article
Biochemistry & Molecular Biology
Christoffer Norn, Ingemar Andre, Douglas L. Theobald
Summary: Evolutionary pressures and thermodynamic stability constraints play key roles in shaping the global amino acid substitution patterns observed in proteins, as evidenced by a new hybrid biophysical and evolutionary model. This model accurately recapitulates the complex yet universal patterns seen in common amino acid substitution matrices, suggesting that selection for thermodynamically stable proteins and nucleotide mutation bias filtered by genetic code structure are primary drivers behind these patterns.
Article
Multidisciplinary Sciences
Veronica Lattanzi, Ingemar Andre, Urs Gasser, Marija Dubackic, Ulf Olsson, Sara Linse
Summary: Amyloid fibrils, specifically A beta 42 fibrils, in neurodegenerative diseases like Alzheimer's, are toxic to neuronal cells. Small-angle scattering is used to study the dimension and shape of these fibrils, revealing an elliptical cross-section with a peptide arrangement of two filaments containing four monomers per plane. Additionally, fitting the data with a continuum model provides an atomistic model of the fibril structure.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Biology
Signe Christensen, Sebastian Raemisch, Ingemar Andre
Summary: Chaperones play a crucial role in cellular quality control by removing misfolded and aggregated proteins. The chaperone DnaK responds to molecular stress by recognizing hydrophobic regions of misfolded proteins. This study found that the level of DnaK response is correlated to protein stability when overexpressing recombinant proteins. Additionally, stable proteins showed variability in protein abundance and DnaK response among cells.
COMMUNICATIONS BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Daniel Varela, Vera Karlin, Ingemar Andre
Summary: In this study, a protein-protein docking algorithm called EvoDOCK was developed, which enables accurate and fast local and global protein-protein docking at the atomic level, improving accuracy and computational speed.
Article
Biochemical Research Methods
Christoffer Norn, Ingemar Andre
Summary: Thermodynamic stability plays a crucial role in protein evolution, affecting mutation rates and residue-residue covariation. By simulating protein evolution and calculating protein stability, researchers have found that stability is related to mutation rates and the spectrum of accepted mutations. These findings provide mechanistic insights into the evolutionary consequences of protein stability variation.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Meeting Abstract
Chemistry, Multidisciplinary
Diego Leonardo, Andre Nascimento, Napoleao Valadares, Ingemar Andre, Isabel Uson, Richard Garratt
ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES
(2019)
Article
Biology
Meliha Mehmeti, Caroline Bergenfelz, Eva Kallberg, Camilla Rydberg Millrud, Per Bjork, Fredrik Ivars, Bengt Johansson-Lindbom, Sven Kjellstrom, Ingemar Andre, Karin Leandersson
COMMUNICATIONS BIOLOGY
(2019)