Article
Biochemistry & Molecular Biology
Claudia Alvarez-Carreno, Petar Penev, Anton S. Petrov, Loren Dean Williams
Summary: The SH3 and OB domains are the simplest, oldest, and most common protein domains in the translation system. These domains share a common core region with significant structure and sequence similarity, suggesting they share a common ancestor. The OB domain likely evolved from duplication and adaptation of the SH3 domain core, or vice versa, in a simple and probable transformation.
MOLECULAR BIOLOGY AND EVOLUTION
(2021)
Article
Biochemical Research Methods
Frank DiMaio, Ryan McHugh
Summary: The study introduces a machine learning model, RoseTTAFoldNA, capable of accurately predicting structures of protein-DNA and protein-RNA complexes, particularly for protein families lacking structural information.
Article
Biochemistry & Molecular Biology
Hu Zhao, Zhuo Tu, Yinmeng Liu, Zhanxiang Zong, Jiacheng Li, Hao Liu, Feng Xiong, Jinling Zhan, Xuehai Hu, Weibo Xie
Summary: Characterizing regulatory effects of genomic variants in plants is a challenge. PlantDeepSEA is a deep learning-based web service designed to predict the regulatory effects of genomic variants in multiple tissues of six plant species. The tool includes two main functions, Variant Effector and Sequence Profiler, which can help discover high-impact sites in sequences.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Biochemical Research Methods
Kaili Wang, Renyi Zhou, Yifan Wu, Min Li
Summary: In this study, a deep learning model called RLBind was proposed to predict RNA-small molecule binding sites. By combining global RNA sequence channel and local neighbor nucleotides channel, RLBind outperformed other state-of-the-art methods in predicting binding sites.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Kaili Wang, Renyi Zhou, Yifan Wu, Min Li
Summary: Identification of RNA-small molecule binding sites is critical for RNA-targeted drug discovery. Only a few methods exist for predicting these sites, necessitating the development of new computational models. This study introduces a deep learning model, RLBind, which combines sequence-dependent and structure-dependent properties to predict RNA-small molecule binding sites. Experimental results show that RLBind outperforms other methods in predicting these sites.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Devlina Chakravarty, Lauren L. Porter
Summary: AlphaFold2 revolutionized protein structure prediction, but its predictions tend to be inaccurate for structurally heterogeneous proteins. Analysis of sequence variation showed that fold-switching regions have similar conservation rates to canonical single-fold proteins, while intrinsically disordered regions have lower prediction confidences.
Article
Biochemistry & Molecular Biology
Anat Etzion-Fuchs, David A. Todd, Mona Singh
Summary: A novel machine learning method dSPRINT has been introduced to predict whether a protein domain binds DNA, RNA, small molecules, ions or peptides, and the positions within it that participate in these interactions. Through stringent cross-validation testing, it has shown excellent performance in uncovering ligand-binding positions and domains.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Biochemistry & Molecular Biology
Gregory D. Martyn, Gianluca Veggiani, Ulrike Kusebauch, Seamus R. Morrone, Bradley P. Yates, Alex U. Singer, Jiefei Tong, Noah Manczyk, Gerald Gish, Zhi Sun, Igor Kurinov, Frank Sicheri, Michael F. Moran, Robert L. Moritz, Sachdev S. Sidhu
Summary: A comprehensive analysis of the phosphoproteome is crucial for understanding human diseases. Researchers have developed new SH2 domains with superior binding abilities to enrich diverse sets of phosphotyrosine peptides.
ACS CHEMICAL BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Sam Giannakoulias, Sumant R. Shringari, John J. Ferrie, E. James Petersson
Summary: The newly developed scoring functions accurately predict the impact of unnatural amino acids on protein yield and solubility, revealing the crucial role in predicting mutation tolerance. This study demonstrates that extracting features from structural models and applying them to machine learning can accurately predict diverse and abstract biological phenomena in biological systems.
SCIENTIFIC REPORTS
(2021)
Article
Biology
Ruben Hervas, Maria del Carmen Fernandez-Ramirez, Albert Galera-Prat, Mari Suzuki, Yoshitaka Nagai, Marta Bruix, Margarita Menendez, Douglas V. Laurents, Mariano Carrion-Vazquez
Summary: The divergent prion-like domains (PLDs) of CPEB proteins from different species retain the ability to form a generic amyloid-like fold through different assembly mechanisms, with structural differences at the beginning of their amyloid assembly pathways.
Article
Biochemistry & Molecular Biology
Fatemeh Hassani Nia, Daniel Woike, Isabel Bento, Stephan Niebling, Debora Tibbe, Kristina Schulz, Daniela Hirnet, Matilda Skiba, Hans-Hinrich Hoenck, Katharina Veith, Christian Guenther, Tasja Scholz, Tatjana Bierhals, Joenna Driemeyer, Renee Bend, Antonio Virgilio Failla, Christian Lohr, Maria Garcia Alai, Hans-Juergen Kreienkamp
Summary: This study investigates the impact of two mutations in the SHANK2 gene on neurodevelopment. The results show that these mutations disrupt the interactions of Shank2 with other proteins, leading to abnormal assembly of postsynaptic protein complexes into nanoclusters. This interference affects the positioning of neurons and synaptic transmission, ultimately impacting normal brain development in humans.
MOLECULAR PSYCHIATRY
(2022)
Article
Multidisciplinary Sciences
Nicole DelRosso, Josh Tycko, Peter Suzuki, Cecelia Andrews, Adi Mukund, Ivan Liongson, Connor Ludwig, Kaitlyn Spees, Polly Fordyce, Michael C. Bassik, Lacramioara Bintu
Summary: Human gene expression is regulated by thousands of transcription factors and chromatin regulators. This study systematically measures the activity of over 100,000 protein fragments from these regulators and annotates previously unknown activation and repression domains. The research reveals the necessary residues for activation domain activity and identifies structures or motifs involved in repression. The findings provide valuable insights into the function of transcription factors and chromatin regulators and can aid in gene expression control and predictive modeling of effector domains.
Article
Biochemical Research Methods
Ke Yan, Jiawei Feng, Jing Huang, Hao Wu
Summary: Nucleic acid-binding proteins are important for regulating gene expression and transcriptional control. This study proposes a new method, iDRPro-SC, to accurately predict the type of nucleic acid-binding proteins based on sequence information. By considering internal differences and using ensemble learning, iDRPro-SC achieved better prediction performance than existing methods.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Upendra Kumar Pradhan, Prabina Kumar Meher, Sanchita Naha, Soumen Pal, Ajit Gupta, Rajender Parsad
Summary: DNA-binding proteins (DBPs) are crucial in cellular processes, but existing computational techniques for identifying them have limited accuracy in plant species. This study developed a comprehensive computational model for predicting plant specific DBPs, using shallow learning methods that outperformed deep learning algorithms. The support vector machine achieved the highest accuracy, with 94.0% AUC-ROC and 93.5% AUC-PR. The developed approach showed higher accuracy compared to existing tools, making it more efficient and reliable for predicting DBPs in plants. A prediction server, PIDBPred, and source code are publicly accessible for convenience.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Upendra Kumar Pradhan, Prabina Kumar Meher, Sanchita Naha, Soumen Pal, Ajit Gupta, Rajender Parsad
Summary: This study developed a computational model for predicting plant-specific DNA-binding proteins. The model achieved high accuracy using shallow learning methods, outperforming existing tools.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Physics, Condensed Matter
Enzo Orlandini, Cristian Micheletti
Summary: This article reviews the latest advances in the study of linking in soft matter systems and organizes the topic from various perspectives, including the concepts and manifestations of entanglement, models of mutual entanglements in polymer mixtures, and the observation of entanglements in liquid crystals and non-filamentous systems.
JOURNAL OF PHYSICS-CONDENSED MATTER
(2022)
Article
Computer Science, Interdisciplinary Applications
Thomas W. Keal, Alin-Marin Elena, Alexey A. Sokol, Karen Stoneham, Matt I. J. Probert, Clotilde S. Cucinotta, David J. Willock, Andrew J. Logsdail, Andrea Zen, Phil J. Hasnip, Ian J. Bush, Matthew Watkins, Dario Alfe, Chris-Kriton Skylaris, Basile F. E. Curchod, Qiong Cai, Scott M. Woodley
Summary: The transition to exascale computing enables simulations of unprecedented accuracy and complexity. The focus is on materials and molecular modeling that aim for high fidelity in silico experiments on technologically interesting complex systems. This progress presents significant challenges to software, particularly in exploiting parallelism and effectively managing workflows and data on such platforms.
COMPUTING IN SCIENCE & ENGINEERING
(2022)
Article
Polymer Science
Giulia Amici, Michele Caraglio, Enzo Orlandini, Cristian Micheletti
Summary: In this study, catenated ring polymers confined inside channels and slits were investigated using Langevin dynamics simulations. It was found that catenation constraints generate a drag that couples the contour motion of the interlocked regions. The strength of this coupling decreases as the interlocking becomes tighter and shorter due to confinement, with the results having implications for linked biomolecules in experimental or biological confining conditions.
Article
Chemistry, Physical
Benjamin X. Shi, Venkat Kapil, Andrea Zen, Ji Chen, Ali Alavi, Angelos Michaelides
Summary: In this work, a systematic and general quantum cluster design protocol is proposed to accurately compute the formation energy of oxygen vacancies in metal-oxides. The protocol is applied to rutile TiO2 and rock salt MgO, providing accurate and well-converged determinations of the formation energy. These results are used to benchmark exchange-correlation functionals in density functional theory.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Andrea Zen, Tai Bui, Tran Thi Bao Le, Weparn J. Tay, Kuhan Chellappah, Ian R. Collins, Richard D. Rickman, Alberto Striolo, Angelos Michaelides
Summary: The aggregation of clay particles in aqueous solution is an important process in both environmental and technological fields. However, the mechanism behind this process at the atomistic level, which involves complex and dynamic interactions such as solvent-mediated electrostatic, hydrogen-bonding, and dispersion interactions, is still poorly understood. In this study, molecular dynamics simulations were conducted to investigate the interactions between kaolinite nanoparticles in pure and salty water. The simulations revealed highly anisotropic behavior, with the interaction between nanoparticles changing from attractive to repulsive depending on their relative orientation. The results of this study highlight the diversity of clay nanoparticle interactions and provide valuable insights for the development of coarse-grained models of clay nanoparticle aggregation.
JOURNAL OF PHYSICAL CHEMISTRY C
(2022)
Article
Polymer Science
Pietro Chiarantoni, Cristian Micheletti
Summary: Molecular dynamics simulations were used to investigate the properties of poly[n]catenanes, revealing that stiffer rings result in more extended and flexible backbones. The internal dynamics of catenanes also slow down with increasing rigidity. Furthermore, it was found that catenanes with rigid rings hinder each other's motion more and have smaller diffusion coefficients in crowded solutions.
Article
Chemistry, Physical
Flaviano Della Pia, Andrea Zen, Dario Alfe, Angelos Michaelides
Summary: Ice is a molecular crystal with important properties and structural diversity. Recent advances in Diffusion Monte Carlo (DMC) have enabled accurate calculations of lattice energies for different ice polymorphs. This study presents the DMC-ICE13 dataset, which includes lattice energies of 13 ice polymorphs. Benchmarking different Density Functional Theory (DFT) functionals shows that there is no single functional that performs well for all ice phases.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Valerio Sorichetti, Andrea Ninarello, Jose Ruiz-Franco, Virginie Hugouvieux, Emanuela Zaccarelli, Cristian Micheletti, Walter Kob, Lorenzo Rovigatti
Summary: We used simulations to self-assemble polymer networks with a mixture of bivalent and tri- or tetravalent patchy particles, resulting in an exponential strand length distribution similar to experimental cross-linked systems. The fractal structure of the network depends on the assembly number density, but systems with the same mean valence and assembly density have the same structural properties. We also examined the dynamics of long strands using the tube model and found a relation between the localization lengths and shear modulus.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Polymer Science
Andrea Tagliabue, Cristian Micheletti, Massimo Mella
Summary: In this study, Langevin dynamics simulations were used to investigate the behavior of coarse-grained knotted copolyelectrolytes in solutions with different salt concentrations, valency, and solvent screening power. The results show that by varying these parameters, the length and position of the knotted region can be tuned, which in turn controls the overall metric properties. Additionally, it was found that similar modulations of knot size and position can be achieved by varying the dielectric constant of the solvent.
Article
Physics, Multidisciplinary
Antonio Suma, Vincenzo Carnevale, Cristian Micheletti
Summary: Using theory and simulations, the authors studied DNA unzipping via nanopore translocation and found three dynamical regimes depending on the applied force. They showed that the normal regime can be modeled as a one-dimensional stochastic process and used the theory of stochastic processes to recover the free-energy landscape. This approach can be applied to other single-molecule systems with periodic potentials to obtain detailed free-energy landscapes from out-of-equilibrium trajectories.
PHYSICAL REVIEW LETTERS
(2023)
Article
Polymer Science
Pietro Chiarantoni, Cristian Micheletti
Summary: We use Langevin dynamics simulations to study the behavior of linear catenanes under confinement in cylindrical channels. We analyze the statics and dynamics of model poly[n]catenanes consisting of n = 100 rings and m = 40 beads in channels of different diameters. By comparing the catenane behavior to an equivalent chain of beads, we show that linear catenanes exhibit different confinement regimes and unique overstretching response under strong confinement. The relaxation dynamics of catenanes also differ from conventional polymers, with slower modes observed at strong confinement. A systematic analysis of the size, shape, and orientation of the concatenated rings and their mechanical bonds provides insights into the underlying mechanisms driving the catenane's response to confinement.
Article
Biochemistry & Molecular Biology
Cesira de Chiara, Gareth A. Prosser, Roksana Ogrodowicz, Luiz P. S. de Carvalho
Summary: Researchers have discovered a resistant mutation, the D322N variant of alanine racemase, which reduces the inhibitory effect of the broad-spectrum antibiotic DCS by 240-fold. Through crystal structures and spectroscopy studies, they have uncovered the mechanism of this resistance mutation.
ACS BIO & MED CHEM AU
(2023)
Article
Chemistry, Physical
Yasmine S. Al-Hamdani, Andrea Zen, Dario Alfe
Summary: Molecular hydrogen has the potential to reduce carbon dioxide emissions, but hydrogen gas storage is a major bottleneck. Physisorbing molecular hydrogen at ambient pressure and temperatures is a promising alternative. However, understanding hydrogen adsorption in well-defined nanomaterials remains experimentally challenging.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Multidisciplinary
Benjamin X. Shi, Andrea Zen, Venkat Kapil, Peter R. Nagy, Andreas Gruneis, Angelos Michaelides
Summary: The adsorption energy of a molecule on a material surface is crucial for various applications and requires agreement between experimental measurements and theoretical calculations. This study addresses the challenge of accurately predicting the adsorption energy of CO on MgO using advanced computational methods. The inconsistencies in experimental results are explained, leading to reliable theoretical predictions for the design of new catalysts and gas storage materials.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2023)
Article
Chemistry, Physical
Matteo Becchi, Riccardo Capelli, Claudio Perego, Giovanni M. Pavan, Cristian Micheletti
Summary: This article discusses the design of self-assembling systems that can be guided towards different target structures depending on external conditions. A minimalistic self-assembly model is proposed, where the density of building blocks can be tuned to control the formation of different types of ordered structures. Simulation methods are used to study the behavior of the system, and key factors influencing the selection of self-assembling pathways are identified. A practical criterion is formulated and validated to determine if a specific molecular design is suitable for density-driven tunability of self-assembled products.