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Author

Giovanni B. Brandani

Other affiliations: University of Edinburgh
Bio: Giovanni B. Brandani is an academic researcher from Kyoto University. The author has contributed to research in topics: Nucleosome & Histone. The author has an hindex of 10, co-authored 21 publications receiving 331 citations. Previous affiliations of Giovanni B. Brandani include University of Edinburgh.
Topics: Nucleosome, Histone, DNA, Chromatin, Translocase

Papers
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Journal ArticleDOI
TL;DR: Analysis of the trajectories via Markov state modeling highlights how the sequence-dependence of the sliding dynamics is due to the different twist defect energy costs, and in particular how nucleosome regions where defects cannot easily form introduce the kinetic bottlenecks slowing down repositioning.
Abstract: While nucleosomes are highly stable structures as fundamental units of chromatin, they also slide along the DNA, either spontaneously or by active remodelers. Here, we investigate the microscopic mechanisms of nucleosome sliding by multiscale molecular simulations, characterizing how the screw-like motion of DNA proceeds via the formation and propagation of twist defects. Firstly, coarse-grained molecular simulations reveal that the sliding dynamics is highly dependent on DNA sequence. Depending on the sequence and the nucleosome super-helical location, we find two distinct types of twist defects: a locally under-twisted DNA region, previously observed in crystal structures, and a locally over-twisted DNA, an unprecedented feature. The stability of the over-twist defect was confirmed via all-atom simulations. Analysis of our trajectories via Markov state modeling highlights how the sequence-dependence of the sliding dynamics is due to the different twist defect energy costs, and in particular how nucleosome regions where defects cannot easily form introduce the kinetic bottlenecks slowing down repositioning. Twist defects can also mediate sliding of nucleosomes made with strong positioning sequences, albeit at a much lower diffusion coefficient, due to a high-energy intermediate state. Finally, we discuss how chromatin remodelers may exploit these spontaneous fluctuations to induce unidirectional sliding of nucleosomes.

67 citations

Journal ArticleDOI
TL;DR: In this paper, it was shown that the amino acids making up the surface-exposed hydrophobic cap in the crystal structure are shielded in aqueous solution by adopting a random coil conformation, enabling the protein to be soluble and monomeric.
Abstract: The majority of bacteria in the natural environment live within the confines of a biofilm. The Gram-positive bacterium Bacillus subtilis forms biofilms that exhibit a characteristic wrinkled morphology and a highly hydrophobic surface. A critical component in generating these properties is the protein BslA, which forms a coat across the surface of the sessile community. We recently reported the structure of BslA, and noted the presence of a large surface-exposed hydrophobic patch. Such surface patches are also observed in the class of surface-active proteins known as hydrophobins, and are thought to mediate their interfacial activity. However, although functionally related to the hydrophobins, BslA shares no sequence nor structural similarity, and here we show that the mechanism of action is also distinct. Specifically, our results suggest that the amino acids making up the large, surface-exposed hydrophobic cap in the crystal structure are shielded in aqueous solution by adopting a random coil conformation, enabling the protein to be soluble and monomeric. At an interface, these cap residues refold, inserting the hydrophobic side chains into the air or oil phase and forming a three-stranded β-sheet. This form then self-assembles into a well-ordered 2D rectangular lattice that stabilizes the interface. By replacing a hydrophobic leucine in the center of the cap with a positively charged lysine, we changed the energetics of adsorption and disrupted the formation of the 2D lattice. This limited structural metamorphosis represents a previously unidentified environmentally responsive mechanism for interfacial stabilization by proteins.

66 citations

Journal ArticleDOI
TL;DR: This work investigated thermally-activated spontaneous nucleosome sliding mechanisms developing and applying a coarse-grained molecular simulation method that incorporates both long-range electrostatic and short-range hydrogen-bond interactions between histone octamer and DNA, revealing two distinct sliding modes depending on the nucleosomal DNA sequence.
Abstract: While nucleosome positioning on eukaryotic genome play important roles for genetic regulation, molecular mechanisms of nucleosome positioning and sliding along DNA are not well understood. Here we investigated thermally-activated spontaneous nucleosome sliding mechanisms developing and applying a coarse-grained molecular simulation method that incorporates both long-range electrostatic and short-range hydrogen-bond interactions between histone octamer and DNA. The simulations revealed two distinct sliding modes depending on the nucleosomal DNA sequence. A uniform DNA sequence showed frequent sliding with one base pair step in a rotation-coupled manner, akin to screw-like motions. On the contrary, a strong positioning sequence, the so-called 601 sequence, exhibits rare, abrupt transitions of five and ten base pair steps without rotation. Moreover, we evaluated the importance of hydrogen bond interactions on the sliding mode, finding that strong and weak bonds favor respectively the rotation-coupled and -uncoupled sliding movements.

53 citations

Journal ArticleDOI
TL;DR: The molecular mechanism of active nucleosome sliding is investigated by means of molecular dynamics simulations of the Snf2 remodeler translocase in complex with a nucleosomes to offer a detailed mechanistic picture of remodeling important for the complete understanding of these key biological processes.
Abstract: ATP-dependent chromatin remodelers are molecular machines that control genome organization by repositioning, ejecting, or editing nucleosomes, activities that confer them essential regulatory roles on gene expression and DNA replication. Here, we investigate the molecular mechanism of active nucleosome sliding by means of molecular dynamics simulations of the Snf2 remodeler translocase in complex with a nucleosome. During its inchworm motion driven by ATP consumption, the translocase overwrites the original nucleosome energy landscape via steric and electrostatic interactions to induce sliding of nucleosomal DNA unidirectionally. The sliding is initiated at the remodeler binding location via the generation of a pair of twist defects, which then spontaneously propagate to complete sliding throughout the entire nucleosome. We also reveal how remodeler mutations and DNA sequence control active nucleosome repositioning, explaining several past experimental observations. These results offer a detailed mechanistic picture of remodeling important for the complete understanding of these key biological processes.

44 citations

Journal ArticleDOI
10 Jun 2013-PLOS ONE
TL;DR: In this article, the authors present a method to quantify the extent of disorder in a system by using conditional entropies, and apply it to mixing and demixing in multicomponent fluid membranes.
Abstract: In this paper, we present a method to quantify the extent of disorder in a system by using conditional entropies. Our approach is especially useful when other global, or mean field, measures of disorder fail. The method is equally suited for both continuum and lattice models, and it can be made rigorous for the latter. We apply it to mixing and demixing in multicomponent fluid membranes, and show that it has advantages over previous measures based on Shannon entropies, such as a much diminished dependence on binning and the ability to capture local correlations. Further potential applications are very diverse, and could include the study of local and global order in fluid mixtures, liquid crystals, magnetic materials, and particularly biomolecular systems.

35 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: This work presents a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space and demonstrates the superiority of this approach compared with existing methods by using both simulated and real scRNA-seq data sets.
Abstract: Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.

1,423 citations

Journal Article
TL;DR: In this article, a nucleosome-DNA interaction model was proposed to predict the genome-wide organization of nucleosomes, and it was shown that genomes encode an intrinsic nucleosomal organization and that this intrinsic organization can explain ∼50% of the in-vivo positions.
Abstract: Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome–DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain ∼50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.

1,399 citations