Publications by authors named "Kaixian Liu"

Homologous meiotic recombination starts with DNA double-strand breaks (DSBs) generated by SPO11 protein. SPO11 is critical for meiosis in most species but the DSBs it makes are also dangerous because of their mutagenic and gametocidal potential, so cells must foster SPO11's beneficial functions while minimizing its risks. SPO11 mechanism and regulation remain poorly understood.

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DNA double-strand breaks that initiate meiotic recombination are formed by the topoisomerase-relative enzyme Spo11, supported by conserved auxiliary factors. Because high-resolution structural data have not been available, many questions remain about the architecture of Spo11 and its partners and how they engage with DNA. We report cryo-electron microscopy structures at up to 3.

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The DNA double-strand breaks that initiate meiotic recombination are formed by topoisomerase relative Spo11, supported by conserved auxiliary factors. Because high-resolution structural data are lacking, many questions remain about the architecture of Spo11 and its partners and how they engage with DNA. We report cryo-EM structures at up to 3.

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The DNA double-strand breaks (DSBs) that initiate meiotic recombination are formed by an evolutionarily conserved suite of factors that includes Rec114 and Mei4 (RM), which regulate DSB formation both spatially and temporally. In vivo, these proteins form large immunostaining foci that are integrated with higher-order chromosome structures. In vitro, they form a 2:1 heterotrimeric complex that binds cooperatively to DNA to form large, dynamic condensates.

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We present a deep background-mismodeling-learned reconstruction framework for high-accuracy fluorescence diffuse optical tomography (FDOT). A learnable regularizer incorporating background mismodeling is formulated in the form of certain mathematical constraints. The regularizer is then learned to obtain the background mismodeling automatically using a physics-informed deep network implicitly.

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Double integrating sphere measurements obtained from thin tissues provides more spectral information and hence allows full estimation of all basic optical properties (OPs) theoretically. However, the ill-conditioned nature of the OP determination increases excessively with the reduction in tissue thickness. Therefore, it is crucial to develop a model for thin tissues that is robust to noise.

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The risk of cardiovascular disease (CVD) is a serious health threat to human society worldwide. The use of machine learning methods to predict the risk of CVD is of great relevance to identify high-risk patients and take timely interventions. In this study, we propose the XGBH machine learning model, which is a CVD risk prediction model based on key contributing features.

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The DNA double-strand breaks (DSBs) that initiate meiotic recombination are formed by an evolutionarily conserved suite of factors that includes Rec114 and Mei4 (RM), which regulate DSB formation both spatially and temporally. , these proteins form large immunostaining foci that are integrated with higher order chromosome structures. , they form a 2:1 heterotrimeric complex that binds cooperatively to DNA to form large, dynamic condensates.

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The imaging fidelity of mesoscopic fluorescence molecular tomography (MFMT) in reflective geometry suffers from spatial nonuniformity of measurement sensitivity and ill-posed reconstruction. In this study, we present a spatially adaptive split Bregman network (SSB-Net) to simultaneously overcome the spatial nonuniformity of measurement sensitivity and promote reconstruction sparsity. The SSB-Net is derived by unfolding the split Bregman algorithm.

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Zika virus (ZIKV) is a positive-sense single-stranded RNA virus that infects humans and can cause birth defects and neurological disorders. Its non-structural protein 3 (NS3) contains a protease domain and a helicase domain, both of which play essential roles during the viral life cycle. However, it has been shown that ZIKV NS3 has an inherently weak helicase activity, making it unable to unwind long RNA duplexes alone.

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In fluorescence diffuse optical tomography (fDOT), the quality of reconstruction is severely limited by mismodeling and ill-posedness of inverse problems. Although data-driven deep learning methods improve the quality of image reconstruction, the network architecture lacks interpretability and requires a lot of data for training. We propose an interpretable model-driven projected gradient descent network (MPGD-Net) to improve the quality of fDOT reconstruction using only a few training samples.

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Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.

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Folding of individual domains in large proteins during translation helps to avoid otherwise prevalent inter-domain misfolding. How folding intermediates observed in vitro for the majority of proteins relate to co-translational folding remains unclear. Combining in vivo and single-molecule experiments, we followed the co-translational folding of the G-domain, encompassing the first 293 amino acids of elongation factor G.

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Large proteins with multiple domains are thought to fold cotranslationally to minimize interdomain misfolding. Once folded, domains interact with each other through the formation of extensive interfaces that are important for protein stability and function. However, multidomain protein folding and the energetics of domain interactions remain poorly understood.

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We present a method to accelerate image reconstruction in fluorescence molecular tomography based on the historical path fluorescence Monte Carlo model. The method exploits a first-order approximation expression during the fluorescence excitation-transmission process to merge the path and state information of the photon in a voxel. The experiments show that our method not only greatly reduces the amount of data required for storage in the hard disk and accelerates image reconstruction, but also maintains the quantitative and positioning accuracy of the conventional method.

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Multi-domain proteins, containing several structural units within a single polypeptide, constitute a large fraction of all proteomes. Co-translational folding is assumed to simplify the conformational search problem for large proteins, but the events leading to correctly folded, functional structures remain poorly characterized. Similarly, how the ribosome and molecular chaperones promote efficient folding remains obscure.

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All cellular proteins are synthesized by the ribosome, an intricate molecular machine that translates the information of protein coding genes into the amino acid alphabet. The linear polypeptides synthesized by the ribosome must generally fold into specific three-dimensional structures to become biologically active. Folding has long been recognized to begin before synthesis is complete.

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Intrinsically disordered proteins (IDPs) present a functional paradox because they lack stable tertiary structure, but nonetheless play a central role in signaling, utilizing a process known as allostery. Historically, allostery in structured proteins has been interpreted in terms of propagated structural changes that are induced by effector binding. Thus, it is not clear how IDPs, lacking such well-defined structures, can allosterically affect function.

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Correct folding is a prerequisite for the biological activity of most proteins. Folding has largely been studied using in vitro refolding assays with isolated small, robustly folding proteins. A substantial fraction of all cellular proteomes is composed of multidomain proteins that are often not amenable to this approach, and their folding remains poorly understood.

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Rtt107 (regulator of Ty1 transposition 107; Esc4) is a DNA repair protein from Saccharomyces cerevisiae that can restore stalled replication forks following DNA damage. There are six BRCT (BRCA1 C-terminal) domains in Rtt107 that act as binding sites for other recruited proteins during DNA repair. Several Rtt107 binding partners have been identified, including Slx4, Rtt101, Rad55, and the Smc5/6 (structural maintenance of chromosome) protein complex.

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