Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.ImportanceTo fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is usually limited by sampling size. Sequence conservation-based methods are further confounded by structural constraints and multifunctionality of proteins. Here we present a method that can systematically identify and annotate functional residues of a given protein. We used a high-throughput functional profiling platform to identify essential residues. Coupling it with homologous-structure comparison, we were able to annotate multiple functions of proteins. We demonstrated the method with the PB1 protein of influenza A virus and identified novel functional residues in addition to its canonical function as an RNA-dependent RNA polymerase. Not limited to virology, this method is generally applicable to other proteins that can be functionally selected and about which homologous-structure information is available

State-wise Constrained Policy Optimization

Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with safety being a major concern. In particular, enforcing state-wise constraints is essential for many challenging tasks such as autonomous driving and robot manipulation. However, existing safe RL algorithms under the framework of Constrained Markov Decision Process (CMDP) do not consider state-wise constraints. To address this gap, we propose State-wise Constrained Policy Optimization (SCPO), the first general-purpose policy search algorithm for state-wise constrained reinforcement learning. SCPO provides guarantees for state-wise constraint satisfaction in expectation. In particular, we introduce the framework of Maximum Markov Decision Process, and prove that the worst-case safety violation is bounded under SCPO. We demonstrate the effectiveness of our approach on training neural network policies for extensive robot locomotion tasks, where the agent must satisfy a variety of state-wise safety constraints. Our results show that SCPO significantly outperforms existing methods and can handle state-wise constraints in high-dimensional robotics tasks.Comment: arXiv admin note: text overlap with arXiv:2305.1368

Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM

Motivated by applications to single-particle cryo-electron microscopy (cryo-EM), we study several problems of function estimation in a low SNR regime, where samples are observed under random rotations of the function domain. In a general framework of group orbit estimation with linear projection, we describe a stratification of the Fisher information eigenvalues according to a sequence of transcendence degrees in the invariant algebra, and relate critical points of the log-likelihood landscape to a sequence of method-of-moments optimization problems. This extends previous results for a discrete rotation group without projection. We then compute these transcendence degrees and the forms of these moment optimization problems for several examples of function estimation under $SO(2)$ and $SO(3)$ rotations, including a simplified model of cryo-EM as introduced by Bandeira, Blum-Smith, Kileel, Perry, Weed, and Wein. For several of these examples, we affirmatively resolve numerical conjectures that $3^\text{rd}$-order moments are sufficient to locally identify a generic signal up to its rotational orbit. For low-dimensional approximations of the electric potential maps of two small protein molecules, we empirically verify that the noise-scalings of the Fisher information eigenvalues conform with these theoretical predictions over a range of SNR, in a model of $SO(3)$ rotations without projection

CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion

Channel and spatial attention mechanism has proven to provide an evident performance boost of deep convolution neural networks (CNNs). Most existing methods focus on one or run them parallel (series), neglecting the collaboration between the two attentions. In order to better establish the feature interaction between the two types of attention, we propose a plug-and-play attention module, which we term "CAT"-activating the Collaboration between spatial and channel Attentions based on learned Traits. Specifically, we represent traits as trainable coefficients (i.e., colla-factors) to adaptively combine contributions of different attention modules to fit different image hierarchies and tasks better. Moreover, we propose the global entropy pooling (GEP) apart from global average pooling (GAP) and global maximum pooling (GMP) operators, an effective component in suppressing noise signals by measuring the information disorder of feature maps. We introduce a three-way pooling operation into attention modules and apply the adaptive mechanism to fuse their outcomes. Extensive experiments on MS COCO, Pascal-VOC, Cifar-100, and ImageNet show that our CAT outperforms existing state-of-the-art attention mechanisms in object detection, instance segmentation, and image classification. The model and code will be released soon.Comment: 8 pages, 5 figure

Application of micro/nanorobot in medicine

The development of micro/nanorobots and their application in medical treatment holds the promise of revolutionizing disease diagnosis and treatment. In comparison to conventional diagnostic and treatment methods, micro/nanorobots exhibit immense potential due to their small size and the ability to penetrate deep tissues. However, the transition of this technology from the laboratory to clinical applications presents significant challenges. This paper provides a comprehensive review of the research progress in micro/nanorobotics, encompassing biosensors, diagnostics, targeted drug delivery, and minimally invasive surgery. It also addresses the key issues and challenges facing this technology. The fusion of micro/nanorobots with medical treatments is poised to have a profound impact on the future of medicine

A Novel Dnmt3a1 Transcript Inhibits Adipogenesis

DNA (cytosine-5)-methyltransferase 3a (Dnmt3a) is an enzyme that catalyzes the transfer of methyl groups to specific CpG forms in DNA. In mammals, two variant transcripts of Dnmt3a have been successfully identified. To the best of our knowledge, no Dnmt3a transcripts in an avian have been successfully identified. This study was performed to detect different transcripts of Dnmt3a in chickens and to examine whether a novel Dnmt3a transcript named Dnmt3a1 may regulate adipogenesis. In addition to cloning, sequencing, transcript detection, and expression studies, a novel Dnmt3a1 transcript overexpression and knockdown were conducted to explore the potential role of Dnmt3a1 in preadipocyte proliferation and the early stage of adipocyte differentiation. In chicken abdominal fat tissue, we detected a novel Dnmt3a1 transcript that differs from Dnmt3a by lacking 23 amino acids at the exon-1/exon-2 border. Dnmt3a1 mRNA was ubiquitously expressed in a variety of tissues or cells and highly expressed in chicken adipose tissue/cells. The expression of Dnmt3a1 was regulated under different physiological conditions including aging, fasting, and high-fat diet. In addition, overexpression of Dnmt3a1 significantly decreased preadipocyte proliferation and induced cell-cycle arrest while its inhibition increased cell proliferation and S-phase cells. Furthermore, the overexpression of Dnmt3a1 significantly upregulated the mRNA level of cell-cycle-related genes, such as CDKN1A, CDKN1B, CCNB3, CCND2, CCNG2, CDKN2B, and CDK9, or the protein level of CDKN1A, CDKN1B, and CCNG2. Conversely, the knockdown of Dnmt3a1 by siRNA had the opposite effects. Moreover, during early adipocyte differentiation, the overexpression of Dnmt3a1 significantly decreased the mRNA and the protein levels of PPAR-γ, C/EBP-α, ADIPOR1, and STAT3, and the mRNA levels of FAS, LEPR, LPL, PRKAB2, and ATGL. In contrast, their expression was significantly increased after the knockdown of Dnmt3a1. Taken together, we identified a novel transcript of Dnmt3a, and it played a potential role in adipogenesis