Protein Condensate Atlas from predictive models of heteromolecular condensate composition

Biomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates

Serological fingerprints link antiviral activity of therapeutic antibodies to affinity and concentration

The effectiveness of therapeutic monoclonal antibodies (mAbs) against variants of the SARS-CoV-2 virus is highly variable. As target recognition of mAbs relies on tight binding affinity, we assessed the affinities of five therapeutic mAbs to the receptor binding domain (RBD) of wild type (A), Delta (B.1.617.2), and Omicron BA.1 SARS-CoV-2 (B.1.1.529.1) spike using microfluidic diffusional sizing (MDS). Four therapeutic mAbs showed strongly reduced affinity to Omicron BA.1 RBD, whereas one (sotrovimab) was less impacted. These affinity reductions correlate with reduced antiviral activities suggesting that affinity could serve as a rapid indicator for activity before time-consuming virus neutralization assays are performed. We also compared the same mAbs to serological fingerprints (affinity and concentration) obtained by MDS of antibodies in sera of 65 convalescent individuals. The affinities of the therapeutic mAbs to wild type and Delta RBD were similar to the serum antibody response, indicating high antiviral activities. For Omicron BA.1 RBD, only sotrovimab retained affinities within the range of the serum antibody response, in agreement with high antiviral activity. These results suggest that serological fingerprints provide a route to evaluating affinity and antiviral activity of mAb drugs and could guide the development of new therapeutics

Catalysts from synthetic genetic polymers

The emergence of catalysis in early genetic polymers like RNA is considered a key transition in the origin of life1, predating the appearance of protein enzymes. DNA also demonstrates the capacity to fold into three-dimensional structures and form catalysts in vitro2. However, to what degree these natural biopolymers comprise functionally privileged chemical scaffolds3 for folding or the evolution of catalysis is not known. The ability of synthetic genetic polymers (XNAs) with alternative backbone chemistries not found in nature to fold into defined structures and bind ligands4 raises the possibility that these too might be capable of forming catalysts (XNAzymes). Here we report the discovery of such XNAzymes, elaborated in four different chemistries (ANA (arabino nucleic acids)5, FANA (2′-fluoroarabino nucleic acids)6, HNA (hexitol nucleic acids) and CeNA (cyclohexene nucleic acids)7 directly from random XNA oligomer pools, exhibiting in trans RNA endonuclease and ligase activities. We also describe an XNA-XNA ligase metalloenzyme in the FANA framework, establishing catalysis in an entirely synthetic system and enabling the synthesis of FANA oligomers and an active RNA endonuclease FANAzyme from its constituent parts. These results extend catalysis beyond biopolymers and establish technologies for the discovery of catalysts in a wide range of polymer scaffolds not found in nature8. Evolution of catalysis independent of any natural polymer has implications for the definition of chemical boundary conditions for the emergence of life on earth and elsewhere in the universe9

Both COVID-19 infection and vaccination induce high-affinity cross-clade responses to SARS-CoV-2 variants

The B.1.1.529 (omicron) variant has rapidly supplanted most other SARS-CoV-2 variants. Using microfluidics-based antibody affinity profiling (MAAP), we have characterized affinity and IgG concentration in the plasma of 39 individuals with multiple trajectories of SARS-CoV-2 infection and/or vaccination. Antibody affinity was similar against the wild-type, delta, and omicron variants (KA ranges: 122 ± 155, 159 ± 148, 211 ± 307 μM-1, respectively), indicating a surprisingly broad and mature cross-clade immune response. Postinfectious and vaccinated subjects showed different IgG profiles, with IgG3 (p-value = 0.002) against spike being more prominent in the former group. Lastly, we found that the ELISA titers correlated linearly with measured concentrations (R = 0.72) but not with affinity (R = 0.29). These findings suggest that the wild-type and delta spike induce a polyclonal immune response capable of binding the omicron spike with similar affinity. Changes in titers were primarily driven by antibody concentration, suggesting that B-cell expansion, rather than affinity maturation, dominated the response after infection or vaccination

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Theoretical and Data-Driven Approaches for Biomolecular Condensates.

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease

Protein Condensate Atlas from predictive models of heteromolecular condensate composition

Acknowledgements: We would like to acknowledge the Schmidt Science Fellowship in partnership with the Rhodes Trust (K.L.S.), St. John’s College Research Fellowship (K.L.S.), the National Institutes of Health Oxford-Cambridge Scholars Programme (L.L.G.), the Cambridge Trust’s Cambridge International Scholarship (L.L.G.), the Intramural Research Programme of the National Institute of Diabetes and Digestive and Kidney Diseases at the National Institutes of Health (L.L.G.), and the European Research Council (T.P.J.K.). The authors gratefully acknowledge funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program through the ERC grant DiProPhys (agreement ID 101001615).Funder: European Research CouncilAbstractBiomolecular condensates help cells organise their content in space and time. Cells harbour a variety of condensate types with diverse composition and many are likely yet to be discovered. Here, we develop a methodology to predict the composition of biomolecular condensates. We first analyse available proteomics data of cellular condensates and find that the biophysical features that determine protein localisation into condensates differ from known drivers of homotypic phase separation processes, with charge mediated protein-RNA and hydrophobicity mediated protein-protein interactions playing a key role in the former process. We then develop a machine learning model that links protein sequence to its propensity to localise into heteromolecular condensates. We apply the model across the proteome and find many of the top-ranked targets outside the original training data to localise into condensates as confirmed by orthogonal immunohistochemical staining imaging. Finally, we segment the condensation-prone proteome into condensate types based on an overlap with biomolecular interaction profiles to generate a Protein Condensate Atlas. Several condensate clusters within the Atlas closely match the composition of experimentally characterised condensates or regions within them, suggesting that the Atlas can be valuable for identifying additional components within known condensate systems and discovering previously uncharacterised condensates.</jats:p

Towards a co‐crediting system for carbon and biodiversity

Funder: Royal Botanical Gardens, Kew; doi: http://dx.doi.org/10.13039/501100001296Funder: SPUNFunder: NWO Gravity grant MICROPSocietal Impact StatementHumankind is facing both climate and biodiversity crises. This article proposes the foundations of a scheme that offers tradable credits for combined aboveground and soil carbon and biodiversity. Multidiversity—as estimated based on high‐throughput molecular identification of soil meiofauna, fungi, bacteria, protists, plants and other organisms shedding DNA into soil, complemented by acoustic and video analyses of aboveground macrobiota—offers a cost‐effective method that captures much of the terrestrial biodiversity. Such a voluntary crediting system would increase the quality of carbon projects and contribute funding for delivering the Kunming‐Montreal Global Biodiversity Framework.SummaryCarbon crediting and land offsets for biodiversity protection have been developed to tackle the challenges of increasing greenhouse gas emissions and the loss of global biodiversity. Unfortunately, these two mechanisms are not optimal when considered separately. Focusing solely on carbon capture—the primary goal of most carbon‐focused crediting and offsetting commitments—often results in the establishment of non‐native, fast‐growing monocultures that negatively affect biodiversity and soil‐related ecosystem services. Soil contributes a vast proportion of global biodiversity and contains traces of aboveground organisms. Here, we outline a carbon and biodiversity co‐crediting scheme based on the multi‐kingdom molecular and carbon analyses of soil samples, along with remote sensing estimation of aboveground carbon as well as video and acoustic analyses‐based monitoring of aboveground macroorganisms. Combined, such a co‐crediting scheme could help halt biodiversity loss by incentivising industry and governments to account for biodiversity in carbon sequestration projects more rigorously, explicitly and equitably than they currently do. In most cases, this would help prioritise protection before restoration and help promote more socially and environmentally sustainable land stewardship towards a ‘nature positive’ future.</jats:sec

Antibody Affinity Governs the Inhibition of SARS-CoV-2 Spike/ACE2 Binding in Patient Serum

The humoral immune response plays a key role in suppressing the pathogenesis of SARS-CoV-2. The molecular determinants underlying the neutralization of the virus remain, however, incompletely understood. Here, we show that the ability of antibodies to disrupt the binding of the viral spike protein to the angiotensin-converting enzyme 2 (ACE2) receptor on the cell, the key molecular event initiating SARS-CoV-2 entry into host cells, is controlled by the affinity of these antibodies to the viral antigen. By using microfluidic antibody-affinity profiling, we were able to quantify the serum-antibody mediated inhibition of ACE2–spike binding in two SARS-CoV-2 seropositive individuals. Measurements to determine the affinity, concentration, and neutralization potential of antibodies were performed directly in human serum. Using this approach, we demonstrate that the level of inhibition in both samples can be quantitatively described using the dissociation constants (KD) of the binary interactions between the ACE2 receptor and the spike protein as well as the spike protein and the neutralizing antibody. These experiments represent a new type of in-solution receptor binding competition assay, which has further potential applications, ranging from decisions on donor selection for convalescent plasma therapy, to identification of lead candidates in therapeutic antibody development, and vaccine development

Microfluidic Antibody Affinity Profiling Reveals the Role of Memory Reactivation and Cross-Reactivity in the Defense Against SARS-CoV-2.

Funder: Universit?tsspital Z?richFunder: Biotechnology and Biological Sciences Research CouncilFunder: NOMIS StiftungFunder: Universit?t Z?richRecent efforts in understanding the course and severity of SARS-CoV-2 infections have highlighted both potentially beneficial and detrimental effects of cross-reactive antibodies derived from memory immunity. Specifically, due to a significant degree of sequence similarity between SARS-CoV-2 and other members of the coronavirus family, memory B-cells that emerged from previous infections with endemic human coronaviruses (HCoVs) could be reactivated upon encountering the newly emerged SARS-CoV-2, thus prompting the production of cross-reactive antibodies. Determining the affinity and concentration of these potentially cross-reactive antibodies to the new SARS-CoV-2 antigens is therefore particularly important when assessing both existing immunity against common HCoVs and adverse effects like antibody-dependent enhancement (ADE) in COVID-19. However, these two fundamental parameters cannot easily be disentangled by surface-based assays like enzyme-linked immunosorbent assays (ELISAs), which are routinely used to assess cross-reactivity. Here, we have used microfluidic antibody affinity profiling (MAAP) to quantitatively evaluate the humoral immune response in COVID-19 convalescent patients by determining both antibody affinity and concentration against spike antigens of SARS-CoV-2 directly in nine convalescent COVID-19 patient and three pre-pandemic sera that were seropositive for common HCoVs. All 12 sera contained low concentrations of high-affinity antibodies against spike antigens of HCoV-NL63 and HCoV-HKU1, indicative of past exposure to these pathogens, while the affinity against the SARS-CoV-2 spike protein was lower. These results suggest that cross-reactivity as a consequence of memory reactivation upon an acute SARS-CoV-2 infection may not be a significant factor in generating immunity against SARS-CoV-2