Digvijay Singh

Nuclear pore complexes (NPCs) enable rapid, selective, and robust nucleocytoplasmic transport. To explain how transport emerges from the system components and their interactions, we used experimental data and theoretical information to construct an integrative Brownian dynamics model of transport through an NPC, coupled to a kinetic model of transport in the cell. The model recapitulates key aspects of transport for a wide range of molecular cargoes, including preribosomes and viral capsids. Our model quantifies how flexible phenylalanine-glycine (FG) repeat proteins create an entropic barrier to passive diffusion and how this barrier is selectively lowered in facilitated diffusion by the many transient interactions of nuclear transport receptors with the FG repeats. Selective transport is enhanced by “fuzzy” multivalent interactions, redundant FG repeat mass, coupling to the energy-dependent RanGTP concentration gradient, and exponential dependence of transport kinetics on the transport barrier. Our model will facilitate rational modulation of the NPC and its artificial mimics.

Predicting the functions of proteins can greatly accelerate biological discovery and applications, where deep learning methods have recently shown great potential. However, these methods predominantly predict protein functions as discrete categories, which fails to capture the nuanced and complex nature of protein functions. Furthermore, existing methods require the development of separate models for each prediction task, a process that can be both resource-heavy and time-consuming. Here, we present ProteinChat, a versatile, multi-modal large language model that takes a protein’s amino acid sequence as input and generates comprehensive narratives describing its function. ProteinChat is trained using over 1,500,000 (protein, prompt, answer) triplets curated from the Swiss-Prot dataset, covering diverse functions. This novel model can universally predict a wide range of protein functions, all within a single, unified framework. Furthermore, ProteinChat supports interactive dialogues with human users, allowing for iterative refinement of predictions and deeper exploration of protein functions. Our experimental results, evaluated through both human expert assessment and automated metrics, demonstrate that ProteinChat outperforms general-purpose LLMs like GPT-4, one of the flagship LLMs, by over ten-fold. In addition, ProteinChat exceeds or matches the performance of task-specific prediction models.

The nuclear pore complex (NPC) is the sole mediator of nucle-ocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPC’s structural variations and the nuclear basket across fungi (yeast; S. cerevisiae ), mammals (mouse; M. musculus ), and protozoa ( T. gondii ). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of Nups in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport

The Nuclear Pore Complex (NPC) facilitates rapid and selective nucleocytoplasmic transport of molecules as large as ribosomal subunits and viral capsids. It is not clear how key emergent properties of this transport arise from the system components and their interactions. To address this question, we constructed an integrative coarse-grained Brownian dynamics model of transport through a single NPC, followed by coupling it with a kinetic model of Ran-dependent transport in an entire cell. The microscopic model parameters were fitted to reflect experimental data and theoretical information regarding the transport, without making any assumptions about its emergent properties. The resulting reductionist model is validated by reproducing several features of transport not used for its construction, such as the morphology of the central transporter, rates of passive and facilitated diffusion as a function of size and valency, in situ radial distributions of pre-ribosomal subunits, and active transport rates for viral capsids. The model suggests that the NPC functions essentially as a virtual gate whose flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to diffusion through the pore. Importantly, this core functionality is greatly enhanced by several key design features, including ‘fuzzy’ and transient interactions, multivalency, redundancy in the copy number of FG nucleoporins, exponential coupling of transport kinetics and thermodynamics in accordance with the transition state theory, and coupling to the energy-reliant RanGTP concentration gradient. These design features result in the robust and resilient rate and selectivity of transport for a wide array of cargo ranging from a few kilodaltons to megadaltons in size. By dissecting these features, our model provides a quantitative starting point for rationally modulating the transport system and its artificial mimics.

The objective of present study was to evaluate the effects of chemical additives on the quality parameters and nutritive value of sugarcane tops (SCT) silages. The trial was carried out in a completely randomized experimental design with total twelve treatments including two controls. The chemical additives (SB; Soidum benzoate, PS; Potassium sorbate, PA; propionic acid and their combinations) were applied onto the fresh SCT before ensiling @1.0% (wet basis) each, with common additives (0.5% Urea, 0.5% NaCl and 2% molasses on wet basis), and packed in 3.5 L vacuum sealed plastic jars of two replicates each. The analyzed variables were: chemical composition, quality parameters and nutritive values. Selection of most promising additive/combination was done with Flieg point and fitness values. The addition of chemical additive before ensiling produced silages with better quality indices (pH, lactic acid, Flieg point and fitness values), higher nutritive value (crude protein and ME) and low NDF as compared to control. Ammonia Nitrogen (NH₃N% TN) and protein fractionation (NDICP and ADICP) were also considered for the identification of best SCT top silage and chemical additive effect. The results of present study suggested that PA and PS+PA were the most promising additive treatments for the improving the SCT silage quality and nutritive value. Furthermore, information extended from the present study is giving future direction towards animal feeding trials with SCT silage with chemical additive.

Nuclear Pore Complexes (NPCs) mediate the nucleocytoplasmic transport of macromolecules. Here we provide a structure of the yeast NPC in which the inner ring is resolved by cryo-EM at - helical resolution to show how flexible connectors tie together different structural and functional layers in the spoke. These connectors are targets for phosphorylation and regulated disassembly in cells with an open mitosis. Moreover, some nucleoporin pairs and karyopherins have similar interaction motifs, which suggests an evolutionary and mechanistic link between assembly and transport. We also provide evidence for three major NPC variants that foreshadow functional specializations at the nuclear periphery. Cryo-electron tomography extended these studies to provide a comprehensive model of the in situ NPC with a radially-expanded inner ring. Our model reveals novel features of the central transporter and nuclear basket, suggests a role for the lumenal ring in restricting dilation and highlights the structural plasticity required for transport by the NPC.

Multi-subunit ring-ATPases carry out a myriad of biological functions, including genome packaging in viruses. Though the basic structures and functions of these motors have been well-established, the mechanisms of ATPase firing and motor coordination are poorly understood. Here, by direct counting using single-molecule fluorescence, we have determined that the active bacteriophage T4 DNA packaging motor consists of five subunits of gp17. By systematically doping motors with an ATPase-defective subunit and selecting single motors containing a precise count of active/inactive subunit(s), we found, unexpectedly, that the packaging motor can tolerate an inactive sub-unit. However, motors containing an inactive subunit(s) exhibit fewer DNA engagements, a higher failure rate in encapsidation, reduced packaging velocity, and increased pausing. These findings suggest a new packaging model in which the motor, by re-adjusting its grip on DNA, can skip an inactive subunit and resume DNA translocation, contrary to the prevailing notion of strict coordination amongst motor subunits of other packaging motors.

The multifunctional nucleocapsid (N) protein in SARS-CoV-2 binds the ~30 kb viral RNA genome to aid its packaging into the 80–90 nm membrane-enveloped virion. The N protein is composed of N-terminal RNA-binding and C-terminal dimerization domains that are flanked by three intrinsically disordered regions. Here we demonstrate that the N protein’s central disordered domain drives phase separation with RNA, and that phosphorylation of an adjacent serine/arginine rich region modulates the physical properties of the resulting condensates. In cells, N forms condensates that recruit the stress granule protein G3BP1, highlighting a potential role for N in G3BP1 sequestration and stress granule inhibition. The SARS-CoV-2 membrane (M) protein independently induces N protein phase separation, and three-component mixtures of N + M + RNA form condensates with mutually exclusive compartments containing N + M or N + RNA, including annular structures in which the M protein coats the outside of an N + RNA condensate. These findings support a model in which phase separation of the SARS-CoV-2 N protein contributes both to suppression of the G3BP1-dependent host immune response and to packaging genomic RNA during virion assembly.