Proteome-wide 3D structure prediction provides insights into the ancestral metabolism of ancient archaea and bacteria

Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability

Protein engineering is a pivotal aspect of synthetic biology, involving the modification of amino acids within existing protein sequences to achieve novel or enhanced functionalities and physical properties. Accurate prediction of protein variant effects requires a thorough understanding of protein sequence, structure, and function. Deep learning methods have demonstrated remarkable performance in guiding protein modification for improved functionality. However, existing approaches predominantly rely on protein sequences, which face challenges in efficiently encoding the geometric aspects of amino acids' local environment and often fall short in capturing crucial details related to protein folding stability, internal molecular interactions, and bio-functions. Furthermore, there lacks a fundamental evaluation for developed methods in predicting protein thermostability, although it is a key physical property that is frequently investigated in practice. To address these challenges, this article introduces a novel pre-training framework that integrates sequential and geometric encoders for protein primary and tertiary structures. This framework guides mutation directions toward desired traits by simulating natural selection on wild-type proteins and evaluates variant effects based on their fitness to perform specific functions. We assess the proposed approach using three benchmarks comprising over 300 deep mutational scanning assays. The prediction results showcase exceptional performance across extensive experiments compared to other zero-shot learning methods, all while maintaining a minimal cost in terms of trainable parameters. This study not only proposes an effective framework for more accurate and comprehensive predictions to facilitate efficient protein engineering, but also enhances the in silico assessment system for future deep learning models to better align with empirical requirements. The PyTorch implementation is available at https://github.com/ai4protein/ProtSSN.

Intranuclear TCA and mitochondrial overload: The nascent sprout of tumors metabolism

Abnormal glucose metabolism in tumors is a well-known form of metabolic reprogramming in tumor cells, the most representative of which, the Warburg effect, has been widely studied and discussed since its discovery. However, contradictions in a large number of studies and suboptimal efficacy of drugs targeting glycolysis have prompted us to further deepen our understanding of glucose metabolism in tumors. Here, we review recent studies on mitochondrial overload, nuclear localization of metabolizing enzymes, and intranuclear TCA (nTCA) in the context of the anomalies produced by inhibition of the Warburg effect. We provide plausible explanations for many of the contradictory points in the existing studies, including the causes of the Warburg effect. Furthermore, we provide a detailed prospective discussion of these studies in the context of these new findings, providing new ideas for the use of nTCA and mitochondrial overload in tumor therapy.

Archaea communities in aerobic granular sludge: A mini-review

Recent research on the archaea community in aerobic granular sludge (AGS) has attracted considerable attention. This review summarizes the existing literature on composition, distribution, and related functions of archaea community in AGS. Furthermore, the effects of granulation, substrate, temperature, process types, and aeration models on the archaea community were discussed. Significantly, the layered structure of AGS facilitates the enrichment of archaea, including methanogenic archaea and ammonia-oxidizing archaea. Archaea engage in metabolic interactions with other microorganisms, enhancing the ecological functionalities of AGS and its tolerance to adverse conditions. Future investigations should focus on minimizing greenhouse gas emissions and exploring the roles and interactive mechanisms of archaea and other microorganisms within AGS.

Decoding the Role of the Global Proteome Dynamics for Cellular Thermal Stability

Molecular mechanisms underlying the thermal response of cells remain elusive. On the basis of the recent result that the short-time diffusive dynamics of the Escherichia coli proteome is an excellent indicator of temperature-dependent bacterial metabolism and death, we used neutron scattering (NS) spectroscopy and molecular dynamics (MD) simulations to investigate the sub-nanosecond proteome mobility in psychro-, meso-, and hyperthermophilic bacteria over a wide temperature range. The magnitude of thermal fluctuations, measured by atomic mean square displacements, is similar among all studied bacteria at their respective thermal cell death. Global roto-translational motions turn out to be the main factor distinguishing the bacterial dynamical properties. We ascribe this behavior to the difference in the average proteome net charge, which becomes less negative for increasing bacterial thermal stability. We propose that the chemical-physical properties of the cytoplasm and the global dynamics of the resulting proteome are fine-tuned by evolution to uphold optimal thermal stability conditions.

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

The metabolic control of DNA replication: mechanism and function

Metabolism and DNA replication are the two most fundamental biological functions in life. The catabolic branch of metabolism breaks down nutrients to produce energy and precursors used by the anabolic branch of metabolism to synthesize macromolecules. DNA replication consumes energy and precursors for faithfully copying genomes, propagating the genetic material from generation to generation. We have exquisite understanding of the mechanisms that underpin and regulate these two biological functions. However, the molecular mechanism coordinating replication to metabolism and its biological function remains mostly unknown. Understanding how and why living organisms respond to fluctuating nutritional stimuli through cell-cycle dynamic changes and reproducibly and distinctly temporalize DNA synthesis in a wide-range of growth conditions is important, with wider implications across all domains of life. After summarizing the seminal studies that founded the concept of the metabolic control of replication, we review data linking metabolism to replication from bacteria to humans. Molecular insights underpinning these links are then presented to propose that the metabolic control of replication uses signalling systems gearing metabolome homeostasis to orchestrate replication temporalization. The remarkable replication phenotypes found in mutants of this control highlight its importance in replication regulation and potentially genetic stability and tumorigenesis.

Identification of a deep-branching thermophilic clade sheds light on early bacterial evolution

It has been proposed that early bacteria, or even the last universal common ancestor of all cells, were thermophilic. However, research on the origin and evolution of thermophily is hampered by the difficulties associated with the isolation of deep-branching thermophilic microorganisms in pure culture. Here, we isolate a deep-branching thermophilic bacterium from a deep-sea hydrothermal vent, using a two-step cultivation strategy ("Subtraction-Suboptimal", StS) designed to isolate rare organisms. The bacterium, which we name Zhurongbacter thermophilus 3DAC, is a sulfur-reducing heterotroph that is phylogenetically related to Coprothermobacterota and other thermophilic bacterial groups, forming a clade that seems to represent a major, early-diverging bacterial lineage. The ancestor of this clade might be a thermophilic, strictly anaerobic, motile, hydrogen-dependent, and mixotrophic bacterium. Thus, our study provides insights into the early evolution of thermophilic bacteria.