DNA Sequences from Formalin-Fixed Nematodes: Integrating Molecular and Morphological Approaches to Taxonomy

To effectively integrate DNA sequence analysis and classical nematode taxonomy, we must be able to obtain DNA sequences from formalin-fixed specimens. Microdissected sections of nematodes were removed from specimens fixed in formalin, using standard protocols and without destroying morphological features. The fixed sections provided sufficient template for multiple polymerase chain reaction-based DNA sequence analyses.     

The influence of alkyl pyridinium sponge toxins on membrane properties, cytotoxicity, transfection and protein expression in mammalian cells

The ability of two alkyl pyridinium sponge toxin preparations (poly-APS and halitoxin) to form transient pores/lesions in cell membranes and allow transfection of plasmid cDNA have been investigated using HEK 293 cells. Poly-APS and halitoxin preparations caused a collapse in membrane potential, reductions in input resistance and increased Ca²⁺ permeability. At least partial recovery was observed after poly-APS application but recovery was more rarely seen with halitoxin. The transfection with plasmid cDNAs for an enhanced green fluorescent protein (EGFP) and human tumour necrosis factor receptor 2 (TNFR2) was assessed for both toxin preparations and compared with lipofectamine. Stable transfection was achieved with poly-APS although it was less efficient than lipofectamine. These results show that viable cells transfected with alien cDNA can be obtained using novel transient pore-forming alkyl pyridinium sponge toxins and a simple pre-incubation protocol. This provides the first proof of principle that pore-forming alkyl pyridinium compounds can be used to deliver cDNA to the intracellular environment without permanently compromising the plasma membrane.     

耐药细菌蛋白质的分泌——新抗攻击的靶点

几十年来,由于抗生素的大量使用,导致了细菌对很多药物的抗药性.为了克服细菌的抗药性问题,需要用新的思路去发掘新的抗生素,包括发掘细菌细胞中存在的抗生素作用的新靶点.蛋白质的分泌过程对于细菌是生死攸关的,它可能成为新药物的适合靶点.     

Trichoderma reesei sequences that bind to the nuclear matrix enhance transformation frequency

Three DNA fragments, trs1, 2 and 3, were isolated from the Trichoderma reesei genome on the basis of their ability to promote autonomous replication of plasmids in Saccharomyces cerevisiae. Each trs element bound specifically to the isolated T. reesei nuclear matrix in vitro, and two of them bound in vivo, indicating that they are matrix attachment regions (MARs). A similar sequence previously isolated from Aspergillus nidulans (ans1) was also shown to bind specifically to the T. reesei nuclear matrix in vitro. The T. reesei MARs are AT-rich sequences containing 70%, 86% and 73% A+T over 2.9, 0.8 and 3.7 kb, respectively for trs1, 2 and 3. They exhibited no significant sequence homology, but were shown to contain a number of sequence motifs that occur frequently in many MARs identified in other eukaryotes. However, these motifs occurred as frequently in the trs elements as in randomly generated sequences with the same A+T content. trs1 and 3 were shown to be present as single copies in the T. reesei genome. The presence of the trs elements in transforming plasmids enhanced the frequency of integrative transformation of T. reesei up to five fold over plasmids without a trs. No evidence was obtained to suggest that the trs elements promoted efficient replication of plasmids in T. reseei. A mechanism for the enhancement of transformation frequency by the trs elements is proposed. Received: 1 March 1997 / Accepted: 13 May 1997     

Indications for a recent Holarctic expansion of pike based on a preliminary study of mtDNA variation

An extremely limited haplotypic diversity within populations and a strikingly low divergence between European and Canadian haplotypes were found in pike Esox lucius . Certain haplotypes were associated with a particular geographical region in Europe: a widespread European and a restricted Hungarian–Norwegian type. The low haplotypic intraspecific divergence suggests a late Pleistocene expansion from an ancestral population.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

MicroID2: A Novel Biotin Ligase Enables Rapid Proximity-Dependent Proteomics

Identifying protein–protein and other proximal interactions is central to dissecting signaling and regulatory processes in cells. BioID is a proximity-dependent biotinylation method that uses an “abortive” biotin ligase to detect proximal interactions in cells in a highly reproducible manner. Recent advancements in proximity-dependent biotinylation tools have improved efficiency and timing of labeling, allowing for measurement of interactions on a cellular timescale. However, issues of size, stability, and background labeling of these constructs persist. Here we modified the structure of BioID2, derived from Aquifex aeolicus BirA, to create a smaller, highly active, biotin ligase that we named MicroID2. Truncation of the C terrminus of BioID2 and addition of mutations to alleviate blockage of biotin/ATP binding at the active site of BioID2 resulted in a smaller and highly active construct with lower background labeling. Several additional point mutations improved the function of our modified MicroID2 construct compared with BioID2 and other biotin ligases, including TurboID and miniTurbo. MicroID2 is the smallest biotin ligase reported so far (180 amino acids [AAs] for MicroID2 versus 257 AAs for miniTurbo and 338 AAs for TurboID), yet it demonstrates only slightly less labeling activity than TurboID and outperforms miniTurbo. MicroID2 also had lower background labeling than TurboID. For experiments where precise temporal control of labeling is essential, we in addition developed a MicroID2 mutant, termed lbMicroID2 (low background MicroID2), that has lower labeling efficiency but significantly reduced biotin scavenging compared with BioID2. Finally, we demonstrate utility of MicroID2 in mass spectrometry experiments by localizing MicroID2 constructs to subcellular organelles and measuring proximal interactions.     

Extending the Horizon of Homology Detection with Coevolution-based Structure Prediction

Traditional sequence analysis algorithms fail to identify distant homologies when they lie beyond a detection horizon. In this review, we discuss how co-evolution-based contact and distance prediction methods are pushing back this homology detection horizon, thereby yielding new functional insights and experimentally testable hypotheses. Based on correlated substitutions, these methods divine three-dimensional constraints among amino acids in protein sequences that were previously devoid of all annotated domains and repeats. The new algorithms discern hidden structure in an otherwise featureless sequence landscape. Their revelatory impact promises to be as profound as the use, by archaeologists, of ground-penetrating radar to discern long-hidden, subterranean structures. As examples of this, we describe how triplicated structures reflecting longin domains in MON1A-like proteins, or UVR-like repeats in DISC1, emerge from their predicted contact and distance maps. These methods also help to resolve structures that do not conform to a “beads-on-a-string” model of protein domains. In one such example, we describe CFAP298 whose ubiquitin-like domain was previously challenging to perceive owing to a large sequence insertion within it. More generally, the new algorithms permit an easier appreciation of domain families and folds whose evolution involved structural insertion or rearrangement. As we exemplify with α1-antitrypsin, coevolution-based predicted contacts may also yield insights into protein dynamics and conformational change. This new combination of structure prediction (using innovative co-evolution based methods) and homology inference (using more traditional sequence analysis approaches) shows great promise for bringing into view a sea of evolutionary relationships that had hitherto lain far beyond the horizon of homology detection.