LLPSDB v2.0: an updated database of proteins undergoing liquid-liquid phase separation in vitro.

SUMMARY: Emerging evidences have suggested that liquid-liquid phase separation (LLPS) of proteins plays a vital role both in a wide range of biological processes and in related diseases. Whether a protein undergoes phase separation not only is determined by the chemical and physical properties of biomolecule themselves, but also is regulated by environmental conditions such as temperature, ionic strength, pH, as well as volume excluded by other macromolecules. A web accessible database LLPSDB was developed recently by our group, in which all the proteins involved in LLPS in vitro as well as corresponding experimental conditions were curated comprehensively from published literatures. With the rapid increase of investigations in biomolecular LLPS and growing popularity of LLPSDB, we updated the database, and developed a new version LLPSDB v2.0. In comparison of the previously released version, more than double contents of data are curated, and a new class 'Ambiguous system' is added. In addition, the web interface is improved, such as that users can search the database by selecting option 'phase separation status' alone or combined with other options. We anticipate that this updated database will serve as a more comprehensive and helpful resource for users. AVAILABILITY AND IMPLEMENTATION: LLPSDB v2.0 is freely available at: http://bio-comp.org.cn/llpsdbv2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning.

Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR ) is identified as the most crucial feature. While, in the further three-class LLPS-Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.

Enhanced permeability, mechanical and antibacterial properties of cellulose acetate ultrafiltration membranes incorporated with lignocellulose nanofibrils.

Cellulose acetate (CA) ultrafiltration membranes are attracting more attention in wastewater purification due to its biodegradability and eco-friendly. The application of CA membranes, however, is limited by high susceptibility to bacterial corrosion and lack of mechanical tolerance that results in loss of life. To solve the above problems, we first fabricated the CA-based composite membranes incorporated with bamboo-based lignocellulose nanofibrils (LCNFs) by a strategy of phase inversion. LCNFs was prepared by using a combined method of one-step chemical pretreatment and acid hydrolysis coupled with high-pressure homogenization. The as-prepared CA/LCNFs composite membranes with 4 wt% lignin in the LCNFs exhibited high tensile strength of 7.08 MPa and strain-at-break of 12.21%, and high filtration permeability of 188.23 L·m-2·h-1 as ultrafiltration membranes for wastewater treatment, which could obviously inhibit the growth of Escherichia coli.