Temperature sensitive (Ts) mutants of proteins provide experimentalists with a powerful and reversible way of conditionally expressing genes. The technique has been widely used in determining the role of gene and gene products in several cellular processes. Traditionally, Ts mutants are generated by random mutagenesis and then selected though laborious large-scale screening. Our web server, TSpred (http://mspc.bii.a-star.edu.sg/TSpred/), now enables users to rationally design Ts mutants for their proteins of interest. TSpred uses hydrophobicity and hydrophobic moment, deduced from primary sequence and residue depth, inferred from 3D structures to predict/identify buried hydrophobic residues. Mutating these residues leads to the creation of Ts mutants. Our method has been experimentally validated in 36 positions in six different proteins. It is an attractive proposition for Ts mutant engineering as it proposes a small number of mutations and with high precision. The accompanying web server is simple and intuitive to use and can handle proteins and protein complexes of different sizes.
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http://dx.doi.org/10.1093/nar/gku319 | DOI Listing |
Crit Rev Oncog
January 2025
GITAM.
Coralyne (COR) is a protoberberine-like isoquinoline alkaloid, and it is known for double-stranded (ds) DNA intercalation and topoisomerase inhibition. It can also sensitize cancer cells through various mechanisms. COR reduces the proliferation and migration of breast cancer cells by inhibiting the expression and activity of matrix metalloproteinase 9 (MMP9).
View Article and Find Full Text PDFPeerJ
January 2025
Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, United States of America.
Despite the recent surge of viral metagenomic studies, it remains a significant challenge to recover complete virus genomes from metagenomic data. The majority of viral contigs generated from de novo assembly programs are highly fragmented, presenting significant challenges to downstream analysis and inference. To address this issue, we have developed Virseqimprover, a computational pipeline that can extend assembled contigs to complete or nearly complete genomes while maintaining extension quality.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi-modal deep learning framework, is developed to predict functional phosphorylation sites.
View Article and Find Full Text PDFHardwareX
March 2025
INRAE - French National Research Institute for Agriculture, Food and Environment, REVERSAAL Research Unit, 5 rue de la Doua, CS 20244, 69625 Villeurbanne Cedex, France.
Sensors play an important role in both the continuous monitoring and intermittent analyses, which are essential for the study of wastewater treatment plant management and conducting related research. Given the significant environmental impact of the issues involved, accurate measurement of the volume of water flowing into and out of treatment plants is a key parameter for plant management, ecotoxicological studies and academic research programs. Traditionally, flow measurements have been based on calibrated weirs or venturi flumes, using water level measurements for conversion into flow, according to established relationships.
View Article and Find Full Text PDFBioinformatics
January 2025
Biocomputing Group, University of Bologna, Italy.
Motivation: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations.
Results: In this work we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations.
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