Our approach to fold recognition for the fourth critical assessment of techniques for protein structure prediction (CASP4) experiment involved the use of the FUGUE sequence-structure homology recognition program (http://www-cryst.bioc.cam.ac.uk/fugue), followed by model building. We treat models as hypotheses and examine these to determine whether they explain the available data. Our method depends heavily on environment-specific substitution tables derived from our database of structural alignments of homologous proteins (HOMSTRAD, http://www-cryst.bioc.cam.ac.uk/homstrad/). FUGUE uses these tables to incorporate structural information into profiles created from HOMSTRAD alignments that are matched against a profile created for the target from multiple sequence alignment. In addition, environment-specific substitution tables are used throughout the modeling procedure and as part of the model evaluation. Annotation of sequence alignments with JOY, to reflect local structural features, proved valuable, both for modifying hypotheses, and for rejecting predictions when the expected pattern of conservation is not observed. Our stringency in rejecting incorrect predictions led us to submit a relatively small number of models, including only a low number of false positives, resulting in a high average score.
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http://dx.doi.org/10.1002/prot.1169 | DOI Listing |
Int J Biol Macromol
October 2024
Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Av. do Café, s/n, Ribeirão Preto, SP 14040-903, Brazil.
There is an urgent need to develop new, safer, and more effective drugs against Chagas disease (CD) as well as related kinetoplastid diseases. Targeting and inhibiting the Trypanosoma cruzi proteasome has emerged as a promising therapeutic approach in this context. To expand the chemical space for this class of inhibitors, we performed virtual screening campaigns with emphasis on shape-based similarity and ADMET prioritization.
View Article and Find Full Text PDFBrief Bioinform
March 2024
Department of Chemical Biology, School of Life Sciences, Southern University of Science and Technology, Xueyuan Avenue, 518055, Shenzhen, China.
Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge.
View Article and Find Full Text PDFInt J Mol Sci
April 2024
Intracellular Parasitism Group, Department of Microbiology, Hellenic Pasteur Institute, 11521 Athens, Greece.
Proteins of the sorting nexin (SNX) family present a modular structural architecture with a phox homology (PX) phosphoinositide (PI)-binding domain and additional PX structural domains, conferring to them a wide variety of vital eukaryotic cell's functions, from signal transduction to membrane deformation and cargo binding. Although SNXs are well studied in human and yeasts, they are poorly investigated in protists. Herein, is presented the characterization of the first SNX identified in protozoan parasites encoded by the BPK_352470 gene.
View Article and Find Full Text PDFNature
October 2023
Biozentrum, University of Basel, Basel, Switzerland.
We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database. These models cover nearly all proteins that are known, including those challenging to annotate for function or putative biological role using standard homology-based approaches. In this study, we examine the extent to which the AlphaFold database has structurally illuminated this 'dark matter' of the natural protein universe at high predicted accuracy.
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