Segovia, "Protein homology detection and fold inference through multiple alignment entropy profiles," Proteins: Structure, Function and Genetics, vol. 70, no. 1, pp. 248-256, 2008.Alejandro S, Ernesto P, Segovia L. Protein homology detection and fold inference through multiple alignment entropy ...
Exploiting sequence–structure–function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec a
With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast
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Moreover, for many practical applications, such as homology modelling and function prediction, sequence alignments are used to infer a structure alignment, which is the real aim. These arguments have motivated the practice to assess the quality of sequence alignments by using structure alignments as ...
Multiple sequence alignment methods are often an essential component for solving challenging bioinformatics problems such as protein function prediction, protein homology identification, protein structure prediction, protein interaction study, mutagenesis analysis, and phylogenetic tree construction. During the ...
Repacking of side chains: The side chains were added, and the clashes between them were removed with the software FAMS Complex, a fully automated homology modeling system for protein complex structure. 5. Normal mode analysis (NMA): Hinge regions that connect to domains are very important for ...
The homology of protein sequences can be analyzed by the following methods. First, a multiple sequence alignment can be used to align the residuals of related protein sequences. Second, a position specific scoring matrix can be constructed. Highly conserved residuals can be identified as motifs. ...
See the wiki on how to use DeepBLAST and TM-vec for remote homology search and alignment. If you have questions on how to use DeepBLAST and TM-vec, feel free to raise questions in the discussions section. If you identify any potential bugs, feel free to raise them in the issuetracker...
Fig. 1: An overview of latent generative landscape (LGL) methodology. aA schematic overview of Hamiltonian mapping of VAE latent space and its applications. Using a multiple sequence alignment as input, DCA and VAE models are independently trained. Maximum probability grid-sampled sequences (S*) ...