Biology subjects

Rubach, P.

Publications and source records attributed to Rubach, P..

3 recordsLinked to original sources

bioRxiv2023

Knot or Not? Sequence-Based Identification of Knotted Proteins With Machine Learning

Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles remain a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold, this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences, with our best-performing model demonstrating a 98.5% overall accuracy. Unveiling the sequence factors that contribute to knot formation, we discover that proteins predicted to be unknotted from known knotted families are typically non-functional fragments missing a significant portion of the knot core. The study further explores the significance of the substrate binding site in knot formation, particularly within the SPOUT protein family. Our findings spotlight the potential of machine learning in enhancing our understanding of protein topology and propose further investigation into the role of knotted structures across other protein families. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/556468v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1466077org.highwire.dtl.DTLVardef@1674829org.highwire.dtl.DTLVardef@1b283a5org.highwire.dtl.DTLVardef@e0e962_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Proteins containing 6-crossing knot types andtheir folding pathways

Studying complex protein knots can provide new insights into potential knot folding mechanisms and other fundamental aspects of why and how proteins knot. This paper presents results of a systematic analysis of the 3D structure of proteins with 6-crossings knots predicted by the artificial intelligence program AlphaFold 2. Furthermore, using a coarse-grained native based model, we found that three representative proteins can self tie to a 63 knot, the most complex knot found in a protein thus far. Because it is not a twist knot, the 63 knot cannot be folded via a simple mechanism involving the threading of a single loop. Based on successful trajectories for each protein, we determined that the 63 knot is formed after folding a significant part of the protein backbone to the native conformation. Moreover, we found that there are two distinct knotting mechanisms, which are described here. Also, building on a loop flipping theory developed earlier, we present two new theories of protein folding involving the creation and threading of two loops, and explain how our theories can describe the successful folding trajectories for each of the three representative 63-knotted proteins.

biophysics↗

bioRxiv2022

New 63 knot and other knots in human proteome from AlphaFold predictions

AlphaFold is a new, highly accurate machine learning protein structure prediction method that outperforms other methods. Recently this method was used to predict the structure of 98.5% of human proteins. We analyze here the structure of these AlphaFold-predicted human proteins for the presence of knots. We found that the human proteome contains 65 robustly knotted proteins, including the most complex type of a knot yet reported in proteins. That knot type, denoted 63 in mathematical notation, would necessitate a more complex folding path than any knotted proteins characterized to date. In some cases AlphaFold structure predictions are not highly accurate, which either makes their topology hard to verify or results in topological artifacts. Other structures that we found, which are knotted, potentially knotted, and structures with artifacts (knots) we deposited in a database available at: https://knotprot.cent.uw.edu.pl/alphafold.

molecular biology↗