Slipknotted and unknotted monovalent cation-proton antiporters evolved from a common ancestor.

While the slipknot topology in proteins has been known for over a decade, its evolutionary origin is still a mystery. We have identified a previously overlooked slipknot motif in a family of two-domain membrane transporters. Moreover, we found that these proteins are homologous to several families of unknotted membrane proteins. This allows us to directly investigate the evolution of the slipknot motif. Based on our comprehensive analysis of 17 distantly related protein families, we have found that slipknotted and unknotted proteins share a common structural motif. Furthermore, this motif is conserved on the sequential level as well. Our results suggest that, regardless of topology, the proteins we studied evolved from a common unknotted ancestor single domain protein. Our phylogenetic analysis suggests the presence of at least seven parallel evolutionary scenarios that led to the current diversity of proteins in question. The tools we have developed in the process can now be used to investigate the evolution of other repeated-domain proteins.

Knot_pull-python package for biopolymer smoothing and knot detection.

SUMMARY: The biggest hurdle in studying topology in biopolymers is the steep learning curve for actually seeing the knots in structure visualization. Knot_pull is a command line utility designed to simplify this process-it presents the user with a smoothing trajectory for provided structures (any number and length of protein, RNA or chromatin chains in PDB, CIF or XYZ format), and calculates the knot type (including presence of any links, and slipknots when a subchain is specified). AVAILABILITY AND IMPLEMENTATION: Knot_pull works under Python >=2.7 and is system independent. Source code and documentation are available at http://github.com/dzarmola/knot_pull under GNU GPL license and include also a wrapper script for PyMOL for easier visualization. Examples of smoothing trajectories can be found at: https://www.youtube.com/watch?v=IzSGDfc1vAY. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PconsFam: An Interactive Database of Structure Predictions of Pfam Families.

At present, about half of the protein domain families lack a structural representative. However, in the last decade, predicting contact maps and using these to model the tertiary structure for these protein families have become an alternative approach to gain structural insight. At present, reliable models for several hundreds of protein families have been created using this approach. To increase the use of this approach, we present PconsFam, which is an intuitive and interactive database for predicted contact maps and tertiary structure models of the entire Pfam database. By modeling all possible families, both with and without a representative structure, using the PconsFold2 pipeline, and running quality assessment estimator on the models, we predict an estimation for how confident the contact maps and structures are for each family.

DCA-MOL: A PyMOL Plugin To Analyze Direct Evolutionary Couplings.

Direct coupling analysis (DCA) is a statistical modeling framework designed to uncover relevant molecular evolutionary relationships from biological sequences. Although DCA has been successfully used in several applications, mapping and visualizing of evolutionary couplings and direct information to a particular set of molecules requires multiple steps and could be prone to errors. DCA-MOL extends PyMOL functionality to allow users to interactively analyze and visualize coevolutionary residue-residue interactions between contact maps and structures. True positive rates for the top N pairs can be computed and visualized in real-time to evaluate the quality of residue-residue contact predictions. Different types of interactions in monomeric proteins, RNA, molecular interfaces, and protein conformational dynamics as well as multiple protein complexes can be studied efficiently within one application. DCA-MOL is available for download from http://dca-mol.cent.uw.edu.pl.

Proteins' Knotty Problems.

Knots in proteins are increasingly being recognized as an important structural concept, and the folding of these peculiar structures still poses considerable challenges. From a functional point of view, most protein knots discovered so far are either enzymes or DNA-binding proteins. Our comprehensive topological analysis of the Protein Data Bank reveals several novel structures including knotted mitochondrial proteins and the most deeply embedded protein knot discovered so far. For the latter, we propose a novel folding pathway based on the idea that a loose knot forms at a terminus and slides to its native position. For the mitochondrial proteins, we discuss the folding problem from the perspective of transport and suggest that they fold inside the mitochondria. We also discuss the evolutionary origin of a novel class of knotted membrane proteins and argue that a novel knotted DNA-binding protein constitutes a new fold. Finally, we have also discovered a knot in an artificially designed protein structure.

KnotGenome: a server to analyze entanglements of chromosomes.

The KnotGenome server enables the topological analysis of chromosome model data using three-dimensional coordinate files of chromosomes as input. In particular, it detects prime and composite knots in single chromosomes, and links between chromosomes. The knotting complexity of the chromosome is presented in the form of a matrix diagram that reveals the knot type of the entire polynucleotide chain and of each of its subchains. Links are determined by means of the Gaussian linking integral and the HOMFLY-PT polynomial. Entangled chromosomes are presented graphically in an intuitive way. It is also possible to relax structure with short molecular dynamics runs before the analysis. KnotGenome is freely available at http://knotgenom.cent.uw.edu.pl/.

GapRepairer: a server to model a structural gap and validate it using topological analysis.

Motivation: Over 25% of protein structures possess unresolved fragments. On the other hand, approximately 6% of protein chains have non-trivial topology (and form knots, slipknots, lassos and links). As the topology is fundamental for the proper function of proteins, modeling of topologically correct structures is decisive in various fields, including biophysics, biotechnology and molecular biology. However, none of the currently existing tools take into account the topology of the model and those which could be modified to include topology, demand experience in bioinformatics, protein topology and knot theory. Results: In this work, we present the GapRepairer-the server that fills the gap in the spectrum of structure modeling methods. Its easy and intuitive interface offers the power of Modeller homology modeling to many non-experts in the field. This server determines the topology of templates and predicted structures. Such information when possible is used by the server to suggest the best model, or it can be used by the user to score models or to design artificially (dis)entangled structures. Availability and implementation: GapRepairer server along with tutorials, usage notes, movies and the database of already repaired structures is available at http://gaprepairer.cent.uw.edu.pl. Supplementary information: Supplementary data are available at Bioinformatics online.

LinkProt: a database collecting information about biological links.

Protein chains are known to fold into topologically complex shapes, such as knots, slipknots or complex lassos. This complex topology of the chain can be considered as an additional feature of a protein, separate from secondary and tertiary structures. Moreover, the complex topology can be defined also as one additional structural level. The LinkProt database (http://linkprot.cent.uw.edu.pl) collects and displays information about protein links - topologically non-trivial structures made by up to four chains and complexes of chains (e.g. in capsids). The database presents deterministic links (with loops closed, e.g. by two disulfide bonds), links formed probabilistically and macromolecular links. The structures are classified according to their topology and presented using the minimal surface area method. The database is also equipped with basic tools which allow users to analyze the topology of arbitrary (bio)polymers.