A library of coiled-coil domains: from regular bundles to peculiar twists.

MOTIVATION: Coiled coils are widespread protein domains involved in diverse processes ranging from providing structural rigidity to the transduction of conformational changes. They comprise two or more α-helices that are wound around each other to form a regular supercoiled bundle. Owing to this regularity, coiled-coil structures can be described with parametric equations, thus enabling the numerical representation of their properties, such as the degree and handedness of supercoiling, rotational state of the helices, and the offset between them. These descriptors are invaluable in understanding the function of coiled coils and designing new structures of this type. The existing tools for such calculations require manual preparation of input and are therefore not suitable for the high-throughput analyses. RESULTS: To address this problem, we developed SamCC-Turbo, a software for fully automated, per-residue measurement of coiled coils. By surveying Protein Data Bank with SamCC-Turbo, we generated a comprehensive atlas of ∼50 000 coiled-coil regions. This machine learning-ready dataset features precise measurements as well as decomposes coiled-coil structures into fragments characterized by various degrees of supercoiling. The potential applications of SamCC-Turbo are exemplified by analyses in which we reveal general structural features of coiled coils involved in functions requiring conformational plasticity. Finally, we discuss further directions in the prediction and modeling of coiled coils. AVAILABILITY AND IMPLEMENTATION: SamCC-Turbo is available as a web server (https://lbs.cent.uw.edu.pl/samcc_turbo) and as a Python library (https://github.com/labstructbioinf/samcc_turbo), whereas the results of the Protein Data Bank scan can be browsed and downloaded at https://lbs.cent.uw.edu.pl/ccdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interactions between motor domains in kinesin-14 Ncd - a molecular dynamics study.

Minus-end directed, non-processive kinesin-14 Ncd is a dimeric protein with C-terminally located motor domains (heads). Generation of the power-stroke by Ncd consists of a lever-like rotation of a long superhelical 'stalk' segment while one of the kinesin's heads is bound to the microtubule. The last ∼30 amino acids of Ncd head play a crucial but still poorly understood role in this process. Here, we used accelerated molecular dynamics simulations to explore the conformational dynamics of several systems built upon two crystal structures of Ncd, the asymmetrical T436S mutant in pre-stroke/post-stroke conformations of two partner subunits and the symmetrical wild-type protein in pre-stroke conformation of both subunits. The results revealed a new conformational state forming following the inward motion of the subunits and stabilized with several hydrogen bonds to residues located on the border or within the C-terminal linker, i.e. a modeled extension of the C-terminus by residues 675-683. Forming of this new, compact Ncd conformation critically depends on the length of the C-terminus extending to at least residue 681. Moreover, the associative motion leading to the compact conformation is accompanied by a partial lateral rotation of the stalk. We propose that the stable compact conformation of Ncd may represent an initial state of the working stroke.

A superposition free method for protein conformational ensemble analyses and local clustering based on a differential geometry representation of backbone.

Here a differential geometry (DG) representation of protein backbone is explored on the analyses of protein conformational ensembles. The protein backbone is described by curvature, κ, and torsion, τ, values per residue and we propose 1) a new dissimilarity and protein flexibility measurement and 2) a local conformational clustering method. The methods were applied to Ubiquitin and c-Myb-KIX protein conformational ensembles and results show that κ\τ metric space allows to properly judge protein flexibility by avoiding the superposition problem. The dmax measurement presents equally good or superior results when compared to RMSF, especially for the intrinsically unstructured protein. The clustering method is unique as it relates protein global to local dynamics by providing a global clustering solutions per residue. The methods proposed can be especially useful to the analyses of highly flexible proteins. The software written for the analyses presented here is available at https://github.com/AMarinhoSN/FleXgeo for academic usage only.

ViralFlow v1.0-a computational workflow for streamlining viral genomic surveillance.

ViralFlow v1.0 is a computational workflow developed for viral genomic surveillance. Several key changes turned ViralFlow into a general-purpose reference-based genome assembler for all viruses with an available reference genome. New virus-agnostic modules were implemented to further study nucleotide and amino acid mutations. ViralFlow v1.0 runs on a broad range of computational infrastructures, from laptop computers to high-performance computing (HPC) environments, and generates standard and well-formatted outputs suited for both public health reporting and scientific problem-solving. ViralFlow v1.0 is available at: https://viralflow.github.io/index-en.html.

PiPred - a deep-learning method for prediction of π-helices in protein sequences.

Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- and ion-binding sites. Given their similarity to α-helices, the prediction of π-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. Here, we present PiPred, a neural network-based tool for predicting π-helices in protein sequences. By performing a rigorous benchmark we show that PiPred can detect π-helices with a per-residue precision of 48% and sensitivity of 46%. Interestingly, some of the α-helices mispredicted by PiPred as π-helices exhibit a geometry characteristic of π-helices. Also, despite being trained only with canonical π-helices, PiPred can identify 6-residue-long α/π-bulges. These observations suggest an even higher effective precision of the method and demonstrate that π-helices, α/π-bulges, and other helical deformations may impose similar constraints on sequences. PiPred is freely accessible at: https://toolkit.tuebingen.mpg.de/#/tools/quick2d . A standalone version is available for download at: https://github.com/labstructbioinf/PiPred , where we also provide the CB6133, CB513, CASP10, and CASP11 datasets, commonly used for training and validation of secondary structure prediction methods, with correctly annotated π-helices.

Analysis of two Schistosoma mansoni uridine phosphorylases isoforms suggests the emergence of a protein with a non-canonical function.

Reports of Schistosoma mansoni strains resistant to praziquantel, the only therapeutic strategy available for the treatment of schistosomiasis, have motivated the scientific community towards the search for new possible therapies. Biochemical characterization of the parasite's metabolism is an essential component for the rational development of new therapeutic alternatives. One of the so far uncharacterized enzymes is uridine phosphorylase (UP) (EC 2.4.2.3), for which the parasite genome presents two isoforms (SmUPa and SmUPb) that share 92% sequence identity. In this paper, we present crystal structures for SmUPa and SmUPb in their free states as well as bound to different ligands. This we have complemented by enzyme kinetic characterization and phylogenetic analyses. Both enzymes present an overall fold and active site structure similar to other known UPs. The kinetic analyses showed conclusively that SmUPa is a regular uridine phosphorylase but by contrast SmUPb presented no detectable activity. This is particularly noteworthy given the high level of sequence identity between the two isoforms and is probably the result of the significant differences observed for SmUPb in the vicinity of the active site itself, suggesting that it is not a UP at all. On the other hand, it was not possible to identify an alternative function for SmUPb, although our phylogenetic analyses and expression data suggest that SmUPb is still functional and plays a role in parasite metabolism. The unusual UPb isoform may open up new opportunities for understanding unique features of S. mansoni metabolism.