Biology subjects

Olechnovic, K.

Publications and source records attributed to Olechnovic, K..

5 recordsLinked to original sources

bioRxiv2024

Voronota-LT: efficient, flexible and solvent-aware tessellation-based analysis of atomic interactions

MotivationIn the fields of structural biology and bioinformatics, understanding molecular interactions is paramount. However, existing advanced geometric methods for describing interatomic contacts considering full structural context have typically demanded substantial computational resources, hindering their practical application. Given the ever-growing volume of structural data, there is an urgent need for more efficient tools for interaction analysis. ResultsWe present Voronota-LT, a new efficient method tailored for computing Voronoi tessellation-based atom-atom contacts within the solvent-accessible surface of molecular structures. Voronota-LT constructs every interatomic contact surface directly, without pre-computing the global Voronoi diagram or Delaunay triangulation. This approach results in a method that is not only fast, but also parallelizable and capable of selectively targeting specific interface areas within molecular complexes. While offering high execution speed, Voronota-LT provides a comprehensive description of every interatomic interaction, taking full account of the relevant structural context. Availability and ImplementationVoronota-LT software is freely available as both a standalone application and a C++ header-only library at https://kliment-olechnovic.github.io/voronota/expansion_lt/.

bioinformatics↗

bioRxiv2023

VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network

We present VoroIF-GNN, a novel single-model method for assessing inter-subunit interfaces in protein-protein complexes. Given a multimeric protein structural model, we derive interface contacts from the Voronoi tessellation of atomic balls, construct a graph of those contacts, and predict accuracy of every contact using an attention-based graph neural network. The contact-level predictions are then summarized to produce whole interface-level scores. VoroIF-GNN was blindly tested for its ability to estimate accuracy of protein complexes during CASP15 and showed strong performance in selecting the best multimeric model out of many. The method implementation is freely available at https://klimentolechnovic.github.io/voronota/expansion_js/.

bioinformatics↗

bioRxiv2023

TemStaPro: protein thermostability prediction using sequence representations from protein language models

MotivationReliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep learning methods for sequence analysis. These methods can facilitate training on more data and, possibly, enable development of more versatile thermostability predictors for multiple ranges of temperatures. ResultsWe applied the principle of transfer learning to predict protein thermostability using embeddings generated by protein language models (pLMs) from an input protein sequence. We used large pLMs that were pre-trained on hundreds of millions of known sequences. The embeddings from such models allowed us to efficiently train and validate a high-performing prediction method using over one million sequences that we collected from organisms with annotated growth temperatures. Our method, TemStaPro (Temperatures of Stability for Proteins), was used to predict thermostability of CRISPR-Cas Class II effector proteins (C2EPs). Predictions indicated sharp differences among groups of C2EPs in terms of thermostability and were largely in tune with previously published and our newly obtained experimental data. Availability and ImplementationTemStaPro software and the related data are freely available from https://github.com/ievapudz/TemStaPro and https://doi.org/10.5281/zenodo.7743637.

bioinformatics↗

bioRxiv2023

Prediction of protein assemblies by structure sampling followed by interface-focused scoring

Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their three-dimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIF-jury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody-antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies.

bioinformatics↗

bioRxiv2020

VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

MotivationEffective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. ResultsFor the first time we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows to efficiently introduce both convolution and pooling operations of the network. We trained our model, called VoroCNN, to predict local qualities of 3D protein folds. The prediction results are competitive to the state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in the recognition of protein binding interfaces. AvailabilityThe model, data, and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. Contactceslovas.venclovas@bti.vu.lt, sergei.grudinin@inria.fr

bioinformatics↗