Volume 4(65)

CONTENTS

  1. Venzel A.S., Ivanisenko T.V., Demenkov P.S., Ivanisenko V.A. Software pipeline for predicting the impact of mutations on the stability of protein spatial structures using free energy change estimation methods and artificial intelligence 
  2. Venzel A. S., Klimenko A. I., Ivanisenko T. V., Demenkov P. S., Lashin S. A., Ivanisenko V. A. An approach for predicting protein abundance in yeast cells based on their genomical sequences 
  3. Demenkov P.S., Mukhin A.M., Ivanisenko V.A., Lashin S.A., Kolchanov N.A. The “Microbitech” digital platform: architecture and purpose 
  4. Ivanisenko T. V., Demenkov P.S., Ivanisenko V.A. Combined Approach to Associative Network Reconstruction: Integrating GraphSAGE and Co-occurrence Statistics 
  5. Lakhova T., Kazantsev F., Khlebodarova T., Matushkin Yu., Lashin S. Software module for studying the regulation of bacterial metabolic pathways by mathematical modeling methods 
  6. Lashin S., Kazantsev F., Lakhova T., Matushkin Yu. DynMicrobiotech: a software module for automatic reconstruction of frame-based dynamic models of microbial gene networks 
  7. Mukhin A., Oschepkov D., Lashin S. A computational pipeline for de novo recognition of transcription factor binding sites in bacterial genomes 
 
Institute of Cytology and Genetics, SB RAS, 630090, Novosibirsk, Russia
Kurchatov Genomic Center of the Institute of Cytology and Genetics, SB RAS, 630090, Novosibirsk, Russia
Novosibirsk State University, 630090, Novosibirsk, Russia

SOFTWARE PIPELINE FOR PREDICTING THE IMPACT OF MUTATIONS ON THE STABILITY OF PROTEIN SPATIAL STRUCTURES USING FREE ENERGY CHANGE ESTIMATION METHODS AND ARTIFICIAL INTELLIGENCE

DOI: 10.24412/2073-0667-2024-4-6-16
EDN:EAMKIP

In this work, a computational pipeline was developed to predict the impact of mutations on the stability of protein structure. The pipeline employs a combined approach, utilizing state-of-the- art artificial intelligence methods for protein structure prediction and classical algorithms for free energy change estimation. The pipeline includes protein structure prediction using the ESM3 model, followed by calculation of free energy changes in mutant forms using pyRosetta. This approach allows overcoming the limitations of existing methods by combining the advantages of deep learning and the interpretability of energy calculations. The developed tool can find applications in structural bioinformatics, biotechnology, and medicine, especially given the limited number of experimentally determined protein structures.

Key words: protein structure prediction, protein structure stability, molecular modeling, ESM3.

References

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  3. Baek M. et al. Accurate prediction of protein structures and interactions using a three-track neural network // Science. 2021. V. 373, № 6557. P. 871-876.
  4. Lin Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model // Science. 2023. V. 379, № 6637. P. 1123-1130.
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  6. Kellogg E.H., Leaver-Fay A., Baker D. Role of conformational sampling in computing mutation- induced changes in protein structure and stability // Proteins: Structure, Function, and Bioinformatics. 2011. V. 79, № 3. P. 830-838.
  7. Dehouck Y. et al. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality // BMC bioinformatics. 2011. V. 12. P. 1-12.
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  11. Nikam R. et al. ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years // Nucleic Acids Research. 2021. V. 49, № DI. P. D420-D424.
  12. Xavier J.S. et al. ThermoMutDB: a thermodynamic database for missense mutations // Nucleic Acids Research. 2021. V. 49, № DI. P. D475-D479.
  13. Stourac J. et al. FireProtDB: database of manually curated protein stability data // Nucleic Acids Research. 2021. V. 49, № DI. P. D319-D324.
  14. Cao H. et al. DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks //J. Chem. Inf. Model. 2019. V. 59, № 4. P. 1508-1514.
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  20. The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023 // Nucleic Acids Research. 2023. V. 51, № DI. P. D523-D531.
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Bibliographic reference: Venzel A.S., Ivanisenko T.V., Demenkov P.S., Ivanisenko V.A. Software pipeline for predicting the impact of mutations on the stability of protein spatial structures using free energy change estimation methods and artificial intelligence //journal “Problems of informatics”. 2024, № 4. P.6-16. DOI: 10.24412/2073-0667-2024-4-6-16.

A.S. Venzel 1,2,3. A. I. Klimenko1,2. T.V. Ivanisenko1,2,3. P. S. Demenkov1,2,3. S.A. Lashin1,2,3 . V. A. Ivanisenko1,2,3

1Institute of Cytology and Genetics, SB RAS, 630090, Novosibirsk, Russia
2Kurchatov Genomic Center of the Institute of Cytology and Genetics, SB RAS, 630090, Novosibirsk, Russia
3Novosibirsk State University, 630090, Novosibirs, Russia

AN APPROACH FOR PREDICTING PROTEIN ABUNDANCE IN YEAST CELLS BASED ON THEIR GENOMICAL SEQUENCES

DOI: 10.24412/2073-0667-2024-4-17-26
EDN:HIAEDZ

In this work presented a new method for predicting protein abundance in Saccharomyces cerevisiae baker’s yeast cells, based on the analysis of their biological sequences using pre-trained language models. For sequence processing, ESM2 family models were applied to amino acid protein sequences, and the GENA-LM model was used for nucleotide gene sequences, which allowed for obtaining informative embedding of input data. The study evaluates the impact of various architectures and sizes of pre­trained language models on prediction accuracy. The proposed method has potential applications in biotechnology, optimization of biosynthesis processes, and computer-aided design of producer strains with enhanced gene expression of target proteins. The results of the study may contribute to a deeper understanding of genetic expression regulation mechanisms and open up prospects for predicting protein abundance in other microorganisms.

Key words: protein abundance, transformer, ESM2, machine learning.

References

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  2. Schwanhausser B. et al. Global quantification of mammalian gene expression control // Nature. 2011. V. 473, № 7347. P. 337-342.
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  4. Ji Y. et al. DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome // Bioinformatics. 2021. V. 37, № 15. P. 2112-2120.
  5. Ferreira M. et al. Protein Abundance Prediction Through Machine Learning Methods // Journal of Molecular Biology. 2021. V. 433, № 22. P. 167267.
  6. Lin Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model // Science. 2023. V. 379, № 6637. P. 1123-1130.
  7. Fishman V. et al. GENA-LM: A Family of Open-Source Foundational DNA Language Models for Long Sequences. 2023.
  8. Cherry J.M. et al. SGD: Saccharomyces Genome Database // Nucleic Acids Research. 1998. V. 26, № 1. P. 73-79.
  9. Huang Q. et al. PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts // Molecular & Cellular Proteomics. 2023. V. 22, № 10.
  10. Schmirler R., Heinzinger M., Rost B. Fine-tuning protein language models boosts predictions across diverse tasks // Nat Commun. 2024. V. 15, № 1. P. 7407.

Bibliographic reference: Venzel A. S., Klimenko A. I., Ivanisenko T. V., Demenkov P. S., Lashin S. A., Ivanisenko V. A. An approach for predicting protein abundance in yeast cells based on their genomical sequences //journal “Problems of informatics”. 2024, № 4. P.17-26. DOI: 10.24412/2073-0667-2024-4-17-26.

P. S. Demenkov, A. M. Mukhin, V.A. Ivanisenko, S.A. Lashin, N.A. Kolchanov

Kurchatov Genome Center of the Institute of Cytology and Genetics of the Siberian Branch
of the Russian Academy of Sciences (KGC ICG SB RAS), 630090, Novosibirsk, Russia

THE “MICROBITECH” DIGITAL PLATFORM: ARCHITECTURE
AND PURPOSE

DOI: 10.24412/2073-0667-2024-4-27-36
EDN:HXJHCY

The article examines the architecture of the developed digital platform “Microbitech” for solving a wide range of problems in systems and structural biology, discusses the use of software integrated into the platform for processing and analyzing large volumes of genetic information, as well as for predicting the structure and function of proteins. The use of the “Microbitech” digital platform allows increasing the productivity of research, improving the accuracy of data analysis, and contributing to the development of new research methods.

Key words: information platform, systems biology, bioinformatics.

References

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  8. Ivanisenko VA, Demenkov PS, Ivanisenko TV, Mishchenko EL, Saik OV. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019;20(Suppl 1):34. doi: 10.1186/sl2859-018-2567-6.

Bibliographic reference: Demenkov P.S., Mukhin A.M., Ivanisenko V.A., Lashin S.A., Kolchanov N.A. The “Microbitech” digital platform: architecture and purpose //journal “Problems of informatics”. 2024, № 4. P.27-36. DOI: 10.24412/2073-0667-2024-4-27-36.

T.V. Ivanisenko, P. S. Demenkov, V.A. Ivanisenko

Kurchatov Genomic Center of ICG SB RAS, Novosibirsk 630090, Russia
Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia

COMBINED APPROACH TO ASSOCIATIVE NETWORK RECONSTRUCTION: INTEGRATING GRAPHSAGE AND CO-OCCURRENCE STATISTICS

DOI: 10.24412/2073-0667-2024-4-37-45
EDN:LEXHCE

This study focuses on developing a hybrid approach for predicting molecular-genetic interactions, combining graph neural networks (GNNs) and co-occurrence analysis of entities in scientific literature. The method’s effectiveness is demonstrated using the associative network of Escherichia coli, reconstructed using the ANDSystem and its ANDDigest module. Results showed a significant improvement in the accuracy of interaction predictions, in terms of conformity to the original graph topology, compared to using GNNs alone. The combination of approaches improved the Fl-score from 0.815 to 0.97 and reduced the loss function value from 0.405 to 0.08. Evaluation on experimentally confirmed protein-protein interactions also demonstrated high model efficiency (Fl-score 0.9799, Matthews correlation coefficient 0.9597). The proposed method can be applied in analyzing complex biological systems, planning experiments, and optimizing biotechnological processes.

Key words: graph neural networks, molecular-genetic interactions, text-mining, Escherichia coli, ANDSystem, ANDDigest, GraphSAGE.

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Bibliographic reference: Ivanisenko T. V., Demenkov P.S., Ivanisenko V.A. Combined Approach to Associative Network Reconstruction: Integrating GraphSAGE and Co-occurrence Statistics //journal “Problems of informatics”. 2024, № 4. P.37-45. DOI: 10.24412/2073-0667-2024-4-37-45.

 
Kurchatov Genomic Center of the Institute of Cytology and Genetics SB RAS, 630090, Novosibirsk, Russia
Institute of Cytology and Genetics SB RAS, 630090, Novosibirsk, Russia
Novosibirsk State University, 630090, Novosibirsk, Russia

SOFTWARE MODULE FOR STUDYING THE REGULATION OF BACTERIAL METABOLIC PATHWAYS BY MATHEMATICAL MODELING METHODS

DOI: 10.24412/2073-0667-2024-4-46-55
EDN:LWFDRD

Mathematical modeling is widely used in microbial biotechnology. It is used to describe and understand metabolite fluxes and changes in their concentrations, allows one to consider pathways of protein biosynthesis and make predictions on the costs of culture media for the yield of target products, etc. Standard approaches to modeling bacterial metabolism usually miss the regulatory processes operating at the genetic level. Meanwhile, the development of computational methods of genomic analysis reveals more and more such regulatory relationships. Accounting for regulatory relationships, in the process of model reconstruction, will allow us to investigate finer details of bacterial metabolism control. This paper presents a program module that generates frame-based mathematical models on the structure of the bacterial gene network, extended with tools to take into account regulatory relationships in the bacterial genome. Model generation is performed in terms of ordinary differential equations within the SBML standard. The study of the resulting mathematical model is finally available in a variety of specialized modeling tools.

Key words: mathematical modeling, operon, gene network, differential equations.

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Bibliographic reference: Lakhova T., Kazantsev F., Khlebodarova T., Matushkin Yu., Lashin S. Software module for studying the regulation of bacterial metabolic pathways by mathematical modeling methods //journal “Problems of informatics”. 2024, № 4. P.46-55. DOI: 10.24412/2073-0667-2024-4-46-55.

S. Lashin, F. Kazantsev, T. Lakhova, Yu. Matushkin

Kurchatov Genomic Center of the Institute of Cytology and Genetics SB RAS,
Institute of Cytology and Genetics SB RAS,
Novosibirsk State University, 630090, Novosibirsk, Russia

DYNMICROBIOTECH: A SOFTWARE MODULE FOR AUTOMATIC RECONSTRUCTION OF FRAME-BASED DYNAMIC MODELS OF MICROBIAL GENE NETWORKS

DOI: 10.24412/2073-0667-2024-4-56-68
EDN: QVDKVH

Modern genetic technologies are used in industrial biotechnology to design microbial strains- producers with target characteristics for based on close integration of experimental and information­computer approaches. The increasing availability of genomic data and methods of their functional annotation requires the development of new methods of systems biology, in particular, methods of reconstruction of gene networks and metabolic pathways controlling target processes and characteristics of microorganisms based on information about sequenced genomes, as well as methods of building mathematical models of these networks and pathways. This paper presents the DynMicrobiotech software module for automatic reconstruction of frame-based mathematical models based on the generalized chemical-kinetic modeling method. The input data for the module are the annotation and markup of the genome, while the output data are the generated model in the form of a system of ordinary differential equations written in SBML format.

Key words: generalized chemical-kinetic method of modeling, differential equations, gene networks.

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Bibliographic reference: Lashin S., Kazantsev F., Lakhova T., Matushkin Yu. DynMicrobiotech: a software module for automatic reconstruction of frame-based dynamic models of microbial gene networks //journal “Problems of informatics”. 2024, № 4. P.56-68. DOI: 10.24412/2073-0667-2024-4-56-68.

A. Mukhin, D. Oschepkov, S. Lashin

Kurchatov Genomic Center Institute Cytology and Genetics SB RAS.
Institute Cytology and Genetics SB RAS.
Novosibirsk State University, 630090, Novosibirsk, Russia

A COMPUTATIONAL PIPELINE FOR DE NOVO RECOGNITION OF TRANSCRIPTION FACTOR BINDING SITES IN BACTERIAL GENOMES

DOI: 10.24412/2073-0667-2024-4-69-83
EDN: UGUBKF

The search for transcription factor binding sites (TFBSs) in bacterial genomes is one of the most important steps for their study and subsequent use in biotechnology and microbiology. The characteristic length of TFBS is 5-20 nucleotide pairs, and each transcription factor has the ability to bind to a set of sites similar in sequence. The concept of motif is used to describe the spectrum of sequences that have substantial (non-random) similarity. That is, a motif in molecular biology is a group (or a representative of a group, depending on the context) of relatively short sequences of nucleotides (or amino acids) that have sufficient similarity due to their performance of a single biological function, e. g., binding of a single transcription factor. The similarity of motifs is directly used by various bioinformatics approaches for their de novo detection in genomic sequence samples, and is possible only if there is sufficient enrichment of the tested sample with the corresponding sequence similarity. In cases where the bacterial genome is insufficiently annotated, such as when working with a newly sequenced genome, it is the de novo motif detection method that proves to be the most effective for finding TFBSs. In this paper, we propose a set of computational motif search pipelines that take as input the bacterial genome data and its primary annotation. The proposed pipelines using two different approaches (full-genome search, when de novo motifs are searched for in a set of promoters of a single genome, and phylogenetic footprinting, when motifs are searched for among a set of promoters of similar genes and/or operons) to search for motifs, provide the researcher with a comprehensive set of settings for obtaining the most complete annotation by sites of both the whole genome and more detailed annotation of the regulatory region of the selected gene. The presented pipelines were implemented using both the modern Nextflow platform and scripts in the Python programming language. Also, the following tools were used within the pipelines: BoBro as a method for searching de novo motifs in promoters of a single organism; MP3, which implements de novo motif searching by phylogenetic footprinting in a set of promoters, GOST to identify similar genes and/or operons between two genome assemblies, OperonMapper to determine the operon structure of the genome, and TomTom for annotation of de novo motifs. We have developed an indexed metadata database for known bacterial genomes using an embedded SQLite DBMS, which allows us to significantly accelerate data retrieval for further calculations.

Key words: pipeline, motifs, TFBS, genomics, Nextflow, Python, SQLite, JBrowse2, bioinformatics.

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Bibliographic reference: Mukhin A., Oschepkov D., Lashin S. A computational pipeline for de novo recognition of transcription factor binding sites in bacterial genomes //journal “Problems of informatics”. 2024, № 4. P.69-83. DOI: 10.24412/2073-0667-2024-4-69-83.