Аннотация (русский):
Методы биоинформатического анализа – вспомогательный инструмент в проведении предварительного этапа исследований процесса биокаталитической конверсии белков с прогнозируемым высвобождением биологически активных пептидов. Однако существует ряд факторов, не учитывающихся в современных стратегиях при проектировании биологически активных пептидов, что препятствует полномасштабному прогнозированию их биологических свойств. Это обуславливает актуальность выбранной цели исследования – разработку гибридной стратегии биоинформатического моделирования для изучения биологически активных пептидов молочного белка с учетом ранжирования ключевых критериев на основе высокопроизводительных алгоритмов протеомных баз данных. Объектом исследования является научная литература, касающаяся методов in silico биологически активных пептидов. Применялись современные таксонометрические методы поиска информации с использованием баз данных РИНЦ, Scopus и Web of Science. Сформирован и поэтапно описан оптимальный алгоритм гибридной стратегии in silico изучения биологически активных пептидов молочного белка с учетом оценки безопасности всех продуктов гидролиза, их физико-химических и технологических свойств. Алгоритм стратегии сформирован исходя из аналитических данных о белковом профиле, аминокислотной последовательности белков, входящих в состав сырья с учетом их полиморфизма, и последующей идентификации биоактивных аминокислотных сайтов в структуре белка. В алгоритм включен подбор оптимальных ферментных препаратов и моделирование гидролиза с оценкой биоактивности пептидов по протеомным базам данных. Предложенная стратегия in silico позволит на предварительном этапе проведения гидролиза белка научно прогнозировать направленное высвобождение стабильных пептидных комплексов биологически активных пептидов с доказанными биоактивностью, безопасностью и сенсорными характеристиками. Гибридный алгоритм будет способствовать аккумулированию необходимых первичных данных для сокращения временных и финансовых затрат на проведение реальных экспериментов.

Ключевые слова:
Молочные белки, пептиды, база данных, биоинформатика, in silico

Introduction
Recent years have seen an increase in the number
of biotechnological studies aimed at assessing the
role of biologically active peptides derived from
food raw materials for regulating body functions,
maintaining immunological status, and reducing the
risk of chronic disease development [1, 2]. Scientists
proved that biologically active peptides demonstrate
antimicrobial, hypocholestermic, antihypertensive,
antioxidant, antidiabetic, immunomodulatory and
otherproperties [3–9]. Peptide of dairy raw materials
is considered one of the most valuable sources for
isolating bioactive peptides encoded in its structure
[10]. Most biologically active peptides identified in dairy
products range from 2 to 20 amino acids in length. This
corresponds to a molecular weight range of 0.24–2.50 kDa.
As the length of the peptide increases, the probability
of forming secondary structure elements rises, which
results in steric hindrances to the manifestation of
various biological activities. Exposure to proteases
brings about the release of bioactive peptides from the
amino acid sequence of a protein. This exposure takes
place during gastrointestinal digestion, fermentation
of milk proteins using proteolytic systems of lactic
acid bacteria in the process of ripening, technological
treatment of raw materials (homogenization, high
temperature treatment, ultrasound, etc.) and bioconversion
of protein raw materials with purified
preparations of proteolytic enzymes [11–13].
The classical strategy for research of biologically
active peptides relies on the unpredictable cleavage
of peptide bonds in the protein structure by proteases
in vitro, followed by the purification of hydrolysis
products and evaluation of their bioactivity in vivo.
However, this strategy suffers from a number of
shortcomings, including a high labor intensity and
a long process, as well as high financial costs [14].
With computer technology and in-depth analytical
research methods developing rapidly, integrated
proteomic data banks, such as NCBI, BIOPEP,
UniProt, PepBank, SwePep, etc. were created. Implementing
bioinformatic analysis algorithms on these
platforms allows the detection of peptide bonds
in the protein structure sensitive to proteolytic
cleavage, amino acid sequences of proteins and derived
peptides, their functionality, allergenicity, chelating
ability, etc. [15–17].
Methods of bioinformatic analysis (in silico) are an
auxiliary tool in preliminary studying the biocatalytic
conversion of proteins (using “digital twin” models)
by different proteases with predicted release of
biologically active peptides. Since peptides, like
proteins, exhibit a high degree of structure-activity
relationship, the presence and location of certain
amino acid residues (biomarkers) can indicate the
properties and potential bioactivity of peptides [18].
For example, E.Yu. Agarkova and A.G. Kruchinin
showed in their article that redox-active amino
acid residues (C, H, Y, W and M) are an important
structural descriptor of antioxidant peptides [19].
Residues of hydrophobic amino acid enhance the
antioxidant properties of peptides in systems containing
the lipid phase. Amino acids with ionogenic groups
in side radicals are responsible for binding metal ions
of variable valence. Thus, predictive modeling of
biological activities in peptides based on biomarkers
reduces the number and duration of experiments to
obtain representative data [18]. Bioinformatic analysis
integrated into research developed new strategies for
discovering bioactive peptides and proving their role
at the organismic level. Most in silico working strategies
are based on a paradigm that selects protein substrate
and enzymes to generate bioactive peptides (taking
into account the frequency and release efficiency
criteria), carry out molecular docking, and screen
virtually peptide sequences for further optimization
of biopeptide release from food protein
substrates [20, 21].
However, the design and generation of biologically
active peptides neglect a number of factors. For
example, the genetic polymorphism of milk proteins
associated with amino acid mutations in its structure can
affect the type and biological activity of the released
peptides [22]. Diversity of the protein matrix of food
raw materials should be considered another important
factor, as well as their bioavailability for enzymatic
cleavage, taking into account the conformational
and intermolecular changes during technological
processing. Considering peptidomics as an integral part of
fudomics, one should pay special attention to predicting
the sensory characteristics of hydrolysis products,
aim to minimize the formation of free amino acids at
the in silico stage, as well as level out the formation
of bitterness and non-specific flavor as much as
possible. A key criterion in the development and identification
of biologically active peptides is food safety.
That is why a bioinformatic approach to modeling
biologically active peptides should predict such factors
as toxicity and allergenicity of the peptides released
from the protein structure. In terms of technological
properties, an important factor is predicting the
stability of biologically active peptides during in silico
modeling. Bioinformatics can predict the average
molecular weight, thermal stability (aliphatic index),
solubility (hydropathy index), etc. This enables
assessment of stability for hydrolysis products during
further technological processing and storage. Since
bioactive peptides can be completely or partially degraded
by digestive proteases in the human gastrointestinal
tract and subsequently lose biological activity,
49
Кручинин А.Г. [и др.] Техника и технология пищевых производств. 2022. Т. 52. № 1 С. 46–57
bioinformatic modeling of the resistance of bioactive
peptides to hydrolysis by digestive enzymes is considered
an important part of the final stage. For example, proline
in biologically active peptides increases their resistance
to GI peptidases [19].
The foregoing determines the relevance of the
study objective, i.e. developing a hybrid strategy
for bioinformatic modeling so as to study biologically
active peptides of milk protein, taking into
account the ranking of key criteria based on highperformance
proteomic database algorithms.
Study objects and methods
Analysis embraced Russian and foreign scientific
publications dealing with the use of bioinformatic
data banks in studying proteins or peptides of food
biosystems. It was carried out on the main scientometric
databases RSCI, Scopus and Web of Science.
The search query excluded teaching materials, as
well as conference materials and proceedings. Search
descriptors in article titles, keywords, and abstracts
included the following words and phrases: food proteins,
bioactive peptides, database, bioinformatics, in silico.
The depth of analysis for scientific publications was
limited to a 20-year period. This approach allowed
us to identify key actualizable databases and form
the fundamental criteria for bioinformatic modeling
of targeted hydrolysis of food proteins in order to
predict the release of biopeptides from their structures.
Results and discussion
Resultant from the development of principles for
the bioinformatic approach in peptidomics, numerous
databases were created, including data banks of proteins,
as well as enzymes, sensory, allergenic, bioactive
and hypothetically bioactive peptides. In addition to
listing members of each group, the databases contain
associated analytical bioinformatics tools. Thanks to
them, one can extract information about the dis-/similarity
of given protein structures, their amino acid sequence,
theoretical enzymatic cleavage, physicochemical
properties, chelating ability, proven or predicted
functionality, allergenicity, toxicity, etc.
In a number of studies, scientists used various
bioinformatic resources successfully to create
algorithms and strategies for predicting the isolation
of biologically active peptide from food raw
materials [23–25]. Taking into account the characteristics
of raw materials or the process of
generating biologically active peptides, the
authors point out that each individual food object requires
appropriate in silico modeling tools.
Analysis and systematization of international
experience resulted in development and thorough
description of an optimal algorithm for a hybrid
strategy of bioinformatic modeling so as to study
biologically active peptides of milk protein. The
strategy takes into account the most significant criteria
that increase the probability of obtaining peptides
with predictable bioactivity, safety, and acceptable
sensory characteristics (Fig. 1).
Analyzing the protein profile of dairy raw materials.
The fractional composition of raw milk is not constant
and depends on paratypical (period of the year,
feeding ration, lactation period, animal health, etc.),
genotypical (heredity, breed, individual genotype, etc.)
and technological (heat treatment, homogenization,
membrane processing, etc.) factors [26]. In this
regard, the preliminary proteomic studies require
qualitative and quantitative determination of
protein fractions for dairy raw materials due to
their instability. To determine the total content of
casein and serum proteins and to identify
protein fractions, one needs to use a set of multidirectional
techniques, such as the Kjeldahl method,
one- or two-dimensional gel electrophoresis with
isoelectric focusing, high-performance liquid
chromatography, etc. In addition, high-performance
liquid chromatography with time-of-flight mass
spectrometry will assess changes in the peptide
profile in dairy raw materials depending on various
technological factors.
Thus, complete systematic mapping of proteins
in dairy raw materials, taking into account the
conformational and proteomic changes associated
with the technological features of modern production,
seems to be a powerful tool at the initial stage of the
bioinformatic modeling strategy.
Analyzing the amino acid sequence for a protein
taking into account genetic polymorphism. The
next stage of the strategy involves obtaining data
on the amino acid sequences of all protein fractions
identified in the composition of raw milk. Data
on the amino acid sequence, including the protein
gene polymorphism (if necessary), its codifiers,
molecular weight, and source, can be retrieved from
bioinformatic databases and associated tools: NCBI,
Uniprot and BIOPEP [27]. These resources are
often used to identify the amino acid sequences
of proteins while studying in silico new bioactive
peptides from animal raw materials and creating
databases of sensory peptides [25, 28, 29].
However, in silico studies do not take into
account information about the genetic variability
of protein structures.
The polymorphism of the gene, encoding the
amino acid sequence in the protein structure, plays
an essential role in the strategy for bioinformatic
modeling of enzymatic bioconversion of milk
proteins. Amino acid mutations result in the random
50
Kruchinin A.G. et al. Food Processing: Techniques and Technology, 2022, vol. 52, no. 1, pp. 46–57
Рисунок 1. Алгоритм гибридной стратегии биоинформатического мод елирования (in silico) для изучения
биологически активных пептидов молочного белка
Figure 1. Hybrid strategy algorithm of bioinformatic in silico modeling to be used in research
on biologically active peptides of milk protein
Analyzing protein
profile of milk raw materials
Analyzing the amino
acid sequence for a protein
taking into account genetic
polymorphism
Identifying bioactive
amino acid sites in
the protein structure
Screening the specificity
of enzyme preparations
Assessing the
bioactivity of peptides
Computer modeling of
the protein bioconversion
Assessing the
physicochemical and
technological properties of
peptides
Assessing peptides for
toxicity, allergenicity,
free amino acids and
sensorics
Stability of biopeptides
during digestion
in the GI model
A digital model of a peptide
complex with predictable
bioactivity, safety, and
sensory characteristics
Substrate
Active Centre
Breaking substrate into subunits
Reaction
products
51
Кручинин А.Г. [и др.] Техника и технология пищевых производств. 2022. Т. 52. № 1 С. 46–57
acid sequence and assessing homology of biofunctional
properties, as well as identifying precursor
proteins [35, 36]. The resultant set contains
data of bioactive peptides with annotated amino
acid sequences included in the studied protein
(peptide mapping), their functions, level of bioactivity,
and references to primary sources of research
data. The data set allows one to simplify the process
and reduce labor costs of releasing bioactive peptides
from complex protein matrices [37, 38]. The targeted
hydrolysis will result in the release of not only the
maximum possible number of functional peptides,
but also those whose bioactivity is not annotated.
The bioinformatic tools BLAST NCBI, Expasy
SIM Alignment Tool and Uniprot (ALIGN) are
used to compare amino acid sequences (alignment)
in order to identify protein structures similar in
motifs and functionality [39]. It is worth noting
that working with these resources requires care in
formulating conclusions. R.A. González-Pech et al.
have drawn attention to cases of incorrect
interpretation of the data obtained through these
algorithms [40].
Most other tools used for identifying bioactive
peptides, such as APD, PeptideDB, BioPepDB,
etc., operate on the basis of an inverse algorithm
[41, 42]. This algorithm focuses on the amino
acid sequences of peptides whose isolation from the
protein requires prior use of resources modelling
enzymatic cleavage. This approach forms many
options for directing the hydrolysis, since enzyme
complexes or individual enzyme preparations
will have an individual bioinformatic scheme of
cleavage. Processing such a data set implies a time
cost, provided that there are no limitations in the number
of enzyme systems. A number of publications on
in silico studies of protein microstructures
of collagens, tomato seeds, mung beans, etc.
also used this classical algorithm – from
enzymatic cleavage to evaluation of peptide
properties [43–45].
Screening the specificity of enzyme preparations.
The task of the next stage of the bioinformatic
modeling strategy is to screen the specificity of enzyme
preparations taking into account the hydrolysable
peptide bonds at the sites of bioactive peptides. The
bioinformatic tool Expasy Peptide Cutter extracts
information about the enzymes appropriate for selected
protein substrates and indicates the hydrolysable
peptide bond between amino acids. Using this information,
BIOPEP’s “Batch Processing” provides a list of selected
amino acid sequences and a list of bioactive peptides
included in it.
Enzymatic screening can also be performed with
another BIOPEP tool, “Find the enzyme for peptide
replacement of single amino acids in the protein
structure, which affects its properties as well as the
bioactivity and degree of peptide release. The effects of
gene polymorphism on the amino acid sequence have
been noted in a number of studies and constitute a proven
fact [30, 31]. Researchers at the University of Limerick
stated that the genetic polymorphism of dairy proteins
in raw milk obtained from producing animals of the
same breed affects the types of bioactive peptides it
contains [24]. The direction of hydrolysis can also
depend on the genetic variation of the protein. This
effect has been mentioned in the study of polymorphic
variants of β-casein and their effect on digestion in the
GI tract ex vivo [32]. Consequently, when modeling
the targeted hydrolysis of milk protein raw materials,
it is necessary to take into account their genotypic
traits because they can determine the direction of
hydrolysis and the composition of bioactive sites within
the protein structure.
The fact that dairy plants receive milk from farms
in a bulk milk tank (mixed) poses the main problem
for genetic identification of expressed protein fractions
in raw milk. Milk collected from different cows is
characterized by heterogeneity of genetic variants of
a certain protein, which complicates its controlled
bioconversion. The laboratory of canned milk at the
All-Russian Dairy Research Institute has developed a
modern technique for molecular genetic evaluation of
the ratio of relative shares of the CSN3 gene alleles
in mixed dairy products [33]. Based on the proposed
technique, the authors developed a bioinformatic analysis
program Calculating the ratio of the relative proportions
of κ-casein alleles in collected milk, available at www.
tinyurl.com/allelesprog. Improving this technique and
projecting it onto other biotechnologically relevant
protein fractions will allow integration of this tool
into the strategy of bioinformatic modeling (in
silico) from the position of rational processing
milk raw materials for the predicted release of
biologically active peptides.
Identifying bioactive amino acid sites in the protein
structure. A key step in in silico modeling of hydrolysis
is identifing locations of bioactive sites encoded
in the amino acid sequences of protein substrates,
taking into account genetic polymorphism with the
aim of their further targeted release. The evaluation
criterion is the frequency of bioactive sites
occurrence in the protein structure. Bioactive peptides
within the amino acid structure of a protein may be
searched by its identifier using the bioinformatic
database tools MBPDB and BIOPEP [34].
Bioinformatic algorithms of these databases are able
to perform a search query in the following variations:
searching for bioactive peptides in the structure of
a particular protein; searching for a specific amino
52
Kruchinin A.G. et al. Food Processing: Techniques and Technology, 2022, vol. 52, no. 1, pp. 46–57
peptide database), AHTPDB (antihypertensive peptide
database), etc. stand out.
Assessing peptides for toxicity, allergenicity,
free amino acids and sensorics. Since one of
the main objectives of biotechnology is to ensure
the safety of isolated substances, a necessary
step consists in testing peptides obtained
by targeted hydrolysis for adverse effects.
According to the publications, there are
approximately 170 food allergens that cause IgEmediated
allergic reactions. 90% of these reactions
are caused by food allergens representing 8
groups, including milk and dairy products [49, 50].
Almost all milk proteins are immunoreactive
due to a large number of antigenic determinants
(epitopes) in their amino acid sequences [51, 52].
On this basis, a prerequisite for in silico analysis
is to predict the residual antigenicity of all hydrolysis
products. It is possible by means of IUIS and
BIOPEP databases containing up-to-date information
on allergenic protein epitopes. In addition to
the search systems of these two bases, there are
bioinformatic tools such as Allergenic Protein
Sequence Searches and AlgPred2 [53]. They help
predict the allergenicity of isolated peptides and the
protein as a whole by amino acid sequence. To
perform alignment, AlgPred2 is paired with IEDB,
which is a database of experimental data on antibody
epitopes studied in the context of infectious
diseases, allergy, autoimmunity and transplantation,
as well as with the NCBI BLAST tool.
It is also coupled with the MERCI software
to identify allergenic sites in the protein
structure [54].
Bioinformatic data on the allergenicity of protein
microstructures will allow correcting the hydrolysis
process by changing the proteolytic system or
adding a second hydrolysis step to break down
allergenic sites, which is used in practice to reduce food
allergenicity [55].
Apart from allergenicity, toxicity of substances
should be taken into account. It is evaluated using
ToxinPred. It is a web server based on a peptide
dataset consisting of 1805 toxic peptides obtained from
various databases (ATDB, Arachno-Server, Conoserver,
DBETH, BTXpred, NTXpred and SwissProt)
[56]. There is evidence that certain amino acid
residues, such as Cys, His, Asn, Pro, or the Phe-Lys-
Lys, Leu-Lys-Leu, Lys-Lys-Leu-Leu, Lys-Trp-Lys,
Cys-Tyr-Cys-Arg sites, are frequently found in
toxic peptides, whereas Arg, Leu, Lys, and Ile are
the least common [56, 57]. Bioinformatic tools for
predicting toxicity in silico work on the principle
of analyzing amino acid sequence for specific
amino acid sites [58]. Current computational
release”, where the raw data are bioactive peptides
and the amino acid sequence of the protein from
which they are to be extracted. It is important to enter
peptides in FASTA format as follows: “> peptide 1 IPP
(amino acid sequence of bioactive peptide)”. There
can be several peptides, and each must be specified
with a new line and a new number. The result of
the data processing is a list of enzymes suitable for
targeted hydrolysis.
Computer modeling of the protein bioconversion.
After suitable enzymes are selected in this way, all
enzymatic cleavage products can be analyzed in
BIOPEP’s section “Enzymes action” by selecting the
option “Enzymes action for your sequence”. This tool
features the complete picture of protein hydrolysis
into peptides. Even taking into account the polyenzyme
system. Computer modeling of bioconversion
should be performed on a “digital twin” model of the
substrate. A digital twin is formed from the analytical
data on the protein profile of the raw milk used.
Bioconversion modeling is carried out for each
protein fraction, after which the hydrolysis products
are combined and analyzed. The only drawback of
this scheme is that this tool does not take into
account the hydrolysis conditions, namely temperature,
duration, substrate-enzyme ratio and pH.
This offers the basis for studies to optimize the
conditions of enzymatic hydrolysis, taking
into account technological factors in vitro.
Assessing the bioactivity of peptides. After
targeted hydrolysis on the “digital twin”
model of the complex protein matrix of dairy raw
materials with enzymes selected after screening,
all reaction products should be evaluated for
biofunctionality by means of tools. They are listed
in “Identification of Bioactive Amino Acid Sites
in Protein Structure”. In addition to the described
bioinformatic resources used to determine the bioactivity
of peptides, another tool, Peptide Ranker, is worth
mentioning. In the study by S. Nebbia et al., it helped
select only 10 out of 30 000 prognostically
formed peptides for further study [35]. This resource
identifies the biological activity of peptides
according to certain structural characteristics
on a scale from 0 to 1, in which any peptide
scoring above 0.5 is considered biologically
active [44, 46]. Using this tool Y. Gu et al.
evaluated the effect of different types of cultures
on the peptide profile of yogurts. M. Tu et al.
studied biologically active peptides derived from
casein hydrolysis [47, 48]. In addition, there
are a number of narrowly focused databases
that will help in the targeted search for bioactivity.
Among such databases, MilkAMP (antimicrobial
53
Кручинин А.Г. [и др.] Техника и технология пищевых производств. 2022. Т. 52. № 1 С. 46–57
approaches used in toxicology are thoroughly
described in studies of antidiabetic, antihypertensive,
antioxidant peptides and other
biological objects for bioinformatic safety
assessments [59–63].
For the food industry or pharmaceuticals to continue
using bioactive peptides, it is necessary to predict
their flavor profile and sensory characteristics
in combination. Sensory characteristics of
biologically active peptides are another significant
descriptor that bioinformatics tools provide
for analysis. The taste profile can be predicted
due to the BIOPEP, which contains a database
of sensory peptides, as well as the BitterDB,
which contains peptides with bitter taste [64].
In addition to sensory peptides with bitter,
sweet and umami tastes, the abnormal taste
profile for hydrolysates can be formed due to
a high index of free amino acids (FAA) [65].
This indicator can be evaluated and corrected
during computer modeling of the targeted protein
bioconversion in silico.
Assessing the physicochemical and technological
properties of peptides. The amino acid sequence
in the structure of peptides obtained as a result of
hydrolysis affects the stability of the system,
physicochemical and technological properties.
They will affect the application scope for the obtained
components. The bioinformatic tool PepCalc was
successfully used in a number of studies to predict
physicochemical properties. It can be used to predict
peptide solubility in water, theoretical molecular
weight, isoelectric point, total charge as a function
of pH, extinction coefficient, and instability
index [66–68]. The importance of predicting the
instability index, characterizing intramolecular
stability, lies in the correlation of this index with
the thermostability of peptides. This is a significant
factor in the technological process (heat treatment)
and in the microbiological safety of hydrolysis
products [69]. Therefore, the instability index can be
viewed as one of the criteria for evaluating
the targeted hydrolysis model or a basis for its
possible adjustment.
The Expasy ProtParam and ProtPi tools can also
be used to predict the instability index, half-life,
extinction coefficient, hydropathicity (GRAVY) and
some other characteristics.
Stability of biopeptides during digestion in the
gastrointestinal model. The structure of biologically
active peptides can be destroyed in the gastrointestinal
tract by the action of digestive enzymes with
complete or partial loss of biofunctional properties.
Therefore, it is pointless to extract biologically
active peptides blindly, without taking into account
degradation in the GI tract. Evaluating peptide
stability during simulated digestion is an important
final step in a hybrid strategy of bioinformatic modeling
(in silico) for targeted hydrolysis. In silico
modeling of digestion can be accomplished via the
bionformatic resources described earlier in “Screening
the Specificity of Enzyme Preparations”. To simulate
digestion in the gastrointestinal tract, three main
digestive enzymes, produced in the human body,
are used: trypsin, chymotrypsin and pancreatic
elastase [70].
Digital model of a peptide complex. Based on
the sequentially generated algorithm in silico, it
seems objectively possible to create a digital model
of the peptide complex. The peptide complex with
predicted bioactivity, safety, and sensory characteristics
may be an object of subsequent scaling studies in real
experimental conditions.
Conclusion
By evaluating the capabilities of multi-directional
bioinformatic analysis methods combined with
high-performance algorithms of proteomic database,
it is possible to combine and integrate them into a
hybrid strategy for the bioinformatic modeling (in silico)
of hydrolysis for targeted release of stable
peptide complexes with predictable bioactivity,
stability, safety and sensory characteristics
from complex protein matrices of dairy raw
materials. In the generated hybrid strategy
algorithm for a bioinformatic modeling, the
mainemphasis is placed on safety due to excluding
the formationof peptide forms that have a negative
impact on the functioning of human organs and
human health in general.
The data obtained by bioinformatic modeling
(in silico) do not always fully correlate with the
experimental data obtained in vitro and in vivo
during targeted hydrolysis of milk protein and yet the
hybrid algorithm presented in this article facilitate
s the accumulation of the necessary primary data
to reduce the time and financial costs of real
experiments.
However, despite all the advantages of bioinformatics
and various strategies, in silico remains only a
preliminary step in a cascade of studies for
biologically active milk protein peptides due to
the impossibility of predicting the theoretical
enzymatic cleavage under various technological
conditions (temperature, duration, active acidity,
substrate-enzyme ratio). This offers the basis for
studies to optimize the conditions of enzymatic
hydrolysis, taking into account technological factors
in vitro.
54
Kruchinin A.G. et al. Food Processing: Techniques and Technology, 2022, vol. 52, no. 1, pp. 46–57
Conflict of interest
The authors declare that there is no conflict of interest
regarding the publication of this article.

1. Karami Z, Akbari-Adergani B. Bioactive food derived peptides: a review on correlation between structure of bioactive peptides and their functional properties. Journal of Food Science and Technology. 2019;56(2):535-547. https://doi.org/10.1007/s13197-018-3549-4

2. Hafeez Z, Cakir-Kiefer C, Roux E, Perrin C, Miclo L, Dary-Mourot A. Strategies of producing bioactive peptides from milk proteins to functionalize fermented milk products. Food Research International. 2014;63:71-80. https://doi.org/10.1016/j.foodres.2014.06.002

3. Kamali Alamdari E, Ehsani MR. Antimicrobial peptides derived from milk: A review. Journal of Food Biosciences and Technology. 2017;7(1):49-56.

4. Ryazantseva KA, Agarkova EYu, Fedotova OB. Continuous hydrolysis of milk proteins in membrane reactors of various configurations. Foods and Raw Materials. 2021;9(2):271-281. https://doi.org/10.21603/2308-4057-2021-2-271-281

5. Kruchinin AG, Savinova OS, Glazunova OA, Moiseenko KV, Agarkova EYu, Fedorova TV. Hypotensive and hepatoprotective properties of the polysaccharide-stabilized foaming composition containing hydrolysate of whey proteins. Nutrients. 2021;13(3). https://doi.org/10.3390/nu13031031

6. Peighambardoust SH, Karami Z, Pateiro M, Lorenzo JM. A review on health-promoting, biological, and functional aspects of bioactive peptides in food applications. Biomolecules. 2021;11(5). https://doi.org/10.3390/biom11050631

7. Bielecka M, Cichosz G, Czeczot H. Antioxidant, antimicrobial and anticarcinogenic activities of bovine milk proteins and their hydrolysates - A review. International Dairy Journal. 2021;127. https://doi.org/10.1016/j.idairyj.2021.105208

8. Huang S, Gong Y, Li Y, Ruan S, Roknul Azam SM, Duan Y, et al. Preparation of ACE-inhibitory peptides from milk protein in continuous enzyme membrane reactor with gradient dilution feeding substrate. Process Biochemistry. 2020;92:130-137. https://doi.org/10.1016/j.procbio.2020.02.030

9. Chalamaiah M, Yu W, Wu J. Immunomodulatory and anticancer protein hydrolysates (peptides) from food proteins: A review. Food Chemistry. 2018;245:205-222. https://doi.org/10.1016/j.foodchem.2017.10.087

10. Giacometti J, Buretić-Tomljanović A. Peptidomics as a tool for characterizing bioactive milk peptides. Food Chemistry. 2017;230:91-98. https://doi.org/10.1016/j.foodchem.2017.03.016

11. Hayes M, Stanton C, Fitzgerald GF, Ross RP. Putting microbes to work: Dairy fermentation, cell factories and bioactive peptides. Part II: Bioactive peptide functions. Biotechnology Journal. 2007;2(4):435-449. https://doi.org/10.1002/biot.200700045

12. Etemadian Y, Ghaemi V, Shaviklo AR, Pourashouri P, Sadeghi Mahoonak AR, Rafipour F. Development of animal/plant-based protein hydrolysate and its application in food, feed and nutraceutical industries: State of the art. Journal of Cleaner Production. 2021;278. https://doi.org/10.1016/j.jclepro.2020.123219

13. Sánchez A, Vázquez A. Bioactive peptides: A review. Food Quality and Safety. 2017;1(1):29-46.

14. Nongonierma AB, FitzGerald RJ. Strategies for the discovery and identification of food protein-derived biologically active peptides. Trends in Food Science and Technology. 2017;69:289-305. https://doi.org/10.1016/j.tifs.2017.03.003

15. Yu Z, Chen Y, Zhao W, Zheng F, Ding L, Liu J. Novel ACE inhibitory tripeptides from ovotransferrin using bioinformatics and peptidomics approaches. Scientific Reports. 2019;9(1). https://doi.org/10.1038/s41598-019-53964-y

16. Tu M, Cheng S, Lu W, Du M. Advancement and prospects of bioinformatics analysis for studying bioactive peptides from food-derived protein: Sequence, structure, and functions. TrAC - Trends in Analytical Chemistry. 2018;105:7-17. https://doi.org/10.1016/j.trac.2018.04.005

17. Barati M, Javanmardi F, Jabbari M, Mokari-Yamchi A, Farahmand F, Eş I, et al. An in silico model to predict and estimate digestion-resistant and bioactive peptide content of dairy products: A primarily study of a time-saving and affordable method for practical research purposes. LWT. 2020;130. https://doi.org/10.1016/j.lwt.2020.109616

18. Panyayai T, Ngamphiw C, Tongsima S, Mhuantong W, Limsripraphan W, Choowongkomon K, et al. FeptideDB: A web application for new bioactive peptides from food protein. Heliyon. 2019;5(7). https://doi.org/10.1016/j.heliyon.2019.e02076

19. Agarkova EYu, Kruchinin AG. Enzymatic conversion as a method of producing biologically active peptides. Vestnik of MSTU. 2018;21(3):412-419. (In Russ.). https://doi.org/10.21443/1560-9278-2018-21-3-412-419

20. FitzGerald RJ, Cermeño M, Khalesi M, Kleekayai T, Amigo-Benavent M. Application of in silico approaches for the generation of milk protein-derived bioactive peptides. Journal of Functional Foods. 2020;64. https://doi.org/10.1016/j.jff.2019.103636

21. Agyei D, Tsopmo A, Udenigwe C. Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides. Analytical and Bioanalytical Chemistry. 2018;410(15):3463-3472. https://doi.org/10.1007/s00216-018-0974-1

22. Ryskaliyeva A, Henry C, Miranda G, Faye B, Konuspayeva G, Martin P. Alternative splicing events expand molecular diversity of camel CSN1S2 increasing its ability to generate potentially bioactive peptides. Scientific Reports. 2019;9(1). https://doi.org/10.1038/s41598-019-41649-5

23. Ryazantzeva KA, Agarkova EYu. Using in silico methods to obtain bioactive peptides of whey. Food Industry. 2021;(5):32-35. (In Russ.). https://doi.org/10.52653/PPI.2021.5.5.007

24. Peredo-Lovillo A, Hernández-Mendoza A, Vallejo-Cordoba B, Romero-Luna HE. Conventional and in silico approaches to select promising food-derived bioactive peptides: A review. Food Chemistry: X. 2021;13. https://doi.org/10.1016/j.fochx.2021.100183

25. Lafarga T, O'Connor P, Hayes M. Identification of novel dipeptidyl peptidase-IV and angiotensin-I-converting enzyme inhibitory peptides from meat proteins using in silico analysis. Peptides. 2014;59:53-62. https://doi.org/10.1016/j.peptides.2014.07.005

26. Kruchinin AG, Turovskaya SN, Illarionova EE, Bigaeva AV. Evaluation of the effect of κ-casein gene polymorphism in milk powder on the technological properties of acid-induced milk gels. Food Processing: Techniques and Technology. 2021;51(1):53-66. (In Russ.). https://doi.org/10.21603/2074-9414-2021-1-53-66

27. Bateman A, Martin MJ, O'Donovan C, Magrane M, Apweiler R, Alpi E, et al. UniProt: a hub for protein information. Nucleic Acids Research. 2015;43(D1):D204-D212. https://doi.org/10.1093/nar/gku989

28. Nongonierma AB, FitzGerald RJ. Enhancing bioactive peptide release and identification using targeted enzymatic hydrolysis of milk proteins. Analytical and Bioanalytical Chemistry. 2018;410(15):3407-3423. https://doi.org/10.1007/s00216-017-0793-9

29. Iwaniak A, Minkiewicz P, Darewicz M, Sieniawski K, Starowicz P. BIOPEP database of sensory peptides and amino acids. Food Research International. 2016;85:155-161. https://doi.org/10.1016/j.foodres.2016.04.031

30. Dyachenko EA, Slugina MA. Intraspecific variability of the Sus1 sucrose synthase gene in Pisum sativui accessions. Vavilov Journal of Genetics and Breeding. 2018;22(1):108-114. (In Russ.). https://doi.org/10.18699/VJ18.338

31. Kruchinin AG, Turovskaya SN, Illarionova EE, Bigaeva AV. Molecular genetic modifications of к-casein. News of Institutes of Higher Education. Food Technology. 2020;376(4):12-16. (In Russ.). https://doi.org/10.26297/0579-3009.2020.4.3

32. Asledottir T, Le TT, Petrat-Melin B, Devold TG, Larsen LB, Vegarud GE. Identification of bioactive peptides and quantification of β-casomorphin-7 from bovine β-casein A1, A2 and I after ex vivo gastrointestinal digestion. International Dairy Journal. 2017;71:98-106. https://doi.org/10.1016/J.IDAIRYJ.2017.03.008

33. Gilmanov KhKh, Semipyatnyi VK, Bigaeva AV, Vafin RR, Turovskaya SN. New determination method for the ratio of the relative proportions of ϰ-casein alleles in milk powder. Food Processing: Techniques and Technology. 2020;50(3):525-535. (In Russ.). https://doi.org/10.21603/2074-9414-2020-3-525-535

34. Nielsen SD, Beverly RL, Qu Y, Dallas DC. Milk bioactive peptide database: A comprehensive database of milk protein-derived bioactive peptides and novel visualization. Food Chemistry. 2017;232:673-682. https://doi.org/10.1016/j.foodchem.2017.04.056

35. Nebbia S, Lamberti C, Lo Bianco G, Cirrincione S, Laroute V, Cocaign-Bousquet M, et al. Antimicrobial potential of food lactic acid bacteria: Bioactive peptide decrypting from caseins and bacteriocin production. Microorganisms. 2021;9(1). https://doi.org/10.3390/microorganisms9010065

36. Nielsen SD, Beverly RL, Underwood MA, Dallas DC. Differences and similarities in the peptide profile of preterm and term mother’s milk, and preterm and term infant gastric samples. Nutrients. 2020;12(9). https://doi.org/10.3390/nu12092825

37. Pa’ee KF, Razali N, Sarbini SR, Ramonaran Nair SN, Yong Tau Len K, Abd-Talib N. The production of collagen type I hydrolyzate derived from tilapia (Oreochromis sp.) skin using thermoase PC10F and its in silico analysis. Food Biotechnology. 2021;35(1):1-21. https://doi.org/10.1080/08905436.2020.1869040

38. Minkiewicz P, Iwaniak A, Darewicz M. BIOPEP-UWM database of bioactive peptides: Current opportunities. International Journal of Molecular Sciences. 2019;20(23). https://doi.org/10.3390/ijms20235978

39. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10. https://doi.org/10.1186/1471-2105-10-421

40. González-Pech RA, Stephens TG, Chan CX. Commonly misunderstood parameters of NCBI BLAST and important considerations for users. Bioinformatics. 2019;35(15):2697-2698. https://doi.org/10.1093/bioinformatics/bty1018

41. Wang Z, Wang G. APD: The antimicrobial peptide database. Nucleic Acids Research. 2004;32:D590-D592.

42. Li Q, Zhang C, Chen H, Xue J, Guo X, Liang M, et al. BioPepDB: an integrated data platform for food-derived bioactive peptides. International Journal of Food Sciences and Nutrition. 2018;69(8):963-968. https://doi.org/10.1080/09637486.2018.1446916

43. Iwaniak A, Minkiewicz P, Pliszka M, Mogut D, Darewicz M. Characteristics of biopeptides released in silico from collagens using quantitative parameters. Foods. 2020;9(7). https://doi.org/10.3390/foods9070965

44. Kartal C, Türköz BK, Otles S. Prediction, identification and evaluation of bioactive peptides from tomato seed proteins using in silico approach. Journal of Food Measurement and Characterization. 2020;14(4):1865-1883. https://doi.org/10.1007/s11694-020-00434-z

45. Kusumah J, Real Hernandez LM, de Mejia EG. Antioxidant potential of mung bean (Vigna radiata) albumin peptides produced by enzymatic hydrolysis analyzed by biochemical and in silico methods. Foods. 2020;9(9). https://doi.org/10.3390/foods9091241

46. Baghban R, Ghasemali S, Farajnia S, Hoseinpoor R, Andarzi S, Zakariazadeh M, et al. Design and in silico evaluation of a novel cyclic disulfide-rich anti-VEGF peptide as a potential antiangiogenic drug. International Journal of Peptide Research and Therapeutics. 2021;27(4):2245-2256. https://doi.org/10.1007/s10989-021-10250-8

47. Gu Y, Li X, Liu H, Li Q, Xiao R, Dudu OE, et al. The impact of multiple-species starters on the peptide profiles of yoghurts. International Dairy Journal. 2020;106. https://doi.org/10.1016/j.idairyj.2020.104684

48. Tu M, Liu H, Zhang R, Chen H, Fan F, Shi P, et al. Bioactive hydrolysates from casein: generation, identification, and in silico toxicity and allergenicity prediction of peptides. Journal of the Science of Food and Agriculture. 2018;98(9):3416-3426. https://doi.org/10.1002/jsfa.8854

49. Nutten S, Schuh S, Dutter T, Heine RG, Kuslys M. Design, quality, safety and efficacy of extensively hydrolyzed formula for management of cow's milk protein allergy: What are the challenges? Advances in Food and Nutrition Research. 2020;93:147-204. https://doi.org/10.1016/bs.afnr.2020.04.004

50. Gromov DA, Borisova AV, Bakharev VV. Food allergens and methods for producing hypoallergenic foods. Food Processing: Techniques and Technology. 2021;51(2):232-247. (In Russ.). https://doi.org/10.21603/2074-9414-2021-2-232-247

51. Loh W, Tang MLK. The epidemiology of food allergy in the global context. International Journal of Environmental Research and Public Health. 2018;15(9). https://doi.org/10.3390/ijerph15092043

52. Flom JD, Sicherer SH. Epidemiology of cow’s milk allergy. Nutrients. 2019;11(5). https://doi.org/10.3390/nu11051051

53. Sharma N, Patiyal S, Dhall A, Pande A, Arora C, Raghava GPS. AlgPred 2.0: An improved method for predicting allergenic proteins and mapping of IgE epitopes. Briefings in Bioinformatics. 2021;22(4). https://doi.org/10.1093/bib/bbaa294

54. Vens C, Rosso MN, Danchin EG. Identifying discriminative classification-based motifs in biological sequences. Bioinformatics. 2011;27(9):1231-1238. https://doi.org/10.1093/bioinformatics/btr110

55. Kharitonov VD, Agarkova EYu, Kruchinin AG, Ryazantseva KA, Korolyeva OV, Fedorova TV, et al. Impact of new fermented dairy product with whey protein hydrolysate on tolerance and dynamics of atopic dermatitis manifestation in children suffering from cow's milk protein allergy. Problems of Nutrition. 2015;84(5):56-63. (In Russ.). https://doi.org/10.24411/0042-8833-2015-00048

56. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GPS. In silico approach for predicting toxicity of peptides and proteins. PloS ONE. 2013;8(9). https://doi.org/10.1371/journal.pone.0073957

57. Chaudhary K, Kumar R, Singh S, Tuknait A, Gautam A, Mathur D, et al. A web server and mobile app for computing hemolytic potency of peptides. Scientific Reports. 2016;6. https://doi.org/10.1038/srep22843

58. Parthasarathi R, Dhawan A. In silico approaches for predictive toxicology. In: Dhawan A, Kwon S, editors. In vitro toxicology. Academic Press; 2018. pp. 91-109. https://doi.org/10.1016/B978-0-12-804667-8.00005-5

59. Yap PG, Gan CY. In vivo challenges of anti-diabetic peptide therapeutics: Gastrointestinal stability, toxicity and allergenicity. Trends in Food Science and Technology. 2020;105:161-175. https://doi.org/10.1016/j.tifs.2020.09.005

60. Guo H, Hao Y, Richel A, Everaert N, Chen Y, Liu M, et al. Antihypertensive effect of quinoa protein under simulated gastrointestinal digestion and peptide characterization. Journal of the Science of Food and Agriculture. 2020;100(15):5569-5576. https://doi.org/10.1002/jsfa.10609

61. Ji D, Udenigwe C, Agyei D. Antioxidant peptides encrypted in flaxseed proteome: An in silico assessment. Food Science and Human Wellness. 2019;8(3):306-314. https://doi.org/10.1016/j.fshw.2019.08.002

62. Lin K, Zhang L-W, Han X, Xin L, Meng Z-X, Gong P-M, et al. Yak milk casein as potential precursor of angiotensin I-converting enzyme inhibitory peptides based on in silico proteolysis. Food Chemistry. 2018;254:340-347. https://doi.org/10.1016/j.foodchem.2018.02.051

63. Tu M, Qiao X, Wang C, Liu H, Cheng S, Xu Z, et al. In vitro and in silico analysis of dual-function peptides derived from casein hydrolysate. Food Science and Human Wellness. 2021;10(1):32-37. https://doi.org/10.1016/j.fshw.2020.08.014

64. Dagan-Wiener A, Di Pizio A, Nissim I, Bahia MS, Dubovski N, Margulis E, et al. BitterDB: taste ligands and receptors database in 2019. Nucleic Acids Research. 2019;47(D1):D1179-D1185. https://doi.org/10.1093/nar/gky974

65. Wang W, Zhang L, Wang Z, Wang X, Liu Y. Physicochemical and sensory variables of Maillard reaction products obtained from Takifugu obscurus muscle hydrolysates. Food Chemistry. 2019;290:40-46. https://doi.org/10.1016/j.foodchem.2019.03.065

66. Stan GM, Constantinescu-Aruxandei D, Oancea F. In silico analysis of the formation of bioactive peptides from silver carp (Hypophthalmichthys molitrix) collagen. Proceedings. 2020;57(1). https://doi.org/10.3390/proceedings2020057024

67. Pooja K, Rani S, Prakash B. In silico approaches towards the exploration of rice bran proteins-derived angiotensin-I-converting enzyme inhibitory peptides. International Journal of Food Properties. 2017;20. https://doi.org/10.1080/10942912.2017.1368552

68. Shi P, Fan F, Chen H, Xu Z, Cheng S, Lu W, et al. A bovine lactoferrin-derived peptide induced osteogenesis via regulation of osteoblast proliferation and differentiation. Journal of Dairy Science. 2020;103(5):3950-3960. https://doi.org/10.3168/jds.2019-17425

69. Tu M, Liu H, Zhang R, Chen H, Mao F, Cheng S, et al. Analysis and evaluation of the inhibitory mechanism of a novel angiotensin-I-converting enzyme inhibitory peptide derived from casein hydrolysate. Journal of Agricultural and Food Chemistry. 2018;66(16):4139-4144. https://doi.org/10.1021/acs.jafc.8b00732

70. Sayd T, Dufour C, Chambon C, Buffière C, Remond D, Santé-Lhoutellier V. Combined in vivo and in silico approaches for predicting the release of bioactive peptides from meat digestion. Food Chemistry. 2018;249:111-118. https://doi.org/10.1016/j.foodchem.2018.01.013