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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5344_Библиотеки_им_академика_М_И_Перельмана.pdf
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
long piece of DNA has been sequenced, the first objective is to regulate if it contains
any genes. This objective is comparatively simple in the case of prokaryotes, but the
search for eukaryotic genes is quite difficult [39].
9.4.1 Identification of genes
With the increase in genomic and functional genomics information, approaches for
disease gene identification are rapidly being developed [40]. Databases are now
indispensable to the process of selecting candidate disease genes. Relating positional
information with disease characteristics and functional information is the normal
approach by which candidate disease genes are selected. Enrichment for candidate
disease genes, however, depends on the skills of the operating researcher [41].
Prior to genome sequencing, gene identification used approaches based on transcript mapping. The genomic clone of interest was exposed to zoo blot hybridization,
i.e., hybridization with the whole genomic DNA of a range of species. It has been
discovered that coding sequences are strongly conserved during development. Thus, a
clone that tested positive in a zoo blot is possible to characterize as a gene. The clone
of interest can be hybridized with cDNA libraries or employed for northern blot or
reverse northern blot assays. A constructive assay identifies a gene as only genes are
transcribed. CpGProD is an application for identifying mammalian promoter regions
associated with CpG islands in large genomic sequences [24]. Detection of CpG
islands in the clone suggests it to be a gene as about 50% of human genes have related
CpG islands. CpG islands are short stretches of G. C-rich DNA often found in
association with vertebrate genes. Other methods of gene identification included
cDNA selection, cDNA capture and exon trapping. These methods provide research
that is appropriate for individual gene identification but unsuitable for genome
annotation. Consequently, the correct reading frame is identified by carrying out a
six-frame translation of the DNA sequence. The accurate reading frame is predicted to
be the longest frame uninterupted by a stop codon (TGA, TAA or TAG); this type of
reading frame is known as an open-reading frame (ORF). The longer this ORF, the
better is the probability that it represents a gene. Locating the 3′-end of an ORF is
relatively easier than locating its 5′-end, although the 5′-end may be indicated by an
ATG. However, more tools are required to locate the 5′-end of an ORF, such as the
presence of a Kozak sequence (CCGCCA UGGG) flanking the AUG codon.
Investigation of codon usage may also be supportive, and the presence of CpG
islands may designate the 5′-end of numerous vertebrate genes. However, determination of ORFs may be hindered by sequencing errors. The most significant single
progress in genome annotation is the use of PCs to predict genes from DNA
sequences. Identification of genes may achieve using one of the following two
approaches. In the first technique, sequences of known genes, cDNAs, ESTs and
proteins present in databases are equated with the genome sequence; this is achieved
by homology search techniques, such as BLAST. In the second method, specific
software is employed to identify genes [42].
In prokaryotes, programs such as GenMark (a modified GeneScan algorithm) and
Glimmercanidentify all the genes, including overlapping genes, present in the genome.
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Numerous sophisticated software programs have been developed for gene prediction
in eukaryotes, e.g. Genie, GeneScan, Grail, GeneFinder, HMM Gene, etc. These
programs required training with information or need to be provided with a set of rules.
Grail can be employed with human, mouse, Arabidopsis, etc, genome sequences, but
Genie has only been trained on human and Drosophila sequences. However, no single
program is 100% perfect at identifying genes. An algorithm trained with the DNA
sequence of one organism, Caenorhabditis elegans, will not execute suitably with that of
another organism, e.g. a plant, without being retrained [43].
The gene prediction programs search for gene-specific characteristics, e.g. promoters,
splice sites and polyadenylation sites, or for pertinent gene content such as ORFs. The
currently available gene search programs are associated with different search criteria and
their sensitivities vary widely. The identification of ORFs, usually exceeding 300
nucleotides, is sufficient to find most genes in prokaryotic genomes. However, such a
simple search criterion will miss smaller genes and overlapping genes. These problems are
resolved by using algorithms that consider differences in base composition between genes
and noncoding DNA, e.g. in GenMark. The gene prediction programs used in eukaryotes
use the output of several algorithms to generate a whole gene model. In this model, a gene
is defined as a series of exons that are coordinately transcribed. The various features of
eukaryotic genes recognized during gene detection include transcriptional and translational controls, e.g. the TATA box, cap site, Kozak consensus and polyadenylation sites.
But problems arise as the TATA box is missing in 70% of human genes, and
polyadenylation signals can differ considerably from the consensus sequence
AATAAA. Moreover, these sequences recognize only the firstandthelastexonsofa
gene. Thus, additional features have been encompassed in modern genesearchtools;these
features include 5′-and3′-splice sites, differences in base composition between coding and
noncoding DNA (typically, comparison of hexamer base composition), etc.
9.4.2 Identification of the function of a new gene
A convenient approach to detect the function of a new gene is as follows. The gene
sequence is translated into the amino acid sequence and thus the protein it is
anticipated to encode. This protein sequence is then compared with a protein
database. A program such as tBLASTx will execute both these functions. If the
encoded protein is homologous to a protein in the databank, it allows identification
of the new gene and also suggests the function of the new gene [44]. The FASTA and
HMMER programs are slower than BLAST, but they are more sensitive.
9.4.3 Identification of functional domains
Several bioinformatics techniques for the detection of protein motifs and protein domains
have been explored. Among these tools are PRINTS, PROSITE, SMART, BLOCKS, etc.
9.4.4 Detection of noncoding RNA
A number of RNAs are noncoding, such as rRNA, tRNA, small RNAs, etc. Of
these, rRNAs are the easiest to find; this is done by sequence similarity search. The
program tRNAScanSE searches for aRNA sequences [44].
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9.4.5 Genome annotation
After the detection of a gene and prediction of its functions, suitable experiments
have to be devised to authenticate these findings. This information is then employed
for genome annotation. Standard genome annotation languages have become
extensively accepted [45]. GAME is a program for describing investigational
confirmation to support annotation. Likewise DAS (the distributed annotation
system) is mainly useful for indexing and visualization. Several software programs
such as BioPerl 2001, BioJava 2001, etc, are employed for storing, manipulating and
imagining the genome annotations [46].
9.4.6 Molecular phylogenetics
DNA and protein sequence statistics can be employed to examine the development of genes and their protein products; this is called molecular phylogeny.
Bioinformatics techniques are employed to define phylogenetic relationships,
which may be accessible in the form of either a phylogenetic tree or a dendogram. While the two depictions look different, they depict exactly the same
relationship [47].
9.5 Computational approaches in bioinformatics
9.5.1 Algorithm development
Sequence alignment is a crucial task in bioinformatics, which provides the
groundwork for several applications, such as functional annotation, structure
prediction, and phylogenetics. The main goal of sequence alignment is to arrange
DNA, RNA, or protein sequences to find comparable sections that may result
from functional, structural, or evolutionary links between the sequences. Sequence
alignment may be roughly classified into multiple sequence alignment (MSA) and
pairwise alignment [48]. Two sequences align in pairwise alignment, while three or
more sequences align in multiple-sequence alignment [49]. The Nee dl eman –
Wunsch technique [50] for global alignment and the Smith–Waterman algorithm
[51] for local alignment are two popular pairwise alignment algorithms. The goal
of global alignment, which works best for equal-length sequences, is to align every
residue in every sequence. Local alignment, on the other hand, finds comparable
sections within lengthy sequences that are often distinct overall. Algorithms like
MUSCLE [52] and ClustalW [53] are widely used in multiple sequence alignment.
MSA is crucial for phylogenetic analyses and identifying conserved domains across
species. It is more complex than pairwise alignment due to the increased computational demands and the complexity of optimally aligning multiple sequences. A
fundamental aspect of sequence alignment algorithms is the scoring system, which
uses substitution matrices to score alignments based on the substitution, insertion,
or deletion of residues. Gap penalties are a crucial part of the scoring system,
imposing penalties for opening and extending gaps, which is essential for managing indels (insertions or deletions) [54]. Various algorithmic strategies are
employed in sequence alignment. Dynamic programming is a common approach
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used in both the Needleman–Wunsch and Smith–Waterman algorithms to ensure
the optimality of the alignment. However, due to the computational intensity of
dynamic programming, empirical methods are often employed to accelerate the
alignment p rocess at the cost of optimality, with BLAST (Basic Local Alignment
Search Tool) [55] and FASTA being prime examples [56]. In the case of multiple
sequence alignment, a progressive alignment strategy is often employed, where
pairwise alignments are extended to multiple alignments b y aligning the most
similar sequences first. Advancements in computing have also impacted the field of
sequence alignment. Parallel computing, for instance, has significantly reduced the
computational time required f or alignment tasks, making real-time analysis of
large datasets feasible. Additionally, the integration of machine learning techniques has emerged as a promising avenue to improve alignment accuracy and
predict novel alignments, signifying a blend of traditional bioinformatics
approaches with modern computational methods [57].
9.5.2 Phylogenetic tree construction algorithms
Phylogenetic tree construction is fundamental for understanding evolutionary
relationships among species or sequences. Various algorithms have been developed
to implement this method, and they fall into different categories based on their
approach [58]. A schematic diagram representing the structure and flow of the
phylogenetic tree is shown in figure 9.4.
Figure 9.4. Phylogenetic tree representation: mapping evolutionary relationships and ancestral lineages of
present-day species.
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9.5.2.1 Distance-based algorithms
Neighbor-Joining (NJ) [59] and UPGMA (Unweighted Pair Group Method with
Arithmetic Mean) are distance-based algorithms [60]. These algorithms begin by
computing a matrix of pairwise distances between sequences. NJ is a widely used
method due to its efficiency. It iteratively joins the nearest neighboring taxa into a
tree, updating the distance matrix in each iteration to reflect the newly formed
clusters. The UPGMA assumes a constant rate of evolution (molecular clock
hypothesis) across different lineages, which may not always hold, thus limiting its
accuracy in some scenarios [61].
9.5.2.2 Character-based algorithms
The character-based algorithms include maximum parsimony [62] and maximum
likelihood. The maximum parsimony method seeks to find the tree that explains the
data with the fewest evolutionary changes. Maximum likelihood estimates tree
topologies and branch lengths based on a probabilistic model of sequence evolution,
aiming to find the tree that maximizes the likelihood of observing the given data [63].
9.5.2.3 Bayesian phylogenetics
The Bayesian phylogenetics approach employs Bayesian assumption to estimate the
posterior probabilities of different tree topologies given in the data, incorporating a
priori knowledge through prior distributions [64].
9.5.3 Machine learning algorithms in bioinformatics
Machine learning (ML) encompasses various algorithms applied to various bioinformatics tasks. Support Vector Machines (SVM) are used for classification tasks
like distinguishing between disease and healthy samples based on gene expression
data. Random Forests are ensemble learning methods used for classification and
regression tasks, providing feature importance scores that can be insightful in
bioinformatics. Unsupervised learning includes K-means clustering [65]. It is
employed for grouping data into clusters based on feature similarity, like clustering
genes based on expression profiles. Hierarchical clustering is useful for generating a
dendrogram of clustered data, often employed in gene expression analysis.
Convolutional neural networks (CNNs) have shown to be very beneficial in image
analysis applications, such as histopathology image analysis, within the field of deep
learning. Since recurrent neural networks (RNNs) are good at processing sequential
data, they are often used for sequence analysis tasks [ 66]. Drug discovery process
optimization may benefit from reinforcement learning, a subfield of ML in which a
method learns how to act in a given environment to maximize some concept of
cumulative reward [67].
9.5.3.1 Structural bioinformatics algorithms
Structural bioinformatics focuses on studying and predicting biological macromolecules’ three-dimensional (3D) structures. Homology modeling, sometimes
called comparison modeling is a technique used to predict the 3D structures of
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proteins and nucleic acids based on the known structures of similar molecules. It is
based on the idea that even when protein sequences differ dramatically, the tertiary
structures of the proteins remain constant throughout evolution [68]. There are four
primary phases in the process:
1. Template identification is used for searching databases for known structures
that share sequence similarity with the target molecule.
2. The target sequence is aligned with the template structures to identify
corresponding residues in the sequence alignment phase.
3. In the model-building phase, a 3D model is generated for the target based on
the template structures, often using spatial restraints derived from the
templates.
4. Finally, the model refinement and validation of the model to improve its
stereochemical quality and validate it using various structural and statistical
checks.
The 3D protein structure homology modeling program MODELLER is frequently used. It
creates models by comparing the modeled sequence to similar sequences [69]. Molecular
docking aims to determine which orientation of two molecules will result in the most
stable combination. Predicting how compounds will react with their intended targets is
essential in the drug design process [70]. Necessary procedures in molecular docking include:
1. Preparing the receptor and ligand structures, including adding hydrogen
atoms, defining rotatable bonds, and assigning charges.
2. Defining scoring functions to evaluate different binding orientations and
conformations.
3. Employing algorithms to explore possible binding modes, including stochastic or deterministic search methods.
4. Ranking the predicted binding modes based on scoring functions and
analyzing the results to understand the binding interactions.
AutoDock is a suite of automated docking tools designed to predict how small
molecules, such as substrates or drug candidates, bind to a receptor of known 3D
structure [71]. Ab initio modeling, or de novo modeling, predicts protein structures
solely from their amino acid sequences without using any template structure. It is
beneficial when no suitable template is available for homology modeling [72, 73].
The de novo modeling process often requires:
1. Building protein conformations by assembling short fragments from a
library of known structures.
2. Employing computational methods to find low-energy conformations that
are likely close to the native structure.
3. Using statistical mechanics techniques to explore conformational space and
find the global minimum energy conformation.
Rosetta is a software suite for predicting and designing protein structures, folding,
and protein –protein interactions from amino acid sequences, using a fragment-based
approach when no homologous templates are available [74].
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Alpha-helices, beta-sheets, and twists are all examples of local protein structures
that can be predicted using just the amino acid sequence [75]. The main procedures
in secondary structure method are:
1. Statistical techniques use the known structures statistics to predict secondary
structure elements.
2. Machine learning techniques like neural networks are used to predict
secondary structures based on patterns in training data.
3. The profile-based methods are used in multiple sequence alignments to
generate profiles and predict secondary structures based on conserved
patterns.
PSIPRED is a technique that uses neural networks to accurately predict secondary
structure components like alpha-helices and beta-sheets from amino acid sequences
[76].
9.5.4 High-performance computing (HPC) in bioinformatics
High-performance computing (HPC) has become vital in bioinformatics, enabling
large-scale biological and genomic data processing and analysis [77]. The advent of
next-generation sequencing (NGS) technologies and the surge in publicly available
biological data have made modern biological experiments both data and computationally intensive [77]. HPC provides the computational power necessary to handle
the ‘Big Data’ challenges posed by bioinformatics, facilitating the extraction of
knowledge from raw data through larger computational platforms known as
supercomputers [78]. Parallel computing is a type of computation in which many
calculations or processes are carried out simultaneously. Bioinformatics accelerates
the analysis of large datasets and complex computational tasks [79]. The HPC in
bioinformatics is particularly used in the following areas:
• Sequence alignment: Parallel algorithms can drastically reduce the time
required to align large or multiple sequences [80].
• Phylogenetic analysis: Constructing phylogenetic trees from large datasets is
computationally demanding, and parallel computing can significantly accelerate this process [81].
• Structural modeling: Parallel computing enables faster processing in structural
bioinformatics, such as molecular docking and homology modeling.
The background in the high-performance computing area, including parallel
computing, opens up significant opportunities for simulating relevant biological
systems and applications in bioinformatics, computational biology, and computational chemistry [82]. A schematic workflow of HPC and how it works is shown in
figure 9.5.
9.5.5 Cloud computing in genomics
Cloud computing provides a flexible and scalable environment for storing,
managing, and analyzing a gigantic amount of genomic data. It has emerged as
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Figure 9.5. HPC framework: data processing from big data storage through hpc nodes to user interface.
a vital resource for bioinformatics and genomics, offering several advantages.
Cloud computing reduces the costs associated with data storage and c omputational resources, which is especially beneficial for smaller research groups or
institutions. It allows for the easy scaling of resources as the data volume grows.
Cloud platforms provide easy access to data and computational resources from
anywhere, anytime. The technology of cloud computing and its applications in
biology and bioinformatics have enormously increased over recent years, providing a platform for the analysis of biological data that would not be possible
without significant computational resources [83]. Resources such as NSF XSEDE,
Google Cloud, and Amazon AWS have become more available, and a growing
community of academicians are working on teaching the utility of HPC resources
in genomics and big data analyses [ 84].
9.5.6 GPGPU (general-purpose computing on graphics processing units)
GPGPU stands for General-Purpose computing on Graphics Processing Units. It
represents an innovative approach in which the powerful processing capabilities of
graphics processing units (GPUs) are employed for general computing tasks beyond
just rendering graphics. In bioinformatics, GPGPU has shown significant promise in
accelerating various computationally intensive tasks. The utilization of GPGPU in
bioinformatics is primarily facilitated through libraries such as Nvidia’s CUDA
(Compute Unified Device Architecture). CUDA is a widely used library for
developing GPU-based tools in bioinformatics, computational biology, and systems
biology. It is specifically tailored to use Nvidia GPUs, although alternative solutions
like Microsoft DirectCompute also exist. The adoption of GPGPU in bioinformatics has been driven by the need to reduce the running time required by standard
CPU-based software, allowing for more intensive investigations of biological
systems. GPGPU offers computational power comparable to a small computer
cluster but at a significantly lower cost, around $400. This advantage, coupled with
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the developing of specific algorithms tailored for GPU computing, has led to a
growing interest in GPGPU within the scientific community [85].
A collection of GPU tools has been developed to perform computational analyses
in life science disciplines, emphasizing the advantages and drawbacks of these
parallel architectures. GPUs can considerably reduce the running time, enabling
more efficient data processing and analysis in bioinformatics [86].
9.5.7 Big data analytics in bioinformatics
Big data analytics in bioinformatics refers to applying advanced analytic techniques
on extensive and complex biological datasets. The emergence of high-throughput
technologies, like NGS, has led to the generation of massive amounts of biological
and genomic data. Big data analytics is crucial for extracting meaningful insights
from these data, including discovering new knowledge, identifying patterns, and
making informed decisions. Several primary approaches are used in data analysis to
derive meaning from large datasets. Data mining is a fundamental approach,
utilizing algorithms to filter through large datasets and reveal hidden patterns and
relationships. ML takes center stage, as complex algorithms are applied to develop
predictive models that can estimate future events based on the existing data.
Statistical analysis is crucial to drawing meaningful conclusions from data, which
uses statistical techniques to analyze the data, identify patterns, and rigorously test
hypotheses. To better grasp a dataset, visualization methods are often used to
construct visual representations of the data, providing a more transparent picture of
the underlying patterns and connections. Several tools and frameworks have been
built expressly for big data analytics in bioinformatics to manage the absolute
volume and variety of biological data. Accelerating research in the biological
sciences, these tools improve data processing, visualization, and interpretation [87].
9.5.8 Systems biology modelling
Systems biology modeling uses computer models to assess biological data and
predict system behavior to comprehend the complicated relationships within biological systems. Network analysis and metabolic pathway analysis are essential
analytical techniques in system biology. In systems biology, network analysis often
involves the examination of biological networks to clarify the intricate relationships
between diverse biological entities, including proteins, metabolites, and genes. This
method helps to comprehend the fundamental design and operation of biological
systems. Building and analyzing networks using molecular data is known as
molecular networking. Vital biological insights can be made by studying the
relationships between molecules, such as finding crucial regulatory molecules or
comprehending the processes behind the disease. Networks are inferred from
experimental data using a variety of instruments and techniques, and these networks
are then analyzed to determine their topological characteristics, dynamics, and
functional consequences [88]. Metabolic pathway analysis includes the study of
metabolic pathways to understand the movement of materials and information
inside biological systems. Flux-based analysis is carried out to learn more about how
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metabolic pathways are controlled and how they contribute to cellular function,
which entails the use of computer models to evaluate the flow of metabolites via
metabolic networks [89]. To offer a thorough knowledge of biological systems,
systems biology analysis is a more complete approach that includes metabolic
pathway analysis, among other techniques. It analyzes and interprets the behavior of
complex biological systems, such as metabolic networks, using computer models and
experimental data [88].
9.5.9 Systems pharmacology
An interdisciplinary field called systems pharmacology combines computational and
experimental methods to study how medications interact with biological networks
and disease states. The goal of system pharmacology is to create mechanistic,
quantitative knowledge that can estimate medication reactions and direct the
development of novel treatment approaches. Quantitative systems pharmacology
(QSP) is a mechanistic platform in pharmacology that models the interplay between
medications, biological networks, and disease states to foretell optimal therapy
responses. Small molecules, nucleic acids, proteins, pathways, cells, organs, and
disease processes are all explored regarding medication interactions. Step-by-step
enhancement of the modeling process to make it more effective and repeatable is
essential to creating and validating QSP models. These models are flexible and everevolving, so they can easily absorb newly available data and insights [90]. Multiscale
modeling in systems pharmacology, especially in disease situations, links the cellular
responses to protein or drug interactions in the context of animal or human
physiology. When it comes to converting preclinical scientific discoveries into useful
clinical applications, these models are essential [91]. In addition, discovering
combination therapy treatments, particularly those targeting the immune system,
might benefit from multiscale systems pharmacology modeling techniques [92].
9.5.10 Multiscale modeling
The goal of multiscale modeling is to combine data from different scales and
different physics in order to determine the processes behind the formation of
function in biological systems. It includes analyses of biological and medical
phenomena at several levels of organization, from the molecular to the organismal.
ML methods are rapidly being included in multiscale modeling because of the
valuable tools they provide for the robust management of challenging issues and the
handling of sparse and noisy data. When multiscale modeling is combined with ML,
the resulting models can better capture the nuanced details of biological systems [93].
Recently, ML has found its way into the multiscale modeling of hierarchical
engineering materials and the solution of high-dimensional partial differential
equations, unveiling its interdisciplinary relevance across various fields [94].
Constructing computational and mathematical models based on experimental
data is a common approach to comprehending and predicting the behavior of
complex biological systems. Simple modeling techniques like differential equations
or Boolean networks benefit from incorporating multiscale modeling, enabling a
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