Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5864_Библиотеки_им_академика_М_И_Перельмана.pdf
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
used to aggregate information from various studies to identify common genetic
traits. Integrative genomics often involves cross-species comparisons to identify
conserved genetic elements and pathways [136]. Multi-omics integration aims to
combine data across different ‘omics’ layers (genomics, transcriptomics, proteomics,
metabolomics) to provide a more comprehensive understanding of biological
systems. Techniques like matrix factorization and network integration are used
to combine data across different omics layers, revealing relationships between
molecular entities across these layers. Principal component analysis (PCA) and
t-distributed stochastic neighbourhood embedding (t-SNE) are two dimensionality
reduction methods that are used to handle the complexity of multi-omics data and
find patterns within the data [137]. Single-cell multi-omics provides a high-resolution
view of the cellular landscape by analyzing multiple omics layers at the single-cell
level. At the single-cell level, multi-omics data is generated using technologies like
single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing
(scATAC-seq). Clustering methods are applied to identify cell kinds and states
based on single-cell multi-omics data. Modeling interactions between molecular
entities across many omics layers requires a network-based methodology. A framework for navigating multi-omics data is provided by networks built based on
statistical correlations between molecular components. Modules inside networks,
frequently corresponding to biological routes or functional groupings, are identified
using community detection techniques. Methods for predicting the effects of
disturbances on a network or locating key regulatory nodes are essential for
understanding how changes in one part of the system can affect the entire network.
References
[1] Baxevanis A D, Bader G D and Wishart D S 2020 Bioinformatics (New York: Wiley)
[2] Gauthier J et al 2019 A brief history of bioinformatics Brief. Bioinform.
[3] Min S, Lee B and Yoon S 2017 Deep learning in bioinformatics Brief. Bioinform. 18 851–69
[4] Zvelebil M J and Baum J O 2007 Understanding Bioinformatics (Garland Science)
[5] Mirsaydaliyevich Y 2022 History of bioinformatics Int. J. Soc. Sci. Interdiscip. Res. 11 72–6
[6] Ouzounis C A and Valencia A 2003 Early bioinformatics: the birth of a discipline—a
personal view Bioinformatics
[7] Jawdat D 2006 The era of bioinformatics 2nd Int. Conf. on Information & Communication
Technologies (Piscataway, NJ: IEEE)
[8] Danchin A 2000 A brief history of genome research and bioinformatics in France
Bioinformatics
[9] Mardis E R 2017 DNA sequencing technologies: 2006–2016 Nat. Protoc. 12 213–8
[10] Pearson W R et al 1997 Comparison of DNA sequences with protein sequences Genomics
46 24–36
[11] Mizrachi I 2007 GenBank: the nucleotide sequence database The NCBI Handbook
[Internet], updated (National Center for Biotechnology Information) 22
[12] Stoesser G et al 1999 The EMBL nucleotide sequence database Nucleic Acids Res.
[13] Kulikova T et al 2004 The EMBL nucleotide sequence database Nucleic Acids Res. 32
D27–30
[14] Baker W et al 2000 The EMBL nucleotide sequence database Nucleic Acids Res. 28 19–23
16 65–75
19 2176–90
9-33
20 1981–96
27 18–24
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
[15] Dunn J J et al 2002 Genomic signature tags (GSTs): a system for profiling genomic DNA
Genome Res.
[16] van der Lelie D et al 2006 Use of single-point genome signature tags as a universal tagging
method for microbial genome surveys Appl. Environ. Microbiol.
[17] Thareau V et al 2003 Automatic design of gene-specific sequence tags for genome-wide
functional studies Bioinformatics
[18] Hilson P et al 2004 Versatile gene-specific sequence tags for Arabidopsis functional
genomics: transcript profiling and reverse genetics applications Genome Res.
[19] Han W et al 2006 DNA methylation mapping by tag-modified bisulfite genomic sequencing
Anal. Biochem.
[20] Cochrane G et al 2016 The international nucleotide sequence database collaboration
Nucleic Acids Res.
[21] Apweiler R, Bairoch A and Wu C H 2004 Protein sequence databases Curr. Opin. Chem.
Biol.
8 76–80
[22] Xu D and Xu Y 2004 Protein databases on the internet Curr. Protoc. Mol. Biol. 68 19.4.
1–19.4. 15
[23] Gasteiger E, Jung E and Bairoch A 2001 SWISS-PROT: connecting biomolecular knowl-
edge via a protein database Curr. Issues Mol. Biol. 3 47–55
[24] Radivojac P et al 2004 Classification and knowledge discovery in protein databases
J. Biomed. Inform.
[25] Cannataro M, Guzzi P H and Veltri P 2010 Protein-to-protein interactions: technologies,
databases, and algorithms ACM Comput. Surv. (CSUR)
[26] Lo Conte L et al 2000 SCOP: a structural classification of proteins database Nucleic Acids
Res.
28 257–9
[27] Altschul S F et al 2005 Protein database searches using compositionally adjusted
substitution matrices FEBS J.
[28] Federhen S 2003 The taxonomy project The NCBI Handbook (National Center for
Biotechnology Information)
[29] Federhen S 2011 Entrez Taxonomy Quick Start. Taxonomy Help [Internet] (Bethesda,
MD: National Center for Biotechnology Information (US))
[30] Page R D 2005 A taxonomic search engine: federating taxonomic databases using web
services BMC Bioinf.
[31] Gibney G and Baxevanis A D 2011 Searching NCBI databases using Entrez Curr. Protoc.
Bioinform.
[32] Wheeler D L et al 2000 Database resources of the national center for biotechnology
information Nucleic Acids Res.
[33] Pruitt K D and Maglott D R 2001 RefSeq and LocusLink: NCBI gene-centered resources
Nucleic Acids Res.
[34] Pruitt K D et al 2000 Introducing RefSeq and LocusLink: curated human genome resources
at the NCBI Trends in Genet.
[35] Sherry S T et al 2001 dbSNP: the NCBI database of genetic variation Nucleic Acids Res. 29
308–11
[36] Sherry S T, Ward M and Sirotkin K 2000 Use of molecular variation in the NCBI dbSNP
database Hum. Mutat.
[37] Pruitt K D, Tatusova T and Maglott D R 2003 NCBI reference sequence project: update
and current status Nucleic Acids Res.
12 1756–65
72 2092–101
19 2191–8
14 2176–89
355 50–61
44 D48–50
37 224–39
43 1–36
272 5101–9
6 1–8
34 1.3. 1–1.3. 25
28 10–4
29 137–40
16 44–7
15 68–75
31 34–7
9-34
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
[38] Wheeler D L et al 2003 Database resources of the national center for biotechnology Nucleic
Acids Res.
[39] Mehmood M A, Sehar U and Ahmad N 2014 Use of bioinformatics tools in different
spheres of life sciences J. Data Min. Genom. Proteomics 5 1
[40] Saraiya P, North C and Duca K 2005 An insight-based methodology for evaluating
bioinformatics visualizations IEEE Trans. Visual Comput. Graphics
[41] Gill S K et al 2016 Emerging role of bioinformatics tools and software in evolution of
clinical research Perspectives Clin. Res.
[42] Arabi Z and Sardari S 2010 An investigation into the antifungal property of fabaceae using
bioinformatics tools Avicenna J. Med. Biotechnol. 2 93
[43] Attwood T K and Miller C J 2001 Which craft is best in bioinformatics? Comput. Chem.
329–39
[44] Gallagher L A, Shendure J and Manoil C 2011 Genome-scale identification of resistance
functions in Pseudomonas aeruginosa using Tn-seq MBio
[45] Okuda T and Kondoh H 1999 Identification of new genes ndr2 and ndr3 which are related
to Ndr1/RTP/Drg1 but show distinct tissue specificity and response to N-myc Biochem.
Biophys. Res. Commun.
[46] Verde F, Mata J and Nurse P 1995 Fission yeast cell morphogenesis: identification of new
genes and analysis of their role during the cell cycle J. Cell Biol.
[47] Abifadel M et al 2014 Living the PCSK9 adventure: from the identification of a new gene in
familial hypercholesterolemia towards a potential new class of anticholesterol drugs Curr.
Atheroscler. Rep.
[48] Lassmann T 2020 Kalign 3: Multiple Sequence Alignment of Large Datasets (Oxford:
Oxford University Press)
[49] Clark C and Kalita J 2014 A comparison of algorithms for the pairwise alignment of
biological networks Bioinformatics
[50] Jararweh Y et al 2019 Improving the performance of the needleman-wunsch algorithm
using parallelization and vectorization techniques Multimedia Tools Appl.
[51] Khajeh-Saeed A, Poole S and Perot J B 2010 Acceleration of the Smith–Waterman
algorithm using single and multiple graphics processors J. Comput. Phys.
[52] Edgar R C 2004 MUSCLE: multiple sequence alignment with high accuracy and high
throughput Nucleic Acids Res.
[53] Thompson J D, Gibson T J and Higgins D G 2003 Multiple sequence alignment using
ClustalW and ClustalX Curr. Protoc. Bioinform.
[54] Marco-Sola S et al 2021 Fast gap-affine pairwise alignment using the wavefront algorithm
Bioinformatics
[55] Altschul S F et al 1990 Basic local alignment search tool J. Mol. Biol. 215 403–10
[56] Pearson W R 1994 Using the FASTA program to search protein and DNA sequence
databases Computer Analysis of Sequence Data: Part I (Springer) pp 307–31
[57] Li H et al 2020 Modern deep learning in bioinformatics J. Mol. Cell. Biol.
[58] Challa S and Neelapu N R R 2019 Phylogenetic trees: applications, construction, and
assessment Essentials of Bioinformatics, Volume III: In Silico Life Sciences: Agriculture
(Springer) pp 167–92
[59] Gascuel O and Steel M 2006 Neighbor-joining revealed Mol. Biol. Evol.
[60] Gronau I and Moran S 2007 Optimal implementations of UPGMA and other common
clustering algorithms Inf. Process. Lett.
31 28–33
11 443–56
7 115
25
2 e00315–10
266 208–15
131 1529–38
16 1–23
30 2351–9
78 3961–77
229 4247–58
32 1792–7
2.3. 1–2.3. 22s
37 456–63
12 823–7
23 1997–2000
104 205–10
9-35
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
[61] Segura-Alabart N et al 2022 Nonunique UPGMA clusterings of microsatellite markers
Brief. Bioinform.
[62] Yan Z et al 2022 Maximum parsimony inference of phylogenetic networks in the presence
of polyploid complexes Syst. Biol.
[63] Kleinbaum D G et al 2010 Maximum likelihood techniques: an overview Logistic
regression: A Self-learning Text (Springer) pp 103–27
[64] Hassler G W et al 2023 Data integration in bayesian phylogenetics Annu. Rev. Stat. Appl.
10 353–77
[65] Shastry K A and Sanjay H 2020 Machine learning for bioinformatics Statistical Modelling
and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications
(Springer) pp 25–39
[66] Chen Z et al 2020 Feature selection may improve deep neural networks for the
bioinformatics problems Bioinformatics
[67] Dara S et al 2022 Machine learning in drug discovery: a review Artif. Intell. Rev. 55
1947–99
[68] Patel B, Singh V and Patel D 2019 Structural bioinformatics Essentials of Bioinformatics,
Volume I: Understanding Bioinformatics: Genes to Proteins (Springer) pp 169–99
[69] Webb B and Sali A 2021 Protein Structure Modeling with Modeller (Berlin: Springer)
[70] Fan J, Fu A and Zhang L 2019 Progress in molecular docking Quant. Biol.
[71] Valdés-Tresanco M S et al 2020 AMDock: a versatile graphical tool for assisting molecular
docking with Autodock Vina and Autodock4 Biol. Direct
[72] Chivian D et al 2003 Ab initio methods Struct. Bioinform. 44 547–57
[73] Wu S, Skolnick J and Zhang Y 2007 Ab initio modeling of small proteins by iterative
TASSER simulations BMC Biol.
[74] Moretti R et al 2018 Web-accessible molecular modeling with Rosetta: the Rosetta online
server that includes everyone (ROSIE) Protein Sci.
[75] Kuhlman B and Bradley P 2019 Advances in protein structure prediction and design Nat.
Rev. Mol. Cell Biol.
[76] Buchan D W and Jones D T 2019 The PSIPRED protein analysis workbench: 20 years on
Nucleic Acids Res.
[77] Castrignanò T et al 2020 ELIXIR-IT HPC@ CINECA: high performance computing
resources for the bioinformatics community BMC Bioinf.
[78] Li Y et al 2019 Deep learning in bioinformatics: Introduction, application, and perspective
in the big data era Methods
[79] Sangaiah A K 2019 Deep Learning and Parallel Computing Environment for Bioengineering
Systems (New York: Academic)
[80] González-Domínguez J 2021 Fast and accurate multiple sequence alignment with msap-
robs-MPI Multiple Sequence Alignment: Methods and Protocols (Springer) pp 39–47
[81] Das A and Huang X 2019 HPC: Hierarchical phylogeny construction PLoS One
e0221357
[82] Behzadi P and Bernabò N 2020 Computational Biololgy and Chemistry (BoD–Books on
Demand)
[83] Hanussek M, Bartusch F and Krüger J 2021 Performance and scaling behavior of
bioinformatic applications in virtualization environments to create awareness for the
efficient use of compute resources PLoS Comput. Biol.
23 bbac312
71 706–20
36 1542–52
7 83–9
15 112
5 1–10
27 259–68
20 681–97
47 W402–7
21 1–17
166 4–21
14
17 e1009244
9-36
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
[84] Sethuraman A 2022 Teaching computational genomics and bioinformatics on a high
performance computing cluster—a primer Biol. Methods Protoc.
[85] Maciá-Lillo A et al 2023 GPU cloud architectures for bioinformatic applications Int. Work-
Conf. on Bioinformatics and Biomedical Engineering (Berlin: Springer)
[86] Tangherloni A et al 2021 FiCoS: a fine-grained and coarse-grained GPU-powered
deterministic simulator for biochemical networks PLoS Comput. Biol.
[87] Patel D T 2019 Big data analytics in bioinformatics Biotechnology: Concepts,
Methodologies, Tools, and Applications (IGI Global) pp 1967–84
[88] Greenyer H 2022 Evaluation of network inference algorithms and their effects on network
analysis for the study of small metabolomic data sets PhD Thesis University of Victoria
[89] Majumder E L-W et al 2021 Cognitive analysis of metabolomics data for systems biology
Nat. Protoc.
[90] Helmlinger G et al 2019 Quantitative systems pharmacology: an exemplar model-building
workflow with applications in cardiovascular, metabolic, and oncology drug development
CPT: Pharmacomet. Syst. Pharmacol.
[91] Schoeberl B 2019 Quantitative systems pharmacology models as a key to translational
medicine Curr. Opin. Syst. Biol.
[92] Van Hasselt J C and Iyengar R 2019 Systems pharmacology: defining the interactions of
drug combinations Annu. Rev. Pharmacol. Toxicol.
[93] Peng G C et al 2021 Multiscale modeling meets machine learning: what can we learn? Arch.
Comput. Meth. Eng.
[94] Alber M et al 2019 Integrating machine learning and multiscale modeling—perspectives,
challenges, and opportunities in the biological, biomedical, and behavioral sciences NPJ
Digit. Med.
[95] Millar-Wilson A et al 2022 Multiscale Modelling in the Framework of Biological Systems
and its Potential for Spaceflight Biology Studies (Iscience)
[96] van der Kolk M et al 2021 Des-ist: a simulation framework to streamline event-based in
silico trials Int. Conf. on Computational Science (Berlin: Springer)
[97] Torri F et al 2012 Next generation sequence analysis and computational genomics using
graphical pipeline workflows Genes
[98] Tao H et al 2021 Computational methods for the prediction of chromatin interaction and
organization using sequence and epigenomic profiles Brief. Bioinform.
[99] Song X et al 2021 Comparative Genomics and Functional Genomics Analyses in Plants
(Frontiers Media SA) p 687966
[100] Lee H H et al 2019 Functional genomics of the rapidly replicating bacterium Vibrio
natriegens by CRISPRi Nat. Microbiol.
[101] Kumwenda B 2013 Comparative Genomics Study of Completely Sequenced Thermus Sp.
Strains to Enhance and Facilitate their Application in Biotechnology (University of Pretoria)
[102] Koonin E and Galperin M Y 2002 Sequence—Evolution—Function: Computational
Approaches in Comparative Genomics (Kluwer Academic)
[103] Wang K C and Chang H Y 2018 Epigenomics: technologies and applications Circ. Res.
1191–9
[104] Koch L 2023 Epigenomes get personal Nat. Rev. Genet. 24 1–3
[105] Zhang L et al 2021 Advances in metagenomics and its application in environmental
microorganisms Front. Microbiol.
16 1376–418
8 380–95
16 25–31
59 21–40
28 1017–37
2 115
3 545–75
4 1105–13
12 766364
7 bpac032
17 e1009410
22 bbaa405
122
9-37
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
[106] Pérez-Cobas A E, Gomez-Valero L and Buchrieser C 2020 Metagenomic approaches in
microbial ecology: an update on whole-genome and marker gene sequencing analyses
Microb. Genom.
[107] Yen S and Johnson J S 2021 Metagenomics: a path to understanding the gut microbiome
Mamm. Genome
[108] Ahmed S et al 2016 Pharmacogenomics of drug metabolizing enzymes and transporters:
relevance to precision medicine Genom. Proteom. Bioinform.
[109] Hasanzad M et al 2022 Genomic medicine on the frontier of precision medicine J. Diabetes
Metab. Disorders
[110] Pirmohamed M 2023 Pharmacogenomics: current status and future perspectives Nat. Rev.
Genet.
24 350–62
[111] Mills R and Haga S B 2014 Genomic counseling: next generation counseling J. Genet.
Couns.
23 689–92
[112] Rehm H L 2017 Evolving health care through personal genomics Nat. Rev. Genet. 18 259–67
[113] Whitley K V, Tueller J A and Weber K S 2020 Genomics education in the era of personal
genomics: academic, professional, and public considerations Int. J. Mol. Sci.
[114] Kim D, Kim J H and Moore J H 2020 Translational bioinformatics: integrating electronic
health record and omics data Biocomputing 2021: Proc. Pacific Symp. (Singapore: World
Scientific)
[115] Stipelman C H et al 2022 Electronic health record-integrated clinical decision support for
clinicians serving populations facing health care disparities: literature review Yearb. Med.
Inform.
[116] Girwar S A M et al 2021 A systematic review of risk stratification tools internationally used
in primary care settings Health Sci. Rep.
[117] Beaulieu-Jones B K et al 2021 Machine learning for patient risk stratification: standing on,
or looking over, the shoulders of clinicians? NPJ Digit. Med.
[118] Bedson J et al 2021 A review and agenda for integrated disease models including social and
behavioural factors Nat. Hum. Behav.
[119] Rowe R G and Daley G Q 2019 Induced pluripotent stem cells in disease modelling and
drug discovery Nat. Rev. Genet.
[120] Yue R and Dutta A 2022 Computational systems biology in disease modeling and control,
review and perspectives npj Syst. Biol. Appl.
[121] Xia X 2017 Bioinformatics and drug discovery Curr. Top. Med. Chem. 17 1709–26
[122] Romano J D and Tatonetti N P 2019 Informatics and computational methods in natural
product drug discovery: a review and perspectives Front. Genet.
[123] Sharma R et al 2023 Bioinformatics paradigms in drug discovery and drug development
Curr. Top. Med. Chem.
[124] Niazi S K and Mariam Z 2023 Recent advances in machine-learning-based chemo-
informatics: a comprehensive review Int. J. Mol. Sci.
[125] Wang Y et al 2022 DrugRepo: a novel approach to repurposing drugs based on chemical
and genomic features Sci. Rep.
[126] Shi W et al 2023 A review on predicting drug target interactions based on machine learning
Int. Conf. on Health Information Science (Berlin: Springer)
[127] Bhardwaj K K, Banyal S and Sharma D K 2019 Artificial intelligence based diagnostics,
therapeutics and applications in biomedical engineering and bioinformatics Internet of
Things in Biomedical Engineering (Amsterdam: Elsevier) pp 161–87
31 184–98
6 1–22
32 282–96
14 298–313
21 853–61
21 768
4 e329
4 62
5 834–46
20 377–88
8 37
10 368
23 579–88
24 11488
12 21116
9-38
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
[128] Auslander N, Gussow A B and Koonin E V 2021 Incorporating machine learning into
established bioinformatics frameworks Int. J. Mol. Sci.
[129] Kaushik A C and Raj U 2020 AI-driven drug discovery: a boon against COVID-19? AI
Open
1 1–4
[130] David L et al 2020 Molecular representations in AI-driven drug discovery: a review and
practical guide J. Cheminf.
[131] Mak K-K, Balijepalli M K and Pichika M R 2022 Success stories of AI in drug discovery-
where do things stand? Expert Opin. Drug Discov.
[132] Alkhnbashi O S et al 2020 CRISPR-Cas bioinformatics Methods 172 3–11
[133] Liu H et al 2015 CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene
editing, repression and activation Bioinformatics
[134] Choudhary S et al 2020 Application of bioinformatics tools in CRISPR/Cas CRISPR/Cas
Genome Editing: Strategies and Potential for Crop Improvement (Springer) pp 31–52
[135] Javed M R et al 2021 Tricks and trends in CRISPR/Cas9-based genome editing and use of
bioinformatics tools for improving on-target efficiency CRISPR and RNAi Systems
(Amsterdam: Elsevier) pp 441–62
[136] Sun Y V and Hu Y-J 2016 Integrative analysis of multi-omics data for discovery and
functional studies of complex human diseases Adv. Genet. 93 147–90
[137] Anowar F, Sadaoui S and Selim B 2021 Conceptual and empirical comparison of
dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP,
LE, ICA, t-SNE) Comput. Sci. Rev.
12 1–22
40 100378
22 2903
17 79–92
31 3676–8
9-39
IOP Publishing
Introduction to Pharmaceutical Biotechnology, Volume 2
(Second Edition)
Enzymes, proteins and bioinformatics
Ahmed Al-Harrasi, Saurabh Bhatia and Ajmal Khan
Chapter 10
Protein and enzyme engineering
10.1 Protein and enzyme engineering
Protein engineering is the design of new enzymes or proteins with new or desirable
functions. It is based on the use of recombinant DNA technology to change amino
acid sequences. Many different protein engineering methods are available today,
owing to the rapid development in biological sciences, more specifically, recombinant DNA technology. In an earlier chapter, we discussed the principles, uses and
techniques of biocatalysis and enzyme technology. In the past, the industry was
largely dependent on these naturally occurring enzymes. However, naturally
occurring enzymes and their related bioprocesses are often limited by a set of
conditions. With advancements in biotechnology, it has been possible to plan and
engineer novel enzymes and other macromolecules with predicted characteristics.
This field of research also gained more support from computer aided molecular
modeling (CAMM), which has contributed considerably, not only to protein and
enzyme engineering, but also to drug design. Different characteristics of protein and
biocatalyst engineering will be discussed in this chapter. To advance the production
of a particular macromolecule or to produce novel products, metabolic pathways are
also being designed in different ways. The various steps involved in protein
engineering are shown in figure 10.1.
10.2 Designing macromolecules
Proteins are versatile macromolecules with various important functions, including
structural, catalytic, sensory and regulatory functions. The rational design of
enzymes is a great challenge to our understanding of protein structure and physical
chemistry and has numerous potential applications. Protein design algorithms have
been useful to design or engineer proteins that fold, fold faster, catalyze, catalyze
faster, signal and adopt preferred conformational states. The field of de novo protein
doi:10.1088/978-0-7503-5387-8ch10 10-1 ª IOP Publishing Ltd 2024. All rights,
including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved.
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
Figure 10.1. The various steps involved in protein engineering.
design, although only a few decades old, is beginning to produce exciting results.
Advances in this field are already having a considerable influence on biotechnology
and chemical biology. The application of powerful computational approaches for
functional protein design has currently succeeded in engineering target activities [1].
In the last quarter of the twentieth century, various major discoveries have made
it possible to modify/alter the structure of novel proteins/molecules/products in a
deliberate way leading to a predicted functional role. Site-directed mutagenesis
allows changes in sequences at identi fied sites, resulting in modification of proteins in
a deliberate way. Moreover, computational and graphical developments have
allowed access to the three-dimensional structure of proteins. These two developments allow us to understand protein–drug interactions more deeply. The steps
involved in protein engineering are as follows:
• The protein sample is characterized with a ligand (drug, enzyme substrate,
receptor hormone or antigen–antibody interaction).
10-2
Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
• The three-dimensional structure is investigated using nuclear magnetic
resonance (NMR) and x-ray diffraction patterns of crystals.
• The three-dimensional structure is displayed with interactive computer
graphics and the available information is used to suggest a novel design
suiting the requirements.
• The desired DNA sequence that is expected to give the novel designed protein
is then either synthesized or obtained by site-directed mutagenesis of an
available gene.
• The novel gene is incorporated into an appropriate expression system and the
gene product is extracted, purified and characterized biochemically.
• If the biochemical profile does not fulfill the previously earlier anticipated
structure, the cycle may be repeated again, until the anticipated structure is
achieved.
It must be noted that the aforementioned steps of protein engineering share steps
employed in the engineering of drugs, insecticides, herbicides and peptide vaccines,
although there are other additional methods that are employed for drug discovery.
The various cycles involved in genetic engineering and drug designing display
common steps, as shown in figure 10.2.
Drug design and protein engineering are different from each other in the sense
that drug design only concerns the ligand, which can be modified more efficiently by
chemical synthesis, whereas in protein engineering rDNA technology is usually used.
Three-dimensional structures for a small number of proteins that are interrelated to
the novel protein of interest will usually be accessible to a protein engineer in
developing a procedure for novel protein production as per the desired features.
Figure 10.2. Various cycles involved in genetic engineering and drug design, demonstration the common steps
of the two cycles.
10-3