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11.19 Expanding structural genomics
to nonprotein biomolecules
Expanding structural genomics beyond proteins to encompass nonprotein biomole-
cules, like lipids, nucleic acids, and small molecules, opens up new avenues for under-
standing the molecular basis of biological processes and disease [182]. Here are
examples of how structural genomics is applied to nonprotein biomolecules:
11.19.1 Structural genomics of nucleic acids
RNA structures: The field of RNA structural genomics aims to determine the three-
dimensional structures of RNA molecules. For example, the use of X-ray crystallogra-
phy to ascertain the structures of riboswitches, which are RNA elements regulating
gene expression through the binding of particular metabolites, has unveiled valuable
insights into how they function. Understanding RNA structures aids in drug design
targeting RNA-related diseases [183].
DNA–protein interactions: Reviewing structures of DN A–protein complexes, as tran-
scription factors binding to DNA, provides insights into gene regulation. For instance,
the crystal structure of DNA-bound transcription factors has elucidated how they rec-
ognize specific DNA sequences and control gene expression [184].
11.19.2 Lipid structural genomics
Lipid membrane dynamics: Structural studies of lipid bilayers and membrane proteins
embedded within them shed light on cell membrane organization and dynamics. For
example, NMR spectroscopy has been utilized to characterize lipid-protein interac-
tions in cell membranes, enhancing our understanding of signal transduction [185].
11.19.3 Structural genomics of small molecules
Drug–target interactions: Structural genomics approaches are applied to study the in-
teractions between small molecules (e.g., drugs) and their target proteins or nucleic
acids. X-ray crystallography and cryo-EM have revealed the binding modes of small
molecules with therapeutic potential, guiding drug design and optimization [186].
Metabolite-binding proteins: Structural studies of proteins that bind metabolites, vita-
mins, and cofactors offer insights into cellular metabolic pathways. For example, the
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structural determination of enzymes involved in vitamin B12 metabolism has en-
hanced our understanding of these processes and facilitated drug development [187].
11.19.4 Noncoding RNA structures
Riboswitches: Apart from protein-coding genes, many nonco ding RNAs play regula-
tory roles. Riboswitches regulate gene expression by undergoing structural alterations
when they bind particular ligands. Structural genomics efforts have led to the deter-
mination of riboswitch structures, revealing their intricate mechanisms [188].
11.19.5 Structural genomics of viral genomes
Viral RNA structures: The structural characterization of viral RNA genomes provides
insights into viral replication and pathogenesis. For example, determining the struc-
tures of RNA elements within the HIV genome has informed the development of anti-
viral therapies targeting essential RNA regions.
Inhibitor design: Structural studies of viral proteins and their interactions with host
molecules aid in designing antiviral inhibitors. For instance, the crystallographic anal-
ysis of the HIV protease complexed with inhibitors has guided drug development
against HIV/AIDS.
Expanding structural genomics to nonprotein biomolecules is yielding crucial insights
into various biological processes, facilitating the development of therapeutics, and
providing a comprehensive view of molecular interactions within cells. These efforts
contribute to a deeper understanding of the roles that nonprotein biomolecules play
in health and disease [189–191].
11.20 Potential impact of cryo-EM on drug discovery
Cryo-EM has had a transformative impact on drug discovery by providing unprece-
dented insights into the three-dimensional structures of biomolecules at near-atomic
resolution. This advanced imaging technique has revolutionized the understanding of
molecular mechanisms, interactions, and dynamics, contributing significantly to vari-
ous aspects of drug development [192]. Here’s an overview of the potential impacts of
cryo-EM on drug discovery:
1. Structure-based drug design: Cryo-EM allows the visualization of complex pro-
tein structures, including membrane proteins and large macromolecular assem-
blies, which were traditionally challenging targets for X-ray crystallography.
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High-resolution cryo-EM structures reveal binding sites, allosteric pockets, and
conformational changes, enabling precise SBDD. This knowledge facilitates the ra-
tional design of molecules which interact with target proteins, enhancing specific-
ity and affinity of drug candidates [193].
2. Fragment-based drug discovery: Cryo-EM can reveal how small molecule frag-
ments bind to a target protein, even at low occupancy. This aids in fragment-
based drug discovery, where small fragments are optimized into high-affinity
drug leads. Cryo-EM provides valuable information on fragment binding modes,
guiding the design of more potent compounds [194].
3. Understanding drug–target interactions: Cryo-EM elucidates the interactions
between drugs and their target proteins, shedding light on binding kinetics, stoi-
chiometry, and structural changes upon ligand binding. This information aids in
predicting drug efficacy, optimizing dosing regimens, and reducing off-target ef-
fect [195].
4. Target validation and mechanism of action: Cryo-EM can elucidate structures
of target proteins and complexes, verifying their roles in disease pathways and
validating them as drug targets. Additionally, it provides insights into the mecha-
nisms by which drugs modulate protein function, helping researchers understand
how therapeutic interventions work at the molecular level [196].
5. Antibody and vaccine development: Cryo-EM shows a decisive role in charac-
terizing structures of antibodies, antibody–antigen complexes, and viral proteins.
This information guides design of antibodies with enhanced binding affinities
and informs development of vaccines targeting specific pathogens [197].
6. Overcoming drug resistance: Cryo-EM can reveal structural details of drug-
resistant mutations, offering insights into the mechanisms underlying resistance.
This information guides the design of next-generation drugs that circumvent re-
sistance mechanisms, extending the effectiveness of therapies [198].
7. Personalized medicine: Cryo-EM contributes to understanding genetic varia-
tions that impa ct drug responses. By visualizing protein structures and their in-
teractions, cryo-EM can help predict how individual genetic differences affect
drug binding, metabolism, and efficacy, facilitating personalized treatment strate-
gies [199].
8. Accelerated drug development: Cryo-EM enables rapid structure determination
without the need for crystallization, saving time in drug development process. It
allows researchers to visualize multiple conformational states of a protein, aiding
in hit identification, lead optimization, and preclinical development [200].
9. Fragment-to-lead optimization: Cryo-EM assists in optimizing fragment hits into
lead compounds by providing detailed information about interactions and bind-
ing mode. This accelerates the iterative process of modifying fragments to im-
prove their potency and selectivity [201].
10. Access to challenging targets: Cryo-EM can study protein complexes, flexible re-
gions, and membrane proteins that are difficult to crystallize or analyze using
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other methods. This widens the scope of drug targets and enhances our under-
standing of their structures and functions. In essence, cryo-EM has revolutionized
drug discovery by providing high-resolution structural insights into biomolecules,
offering a deeper understanding of their roles in diseases, and enabling the de-
sign of more effective and targeted therapeutics. As cryo-EM techniques continue
to evolve and become more accessible, their impact on drug discovery is expected
to grow even further [202, 203].
11.21 Recapitulation of the role of structural
genomics in drug discovery
In the realm of drug discovery, structural genomics stands as a paramount force, orches-
trating a profound transformation in our understanding and approach. By unveiling the
intricate three-dimensional architectures of proteins, nucleic acids, and diverse biomole-
cules, structural genomics assumes the critical role of a guiding compass in the pursuit
of effective therapeutics. Its significance spans across a spectrum of facets within drug
development. At the forefront, structural genomics bestows a meticulous lens for target
identification and validation, unraveling the molecular signatures of disease-associated
entities. This foundational knowledge, in turn, fuels the engine of rational drug design,
where atomic-level insights into binding sites and conformational changes steer the pre-
cise engineering of pharmacological agents with heightened specificity and potency
[204, 205].
Beyond this, structural genomics forms an anchor for the elucidation of mecha-
nisms that underlie drug–target interactions, a fundamental facet in predicting effi-
cacy, optimizing dosages, and minimizing unintended consequences. The impact
reverberates through the intricate tapestry of fragment-based discovery of drug, as it
aids in the strategic transformation of modest molecular fragments into potent thera-
peutic leads. Moreover, the canvas expands to encompass the realms of antibody and
vaccine development , enabling the strategic crafting of enhanced immunotherapies
and preventive measures against pathogens [206, 207].
A pivotal promise held by structural genomics lies in its ability to surmount chal-
lenges presented by drug resistance. By laying bare the structural basis of resistant
mutations, it fosters a roadmap for the creation of innovative treatments, rendering
obsolescence a transient concern. Embracing the era of personalized medicine, struc-
tural genomics entwines genetics and structure, allowing tailored treatments rooted
in individual genetic profiles, ushering forth a new dawn of patient-centric care [208].
In the symphony of drug development, structural genomics orchestrates harmony
with acceleration. Cryo-EM, as a virtuoso, empowers swift and comprehensive struc-
tural elucidation, streamlining the intricate dance of hit identification, lead optimiza-
tion, and preclinical evaluation. As the curtain rises, structural genomics spotlights an
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array of challenging targets, once ensconced in the shadows, illuminating their poten-
tial for therapeutic intervention [209].
In the narrative of decision-making, structural genomics becomes the sage advisor,
guiding choices from the embryonic stages of target selection through the labyrinthine
corridors of optimization, bolstering the journey with data-driven precision. It, thus,
embodies the very essence of innovation and progress, emboldening the landscape of
drug discovery with the promise of safer, more efficacious, and tailored treatments. In
its tapestry of roles, structural genomics weaves together the threads of knowledge,
design, and discovery, forever altering the trajectory of modern medicine [210, 211].
11.22 Outlook for the future of structural genomics
in advancing medicine
The future of structural genomics gleams with immense promise, poised to chart a
transformative course in advancing medicine to unprecedented heights. As we stand
at the nexus of technological innovation and scientific discovery, part of structural
genomics is set to develop even more profound and integral to the evolution of medi-
cal practice [212, 213].
One of the most captivating prospects lies in the convergence of structural genomics
with the burgeoning fields of AI and ML. This symbiotic partnership holds the potential
to unravel complexities on a scale previously unimaginable. AI-driven algorithms will de-
code the intricate relationships between molecular structures, functions, and disease
pathways, hastening the identification of novel drug targets, facilitating rapid hit-to-lead
transformations, and designing tailored therapeutics with remarkable precision. ML’s
ability to predict interactions, model dynamic behavior, and navigate the intricate land-
scape of biomolecular systems will catalyze breakthroughs that redefine therapeutic
strategies [93, 214, 215].
Furthermore, the advent of cryo-EM, like a beacon illuminating uncharted territo-
ries, will continue to reshape structural genomics. Its increasing accessibility and res-
olution will usher in an era of high-throughput structural elucidation, where complex
biomolecules, once deemed elusive, will stand revealed in exquisite detail. This de-
mocratization of cryo-EM will democratize access to structural insights, fostering col-
laborations, igniting innovation, and acce lerating the development of life-changing
interventions [216].
In tandem, the expansion of structural genomics beyond proteins to encompass
nonprotein biomolecules will unveil a more holistic understanding of biological sys-
tems. Nucleic acids, lipids, and metabolites will emerge as key players, revealing their
roles as orchestrators of cellular symphonies. These insights will illuminate novel ave-
nues for therapeutic interventions, where precision medicine takes center stage, and
interventions are tailored not only to an individual’s genetics but also to the intricacies
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of their molecular ensemble. The horizon also holds the promise of enhanced target
validation, as structural genomics elucidates the intimate choreography of disease-
related pathways. This molecular choreography will enable the identification of vulner-
abilities, guiding the design of interventions that disrupt disease progression at its most
fundamental level [217, 218].
As data sharing initiatives and collaborative platforms flourish, structural genomics
will foster a vibrant ecosystem of knowledge exchange, where researchers, clinicians,
and computational experts converge to collectively shape the future of medicine. Open-
access databases will flourish databases that are enriched not only with static structural
snapshots but also with dynamic, interactive models that paint a holistic portrait of mo-
lecular behavior [219, 220].
11.23 Conclusion
In conclusion, the realm of structural genomics encompasses a dynamic landscape of
innovations and advancements that are revolutionizing our understanding of biologi-
cal macromolecules and driving drug d iscovery forward. Various solutions have
emerged to enhance data quality and validation in structural biology, ensuring the
robustness and reliability of findings. Overcoming challenges in protein crystalliza-
tion and sample preparation has led to a diverse array of techniques that enable the
acquisition of high-quality crystals for detailed structural analyses.
The integration of structural data with other omics technologies enriches our
comprehension of complex biological systems, opening doors to personalized medi-
cine, targeted therapies, and a deeper grasp of disease mechanisms. This integration
is powered by the collaboration of diverse scientific disciplines and the advantage of
ML and AI, which synergize to refine predictions, optimize drug designs, and propel
data-driven drug discovery.
Expanding structural genomics beyond proteins to include nonprotein biomole-
cules, as lipids, nucleic acids, and molecules, augments our insights into cellular pro-
cesses, facilitating the design of novel therapeutics. Notably, the impending impact of
cryo-EM on drug discovery is profound, offering unprecedented structural clarity and
accelerating the development of precise, effective drugs.
As we stand on the cusp of transformative advancements, the integration of
these multifaceted approaches and trends in structural genomics and drug discov-
ery holds immense promise. This fusion of knowledge and technology is poised to
usher in a new era of targeted treatments, personalized interventions, and innova-
tive solutions for addressing complex diseases, ultimately improving global health-
care and well-being.
In the grand tapestry of advancing medicine, structural genomics assumes the
role of an architect, blueprinting a future where treatments are precision-crafted, dis-
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eases are intercepted at their molecular origins, and patient care is individualized to
an unparalleled degree. The symphony of innovation, fuelled by structural insights
and driven by interdisciplinary collaboration, promises a harmonious blend of scien-
tific rigor and medical compassion. As we navigate this transformative journey, the
future of structural genomics stands as a beacon of hope, illuminating a path toward
a healthier, more resilient, and profoundly personalized realm of medicine.
References
[1] Burley, S. K., Berman, H. M., Bhikadiya, C., Bi, C., Chen, L., Di Costanzo, L., Christie, C., Dalenberg, K.,
Duarte, J. M., Dutta, S., Feng, Z., Ghosh, S., Goodsell, D. S., Green, R. K., Guranović, V., Guzenko, D.,
Hudson, B. P., Kalro, T., Liang, Y., Lowe, R., Namkoong, H., Peisach, E., Periskova, I., Prlić, A., Randle,
C., Rose, A., Rose, P., Sala, R., Sekharan, M., Shao, C., Tan, L., Tao, Y. P., Valasatava, Y., Voigt, M.,
Westbrook, J., Woo, J., Yang, H., Young, J., Zhuravleva, M., & Zardecki, C. RCSB Protein Data Bank:
Biological macromolecular structures enabling research and education in fundamental biology,
biomedicine, biotechnology and energy. Nucleic Acids Research, 2019, 47, D464–D474. https://doi.
org/10.1093/NAR/GKY1004.
[2] Mariani, V., Biasini, M., Barbato, A., & Schwede, T. lDDT: A local superposition-free score for
comparing protein structures and models using distance difference tests. Bioinformatics, 2013, 29,
2722–2728. https://doi.org/10.1093/BIOINFORMATICS/BTT473.
[3] Storici, F., Bebenek, K., Kunkel, T. A., Gordenin, D. A., & Resnick, M. A. RNA-templated DNA repair.
Nature, 2007, 447, 338–341. https://doi.org/10.1038/nature05720.
[4] Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., &
Bourne, P. E. The Protein Data Bank. Nucleic Acids Research, 2000, 28, 235–242. https://doi.org/10.
1093/NAR/28.1.235.
[5] Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. The I-TASSER Suite: Protein structure and
function prediction. Nature Methods, 2015, 12, 7–8. https://doi.org/10.1038/NMETH.3213.
[6] Mollica, L., Bessa, L. M., Hanoulle, X., Jensen, M. R., Blackledge, M., & Schneider, R. Binding
Mechanisms of Intrinsically Disordered Proteins: Theory, Simulation, and Experiment. Frontiers in
Molecular Biosciences, 2016, 3(52), https://doi.org/10.3389/fmolb.2016.00052.
[7] Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V.,
Renfrew, P. D., Mulligan, V. K., Kappel, K., Labonte, J. W., Pacella, M. S., Bonneau, R., Bradley, P.,
Dunbrack, R. L., Das, R., Baker, D., Kuhlman, B., Kortemme, T., & Gray, J. J. The Rosetta all-atom
energy function for macromolecular modeling and design. Journal of Chemical Theory and
Computation, 2017, 13, 3031–3048. https://doi.org/10.1021/ACS.JCTC.7B00125.
[8] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K.,
Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A.,
Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E.,
Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O.,
Senior, A. W., Kavukcuoglu, K., Kohli, P., & Hassabis, D. Highly accurate protein structure prediction
with AlphaFold. Nature, 2021, 596, 583–589. https://doi.org/10.1038/S41586-021-03819-2.
[9] Chaker-Margot, M., Barandun, J., Hunziker, M., & Klinge, S. Architecture of the yeast small subunit
processome. Science, 2017, 355. https://doi.org/10.1126/SCIENCE.AAL1880.
[10] Liu, R., Xia, S., & Li, H. Native top-down mass spectrometry for higher-order structural
characterization of proteins and complexes. Mass Spectrometry Reviews, 2023, 42. https://doi.org/
10.1002/MAS.21793.
280 Anuradha Mehra et al.
https://t.me/med1917
[11] Ovchinnikov, S., Park, H., Varghese, N., Huang, P. S., Pavlopoulos, G. A., Kim, D. E., Kamisetty, H.,
Kyrpides, N. C., & Baker, D. Protein structure determination using metagenome sequence data.
Science, 2017, 355, 294–298. https://doi.org/10.1126/SCIENCE.AAH4043.
[12] Uversky, V. N. Intrinsic disorder, protein-protein interactions, and disease. Advances in Protein
Chemistry and Structural Biology, 2018, 110, 85–121. https://doi.org/10.1016/BS.APCSB.2017.06.005.
[13] Maturi, V., Morén, A., Enroth, S., Heldin, C. H., & Moustakas, A. Genomewide binding of
transcription factor Snail1 in triple-negative breast cancer cells. Molecular Oncology, 2018, 12,
1153–1174. https://doi.org/10.1002/1878-0261.12317.
[14] Zhu, Q., Venzke, D., Walimbe, A. S., Anderson, M. E., Fu, Q., Kinch, L. N., Wang, W., Chen, X., Grishin,
N. V., Huang, N., Yu, L., Dixon, J. E., Campbell, K. P., & Xiao, J. Structure of protein O-mannose kinase
reveals a unique active site architecture. Elife, 2016, 5. https://doi.org/10.7554/ELIFE.22238.
[15] Adams, P. D., Afonine, P. V., Bunkóczi, G., Chen, V. B., Echols, N., Headd, J. J., Hung, L. W., Jain, S.,
Kapral, G. J., Grosse Kunstleve, R. W., McCoy, A. J., Moriarty, N. W., Oeffner, R. D., Read, R. J.,
Richardson, D. C., Richardson, J. S., Terwilliger, T. C., & Zwart, P. H. The Phenix software for
automated determination of macromolecular structures. Methods, 2011, 55, 94–106. https://doi.
org/10.1016/J.YMETH.2011.07.005.
[16] Lasker, K., Förster, F., Bohn, S., Walzthoeni, T., Villa, E., Unverdorben, P., Beck, F., Aebersold, R., Sali,
A., & Baumeister, W. Molecular architecture of the 26S proteasome holocomplex determined by an
integrative approach. Proceedings of the National Academy of Sciences, 2012, 109, 1380–1387.
https://doi.org/10.1073/PNAS.1120559109/-/DCSUPPLEMENTAL/PNAS.1120559109_SI.PDF.
[17] Goulding, C. W., & Perry, L. J. Protein production in Escherichia coli for structural studies by X-ray
crystallography. Journal of Structural Biology, 2003, 142, 133–143. https://doi.org/10.1016/S1047-
8477(03)00044-3.
[18] Maeda, T., Tanaka, Y., Takemoto, N., Hamamoto, N., & Inui, M. RNase III mediated cleavage of the
coding region of mraZ mRNA is required for efficient cell division in Corynebacterium glutamicum.
Molecular Microbiology, 2016, 99, 1149–1166. https://doi.org/10.1111/MMI.13295.
[19] Sahu, R., & Nayar, D. Crowding effects on water-mediated hydrophobic interactions. Journal of
Chemical Physics, 2021, 155. https://doi.org/10.1063/5.0054410.
[20] Klinge, S., Voigts-Hoffmann, F., Leibundgut, M., Arpagaus, S., & Ban, N. Crystal structure of the
eukaryotic 60S ribosomal subunit in complex with initiation factor 6. Science, 2011, 334, 941–948.
https://doi.org/10.1126/SCIENCE.1211204.
[21] Zivanov, J., Nakane, T., Forsberg, B. O., Kimanius, D., Hagen, W. J. H., Lindahl, E., & Scheres,
S. H. W. New tools for automated high-resolution cryo-EM structure determination in RELION-3.
Elife, 2018, 7. https://doi.org/10.7554/ELIFE.42166.
[22] Baradaran, R., Berrisford, J. M., Minhas, G. S., & Sazanov, L. A. Crystal structure of the entire
respiratory complex I. Nature, 2013, 494, 443–448. https://doi.org/10.1038/NATURE11871.
[23] Ameh, E. S. A review of basic crystallography and x-ray diffraction applications. International Journal
of Advanced Manufacturing Technology, 2019, 105, 3289–3302. https://doi.org/10.1007/S00170-019-
04508-1.
[24] Maveyraud, L., & Mourey, L. Protein X-ray crystallography and drug discovery. Molecules, 2020, 25.
https://doi.org/10.3390/MOLECULES25051030.
[25] Zia, K., Siddiqui, T., Ali, S., Farooq, I., Zafar, M. S., & Khurshid, Z. Nuclear magnetic resonance
spectroscopy for medical and dental applications: A comprehensive review. European Journal of
Dentistry, 2019, 13, 124–128. https://doi.org/10.1055/S-0039-1688654.
[26] Ahluwalia, V. K. Nuclear magnetic resonance (NMR) spectroscopy. Instrumental Methods of
Chemical Analysis, 2023, 271–343. https://doi.org/10.1007/978-3-031-38355-7_25.
[27] Danev, R., Yanagisawa, H., & Kikkawa, M. Cryo-electron microscopy methodology: Current aspects
and future directions. Trends in Biochemical Sciences, 2019, 44, 837–848. https://doi.org/10.1016/J.
TIBS.2019.04.008.
11 Role of structural genomics in drug discovery 281
https://t.me/med1917
[28] Fernandez-Leiro, R., & Scheres, S. H. W. Unravelling biological macromolecules with cryo-electron
microscopy. Nature, 2016, 537, 339–346. https://doi.org/10.1038/NATURE19948.
[29] Muhammed, M. T., & Aki-Yalcin, E. Homology modeling in drug discovery: Overview, current
applications, and future perspectives. Chemical Biology & Drug Design, 2019, 93, 12–20. https://doi.
org/10.1111/CBDD.13388.
[30] Cavasotto, C. N., & Phatak, S. S. Homology modeling in drug discovery: Current trends and
applications. Drug Discovery Today, 2009, 14, 676–683. https://doi.org/10.1016/J.DRUDIS.2009.
04.006.
[31] Rout, M. P., & Sali, A. Principles for integrative structural biology studies. Cell, 2019, 177, 1384–1403.
https://doi.org/10.1016/J.CELL.2019.05.016.
[32] Sali, A. From integrative structural biology to cell biology. Journal of Biological Chemistry, 2021, 296.
https://doi.org/10.1016/J.JBC.2021.100743.
[33] Rao, V. S., Srinivas, K., Sujini, G. N., & Kumar, G. N. S. Protein-protein interaction detection: Methods
and analysis. International Journal of Proteomics, 2014, 2014, 1–12. https://doi.org/10.1155/2014/
147648.
[34] Zhang, Q. C., Petrey, D., Deng, L., Qiang, L., Shi, Y., Thu, C. A., Bisikirska, B., Lefebvre, C., Accili, D.,
Hunter, T., Maniatis, T., Califano, A., & Honig, B. Structure-based prediction of protein-protein
interactions on a genome-wide scale. Nature, 2012, 490, 556–560. https://doi.org/10.1038/
NATURE11503.
[35] Dago, A. E., Schug, A., Procaccini, A., Hoch, J. A., Weigt, M., & Szurmant, H. Structural basis of
histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and
mutagenesis. Proceedings of the National Academy of Sciences, 2012, 109. https://doi.org/10.1073/
PNAS.1201301109.
[36] Osman, K. T., & Edwards, A. Structural genomics of human proteins. Methods in Molecular Biology,
2014, 1140, 27–34. https://doi.org/10.1007/978-1-4939-0354-2_2.
[37] Mallela, K. Pharmaceutical biotechnology – Concepts and applications. Human Genetics, 2010, 4,
218. https://doi.org/10.1186/1479-7364-4-3-218.
[38] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K.,
Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A.,
Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E.,
Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O.,
Senior, A. W., Kavukcuoglu, K., Kohli, P., & Hassabis, D. Highly accurate protein structure prediction
with AlphaFold. Nature, 2021, 596, 583–589. https://doi.org/10.1038/S41586-021-03819-2.
[39] Seong, K., & Krasileva, K. V. Prediction of effector protein structures from fungal phytopathogens
enables evolutionary analyses. Nature Microbiology, 2023 2023, 8:1. 8, 174–187. https://doi.org/10.
1038/s41564-022-01287-6.
[40] Naqvi, A. A. T., Fatima, K., Mohammad, T., Fatima, U., Singh, I. K., Singh, A., Atif, S. M., Hariprasad,
G., Hasan, G. M., & Hassan, M. I. Insights into SARS-CoV-2 genome, structure, evolution,
pathogenesis and therapies: Structural genomics approach. Biochimica Et Biophysica Acta (BBA)-
molecular Basis of Disease, 2020, 1866. https://doi.org/10.1016/J.BBADIS.2020.165878.
[41] Wellenreuther, M., Mérot, C., Berdan, E., & Bernatchez, L. Going beyond SNPs: The role of structural
genomic variants in adaptive evolution and species diversification. Molecular Ecology, 2019, 28,
1203–1209. https://doi.org/10.1111/MEC.15066.
[42] Srinivasan, S., Cui, H., Gao, Z., Liu, M., Lu, S., Mkandawire, W., Narykov, O., Sun, M., & Korkin,
D. Structural genomics of SARS-CoV-2 indicates evolutionary conserved functional regions of viral
proteins. Viruses, 2020, 12. https://doi.org/10.3390/V12040360.
[43] Audano, P. A., Sulovari, A., Graves-Lindsay, T. A., Cantsilieris, S., Sorensen, M., Welch, A. M. E.,
Dougherty, M. L., Nelson, B. J., Shah, A., Dutcher, S. K., Warren, W. C., Magrini, V., McGrath, S. D., Li,
282 Anuradha Mehra et al.
https://t.me/med1917
Y. I., Wilson, R. K., & Eichler, E. E. Characterizing the major structural variant alleles of the human
genome. Cell, 2019, 176, 663–675. https://doi.org/10.1016/J.CELL.2018.12.019.
[44] Ho, S. S., Urban, A. E., & Mills, R. E. Structural variation in the sequencing era. Nature Reviews
Genetics, 2020, 21, 171–189. https://doi.org/10.1038/S41576-019-0180-9.
[45] Adamson, C. S., Chibale, K., Goss, R. J. M., Jaspars, M., Newman, D. J., & Dorrington, R. A. Antiviral
drug discovery: Preparing for the next pandemic. Chemical Society Reviews, 2021, 50, 3647–3655.
https://doi.org/10.1039/D0CS01118E.
[46] Menéndez-Arias, L., & Gago, F. Antiviral agents: Structural basis of action and rational design. Sub-
Cellular Biochemistry, 2013, 68, 599–630. https://doi.org/10.1007/978-94-007-6552-8_20.
[47] Roviello, G., Aprile, G., D’Angelo, A., Iannone, L. F., Roviello, F., Polom, K., Mini, E., & Catalano,
M. Human epidermal growth factor receptor 2 (HER2) in advanced gastric cancer: Where do we
stand? Gastric Cancer, 2021, 24, 765–779. https://doi.org/10.1007/S10120-021-01182-9.
[48] Furrer, D., Paquet, C., Jacob, S., Diorio, C., Furrer, D., Paquet, C., Jacob, S., & Diorio, C. The human
epidermal growth factor receptor 2 (HER2) as a prognostic and predictive biomarker: molecular
insights into HER2 activation and diagnostic implications. Cancer Prognosis, 2018. https://doi.org/
10.5772/INTECHOPEN.78271.
[49] Kovacs, E., Zorn, J. A., Huang, Y., Barros, T., & Kuriyan, J. A structural perspective on the regulation of
the epidermal growth factor receptor. Annual Review of Biochemistry, 2015, 84, 739–764.
https://doi.org/10.1146/ANNUREV-BIOCHEM-060614-034402.
[50] Lim, H., He, D., Qiu, Y., Krawczuk, P., Sun, X., & Xie, L. Rational discovery of dual-indication multi-
target PDE/kinase inhibitor for precision anti-cancer therapy using structural systems
pharmacology. PLOS Computational Biology, 2019, 15. https://doi.org/10.1371/JOURNAL.PCBI.
1006619.
[51] Bhullar, K. S., Lagarón, N. O., McGowan, E. M., Parmar, I., Jha, A., Hubbard, B. P., & Rupasinghe,
H. P. V. Kinase-targeted cancer therapies: Progress, challenges and future directions. Molecular
Cancer, 2018, 17. https://doi.org/10.1186/S12943-018-0804-2.
[52] Cui, W., Yang, K., & Yang, H. Recent progress in the drug development targeting SARS-CoV-2 main
protease as treatment for COVID-19. Frontiers in Molecular Biosciences, 2020, 7. https://doi.org/10.
3389/FMOLB.2020.616341.
[53] Wu, C. R., Yin, W. C., Jiang, Y., & Xu, H. E. Structure genomics of SARS-CoV-2 and its Omicron variant:
Drug design templates for COVID-19. Acta Pharmacologica Sinica, 2022, 43, 3021–3033. https://doi.
org/10.1038/S41401-021-00851-W.
[54] Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. Role of computer-aided drug design in modern drug
discovery. Archives of Pharmacal Research, 2015, 38, 1686–1701. https://doi.org/10.1007/S12272-015-
0640-5.
[55] Achary, P. G. R. Applications of quantitative structure-activity relationships (QSAR) based virtual
screening in drug design: A review. Mini-Reviews in Medicinal Chemistry, 2020, 20, 1375–1388.
https://doi.org/10.2174/1389557520666200429102334.
[56] Basith, S., Cui, M., Macalino, S. J. Y., Park, J., Clavio, N. A. B., Kang, S., & Choi, S. Exploring G Protein-
coupled receptors (GPCRs) ligand space via cheminformatics approaches: Impact on rational drug
design. Frontiers in Pharmacology, 2018, 9. https://doi.org/10.3389/FPHAR.2018.00128.
[57] Naderi, M., Lemoine, J. M., Govindaraj, R. G., Kana, O. Z., Feinstein, W. P., & Brylinski, M. Binding site
matching in rational drug design: Algorithms and applications. Briefings in Bioinformatics, 2019, 20,
2167–2184. https://doi.org/10.1093/BIB/BBY078.
[58] Li, Q., & Shah, S. Structure-based virtual screening. Methods in Molecular Biology, 2017, 1558,
111–124. https://doi.org/10.1007/978-1-4939-6783-4_5.
[59] Nazarshodeh, E., Marashi, S. A., & Gharaghani, S. Structural systems pharmacology: A framework
for integrating metabolic network and structure-based virtual screening for drug discovery against
bacteria. PLoS One, 2021, 16. https://doi.org/10.1371/JOURNAL.PONE.0261267.
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