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Ullah S, Ullah A, Waqas M, Halim SA, Pasha AR, Shafiq Z, Mali SN, Jawarkar RD, Khan A, Khalid A, Abdalla AN, Kashtoh H, Al-Harrasi A. Structural, dynamic behaviour, in-vitro and computational investigations of Schiff's bases of 1,3-diphenyl urea derivatives against SARS-CoV-2 spike protein. Sci Rep 2024; 14:12588. [PMID: 38822113 PMCID: PMC11143201 DOI: 10.1038/s41598-024-63345-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/28/2024] [Indexed: 06/02/2024] Open
Abstract
The COVID-19 has had a significant influence on people's lives across the world. The viral genome has undergone numerous unanticipated changes that have given rise to new varieties, raising alarm on a global scale. Bioactive phytochemicals derived from nature and synthetic sources possess lot of potential as pathogenic virus inhibitors. The goal of the recent study is to report new inhibitors of Schiff bases of 1,3-dipheny urea derivatives against SARS COV-2 spike protein through in-vitro and in-silico approach. Total 14 compounds were evaluated, surprisingly, all the compounds showed strong inhibition with inhibitory values between 79.60% and 96.00% inhibition. Here, compounds 3a (96.00%), 3d (89.60%), 3e (84.30%), 3f (86.20%), 3g (88.30%), 3h (86.80%), 3k (82.10%), 3l (90.10%), 3m (93.49%), 3n (85.64%), and 3o (81.79%) exhibited high inhibitory potential against SARS COV-2 spike protein. While 3c also showed significant inhibitory potential with 79.60% inhibition. The molecular docking of these compounds revealed excellent fitting of molecules in the spike protein receptor binding domain (RBD) with good interactions with the key residues of RBD and docking scores ranging from - 4.73 to - 5.60 kcal/mol. Furthermore, molecular dynamics simulation for 150 ns indicated a strong stability of a complex 3a:6MOJ. These findings obtained from the in-vitro and in-silico study reflect higher potency of the Schiff bases of 1,3-diphenyl urea derivatives. Furthermore, also highlight their medicinal importance for the treatment of SARS COV-2 infection. Therefore, these small molecules could be a possible drug candidate.
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Affiliation(s)
- Saeed Ullah
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
| | - Atta Ullah
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
| | - Muhammad Waqas
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
| | - Sobia Ahsan Halim
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
| | - Anam Rubbab Pasha
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, 60800, Pakistan
| | - Zahid Shafiq
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, 60800, Pakistan.
| | - Suraj N Mali
- School of Pharmacy, D.Y. Patil University (Deemed to be University), Sector 7, Nerul, Navi Mumbai, 400706, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr. Rajendra Gode Institute of Pharmacy, University Mardi Road, Amravati, 444603, India
| | - Ajmal Khan
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman.
| | - Asaad Khalid
- Substance Abuse and Toxicology Research Center, Jazan University, P.O. Box: 114, 45142, Jazan, Saudi Arabia
| | - Ashraf N Abdalla
- Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, 21955, Makkah, Saudi Arabia
| | - Hamdy Kashtoh
- Department of Biotechnology, Yeungnam University, Gyeongsan, 38541, Gyeongbuk, Republic of Korea.
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman.
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2
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Kole A, Bag AK, Pal AJ, De D. Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology. BMC Bioinformatics 2024; 25:74. [PMID: 38365632 PMCID: PMC10874019 DOI: 10.1186/s12859-024-05690-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024] Open
Abstract
PURPOSE Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.
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Affiliation(s)
- Arnab Kole
- Department of Computer Application, The Heritage Academy, Kolkata, W.B., 700107, India.
| | - Arup Kumar Bag
- Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA
| | | | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Nadia, W.B., 741249, India
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Ullah A, Ullah S, Halim SA, Waqas M, Ali B, Ataya FS, El-Sabbagh NM, Batiha GES, Avula SK, Csuk R, Khan A, Al-Harrasi A. Identification of new pharmacophore against SARS-CoV-2 spike protein by multi-fold computational and biochemical techniques. Sci Rep 2024; 14:3590. [PMID: 38351259 PMCID: PMC10864406 DOI: 10.1038/s41598-024-53911-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 02/06/2024] [Indexed: 02/16/2024] Open
Abstract
COVID-19 appeared as a highly contagious disease after its outbreak in December 2019 by the virus, named SARS-CoV-2. The threat, which originated in Wuhan, China, swiftly became an international emergency. Among different genomic products, spike protein of virus plays a crucial role in the initiation of the infection by binding to the human lung cells, therefore, SARS-CoV-2's spike protein is a promising therapeutic target. Using a combination of a structure-based virtual screening and biochemical assay, this study seeks possible therapeutic candidates that specifically target the viral spike protein. A database of ~ 850 naturally derived compounds was screened against SARS-CoV-2 spike protein to find natural inhibitors. Using virtual screening and inhibitory experiments, we identified acetyl 11-keto-boswellic acid (AKBA) as a promising molecule for spike protein, which encouraged us to scan the rest of AKBA derivatives in our in-house database via 2D-similarity searching. Later 19 compounds with > 85% similarity with AKBA were selected and docked with receptor binding domain (RBD) of spike protein. Those hits declared significant interactions at the RBD interface, best possess and excellent drug-likeness and pharmacokinetics properties with high gastrointestinal absorption (GIA) without toxicity and allergenicity. Our in-silico observations were eventually validated by in vitro bioassay, interestingly, 10 compounds (A3, A4, C3, C6A, C6B, C6C, C6E, C6H, C6I, and C6J) displayed significant inhibitory ability with good percent inhibition (range: > 72-90). The compounds C3 (90.00%), C6E (91.00%), C6C (87.20%), and C6D (86.23%) demonstrated excellent anti-SARS CoV-2 spike protein activities. The docking interaction of high percent inhibition of inhibitor compounds C3 and C6E was confirmed by MD Simulation. In the molecular dynamics simulation, we observed the stable dynamics of spike protein inhibitor complexes and the influence of inhibitor binding on the protein's conformational arrangements. The binding free energy ΔGTOTAL of C3 (-38.0 ± 0.08 kcal/mol) and C6E (-41.98 ± 0.08 kcal/mol) respectively indicate a strong binding affinity to Spike protein active pocket. These findings demonstrate that these molecules particularly inhibit the function of spike protein and, therefore have the potential to be evaluated as drug candidates against SARS-CoV-2.
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Affiliation(s)
- Atta Ullah
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-Ul-Mouz, P.O Box 33, Postal Code 616, Nizwa, Sultanate of Oman
| | - Saeed Ullah
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-Ul-Mouz, P.O Box 33, Postal Code 616, Nizwa, Sultanate of Oman
| | - Sobia Ahsan Halim
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-Ul-Mouz, P.O Box 33, Postal Code 616, Nizwa, Sultanate of Oman
| | - Muhammad Waqas
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-Ul-Mouz, P.O Box 33, Postal Code 616, Nizwa, Sultanate of Oman
| | - Basharat Ali
- Sulaiman Bin Abdullah Aba Al-Khail-Centre for Interdisciplinary Research in Basic Sciences (SA-CIRBS), International Islamic University, Islamabad, Pakistan
| | - Farid S Ataya
- Department of Biochemistry, College of Science, King Saud University, PO Box 2455, 11451, Riyadh, Saudi Arabia
| | - Nasser M El-Sabbagh
- Department of Veterinary Pharmacology, Faculty of Veterinary Medicine, Alexandria University, Edfina, Egypt
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, 22511, AlBeheira, Egypt
| | - Satya Kumar Avula
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-Ul-Mouz, P.O Box 33, Postal Code 616, Nizwa, Sultanate of Oman
| | - Rene Csuk
- Organic Chemistry, Martin-Luther-University Halle-Wittenberg, Kurt-Mothes-Str. 2, 06120, Halle (Saale), Germany
| | - Ajmal Khan
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-Ul-Mouz, P.O Box 33, Postal Code 616, Nizwa, Sultanate of Oman.
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat-Ul-Mouz, P.O Box 33, Postal Code 616, Nizwa, Sultanate of Oman.
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4
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Yang ZS, Li TS, Huang YS, Chang CC, Chien CM. Targeting the receptor binding domain and heparan sulfate binding for antiviral drug development against SARS-CoV-2 variants. Sci Rep 2024; 14:2753. [PMID: 38307890 PMCID: PMC10837157 DOI: 10.1038/s41598-024-53111-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
The emergence of SARS-CoV-2 variants diminished the efficacy of current antiviral drugs and vaccines. Hence, identifying highly conserved sequences and potentially druggable pockets for drug development was a promising strategy against SARS-CoV-2 variants. In viral infection, the receptor-binding domain (RBD) proteins are essential in binding to the host receptor. Others, Heparan sulfate (HS), widely distributed on the surface of host cells, is thought to play a central role in the viral infection cycle of SARS-CoV-2. Therefore, it might be a reasonable strategy for antiviral drug design to interfere with the RBD in the HS binding site. In this study, we used computational approaches to analyze multiple sequences of coronaviruses and reveal important information about the binding of HS to RBD in the SARS-CoV-2 spike protein. Our results showed that the potential hot-spots, including R454 and E471, in RBD, exhibited strong interactions in the HS-RBD binding region. Therefore, we screened different compounds in the natural product database towards these hot-spots to find potential antiviral candidates using LibDock, Autodock vina and furthermore applying the MD simulation in AMBER20. The results showed three potential natural compounds, including Acetoside (ACE), Hyperoside (HYP), and Isoquercitrin (ISO), had a strong affinity to the RBD. Our results demonstrate a feasible approach to identify potential antiviral agents by evaluating the binding interaction between viral glycoproteins and host receptors. The present study provided the applications of the structure-based computational approach for designing and developing of new antiviral drugs against SARS-CoV-2 variants.
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Affiliation(s)
- Zi-Sin Yang
- Department of Medical Sciences Industry, College of Health Sciences, Chang Jung Christian University, Tainan, 711, Taiwan
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 300, Taiwan
| | - Tzong-Shiun Li
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung, 402, Taiwan
- Department of Plastic Surgery, Chang Bing Show Chwan Memorial Hospital, Changhua, 500, Taiwan
| | - Yu-Sung Huang
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, 300, Taiwan
| | - Cheng-Chung Chang
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, Taichung, 402, Taiwan
| | - Ching-Ming Chien
- Department of Medical Sciences Industry, College of Health Sciences, Chang Jung Christian University, Tainan, 711, Taiwan.
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5
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Çınaroğlu S, Biggin PC. Computed Protein-Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems. J Chem Inf Model 2023; 63:6095-6108. [PMID: 37759363 PMCID: PMC10565830 DOI: 10.1021/acs.jcim.3c01041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Indexed: 09/29/2023]
Abstract
Understanding the thermodynamic signature of protein-peptide binding events is a major challenge in computational chemistry. The complexity generated by both components possessing many degrees of freedom poses a significant issue for methods that attempt to directly compute the enthalpic contribution to binding. Indeed, the prevailing assumption has been that the errors associated with such approaches would be too large for them to be meaningful. Nevertheless, we currently have no indication of how well the present methods would perform in terms of predicting the enthalpy of binding for protein-peptide complexes. To that end, we carefully assembled and curated a set of 11 protein-peptide complexes where there is structural and isothermal titration calorimetry data available and then computed the absolute enthalpy of binding. The initial "out of the box" calculations were, as expected, very modest in terms of agreement with the experiment. However, careful inspection of the outliers allows for the identification of key sampling problems such as distinct conformations of peptide termini not being sampled or suboptimal cofactor parameters. Additional simulations guided by these aspects can lead to a respectable correlation with isothermal titration calorimetry (ITC) experiments (R2 of 0.88 and an RMSE of 1.48 kcal/mol overall). Although one cannot know prospectively whether computed ITC values will be correct or not, this work shows that if experimental ITC data are available, then this in conjunction with computed ITC, can be used as a tool to know if the ensemble being simulated is representative of the true ensemble or not. That is important for allowing the correct interpretation of the detailed dynamics of the system with respect to the measured enthalpy. The results also suggest that computational calorimetry is becoming increasingly feasible. We provide the data set as a resource for the community, which could be used as a benchmark to help further progress in this area.
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Affiliation(s)
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, U.K.
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6
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Okada S, Muto Y, Zhu B, Ueda H, Nakamura H. Development of a Peptide Sensor Derived from Human ACE2 for Fluorescence Polarization Assays of the SARS-CoV-2 Receptor Binding Domain. Anal Chem 2023; 95:6198-6202. [PMID: 37028948 PMCID: PMC10107661 DOI: 10.1021/acs.analchem.2c05818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/05/2023] [Indexed: 04/09/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the continuing emergence of infectious variants have caused a serious pandemic and a global economic slump since 2019. To overcome the situation and prepare for future pandemic-prone diseases, there is a need to establish a convenient diagnostic test that is quickly adaptable to unexpected emergence of virus variants. Here we report a fluorescent peptide sensor 26-Dan and its application to the fluorescence polarization (FP) assay for the highly sensitive and convenient detection of SARS-CoV-2. The 26-Dan sensor was developed by fluorescent labeling of the 26th amino acid of a peptide derived from the N-terminal α-helix of human angiotensin-converting enzyme 2 (hACE2) receptor. The 26-Dan sensor maintained the α-helical structure and showed FP changes in a concentration-dependent manner of the receptor binding domain (RBD) of the virus. The half maximal effective concentrations (EC50's) for RBD of Wuhan-Hu-1 strain, Delta (B.1.617.2), and Omicron (BA.5) variants were 51, 5.2, and 2.2 nM, respectively, demonstrating that the 26-Dan-based FP assay can be adaptable to virus variants that evade standard diagnostic tests. The 26-Dan-based FP assay could also be applied to model screening of a small molecule that inhibits RBD binding to hACE2 and identified glycyrrhizin as a potential inhibitor. The combination of the sensor with a portable microfluidic fluorescence polarization analyzer allowed for the detection of RBD in a femtomolar range within 3 min, demonstrating the assay could be a promising step toward a rapid and convenient test for SARS-CoV-2 and other possible future pandemic-prone diseases.
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Affiliation(s)
- Satoshi Okada
- Laboratory
for Chemistry and Life Science, Institute
of Innovative Research, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
- School
of Life Science and Technology, Tokyo Institute
of Technology, 4259 Nagatsuta-cho,
Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Yuka Muto
- School
of Life Science and Technology, Tokyo Institute
of Technology, 4259 Nagatsuta-cho,
Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Bo Zhu
- Laboratory
for Chemistry and Life Science, Institute
of Innovative Research, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Hiroshi Ueda
- Laboratory
for Chemistry and Life Science, Institute
of Innovative Research, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
- World
Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
| | - Hiroyuki Nakamura
- Laboratory
for Chemistry and Life Science, Institute
of Innovative Research, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
- School
of Life Science and Technology, Tokyo Institute
of Technology, 4259 Nagatsuta-cho,
Midori-ku, Yokohama, Kanagawa 226-8503, Japan
- World
Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
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7
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Gurusinghe SN, Oppenheimer B, Shifman JM. Cold spots are universal in protein–protein interactions. Protein Sci 2022; 31:e4435. [PMID: 36173158 PMCID: PMC9490803 DOI: 10.1002/pro.4435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 07/22/2022] [Accepted: 08/26/2022] [Indexed: 12/02/2022]
Abstract
Proteins interact with each other through binding interfaces that differ greatly in size and physico‐chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein–protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo‐ and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high‐affinity complexes showing fewer centrally located colds spots than low‐affinity complexes. A larger number of cold spots is also found in non‐cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo‐dimeric complexes compared to hetero‐complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.
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Affiliation(s)
- Sagara N.S. Gurusinghe
- Department of Biological Chemistry The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem Jerusalem Israel
| | - Ben Oppenheimer
- Department of Biological Chemistry The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem Jerusalem Israel
| | - Julia M. Shifman
- Department of Biological Chemistry The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem Jerusalem Israel
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Abstract
![]()
AlphaFold has burst into our lives. A powerful algorithm
that underscores
the strength of biological sequence data and artificial intelligence
(AI). AlphaFold has appended projects and research directions. The
database it has been creating promises an untold number of applications
with vast potential impacts that are still difficult to surmise. AI
approaches can revolutionize personalized treatments and usher in
better-informed clinical trials. They promise to make giant leaps
toward reshaping and revamping drug discovery strategies, selecting
and prioritizing combinations of drug targets. Here, we briefly overview
AI in structural biology, including in molecular dynamics simulations
and prediction of microbiota–human protein–protein interactions.
We highlight the advancements accomplished by the deep-learning-powered
AlphaFold in protein structure prediction and their powerful impact
on the life sciences. At the same time, AlphaFold does not resolve
the decades-long protein folding challenge, nor does it identify the
folding pathways. The models that AlphaFold provides do not capture
conformational mechanisms like frustration and allostery, which are
rooted in ensembles, and controlled by their dynamic distributions.
Allostery and signaling are properties of populations. AlphaFold also
does not generate ensembles of intrinsically disordered proteins and
regions, instead describing them by their low structural probabilities.
Since AlphaFold generates single ranked structures, rather than conformational
ensembles, it cannot elucidate the mechanisms of allosteric activating
driver hotspot mutations nor of allosteric drug resistance. However,
by capturing key features, deep learning techniques can use the single
predicted conformation as the basis for generating a diverse ensemble.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
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9
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Cong X, Zhang X, Liang X, He X, Tang Y, Zheng X, Lu S, Zhang J, Chen T. Delineating the conformational landscape and intrinsic properties of the angiotensin II type 2 receptor using a computational study. Comput Struct Biotechnol J 2022; 20:2268-2279. [PMID: 35615027 PMCID: PMC9117689 DOI: 10.1016/j.csbj.2022.05.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 12/22/2022] Open
Abstract
As a key regulator for the renin-angiotensin system, a class A G protein-coupled receptor (GPCR), AngII type 2 receptor (AT2R), plays a pivotal role in the homeostasis of the cardiovascular system. Compared with other GPCRs, AT2R has a unique antagonist-bound conformation and its mechanism is still an enigma. Here, we applied combined dynamic and evolutional approaches to investigate the conformational space and intrinsic properties of AT2R. With molecular dynamic simulations, Markov State Models, and statistics coupled analysis, we captured the conformational landscape of AT2R and identified its uniquity from both dynamical and evolutional viewpoints. A cryptic pocket was also discovered in the intermediate state during conformation transitions. These findings offer a deeper understanding of the AT2R mechanism at an atomic level and provide hints for the design of novel AT2R modulators.
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Affiliation(s)
- Xiaoliang Cong
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
| | - Xiaogang Zhang
- Department of Cardiology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai 201318, China
| | - Xin Liang
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
| | - Xinheng He
- Medicinal Chemistry and Bioinformatics Centre, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Yehua Tang
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
| | - Xing Zheng
- Department of Cardiology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Shaoyong Lu
- Medicinal Chemistry and Bioinformatics Centre, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Corresponding authors.
| | - Jiayou Zhang
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
- Corresponding authors.
| | - Ting Chen
- Department of Cardiology, Shanghai Changzheng Hospital, the Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China
- Corresponding authors.
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10
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High predictive QSAR models for predicting the SARS coronavirus main protease inhibition activity of ketone-based covalent inhibitors. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2022. [PMCID: PMC8547569 DOI: 10.1007/s13738-021-02426-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In this research, a dataset including 29 ketone-based covalent inhibitors with SARS-CoV-1 3CLpro inhibition activity was used to develop high predictive QSAR models. Twenty-two molecules were put in train set and seven molecules in test set. By using stepwise MLR method for molecules in train set, four molecular descriptors including Mor26p, Hy, GATS7p and Mor04v were selected to build QSAR models. MLR and ANN methods were used to create QSAR models for predicting the activity of molecules in both train and test sets. Both QSAR models were validated by calculating several statistical parameters. R2 values for the test set of MLR and ANN models were 0.93 and 0.95, respectively, and RMSE values for their test sets were 0.24 and 0.17, respectively. Other calculated statistical parameters (especially \documentclass[12pt]{minimal}
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\begin{document}$$Q_{F3}^{2}$$\end{document}QF32 parameter) show that created ANN model has more predictive power with respect to developed MLR model (with four descriptor). Calculated leverages for all molecules show that predicted pIC50 (by both QSAR models) for all molecules is acceptable, and drawn residuals plots show that there is no systematic error in building both QSAR modes. Also, based on developed MLR model, used molecular descriptors were interpreted.
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Elwakeel A. Abrogating the Interaction Between p53 and Mortalin (Grp75/HSPA9/mtHsp70) for Cancer Therapy: The Story so far. Front Cell Dev Biol 2022; 10:879632. [PMID: 35493098 PMCID: PMC9047732 DOI: 10.3389/fcell.2022.879632] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 03/15/2022] [Indexed: 11/22/2022] Open
Abstract
p53 is a transcription factor that activates the expression of a set of genes that serve as a critical barrier to oncogenesis. Inactivation of p53 is the most common characteristic in sporadic human cancers. Mortalin is a differentially sub-cellularly localized member of the heat shock protein 70 family of chaperones that has essential mitochondrial and extra-mitochondrial functions. Elevated mortalin levels in multiple cancerous tissues and tumor-derived cell lines emphasized its key role in oncogenesis. One of mortalin’s major oncogenic roles is the inactivation of p53. Mortalin binds to p53 sequestering it in the cytoplasm. Hence, p53 cannot freely shuttle to the nucleus to perform its tumor suppressor functions as a transcription factor. This protein-protein interaction was reported to be cancer-specific, hence, a selective druggable target for a rationalistic cancer therapeutic strategy. In this review article, the chronological identification of mortalin-p53 interactions is summarized, the challenges and general strategies for targeting protein-protein interactions are briefly discussed, and information about compounds that have been reported to abrogate mortalin-p53 interaction is provided. Finally, the reasons why the disruption of this druggable interaction has not yet been applied clinically are discussed.
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Lim H, Cankara F, Tsai CJ, Keskin O, Nussinov R, Gursoy A. Artificial intelligence approaches to human-microbiome protein–protein interactions. Curr Opin Struct Biol 2022; 73:102328. [DOI: 10.1016/j.sbi.2022.102328] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/01/2021] [Accepted: 12/31/2021] [Indexed: 02/08/2023]
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Marques-Pereira C, Pires MN, Gouveia RP, Pereira NN, Caniceiro AB, Rosário-Ferreira N, Moreira IS. SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights. Int J Mol Sci 2022; 23:2986. [PMID: 35328409 PMCID: PMC8948900 DOI: 10.3390/ijms23062986] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/07/2022] [Accepted: 03/09/2022] [Indexed: 01/27/2023] Open
Abstract
Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2's most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2.
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Affiliation(s)
- Catarina Marques-Pereira
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-535 Coimbra, Portugal; (C.M.-P.); (M.N.P.); (R.P.G.); (N.N.P.); (A.B.C.); (N.R.-F.)
- IIIs—Institute for Interdisciplinary Research, University of Coimbra, 3030-789 Coimbra, Portugal
| | - Manuel N. Pires
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-535 Coimbra, Portugal; (C.M.-P.); (M.N.P.); (R.P.G.); (N.N.P.); (A.B.C.); (N.R.-F.)
- Department of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Raquel P. Gouveia
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-535 Coimbra, Portugal; (C.M.-P.); (M.N.P.); (R.P.G.); (N.N.P.); (A.B.C.); (N.R.-F.)
| | - Nádia N. Pereira
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-535 Coimbra, Portugal; (C.M.-P.); (M.N.P.); (R.P.G.); (N.N.P.); (A.B.C.); (N.R.-F.)
| | - Ana B. Caniceiro
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-535 Coimbra, Portugal; (C.M.-P.); (M.N.P.); (R.P.G.); (N.N.P.); (A.B.C.); (N.R.-F.)
| | - Nícia Rosário-Ferreira
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-535 Coimbra, Portugal; (C.M.-P.); (M.N.P.); (R.P.G.); (N.N.P.); (A.B.C.); (N.R.-F.)
- CQC—Coimbra Chemistry Center, Chemistry Department, Faculty of Science and Technology, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Irina S. Moreira
- Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-535 Coimbra, Portugal
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Guo Y, Esfahani F, Shao X, Srinivasan V, Thomo A, Xing L, Zhang X. Integrative COVID-19 biological network inference with probabilistic core decomposition. Brief Bioinform 2022; 23:6425808. [PMID: 34791019 PMCID: PMC8689992 DOI: 10.1093/bib/bbab455] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/15/2021] [Accepted: 10/07/2021] [Indexed: 12/15/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.
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Affiliation(s)
- Yang Guo
- Department of Mathematics and Statistics, University of Victoria, 3800 Finnerty Road, V8P 5C2, Victoria, BC, Canada
| | - Fatemeh Esfahani
- Department of Computer Science, University of Victoria, 3800 Finnerty Road, V8P 5C2, Victoria, BC, Canada
| | - Xiaojian Shao
- Digital Technologies Research Centre, National Research Council Canada, 1200 Montreal Road, K1A 0R6, Ottawa, ON, Canada
| | - Venkatesh Srinivasan
- Department of Computer Science, University of Victoria, 3800 Finnerty Road, V8P 5C2, Victoria, BC, Canada
| | - Alex Thomo
- Department of Computer Science, University of Victoria, 3800 Finnerty Road, V8P 5C2, Victoria, BC, Canada
| | - Li Xing
- Department of Mathematics and Statistics, University of Saskatchewan, 110 Science Place, S7N 5A2, Saskatoon, SK, Canada
| | - Xuekui Zhang
- Corresponding author: Xuekui Zhang, Department of Mathematics and Statistics, University of Victoria, 3800 Finnerty Road, V8P 5C2, Victoria, BC, Canada.
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Ershov PV, Mezentsev YV, Ivanov AS. Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions. Biomolecules 2022; 12:106. [PMID: 35053254 PMCID: PMC8773757 DOI: 10.3390/biom12010106] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/06/2022] [Accepted: 01/06/2022] [Indexed: 12/25/2022] Open
Abstract
The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, "hot spots", as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ-TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.
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Affiliation(s)
- Pavel V. Ershov
- Institute of Biomedical Chemistry, 119121 Moscow, Russia; (Y.V.M.); (A.S.I.)
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Kosugi T, Ohue M. Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions. Int J Mol Sci 2021; 22:10925. [PMID: 34681589 PMCID: PMC8539639 DOI: 10.3390/ijms222010925] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/02/2021] [Accepted: 10/07/2021] [Indexed: 12/13/2022] Open
Abstract
Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the "Rule-of-Four" (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.
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Affiliation(s)
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, G3-56-4259 Nagatsutacho, Midori-ku, Yokohama 226-8501, Kanagawa, Japan;
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