Restriction of S-adenosylmethionine conformational freedom by knotted protein binding sites

AbstractS-adenosylmethionine (SAM) is one of the most important enzyme substrates. It is vital for the function of various proteins, including large group of methyltransferases (MTs). Intriguingly, some bacterial and eukaryotic MTs, while catalysing the same reaction, possess significantly different topologies, with the former being a knotted one. Here, we conducted a comprehensive analysis of SAM conformational space and factors that affect its vastness. We investigated SAM in two forms: free in water (via NMR studies and explicit solvent simulations) and bound to proteins (based on all data available in the PDB). We identified structural descriptors – angles which show the major differences in SAM conformation between unknotted and knotted methyltransferases. Moreover, we report that this is caused mainly by a characteristic for knotted MTs tight binding site formed by the knot and the presence of adenine-binding loop. Additionally, we elucidate conformational restrictions imposed on SAM molecules by other protein groups in comparison to conformational space in water.Author summaryThe topology of a folded polypeptide chain has great impact on the resulting protein function and its interaction with ligands. Interestingly, topological constraints appear to affect binding of one of the most ubiquitous substrates in the cell, S-adenosylmethionine (SAM), to its target proteins. Here, we demonstrate how binding sites of specific proteins restrict SAM conformational freedom in comparison to its unbound state, with a special interest in proteins with non-trivial topology, including an exciting group of knotted methyltransferases. Using a vast array of computational methods combined with NMR experiments, we identify key structural features of knotted methyltransferases that impose unorthodox SAM conformations. We compare them with the characteristics of standard, unknotted SAM binding proteins. These results are significant for understanding differences between analogous, yet topologically different enzymes, as well as for future rational drug design.

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.QSAR-QMMM Cryptographic Mining on Chern-Simons Topologies for the generation of a Ligand Targeting SARS-COV-2 D614G Binding Sites.

10.21203/rs.3.rs-128623/v2 ◽

2021 ◽

Author(s):

Ioannis Grigoriadis

Keyword(s):

Binding Sites ◽

High Accuracy ◽

Viral Transmission ◽

Rational Drug Design ◽

Design Strategies ◽

Geometric Methods ◽

Rational Drug ◽

Chern Simons ◽

Very High ◽

Over Time

Abstract SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. It has also been observed that retroviruses infected ACE2-expressing cells pseudotyped with SG614 that is presently affecting a growing number of countries markedly more efficiently than those with SD614. In this paper, we strongly combine topology geometric methods targeting at the atomistic level the protein apparatus of the SARS-COV-2 virus that are simple in machine learning anti-viral characteristics, to propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of the AI-Quantum designed molecule of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM ligands with Preferred IUPAC Names of (7aR)‐5‐amino‐N‐[(S)‐ {2‐[(S)‐[(E)‐(amino methylidene)amino](cyano)methyl]hydrazin‐1‐yl} (aziridin‐1‐yl)phosphoryl]‐ 1‐[(2E)‐2‐ [(fluoromethanimidoyl) imino]acetyl]‐7‐oxo‐1H,7H,7aH‐pyrazolo[4,3‐d]pyrimidine‐3‐carboxamide; N‐{[(2‐ amino‐6‐oxo‐6,9‐dihydro‐1H‐purin‐9‐yl)amino]({1‐[5‐({[cyano({1‐[(diamino methylidene)amino] ethenyl})amino]oxy}methyl)‐3,4‐dihydroxyoxolan‐2‐yl]‐1H‐1,2,4‐triazol‐3‐yl}formamido)phosphoryl}‐6‐fluoro‐3,4‐dihydropyrazine‐2‐carboxamide;[3‐(2‐amino‐5‐sulfanylidene‐1,2,4‐triazolidin‐3‐yl)oxaziridin‐2‐yl]({3‐sulfanylidene‐1,2,4,6‐tetraazabicyclo[3.1.0]hexan‐6‐yl})phosphoroso1‐(3,4,5‐trifluorooxolan‐2‐yl)‐1H‐1,2,4‐triazole‐3‐carboxylate targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation using Chern-Simons Topology Euclidean Geometric in a Lindenbaum-Tarski generated QSAR automating modeling and Artificial Intelligence-Driven Predictive Neural Networks.

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Computational molecular modelling as a platform for a deeper understanding of protein dynamics and rational drug design

Biomedical Science and Engineering ◽

10.4081/bse.2019.87 ◽

2020 ◽

Author(s):

Gianvito Grasso ◽

Lorenzo Pallante ◽

Jack A. Tuszynski ◽

Umberto Morbiducci ◽

Marco A. Deriu

Keyword(s):

Molecular Modelling ◽

Molecular Level ◽

Structural Features ◽

Rational Drug Design ◽

Therapeutic Approaches ◽

Amyloidogenic Proteins ◽

Rational Drug ◽

Aggregation Inhibitors ◽

The Stability ◽

Toxicity Pathways

Elucidating structural features of protein aggregation at molecular level may provide novel opportunities for overarching therapeutic approaches such as blocking common aggregation-induced cellular toxicity pathways. In this context molecular modelling stimulates further research on amyloid aggregation modulators and modelling platforms can be used to test the efficiency of potential aggregation inhibitors aimed at destabilizing/reducing the stability of the amyloidogenic proteins

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Structure of the human gonadotropin-releasing hormone receptor GnRH1R reveals an unusual ligand binding mode

Nature Communications ◽

10.1038/s41467-020-19109-w ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Wei Yan ◽

Lin Cheng ◽

Wei Wang ◽

Chao Wu ◽

Xin Yang ◽

...

Keyword(s):

Ligand Binding ◽

Reproductive Function ◽

Binding Mode ◽

Structural Features ◽

Docking Studies ◽

Rational Drug Design ◽

Releasing Hormone ◽

Promising Therapeutic Target ◽

Rational Drug ◽

Main Regulator

Abstract Gonadotrophin-releasing hormone (GnRH), also known as luteinizing hormone-releasing hormone, is the main regulator of the reproductive system, acting on gonadotropic cells by binding to the GnRH1 receptor (GnRH1R). The GnRH-GnRH1R system is a promising therapeutic target for maintaining reproductive function; to date, a number of ligands targeting GnRH1R for disease treatment are available on the market. Here, we report the crystal structure of GnRH1R bound to the small-molecule drug elagolix at 2.8 Å resolution. The structure reveals an interesting N-terminus that could co-occupy the enlarged orthosteric binding site together with elagolix. The unusual ligand binding mode was further investigated by structural analyses, functional assays and molecular docking studies. On the other hand, because of the unique characteristic of lacking a cytoplasmic C-terminal helix, GnRH1R exhibits different microswitch structural features from other class A GPCRs. In summary, this study provides insight into the ligand binding mode of GnRH1R and offers an atomic framework for rational drug design.

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Cyclin Dependent Kinase as a Novel Therapeutic Target: An Endless Story

Current Chemical Biology ◽

10.2174/2212796814999201123194016 ◽

2020 ◽

Vol 14 ◽

Author(s):

Ahmed Mohamed Etman ◽

Sherif Sabry Abdel Mageed ◽

Mohamed Ahmed Ali ◽

Mahmoud Abd El Monem El Hassab

Keyword(s):

Molecular Mechanisms ◽

Binding Pocket ◽

Structural Features ◽

Rational Drug Design ◽

Cyclin Dependent Kinase ◽

Cyclin Dependent Kinases ◽

Therapeutic Class ◽

Pharmacological Targets ◽

Rational Drug ◽

Cycle Regulation

Abstract:: Cyclin Dependent Kinases (CDKs) are a family of enzymes that along with their Cyclin partners play a crucial role in cell cycle regulation at many biological functions such as proliferation, differentiation, DNA repair and apoptosis. Thus, they are tightly regulated by a vast of inhibitory and activating enzymes. Deregulation of these kinases’ activity either by amplification, overexpression or mutation of CDKs or Cyclins leads to uncontrolled proliferation of cancer cells. Hyperactivity of these kinases has been reported in wide variety of human cancers. Hence, CDKs has been established as one of the most attractive pharmacological targets in the development of promising anticancer drugs. The elucidated structural features and the well characterized molecular mechanisms of CDKs have been the guide in designing inhibitors to these kinases. Yet they remain a challenging therapeutic class as they share conserved structure similarity in their active site. Several inhibitors have been discovered from natural sources or identified through high through put screening and rational drug design approaches. Most of these inhibitors target the ATP binding pocket, so they suffer from many limitations. Now a growing number of ATP non-competitive peptides and small molecules have been reported.

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Structural similarity between binding sites in influenza sialidase and isocitrate dehydrogenase: Implications for an alternative approach to rational drug design

Protein Science ◽

10.1002/pro.5560030719 ◽

1994 ◽

Vol 3 (7) ◽

pp. 1128-1130 ◽

Cited By ~ 7

Author(s):

Andrew R. Poirrette ◽

Helen M. Grindley ◽

David W. Rice ◽

Peter J. Artymiuk ◽

Peter Willett

Keyword(s):

Drug Design ◽

Isocitrate Dehydrogenase ◽

Binding Sites ◽

Structural Similarity ◽

Rational Drug Design ◽

Rational Drug ◽

Alternative Approach

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Structural Features Underlying Selective Inhibition of GSK3β by Dibromocantharelline: Implications for Rational Drug Design

Chemical Biology & Drug Design ◽

10.1111/j.1747-0285.2010.01069.x ◽

2011 ◽

Vol 77 (3) ◽

pp. 199-205 ◽

Cited By ~ 15

Author(s):

Na Zhang ◽

Rugang Zhong ◽

Hong Yan ◽

Yongjun Jiang

Keyword(s):

Drug Design ◽

Selective Inhibition ◽

Structural Features ◽

Rational Drug Design ◽

Rational Drug

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Identification of common structural features of binding sites in galactose-specific proteins

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.10612 ◽

2004 ◽

Vol 55 (1) ◽

pp. 44-65 ◽

Cited By ~ 56

Author(s):

M.S. Sujatha ◽

Petety V. Balaji

Keyword(s):

Binding Sites ◽

Structural Features ◽

Specific Proteins

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QSAR-QMMM Cryptographic Mining on Chern-Simons Topologies for the generation of a Ligand Targeting SARS-COV-2 D614G Binding Sites.

10.21203/rs.3.rs-128623/v3 ◽

2021 ◽

Author(s):

Ioannis Grigoriadis

Keyword(s):

Binding Sites ◽

High Accuracy ◽

Viral Transmission ◽

Rational Drug Design ◽

Design Strategies ◽

Geometric Methods ◽

Rational Drug ◽

Chern Simons ◽

Very High ◽

Over Time

Abstract SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. In this paper, we strongly combine topology geometric methods targeting at the atomistic level the protein apparatus of the SARS-COV-2 virus that are simple in machine learning anti-viral characteristics, to propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of the AI-Quantum designed molecule of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM ligands with Preferred IUPAC Names of (7aR) ‐5‐amino‐N‐[(S) ‐ {2‐[(S) ‐[(E) ‐(amino methylidene) amino](cyano) methyl]hydrazin‐1‐yl} (aziridin‐1‐yl) phosphoryl]‐ 1‐[(2E) ‐2‐ [(fluoromethanimidoyl) imino]acetyl]‐7‐oxo‐1H,7H,7aH‐pyrazolo[4,3‐d]pyrimidine‐3‐carboxamide;N‐{[(2‐amino‐6‐oxo‐6,9‐dihydro‐1H‐purin‐9‐yl) amino]({1‐[5‐({[cyano({1‐[(diamino methylidene) amino] ethenyl}) amino]oxy} methyl) ‐3,4‐dihydroxyoxolan‐2‐yl]‐1H‐1,2,4‐triazol‐3‐yl}formamido) phosphoryl} ‐6‐fluoro‐3,4‐dihydropyrazine ‐2‐carboxamide; [3‐(2‐amino‐5‐sulfanylidene‐1,2,4‐triazolidin‐3‐yl) oxaziridin‐2‐yl]({3‐sulfanylidene‐1,2,4,6 ‐tetraazabicyclo [3.1.0]hexan‐6‐yl}) phosphoroso1‐(3,4,5‐trifluorooxolan‐2‐yl) ‐1H‐1,2,4‐triazole‐3‐carboxylate targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation using Chern-Simons Topology Euclidean Geometric in a Lindenbaum-Tarski generated QSAR automating modeling and Artificial Intelligence-Driven Predictive Neural Networks.

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Improved (DFT) Generalized k-nearest information systems on Molecular QSAR-QMMM Cryptographic Mining and Chern-Simons Weighted ℓneuron(ι):=φ∘D∘R2∘S∘R1 Topologies for the generation of the Roccustyrna Ligand Targeting SARS-COV-2 D614G Binding Sites.

10.21203/rs.3.rs-678256/v1 ◽

2021 ◽

Author(s):

Ioannis Grigoriadis

Keyword(s):

Machine Learning ◽

Binding Sites ◽

Nearest Neighbors ◽

High Accuracy ◽

Rational Drug Design ◽

Design Strategies ◽

K Nearest Neighbors ◽

Rational Drug ◽

Chern Simons ◽

Very High

Abstract SARS coronavirus 2 (SARS-CoV-2) in the viral spike (S) encoding a SARS-COV-2 SPIKE D614G mutation protein predominate over time in locales revealing the dynamic aspects of its key viral processes where it is found, implying that this change enhances viral transmission. In this paper, we strongly combine topology geometric methods for generalized formalisms of k-nearest neighbors as a Tipping–Ogilvie and Machine Learning application within the quantum computing context targeting the atomistic level of the protein apparatus of the SARS-COV-2 viral characteristics. In this effort, we propose computer-aided rational drug design strategies efficient in computing docking usage, and powerful enough to achieve very high accuracy levels for this in-silico effort for the generation of AI-Quantum designed molecules of GisitorviffirnaTM, Roccustyrna_gs1_TM, and Roccustyrna_fr1_TM ligands targeting the COVID-19-SARS-COV-2 SPIKE D614G mutation by unifying Molecular Pairs (MMP), Lindenbaum-Tarski logical spaces and Adaptive Weighted KNN Positioning for Matched Bemis and Murko (BM) driven eigenvalue statements into Shannon entropy quantities as composed by Tipping–Ogilvie driven Machine Learning potentials on a (DFT) ℓneuron(ι):=φ∘D∘R2∘S∘R1 𝑐0𝜁2(1+∑𝑖)=[A∧A’(p)] ⊗[∂vˆ∂qˆ,∂uˆ∂pˆ] − [∂vˆ⊗∂pˆ,⊗∂uˆ⊗∂qˆ]=1𝑐𝑖𝜉𝑖{−ℏ20<≡===〈ψ⁎∣∣ψ⁎〉=[A∧A’(p)] ↦(𝑐𝑜𝑠𝜙−𝑠𝑖𝑛𝜙𝑠𝑖𝑛𝜙𝑐𝑜𝑠𝜙) (𝑥𝑝),𝐷(𝛼):(𝑥𝑝)↦(𝑥+𝑅𝑒(𝛼)𝑝+𝐼𝑚(𝛼)),𝑆(𝑟): (𝑥𝑝)↦(𝑒−𝑟00𝑒𝑟)(𝑥𝑝),𝐵𝑆(𝜃) :𝑥1𝑥2𝑝1𝑝2↦𝑐𝑜𝑠𝜃𝑠𝑖𝑛𝜃−𝑠𝑖𝑛𝜃𝑐𝑜𝑠𝜃𝑐𝑜𝑠𝜃𝑠𝑖𝑛𝜃00−𝑠𝑖𝑛𝜃𝑐𝑜𝑠𝜃𝑥1𝑥2𝑝1𝑝2𝑣=0,1,2….𝜆=1/2+𝛾2+1/4𝑐1⟨ψi∣∣(𝟙ˆ−ρˆ0)∣∣ψj⟩[A∧A’(p)] ↦(𝑐𝑜𝑠𝜙−𝑠𝑖𝑛𝜙𝑠𝑖𝑛𝜙𝑐𝑜𝑠𝜙) (𝑥𝑝),𝐷(𝛼):(𝑥𝑝)↦(𝑥+𝑅𝑒(𝛼)𝑝+ j<i⟨ψi∣∣ρˆ0∣∣ψj⟩𝑎22vKS1(r)a 4cos2θ+a2r2+r4) Σ3’⟨ψi∣∣ρˆ0∣∣ψj⟩𝑎22vKS1(r)=vext(r)+∫dr′ρ(r′)∣∣r−r′∣∣+ vXC1,(r)𝑚𝑏2𝑟2𝑒𝑑2πˆ2B2mB∇ [𝑃‾‾√𝑄‾‾(𝑟𝑟𝑦𝑣Γ(𝑏+𝑣)Γ(𝑐)𝑣!𝑐0𝑎2√−‾‾√𝑄‾‾(𝑟𝑟𝑦𝑣Γ(𝑏+𝑣)Γ(𝑐)𝑣!𝑐0𝑎2𝑄‾‾√𝑃‾‾√]≡∣CmdπCdπ2∣ϕ(Wxˆ+α)⟩, 𝑑𝑦2ΣΣ¯Ǫ ⊗ ¯σ − ¯σσ¯ǫ −i_+𝑐0𝑎2[1−𝑦𝑦]2}𝜓(𝑦)𝑣) improver for Chern-Simons Topology Euclidean Geometrics.

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