A Computational Study to Prevent HIV Invasion by Bovine LF in Mucosal-Layer via Blocking of DC-SIGN_GP120 Interaction

2020 ◽  
Vol 17 (5) ◽  
pp. 413-424
Author(s):  
Arundhati Banerjee ◽  
Rakhi Dasgupta ◽  
Sujay Ray
Keyword(s):  
Computational Study ◽  
Sexual Transmission ◽  
Pharmaceutical Research ◽  
Active Role ◽  
Binding Pocket ◽  
Protein Docking ◽  
Bovine Lactoferrin ◽  
Stability Parameters ◽  
Mucosal Layer ◽  
Mucosal Lining

Background: Invasion of HIV in human occurs through DC-SIGN’s interaction via the mucosal lining during sexual transmission. Bovine Lactoferrin (bLF) has been known to hinder this invasion via its interaction with DC-SIGN. Hitherto, protein assays have taken place but molecular-level studies remain unexplored. Methodology: The 3D structures of the three proteins were studied. After protein docking (bLF_DCSIGN and gp120_DC-SIGN), the complexes underwent simulation. Stability parameters and binding patterns with residues were explored. Results and Conclusion: ΔG values, net area for solvent accessibilities and conformational fluctuations in DC-SIGN affirm the binding of bLF with DC-SIGN to be more spontaneous and steadier contrary to that with gp120. Residue participation inferred more interactions to occur from bLF complex with a greater percentage of arginine (which strengthens the interaction) while electrostatic interaction between Lys45 (bLF) and Glu26 (DC-SIGN) strengthened the complex. Arg37 played an active role from DC-SIGN to form the stabilizing charged-neutral H-bond, while Lys63 from bLF formed two more such stabilizing charged-neutral H-bond with DC-SIGN. The prime binding sites in DC-SIGN; Arg37 and Gln34 occupy helices. The binding pockets in DC-SIGN may be blocked by bLF spontaneously, to hinder their interaction with gp120. No ionic-ionic interaction was observed from gp120_DCSIGN complex. 88th residue, which was a predominant residue in the binding pocket was found to experience a conformational shift from coils to sheets after interaction of DC-SIGN with bLF. This would instigate the pharmaceutical research as non-toxic LF would be economic as a remarkable peptide inhibitor opposing HIV.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals Formamide reaction network in gas phase and solution via a unified theoretical approach: Toward a reconciliation of different prebiotic scenarios

2015 ◽  
Vol 112 (49) ◽  
pp. 15030-15035 ◽  
Author(s):  
Fabio Pietrucci ◽  
Antonino Marco Saitta
Keyword(s):  
Gas Phase ◽  
Computational Study ◽  
Reaction Network ◽  
Theoretical Method ◽  
Active Role ◽  
Decomposition Reaction ◽  
Ab Initio Molecular Dynamics ◽  
Collective Variables ◽  
Wide Range ◽  
Reaction Channels

Increasing experimental and theoretical evidence points to formamide as a possible hub in the complex network of prebiotic chemical reactions leading from simple precursors like H2, H2O, N2, NH3, CO, and CO2 to key biological molecules like proteins, nucleic acids, and sugars. We present an in-depth computational study of the formation and decomposition reaction channels of formamide by means of ab initio molecular dynamics. To this aim we introduce a new theoretical method combining the metadynamics sampling scheme with a general purpose topological formulation of collective variables able to track a wide range of different reaction mechanisms. Our approach is flexible enough to discover multiple pathways and intermediates starting from minimal insight on the systems, and it allows passing in a seamless way from reactions in gas phase to reactions in liquid phase, with the solvent active role fully taken into account. We obtain crucial new insight into the interplay of the different formamide reaction channels and into environment effects on pathways and barriers. In particular, our results indicate a similar stability of formamide and hydrogen cyanide in solution as well as their relatively facile interconversion, thus reconciling experiments and theory and, possibly, two different and competing prebiotic scenarios. Moreover, although not explicitly sought, formic acid/ammonium formate is produced as an important formamide decomposition byproduct in solution.

scholarly journals GNINA 1.0: Molecular Docking with Deep Learning

2021 ◽  
Author(s):  
Andrew McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2A root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under and open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

scholarly journals LiF-MS: Mapping unstructured peptide-protein interactions using Ligand-Footprinting Mass Spectrometry

2018 ◽  
Author(s):  
Benjamin Parker ◽  
Edward Goncz ◽  
David T. Krist ◽  
Alexander Statsyuk ◽  
Alexey I. Nesvizhskii ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Amino Acid Level ◽  
Structural Data ◽  
Binding Pocket ◽  
Protein Docking ◽  
Model Quality ◽  
Linear Motif ◽  
Docking Simulations ◽  
Improve Model

AbstractUnstructured peptides, or linear motifs, present a poorly understood molecular language within the context of cellular signaling. These modular regions are often short, unstructured and interact weakly and transiently with folded target proteins. Thus, they are difficult to study with conventional structural biology methods. We present Ligand-Footprinting Mass Spectrometry, or LiF-MS, as a method of mapping the binding sites and dynamic disorder of these peptides on folded protein domains. LiF-MS uses a cleavable crosslinker to mark regions of a protein contacted by a bound linear motif. We demonstrate this method can detect both conformation ensembles and binding orientations of a linear motif in its binding pocket to amino-acid-level detail. Furthermore, marked amino acids can be used as constraints in peptide-protein docking simulations to improve model quality. In conclusion, LiF-MS proves a simple and novel method of elucidating peptide docking structural data not accessible by other methods in the context of a purified system.

Molecular Modeling, Interacting Residues and other Structural Analyses for Human SOCS3, Gp130 and JAK Proteins: A Detailed Computational Approach for Proteins Involved in Feedback Inhibition

2021 ◽  
Vol 11 ◽  
Author(s):  
Arundhati Banerjee ◽  
Rakhi Dasgupta
Keyword(s):  
Feedback Inhibition ◽  
Solvent Accessibility ◽  
3D Structure ◽  
Protein Docking ◽  
Sh2 Domain ◽  
Dynamics Simulation ◽  
Signaling Cascade ◽  
Protein Kinase Activity ◽  
Stability Parameters ◽  
Socs3 Protein

Background: When STAT3 is activated only by the IL6 family of proteins, then gp130 (having a phosphopeptide motif) interacts with human SOCS3 which further binds to JAK and inhibits its protein kinase activity. Interaction of gp130 with SOCS3 targets only the IL-6 signaling cascade. The interaction occurs when SOCS3 binds to a particular motif on gp130 (centered upon pTyr759) after its phosphorylation. Previously, wet laboratory studies were done but computational exploration for the participating residues remained unexplored. Methodology: The 3D structure of human SOCS3 protein was modeled and its stereo-chemical parameters were satisfied. Crystallographic structures of gp130-phosphopeptide and JAK were studied. After protein docking, the complex underwent minimization and molecular dynamics simulation. Different stability parameters and binding patterns with residues were evaluated Results, Discussion and Conclusion: The best modeled structure of SOCS3 protein was selected and found that it had three helices and seven sheets interspersed with coils. Arg133, Tyr137 and Tyr98 from SOCS3 formed manifold binding patterns with gp130 (mainly with pTyr759 and Glu758). Lys62, Lys63 and Arg65 from SOCS3 were also found to interact with Val762 of gp130. Interactions with JAK were also studied. Residue 53, 62-65, 98, 133, 136 and 137 formed the predominant binding pockets in SOCS3. They can serve as important target sites as well. Altogether, it created elctrostatically charged pockets to accommodate the partner proteins for each other. Gp130 phosphopeptide was observed to be tightly accommodated in the electrostatically positive zones on SOCS3 surface. Net area for solvent accessibility was also found to get drastically reduced implying high participation of residues. Earlier studies documented that the interaction of these three proteins occurs with affinity and have satisfactory association with each other. Here in this study, free energy of binding for the triple protein interaction through the ΔG values helped to infer that SOCS3 interacted spontaneously (in thermodynamic sense). Many helical conformations formed coiled-coils providing high flexibility to interact spontaneously. Most of the interactions were through the responsible SH2 domain (46-127 residue length) of SOCS3. Residues 53, 62-64 and 98 formed coils while the residue number 137adopted sheet conformation from coils. Future Scope: This study shall instigate to block the gp130-binding sites of SOCS3 through targeting of drugs, thereby preventing SOCS3-gp130 interaction. This would allow JAK-STAT signaling cascade which is paramount for several biological functions

scholarly journals Structural Insights into the Interaction of Filovirus Glycoproteins with the Endosomal Receptor Niemann-Pick C1: A Computational Study

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 913
Author(s):  
Manabu Igarashi ◽  
Takatsugu Hirokawa ◽  
Yoshihiro Takadate ◽  
Ayato Takada
Keyword(s):  
Receptor Binding ◽  
Ebola Virus ◽  
Computational Study ◽  
Binding Pocket ◽  
Crystal Structure Analysis ◽  
Receptor Binding Site ◽  
Structure Based Drug Design ◽  
Niemann Pick ◽  
Dynamics Simulations ◽  
Loop 2

Filoviruses, including marburgviruses and ebolaviruses, have a single transmembrane glycoprotein (GP) that facilitates their entry into cells. During entry, GP needs to be cleaved by host proteases to expose the receptor-binding site that binds to the endosomal receptor Niemann-Pick C1 (NPC1) protein. The crystal structure analysis of the cleaved GP (GPcl) of Ebola virus (EBOV) in complex with human NPC1 has demonstrated that NPC1 has two protruding loops (loops 1 and 2), which engage a hydrophobic pocket on the head of EBOV GPcl. However, the molecular interactions between NPC1 and the GPcl of other filoviruses remain unexplored. In the present study, we performed molecular modeling and molecular dynamics simulations of NPC1 complexed with GPcls of two ebolaviruses, EBOV and Sudan virus (SUDV), and one marburgvirus, Ravn virus (RAVV). Similar binding structures were observed in the GPcl–NPC1 complexes of EBOV and SUDV, which differed from that of RAVV. Specifically, in the RAVV GPcl–NPC1 complex, the tip of loop 2 was closer to the pocket edge comprising residues at positions 79–88 of GPcl; the root of loop 1 was predicted to interact with P116 and Q144 of GPcl. Furthermore, in the SUDV GPcl–NPC1 complex, the tip of loop 2 was slightly closer to the residue at position 141 than those in the EBOV and RAVV GPcl–NPC1 complexes. These structural differences may affect the size and/or shape of the receptor-binding pocket of GPcl. Our structural models could provide useful information for improving our understanding the differences in host preference among filoviruses as well as contributing to structure-based drug design.

scholarly journals In Silico Docking Studies of Antimalarial Drug Hydroxychloroquine to SARS-CoV Proteins :An Emerging Pandemic Worldwide

2020 ◽  
Author(s):  
Priyanka H. Jokhakar ◽  
Rishee Kalaria ◽  
Hiren K. Patel
Keyword(s):  
Antimalarial Drug ◽  
Hydrophobic Interactions ◽  
Computational Study ◽  
Covalent Bond ◽  
Docking Studies ◽  
Protein Docking ◽  
Viral Attachment ◽  
Discovery Studio ◽  
In Silico Docking ◽  
Covalent Bond Formation

<p>This computational study comprises screening and prediction of interaction of selected antimalarial drug hydroxychloroquine with targeted two proteins of coronavirus. One is SARS enveloped E pantameric ion channel protein and another is SARS-CoV-2 main apoprotein protease. Both are vital for viral attachment and entry to the host cell for infection. After molecular protein docking with different confirmations, stable interacting complex of ligand and macromolecules were obtained. Interacting Lysine, Threonine and Tyrosine of E protein were found for participation of stable interaction with selected drug having docking affinity energy of -6.3kcal/mol. For apoprotein protease stable confirmation was screened out having bonding Threonine residue with same drug of energy -6.0 kcal/mol. Irreversible covalent bond formation and van der Waals interaction favours the selectivity and stability of both targeted proteins towards selected drug. Conventional as well as hydrophobic interactions are found in Ligplot and Discovery studio analysis also indicates stabilized confirmations between ligand and drug. Thus, this study delivers the putative mechanism of the drug interactions to target proteins hence comprising landmark for future investigation for antimalarial hydroxychloroquine as anti COVID 19 drug in this experimental time.</p>

scholarly journals In Silico Docking Studies of Antimalarial Drug Hydroxychloroquine to SARS-CoV Proteins :An Emerging Pandemic Worldwide

2020 ◽  
Author(s):  
Priyanka H. Jokhakar ◽  
Rishee Kalaria ◽  
Hiren K. Patel
Keyword(s):  
Antimalarial Drug ◽  
Hydrophobic Interactions ◽  
Computational Study ◽  
Covalent Bond ◽  
Docking Studies ◽  
Protein Docking ◽  
Viral Attachment ◽  
Discovery Studio ◽  
In Silico Docking ◽  
Covalent Bond Formation

<p>This computational study comprises screening and prediction of interaction of selected antimalarial drug hydroxychloroquine with targeted two proteins of coronavirus. One is SARS enveloped E pantameric ion channel protein and another is SARS-CoV-2 main apoprotein protease. Both are vital for viral attachment and entry to the host cell for infection. After molecular protein docking with different confirmations, stable interacting complex of ligand and macromolecules were obtained. Interacting Lysine, Threonine and Tyrosine of E protein were found for participation of stable interaction with selected drug having docking affinity energy of -6.3kcal/mol. For apoprotein protease stable confirmation was screened out having bonding Threonine residue with same drug of energy -6.0 kcal/mol. Irreversible covalent bond formation and van der Waals interaction favours the selectivity and stability of both targeted proteins towards selected drug. Conventional as well as hydrophobic interactions are found in Ligplot and Discovery studio analysis also indicates stabilized confirmations between ligand and drug. Thus, this study delivers the putative mechanism of the drug interactions to target proteins hence comprising landmark for future investigation for antimalarial hydroxychloroquine as anti COVID 19 drug in this experimental time.</p>

scholarly journals High-order epistasis in catalytic power of dihydrofolate reductase gives rise to a rugged fitness landscape in the presence of trimethoprim selection

2018 ◽  
Author(s):  
Yusuf Talha Tamer ◽  
Ilona K. Gaszek ◽  
Haleh Abdizadeh ◽  
Tugce Altinusak Batur ◽  
Kimberly Reynolds ◽  
...  
Keyword(s):  
Dihydrofolate Reductase ◽  
Biochemical Parameters ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Computational Study ◽  
Binding Pocket ◽  
Resistance Mechanisms ◽  
Fitness Landscapes ◽  
High Order ◽  
Catalytic Power

ABSTRACTEvolutionary fitness landscapes of certain antibiotic target enzymes have been comprehensively mapped showing strong high order epistasis between mutations, but understanding these effects at the biochemical and molecular levels remained open. Here, we carried out an extensive experimental and computational study to quantitatively understand the evolutionary dynamics of Escherichia coli dihydrofolate reductase (DHFR) enzyme in the presence of trimethoprim induced selection. Biochemical and structural characterization of resistance-conferring mutations targeting a total of ten residues spanning the substrate binding pocket of DHFR revealed distinct resistance mechanisms. Next, we experimentally measured biochemical parameters (Km, Ki, and kcat) for a mutant library carrying all possible combinations of six resistance-conferring DHFR mutations and quantified epistatic interactions between them. We found that the epistasis between DHFR mutations is high-order for catalytic power of DHFR (kcat and Km), but less prevalent for trimethoprim affinity (Ki). Taken together our data provide a concrete illustration of how epistatic coupling at the level of biochemical parameters can give rise to complex fitness landscapes, and suggest new strategies for developing mutant specific inhibitors.

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