scholarly journals Reliable in silico ranking of engineered therapeutic TCR binding affinities using MMPBSA and MMGBSA calculations.

2021 ◽  
Author(s):  
Marc W. Van der Kamp ◽  
Rory M. Crean ◽  
David K. Cole ◽  
Christopher R. Pudney
Keyword(s):  
Protein Design ◽  
In Silico ◽  
Single Point ◽  
Computational Cost ◽  
Human Leukocyte ◽  
Md Simulations ◽  
Cell Receptor ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Binding Interface

Accurate and efficient in silico ranking of protein-protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics trajectories. This provides a compromise between rapid but approximate scoring functions of single structures and more sophisticated methods such as free energy perturbation. Optimal MMPB/GBSA parameters tend to be system specific. Here, we identify protocols that enable reliable evaluation of the effect of mutations in a T-cell receptor (TCR) in complex with its natural target, the peptide-human leukocyte antigen (pHLA). The development of affinity-enhanced engineered TCRs towards a specific pHLA is of great interest in the field of immunotherapy. Our study highlights the importance of using a higher than default internal dielectric constant, especially in the case of charge changing mutations. Including explicit solvation and/or entropy corrections may deteriorate the ranking of single point variants due to the errors associated with these additions. For multi-point variants, however, these corrections were important for accurate ranking. We also demonstrate how potential outliers could be identified in advance by analyzing changes in the hydrogen bonding networks at the binding interface. Finally, using bootstrapping we show that as few as 5-10 replicas of short (4 ns) MD simulations may be sufficient for reproducible and accurate ranking of candidate TCR variants. Our work demonstrates that reliably ranking TCR variant binding affinities can be achieved at moderate computational cost. The protocols developed here can be applied towards in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

scholarly journals Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Beihong Ji ◽  
Xibing He ◽  
Yuzhao Zhang ◽  
Jingchen Zhai ◽  
Viet Hoang Man ◽  
...  
Keyword(s):  
Computational Cost ◽  
Scoring Function ◽  
Structural Similarity ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Autodock Vina ◽  
Predictive Index ◽  
Drug Lead ◽  
Screening Performance ◽  
Calibration Algorithm

AbstractIn this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S4 (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost.

scholarly journals The SARS-CoV-2 Exerts a Distinctive Strategy for Interacting with the ACE2 Human Receptor

Viruses ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 497 ◽  
Author(s):  
Esther S. Brielle ◽  
Dina Schneidman-Duhovny ◽  
Michal Linial
Keyword(s):  
Cell Receptor ◽  
Host Recognition ◽  
Interface Residue ◽  
Binding Affinities ◽  
Binding Interface ◽  
Residue Contacts ◽  
High Infection ◽  
Dynamics Simulations ◽  
Insight Into ◽  
Human Receptor

The COVID-19 disease has plagued over 200 countries with over three million cases and has resulted in over 200,000 deaths within 3 months. To gain insight into the high infection rate of the SARS-CoV-2 virus, we compare the interaction between the human ACE2 receptor and the SARS-CoV-2 spike protein with that of other pathogenic coronaviruses using molecular dynamics simulations. SARS-CoV, SARS-CoV-2, and HCoV-NL63 recognize ACE2 as the natural receptor but present a distinct binding interface to ACE2 and a different network of residue–residue contacts. SARS-CoV and SARS-CoV-2 have comparable binding affinities achieved by balancing energetics and dynamics. The SARS-CoV-2–ACE2 complex contains a higher number of contacts, a larger interface area, and decreased interface residue fluctuations relative to the SARS-CoV–ACE2 complex. These findings expose an exceptional evolutionary exploration exerted by coronaviruses toward host recognition. We postulate that the versatility of cell receptor binding strategies has immediate implications for therapeutic strategies.

scholarly journals In silico investigation of binding affinities between human leukocyte antigen class I molecules and SARS-CoV-2 virus spike and ORF1ab proteins

2021 ◽  
Author(s):  
Spyros A. Charonis ◽  
Effie-Photini Tsilibary ◽  
Apostolos P. Georgopoulos
Keyword(s):  
Human Leukocyte Antigen ◽  
In Silico ◽  
Human Leukocyte ◽  
Hla Class I ◽  
Hla Class Ii ◽  
Class I ◽  
Binding Affinities ◽  
S Protein ◽  
Leukocyte Antigen ◽  
Hla Class I Molecules

Aim: The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019, a global pandemic. There is hence an urgent need for effective approaches to understand the mechanism of viral interaction with immune cells that lead to viral elimination and subsequent long-term immunity. The first, immediate response to the viral infection involves mobilization of native immunity and human leukocyte antigen (HLA) class I mechanisms to kill infected cells and eliminate the virus. The second line of defense involves the activation of HLA class II system for the production of antibodies against the virus which will add to the elimination of the virus and prevent future infections. In a previous study, investigated the relations between SARS-CoV-2 spike glycoprotein (S protein) and HLA class II alleles were investigaed; here report on the relations of the S protein and the open reading frame 1ab (ORF1ab) of SARS-CoV-2 to HLA class I alleles. Methods: An in silico sliding window approach was used to determine exhaustively the binding affinities of linear epitopes of 10 amino acid length (10-mers) to each of 61 common (global frequency ≥ 0.01) HLA class I molecules (17, 24 and 20 from gene loci A, B and C, respectively). A total of 8,354 epitopes were analyzed; 1,263 from the S protein and 7,091 from ORF1ab. Results: HLA-A genes were the most effective at binding SARS-CoV-2 epitopes for both spike and ORF1ab proteins. Good binding affinities were found for all three genes and were distributed throughout the length of the S protein and ORF1ab polyprotein sequence. Conclusions: Common HLA class I molecules, as a population, are very well suited to binding with high affinity to SARS-CoV-2 spike and ORF1ab proteins and hence should be effective in aiding the early elimination of the virus.

scholarly journals The SARS-CoV-2 exerts a distinctive strategy for interacting with the ACE2 human receptor

2020 ◽  
Author(s):  
Esther S. Brielle ◽  
Dina Schneidman-Duhovny ◽  
Michal Linial
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Cell Receptor ◽  
Host Recognition ◽  
Interface Residue ◽  
Binding Affinities ◽  
Temporal Dimension ◽  
Binding Interface ◽  
Residue Contacts ◽  
Dynamics Simulations

AbstractThe COVID-19 disease has plagued over 110 countries and has resulted in over 4,000 deaths within 10 weeks. We compare the interaction between the human ACE2 receptor and the SARS-CoV-2 spike protein with that of other pathogenic coronaviruses using molecular dynamics simulations. SARS-CoV, SARS-CoV-2, and HCoV-NL63 recognize ACE2 as the natural receptor but present a distinct binding interface to ACE2 and a different network of residue-residue contacts. SARS-CoV and SARS-CoV-2 have comparable binding affinities achieved by balancing energetics and dynamics. The SARS-CoV-2–ACE2 complex contains a higher number of contacts, a larger interface area, and decreased interface residue fluctuations relative to SARS-CoV. These findings expose an exceptional evolutionary exploration exerted by coronaviruses toward host recognition. We postulate that the versatility of cell receptor binding strategies has immediate implications on therapeutic strategies.One Sentence SummaryMolecular dynamics simulations reveal a temporal dimension of coronaviruses interactions with the host receptor.

scholarly journals SARS-CoV-2 Virus and Human Leukocyte Antigen (HLA) Class II: Investigation in silico of Binding Affinities for COVID-19 Protection and Vaccine Development

2020 ◽  
Vol 4 (4) ◽  
pp. 12-23 ◽  
Author(s):  
Spyros Charonis ◽  
Effie-Photini Tsilibary ◽  
Apostolos Georgopoulos
Keyword(s):  
Human Leukocyte Antigen ◽  
Binding Affinity ◽  
In Silico ◽  
Vaccine Development ◽  
Human Leukocyte ◽  
Class Ii ◽  
Hla Class Ii ◽  
Effective Vaccine ◽  
Binding Affinities ◽  
Leukocyte Antigen

SARS-CoV-2 causes COVID-19, urgently requiring the development of effective vaccine(s). Much of current efforts focus on the SARS-CoV-2 spike-glycoprotein by identifying highly antigenic epitopes as good vaccine candidates. However, high antigenicity is not sufficient, since the activation of relevant T cells depends on the presence of the complex of the antigen with a suitably matching Human Leukocyte Antigen (HLA) Class II molecule, not the antigen alone: in the absence of such a match, even a highly antigenic epitope in vitro will not elicit antibody formation in vivo. Here we assessed systematically in silico the binding affinity of epitopes of the spike-glycoprotein to 66 common HLA-Class-II alleles (frequency ≥ 0.01). We used a sliding epitope window of 22-amino-acid-width to scan the entire protein and determined the binding affinity of each subsequence to each HLA allele. DPB1 had highest binding affinities, followed by DRB1 and DQB1. Higher binding affinities were concentrated in the initial part of the glycoprotein (S1-S460), with a peak at S223-S238. This region would be well suited for effective vaccine development by ensuring high probability for successful matching of the vaccine antigen from that region to a HLA Class II molecule for CD4+ T cell activation by the antigen-HLA molecule complex.

scholarly journals An Accurate Free Energy Method for Solvation of Organic Compounds and Binding to Proteins

2020 ◽  
Author(s):  
Omer Tayfuroglu ◽  
Muslum Yildiz ◽  
Lee-Wright Pearson ◽  
Abdulkadir Kocak
Keyword(s):  
Molecular Dynamics ◽  
Organic Compounds ◽  
Drug Targets ◽  
Single Point ◽  
Computational Cost ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Free Energies ◽  
Atomic Simulation ◽  
New Strategy

ABSTRACTHere, we introduce a new strategy to estimate free energies using single end-state molecular dynamics simulation trajectories. The method is adopted from ANI-1ccx neural network potentials (Machine Learning) for the Atomic Simulation Environment (ASE) and predicts the single point energies at the accuracy of CCSD(T)/CBS level for the entire configurational space that is sampled by Molecular Dynamics (MD) simulations. Our preliminary results show that the method can be as accurate as Bennet-Acceptance-Ration (BAR) with much reduced computational cost. Not only does it enable to calculate solvation free energies of small organic compounds, but it is also possible to predict absolute and relative binding free energies in ligand-protein complex systems. Rapid calculation also enables to screen small organic molecules from databases as potent inhibitors to any drug targets.

scholarly journals Single point mutations can potentially enhance infectivity of SARS-CoV-2 revealed by in silico affinity maturation and SPR assay

2020 ◽  
Author(s):  
Ting Xue ◽  
Weikun Wu ◽  
Ning Guo ◽  
Chengyong Wu ◽  
Jian Huang ◽  
...  
Keyword(s):  
Binding Affinity ◽  
In Silico ◽  
Cell Attachment ◽  
Single Point ◽  
Point Mutations ◽  
Cell Receptor ◽  
Affinity Maturation ◽  
Promising Target ◽  
Assay Result ◽  
Single Point Mutations

AbstractThe RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates the viral cell attachment and serves as a promising target for therapeutics development. Mutations on the S-RBD may alter its affinity to cell receptor and affect the potency of vaccines and antibodies. Here we used an in-silico approach to predict how mutations on RBD affect its binding affinity to hACE2 (human angiotensin-converting enzyme2). The effect of all single point mutations on the interface was predicted. SPR assay result shows that 6 out of 9 selected mutations can strengthen binding affinity. Our prediction has reasonable agreement with the previous deep mutational scan results and recently reported mutants. Our work demonstrated in silico method as a powerful tool to forecast more powerful virus mutants, which will significantly benefit for the development of broadly neutralizing vaccine and antibody.

scholarly journals Association of Dementia Human Leukocyte Antigen (HLA) Profile with Human Herpes Viruses 3 and 7: An in-silico Investigation

2021 ◽  
Vol 5 (3) ◽  
pp. 7-14
Author(s):  
Lisa M. James ◽  
Spyros A. Charonis ◽  
Apostolos P. Georgopoulos
Keyword(s):  
Human Leukocyte Antigen ◽  
In Silico ◽  
Human Leukocyte ◽  
Binding Affinities ◽  
Herpes Viruses ◽  
Human Herpes ◽  
Hla Alleles ◽  
Leukocyte Antigen ◽  
Dementia Risk ◽  
Human Herpes Viruses

Human leukocyte antigen (HLA), the most highly polymorphic region of the human genome, is increasingly recognized as an important genetic contributor to dementia risk and resilience. HLA is involved in protection against foreign antigens including human herpes viruses (HHV), which have been widely implicated in dementia. Here we used an in silico approach1 to determine binding affinities of glycoproteins from 9 human herpes virus (HHV) strains to 113 HLA alleles, and to examine the association of a previously identified HLA-dementia risk profile2 to those affinities. We found a highly significant correlation between high binding affinities of HLA alleles to HHV 3 and 7 and the dementia risk scores of those alleles, such that the higher the estimated binding affinity, the lower the dementia risk score. These findings suggest that protection conferred by HLA alleles may be related to their ability to bind and eliminate HHV3 and HHV7 and point to the possibility that protection against these viruses may reduce dementia incidence.

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