SARS-CoV-2 Variants, RBD Mutations, Binding Affinity, and Antibody Escape

Since 2020, the receptor-binding domain (RBD) of the spike protein of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been constantly mutating, producing most of the notable missense mutations in the context of “variants of concern”, probably in response to the vaccine-driven alteration of immune profiles of the human population. The Delta variant, in particular, has become the most prevalent variant of the epidemic, and it is spreading in countries with the highest vaccination rates, causing the world to face the risk of a new wave of the contagion. Understanding the physical mechanism responsible for the mutation-induced changes in the RBD’s binding affinity, its transmissibility, and its capacity to escape vaccine-induced immunity is the “urgent challenge” in the development of preventive measures, vaccines, and therapeutic antibodies against the coronavirus disease 2019 (COVID-19) pandemic. In this study, entropy–enthalpy compensation and the Gibbs free energy change were used to analyze the impact of the RBD mutations on the binding affinity of SARS-CoV-2 variants with the receptor angiotensin converting enzyme 2 (ACE2) and existing antibodies. Through the analysis, we found that the existing mutations have already covered almost all possible detrimental mutations that could result in an increase of transmissibility, and that a possible mutation in amino-acid position 498 of the RBD can potentially enhance its binding affinity. A new calculation method for the binding energies of protein–protein complexes is proposed based on the entropy–enthalpy compensation rule. All known structures of RBD–antibody complexes and the RBD–ACE2 complex comply with the entropy–enthalpy compensation rule in providing the driving force behind the spontaneous protein–protein docking. The variant-induced risk of breakthrough infections in vaccinated people is attributed to the L452R mutation’s reduction of the binding affinity of many antibodies. Mutations reversing the hydrophobic or hydrophilic performance of residues in the spike RBD potentially cause breakthrough infections of coronaviruses due to the changes in geometric complementarity in the entropy–enthalpy compensations between antibodies and the virus at the binding sites.

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The Effect of Vaccination Rates on the Infection of COVID-19 under the Vaccination Rate below the Herd Immunity Threshold

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147491 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7491

Author(s):

Yi-Tui Chen

Keyword(s):

Infection Rate ◽

Herd Immunity ◽

The United States ◽

Vaccination Rate ◽

Infection Rates ◽

Vaccination Rates ◽

The United Kingdom ◽

The World ◽

Almost All ◽

The Impact

Although vaccination is carried out worldwide, the vaccination rate varies greatly. As of 24 May 2021, in some countries, the proportion of the population fully vaccinated against COVID-19 has exceeded 50%, but in many countries, this proportion is still very low, less than 1%. This article aims to explore the impact of vaccination on the spread of the COVID-19 pandemic. As the herd immunity of almost all countries in the world has not been reached, several countries were selected as sample cases by employing the following criteria: more than 60 vaccine doses per 100 people and a population of more than one million people. In the end, a total of eight countries/regions were selected, including Israel, the UAE, Chile, the United Kingdom, the United States, Hungary, and Qatar. The results find that vaccination has a major impact on reducing infection rates in all countries. However, the infection rate after vaccination showed two trends. One is an inverted U-shaped trend, and the other is an L-shaped trend. For those countries with an inverted U-shaped trend, the infection rate begins to decline when the vaccination rate reaches 1.46–50.91 doses per 100 people.

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Molecular Docking Senyawa Gingerol dan Zingiberol pada Tanaman Jahe sebagai Penanganan COVID-19

Jurnal e-Biomedik ◽

10.35790/ebm.v9i1.32361 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Belinda D. P. M. Ratu ◽

Widdhi Bodhi ◽

Fona Budiarso ◽

Billy J. Kepel ◽

. Fatimawali ◽

...

Keyword(s):

Molecular Docking ◽

Binding Affinity ◽

Amino Acid Residues ◽

Autodock Vina ◽

Discovery Studio ◽

Main Protease ◽

Docking Method ◽

Pubmed Database ◽

Almost All ◽

The Impact

Abstract: COVID-19 is a new disease. Many people feel the impact of this disease. There is no definite cure for COVID-19, so many people use traditional medicine to ward off COVID-19, including ginger. This study aims to determine whether there is an interaction between compounds in ginger (gingerol and zingiberol) and the COVID-19’s main protease (6LU7). This study uses a molecular docking method using 4 main applications, namely Autodock Tools, Autodock Vina, Biovia Discovery Studio 2020, and Open Babel GUI. The samples used were gingerol and zingiberol compounds in ginger plants downloaded from Pubchem. The data used in this study used Mendeley, Clinical Key, and PubMed database. The study showed that almost all of the amino acid residues in the gingerol compound acted on the 6LU7 active site, whereas the zingiberol did not. The results of the binding affinity of ginger compounds, both gingerol and zingiberol, do not exceed the binding affinity of remdesivir, a drug that is widely researched as a COVID-19 handling drug. In conclusion, gingerol and zingiberol compounds in ginger can’t be considered as COVID-19’s treatment.Keywords: molecular docking, gingerol, zingiberol Abstrak: COVID-19 merupakan sebuah penyakit yang baru. Banyak masyarakat yang merasakan dampak dari penyakit ini. Belum ada pengobatan pasti untuk menyembuhkan COVID-19, sehingga banyak masyarakat yang menggunakan pengobatan tradisional untuk menangkal COVID-19, termasuk jahe. Penelitian ini bertujuan untuk mengetahui apakah ada interaksi antara senyawa pada jahe (gingerol dan zingiberol) dengan main protease COVID-19 (6LU7). Penelitian ini menggunakan metode molecular docking dengan menggunakan 4 aplikasi utama, yaitu Autodock Tools, Autodock Vina, Biovia Discovery Studio 2020, dan Open Babel GUI. Sampel yang digunakan yaitu senyawa gingerol dan zingiberol pada tanaman jahe yang diunduh di Pubchem. Data yang digunakan dalam penelitian ini menggunakan database Mendeley, Clinical Key, dan PubMed. Penelitian menunjukkan bahwa hampir semua residu asam amino pada senyawa gingerol bekerja pada sisi aktif 6LU7, sedangkan tidak demikian pada zingiberol. Hasil binding affinity senyawa jahe, baik gingerol maupun zingiberol tidak melebihi binding affinity remdesivir, obat yang banyak diteliti sebagai obat penanganan COVID-19. Sebagai simpulan, senyawa gingerol dan zingiberol pada tanaman jahe tidak dapat dipertimbangkan sebagai penanganan COVID-19Kata Kunci: molecular docking, gingerol, zingiberol

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Analysis of the impact of molecular motions on the efficiency of XL-MS and the distance restraints in hybrid structural biology

10.1101/379289 ◽

2018 ◽

Author(s):

Vladimir Svetlov ◽

Evgeny Nudler

Keyword(s):

Structural Biology ◽

Protein Complexes ◽

Protein Docking ◽

Protein Fold ◽

Molecular Motions ◽

Cross Link ◽

Distance Restraints ◽

Cross Links ◽

Cross Linker ◽

The Impact

AbstractCovalent cross-link mapping by mass spectrometry (XL-MS) is rapidly becoming the most widely used method of hybrid structural biology. We investigated the impact of incremental variations of cross-linker length have on the depth of XL-MS interrogation of protein-protein complexes, and assessed the role molecular motions in solution play in generation of cross-link-derived distance restraints. Supplementation of a popular NHS-ester cross-linker, DSS, with 2 reagents shorter or longer by CH2-CH2, increased the number of non-reductant cross-links by ~50%. Molecular dynamics simulations of these cross-linkers revealed 3 individual, partially overlapping ranges of motion, consistent with partially overlapping sets of cross-links formed by each reagent. Similar simulations elucidated protein fold-specific ranges of motions for the reactive and backbone atoms from rigid and flexible target domains. Together these findings create a quantitative framework for generation of cross-linker- and protein fold-specific distance restraints for XL-MS-guided protein-protein docking.

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Contacts-based prediction of binding affinity in protein–protein complexes

eLife ◽

10.7554/elife.07454 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 119

Author(s):

Anna Vangone ◽

Alexandre MJJ Bonvin

Keyword(s):

Binding Affinity ◽

Conformational Changes ◽

Protein Interactions ◽

Prediction Accuracy ◽

Protein Complexes ◽

Structural Features ◽

Specific Protein ◽

Strong Impact ◽

Protein Protein Interactions ◽

Almost All

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

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UEP: an open-source and fast classifier for predicting the impact of mutations in protein–protein complexes

Bioinformatics ◽

10.1093/bioinformatics/btaa708 ◽

2020 ◽

Author(s):

Pep Amengual-Rigo ◽

Juan Fernández-Recio ◽

Victor Guallar

Keyword(s):

Open Source ◽

Binding Affinity ◽

Protein Interactions ◽

Protein Complexes ◽

Selection Procedure ◽

Supplementary Information ◽

Computational Time ◽

Contact Potential ◽

Three Body ◽

The Impact

Abstract Motivation Single protein residue mutations may reshape the binding affinity of protein–protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein–protein complexes trained on interactome data. Results Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein–protein mutations using UEP, a fast and reliable open-source code. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online.

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pyDockEneRes: per-residue decomposition of protein–protein docking energy

Bioinformatics ◽

10.1093/bioinformatics/btz884 ◽

2019 ◽

Vol 36 (7) ◽

pp. 2284-2285 ◽

Cited By ~ 1

Author(s):

Miguel Romero-Durana ◽

Brian Jiménez-García ◽

Juan Fernández-Recio

Keyword(s):

Binding Affinity ◽

Protein Interactions ◽

Structural Model ◽

Protein Complexes ◽

Complex Structure ◽

Protein Docking ◽

Supplementary Information ◽

Scoring Functions ◽

Residue Decomposition ◽

Docking Energy

Abstract Motivation Protein–protein interactions are key to understand biological processes at the molecular level. As a complement to experimental characterization of protein interactions, computational docking methods have become useful tools for the structural and energetics modeling of protein–protein complexes. A key aspect of such algorithms is the use of scoring functions to evaluate the generated docking poses and try to identify the best models. When the scoring functions are based on energetic considerations, they can help not only to provide a reliable structural model for the complex, but also to describe energetic aspects of the interaction. This is the case of the scoring function used in pyDock, a combination of electrostatics, desolvation and van der Waals energy terms. Its correlation with experimental binding affinity values of protein–protein complexes was explored in the past, but the per-residue decomposition of the docking energy was never systematically analyzed. Results Here, we present pyDockEneRes (pyDock Energy per-Residue), a web server that provides pyDock docking energy partitioned at the residue level, giving a much more detailed description of the docking energy landscape. Additionally, pyDockEneRes computes the contribution to the docking energy of the side-chain atoms. This fast approach can be applied to characterize a complex structure in order to identify energetically relevant residues (hot-spots) and estimate binding affinity changes upon mutation to alanine. Availability and implementation The server does not require registration by the user and is freely accessible for academics at https://life.bsc.es/pid/pydockeneres. Supplementary information Supplementary data are available at Bioinformatics online.

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Udock, the interactive docking entertainment system

Faraday Discussions ◽

10.1039/c3fd00147d ◽

2014 ◽

Vol 169 ◽

pp. 425-441 ◽

Cited By ~ 10

Author(s):

Guillaume Levieux ◽

Guillaume Tiger ◽

Stéphanie Mader ◽

Jean-François Zagury ◽

Stéphane Natkin ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Protein Structures ◽

Protein Docking ◽

Conformational Space ◽

Protein Protein Interactions ◽

Binding Interface ◽

Protein Interfaces ◽

Almost All ◽

Experimental Partner

Protein–protein interactions play a crucial role in biological processes. Protein docking calculations' goal is to predict, given two proteins of known structures, the associate conformation of the corresponding complex. Here, we present a new interactive protein docking system, Udock, that makes use of users' cognitive capabilities added up. In Udock, the users tackle simplified representations of protein structures and explore protein–protein interfaces’ conformational space using a gamified interactive docking system with on the fly scoring. We assumed that if given appropriate tools, a naïve user's cognitive capabilities could provide relevant data for (1) the prediction of correct interfaces in binary protein complexes and (2) the identification of the experimental partner in interaction among a set of decoys. To explore this approach experimentally, we conducted a preliminary two week long playtest where the registered users could perform a cross-docking on a dataset comprising 4 binary protein complexes. The users explored almost all the surface of the proteins that were available in the dataset but favored certain regions that seemed more attractive as potential docking spots. These favored regions were located inside or nearby the experimental binding interface for 5 out of the 8 proteins in the dataset. For most of them, the best scores were obtained with the experimental partner. The alpha version of Udock is freely accessible at http://udock.fr.

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Structural-bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild-type reference.

10.21203/rs.3.rs-1153124/v1 ◽

2021 ◽

Author(s):

Vedat Durmaz ◽

Katharina Köchl ◽

Amit Singh ◽

Michael Hetmann ◽

Lena Parigger ◽

...

Keyword(s):

Binding Affinity ◽

Receptor Binding ◽

Bioinformatics Analysis ◽

Structural Bioinformatics ◽

Binding Energies ◽

Large Set ◽

Wild Type ◽

Linear Interaction ◽

Binding Interface ◽

The Impact

Abstract To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread came in several pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here, we report on a sequence- and structural-bioinformatics analysis by which we estimate the impact of amino acid exchanges on the affinity of the SARS-CoV-2 spike receptor-binding domain (RBD) to the human receptor hACE2. This is carried out by qualitative electrostatics and hydrophobicity analysis as well as through molecular dynamics simulations used for the development of a highly accurate linear interaction energy (LIE) binding affinity model that was calibrated on a large set of experimental binding energies. For the newest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to any other VOC. Moreover, according to our LIE model, Omicron achieved a substantially higher ACE2 binding affinity than the wild-type and in particular the highest among all VOCs except for Alpha and therefore requires special attention and monitoring. Using this prediction model we provide early structural insight and binding properties before experimentally determined complex structures and binding affinity data become available in the upcoming months.

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Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity

Journal of Chemical Theory and Computation ◽

10.1021/ct401022c ◽

2014 ◽

Vol 10 (4) ◽

pp. 1770-1780 ◽

Cited By ~ 67

Author(s):

Minghui Li ◽

Marharyta Petukh ◽

Emil Alexov ◽

Anna R. Panchenko

Keyword(s):

Protein Binding ◽

Binding Affinity ◽

Missense Mutations ◽

The Impact

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Native or non-native protein-protein docking models? Molecular dynamics to the rescue.

10.1101/2021.04.02.438171 ◽

2021 ◽

Author(s):

Zuzana Jandova ◽

Attilio Vittorio Vargiu ◽

Alexandre M.J.J. Bonvin

Keyword(s):

Molecular Dynamics ◽

Protein Complexes ◽

Md Simulations ◽

Protein Docking ◽

Random Forest Classifier ◽

Scoring Functions ◽

Mean Square ◽

Potential Models ◽

The Stability ◽

Almost All

Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favourable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow to distinguish native from non-native models to complement scoring functions used in docking. To this end, first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the CAPRI competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A Random Forest classifier was trained, reaching 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths in the order of 50 to 100 ns are already sufficient to reach this accuracy, which makes this approach applicable in practice.

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