An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design.

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.

Investigation on the binding behavior of human α1-acid glycoprotein with Janus Kinase inhibitor baricitinib: Multi-spectroscopic and molecular simulation methodologies.

Baricitinib is a Janus Kinase (JAK) inhibitor that is primarily used to treat moderately to severely active rheumatoid arthritis in adults and has recently been reported for the treatment of patients with severe COVID-19. This paper describes the investigation of the binding behavior of baricitinib to human α1-acid glycoprotein (HAG) employing a variety of spectroscopic techniques, molecular docking and dynamics simulations. Baricitinib can quench the fluorescence from amino acids in HAG through a mix of dynamic and static quenching, according to steady-state fluorescence and UV spectra observations, but it is mainly static quenching at low concentration. The binding constant (Kb) of baricitinib to HAG at 298 K was at the level of 104 M-1, indicating a moderate affinity of baricitinib to HAG. Hydrogen bonding and hydrophobic interactions conducted the main effect, according to thermodynamic characteristics, competition studies between ANS and sucrose, and molecular dynamics simulations. For the change in HAG conformation, the results of multiple spectra showed that baricitinib was able to alter the secondary structure of HAG as well as increase the polarity of the microenvironment around the Trp amino acid. Furthermore, the binding behavior of baricitinib to HAG was investigated by molecular docking and molecular dynamics simulations, which validated experimental results. Also explored is the influence of K+, Co2+, Ni2+, Ca2+, Fe3+, Zn2+, Mg2+ and Cu2+plasma on binding affinity.

Sonocrystallization of a novel ZIF/zeolite composite adsorbent with high chemical stability for removal of the pharmaceutical pollutant azithromycin from contaminated water.

Water pollution management, reduction, and elimination are critical challenges of the current era that threaten millions of lives. By spreading the coronavirus in December 2019, the use of antibiotics, such as azithromycin increased. This drug was not metabolized, and entered the surface waters. ZIF-8/Zeolit composite was made by the sonochemical method. Furthermore, the effect of pH, the regeneration of adsorbents, kinetics, isotherms, and thermodynamics were attended. The adsorption capacity of zeolite, ZIF-8, and the composite ZIF-8/Zeolite were 22.37, 235.3, and 131 mg/g, respectively. The adsorbent reaches the equilibrium in 60 min, and at pH = 8. The adsorption process was spontaneous, endothermic associated with increased entropy. The results of the experiment were analyzed using Langmuir isotherms and pseudo-second order kinetic models with a R2 of 0.99, and successfully removing the composite by 85% in 10 cycles. It indicated that the maximum amount of drug could be removed with a small amount of composite.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies.

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

Vaccination dilemma in the thermodynamic limit.

The vaccination game is a social dilemma that refers to the conundrum individuals face (to get immunized or not) when the population is exposed to an infectious disease. The model has recently gained much traction due to the COVID-19 pandemic since the public perception of vaccines plays a significant role in disease dynamics. This paper studies the vaccination game in the thermodynamic limit with an analytical method derived from the 1D Ising model called Nash equilibrium mapping. The individual dilemma regarding vaccination comes from an internal conflict wherein one tries to balance the perceived advantages of immunizing with the apparent risks associated with vaccination, which they hear through different news media. We compare the results of Nash equilibrium (NE) mapping from other 1D Ising-based models, namely, Darwinian evolution (DE) and agent-based simulation. This study aims to analyze the behavior of an infinite population regarding what fraction of people choose to vaccinate or not vaccinate. While Nash equilibrium mapping and agent-based simulation agree mostly, DE strays far from the two models. DE fails to predict the equilibrium behavior of players in the population reasonably. We apply the results of our study to analyze the AstraZeneca (AZ) COVID-19 vaccine risk vs disease deaths debate, both via NE mapping and the agent-based method. Both predict nearly 100% AZ vaccine coverage for people aged above 40, notwithstanding the risk. At the same time, younger people show a slight reluctance. We predict that while government intervention via vaccination mandates and/or advertisement campaigns are unnecessary for the older population, for the younger population (ages: 20-39), some encouragement from the government via media campaigns and/or vaccine mandates may be necessary.

Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling.

In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE). Thermodynamics integration (TI) based on molecular dynamics (MD) simulations is a BFE calculation method capable of acquiring accurate BFE, but it is computationally expensive and time-consuming. In this work, we have developed an efficient automated workflow for identifying compounds with the lowest BFE among thousands of congeneric ligands, which requires only hundreds of TI calculations. Automated machine learning (AutoML) orchestrated by active learning (AL) in an AL-AutoML workflow allows unbiased and efficient search for a small set of best-performing molecules. We have applied this workflow to select inhibitors of the SARS-CoV-2 papain-like protease and were able to find 133 compounds with improved binding affinity, including 16 compounds with better than 100-fold binding affinity improvement. We obtained a hit rate that outperforms that expected of traditional expert medicinal chemist-guided campaigns. Thus, we demonstrate that the combination of AL and AutoML with free energy simulations provides at least 20× speedup relative to the naïve brute force approaches.

RAFFT: Efficient prediction of RNA folding pathways using the fast Fourier transform.

We propose a novel heuristic to predict RNA secondary structure formation pathways that has two components: (i) a folding algorithm and (ii) a kinetic ansatz. This heuristic is inspired by the kinetic partitioning mechanism, by which molecules follow alternative folding pathways to their native structure, some much faster than others. Similarly, our algorithm RAFFT starts by generating an ensemble of concurrent folding pathways ending in multiple metastable structures, which is in contrast with traditional thermodynamic approaches that find single structures with minimal free energies. When we constrained the algorithm to predict only 50 structures per sequence, near-native structures were found for RNA molecules of length ≤ 200 nucleotides. Our heuristic has been tested on the coronavirus frameshifting stimulation element (CFSE): an ensemble of 68 distinct structures allowed us to produce complete folding kinetic trajectories, whereas known methods require evaluating millions of sub-optimal structures to achieve this result. Thanks to the fast Fourier transform on which RAFFT (RNA folding Algorithm wih Fast Fourier Transform) is based, these computations are efficient, with complexity [Formula: see text].

Large-scale application of free energy perturbation calculations for antibody design.

Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

Plasticity of the 340-Loop in Influenza Neuraminidase Offers New Insight for Antiviral Drug Development.

The recently discovered 340-cavity in influenza neuraminidase (NA) N6 and N7 subtypes has introduced new possibilities for rational structure-based drug design. However, the plasticity of the 340-loop (residues 342-347) and the role of the 340-loop in NA activity and substrate binding have not been deeply exploited. Here, we investigate the mechanism of 340-cavity formation and demonstrate for the first time that seven of nine NA subtypes are able to adopt an open 340-cavity over 1.8 µs total molecular dynamics simulation time. The finding that the 340-loop plays a role in the sialic acid binding pathway suggests that the 340-cavity can function as a druggable pocket. Comparing the open and closed conformations of the 340-loop, the side chain orientation of residue 344 was found to govern the formation of the 340-cavity. Additionally, the conserved calcium ion was found to substantially influence the stability of the 340-loop. Our study provides dynamical evidence supporting the 340-cavity as a druggable hotspot at the atomic level and offers new structural insight in designing antiviral drugs.

Exploring the multiple conformational states of RNA genome through interhelical dynamics and network analysis.

The structural variation of RNA is often very transient and can be easily missed in experiments. Molecular dynamics simulation studies along with network analysis can be an effective tool to identify prominent conformations of such dynamic biomolecular systems. Here we describe a method to effectively sample different RNA conformations at six different temperatures based on the changes in the interhelical orientations. This method gives the information about prominent states of the RNA as well as the probability of the existence of different conformations and their interconnections during the process of evolution. In the case of the SARS-CoV-2 genome, the change of prominent structures was found to be faster at 333 K as compared to higher temperatures due to the formation of the non-native base pairs. ΔΔG calculated between 288 K and 363 K are found to be 10.31 kcal/mol (88 nt) considering the contribution from the multiple states of the RNA which agrees well with the experimentally reported denaturation energy for E. coli α mRNA pseudoknot (∼16 kcal/mol, 112 nt) determined by calorimetry/UV hyperchromicity and human telomerase RNA telomerase (4.5-6.6 kcal/mol, 54 nt) determined by FRET analysis.

Environmentally azithromycin pharmaceutical wastewater management and synergetic biocompatible approaches of loaded azithromycin@hematite nanoparticles.

Pharmaceutical wastewater contamination via azithromycin antibiotic and the continuous emergence of some strains of bacteria, cancer, and the Covid-19 virus. Azithromycin wastewater treatment using the biosynthesized Hematite nanoparticles (α-HNPs) and the biocompatible activities of the resulted nanosystem were reported. Biofabrication of α-HNPs using Echinacea purpurea liquid extract as a previously reported approach was implemented. An evaluation of the adsorption technique via the biofabricated α-HNPs for the removal of the Azr drug contaminant from the pharmaceutical wastewater was conducted. Adsorption isotherm, kinetics, and thermodynamic parameters of the Azr on the α-HNPs surface have been investigated as a batch mode of equilibrium experiments. Antibacterial, anticancer, and antiviral activities were conducted as Azr@α-HNPs. The optimum conditions for the adsorption study were conducted as solution pH = 10, 150 mg dose of α-HNPs, and Azr concentration 400 mg/L at 293 K. The most fitted isothermal model was described according to the Langmuir model at adsorption capacity 114.05 mg/g in a pseudo-second-order kinetic mechanistic at R2 0.9999. Thermodynamic study manifested that the adsorption behavior is a spontaneous endothermic chemisorption process. Subsequently, studying the biocompatible applications of the Azr@α-HNPs. Azr@α-HNPs antibacterial activity revealed a synergistic effect in the case of Gram-positive more than Gram-negative bacteria. IC50 of Azr@α-HNPs cytotoxicity against MCF7, HepG2, and HCT116 cell lines was investigated and it was found to be 78.1, 81.7, and 93.4 µg/mL respectively. As the first investigation of the antiviral use of Azr@α-HNPs against SARS-CoV-2, it was achieved a safety therapeutic index equal to 25.4 revealing a promising antiviral activity. An admirable impact of the use of the biosynthesized α-HNPs and its removal nanosystem product Azr@α-HNPs was manifested and it may be used soon as a platform of the drug delivery nanosystem for the biomedical applications.

Hazardous Shortcuts in Standard Binding Free Energy Calculations.

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Interaction of the new inhibitor paxlovid (PF-07321332) and ivermectin with the monomer of the main protease SARS-CoV-2: A volumetric study based on molecular dynamics, elastic networks, classical thermodynamics and SPT.

The COVID-19 pandemic has accelerated the study of drugs, most notably ivermectin and more recently Paxlovid (PF-07321332) which is in phase III clinical trials with experimental data showing covalent binding to the viral protease Mpro. Theoretical developments of catalytic site-directed docking support thermodynamically feasible non-covalent binding to Mpro. Here we show that Paxlovid binds non-covalently at regions other than the catalytic sites with energies stronger than reported and at the same binding site as the ivermectin B1a homologue, all through theoretical methodologies, including blind docking. We volumetrically characterize the non-covalent interaction of the ivermectin homologues (avermectins B1a and B1b) and Paxlovid with the mMpro monomer, through molecular dynamics and scaled particle theory (SPT). Using the fluctuation-dissipation theorem (FDT), we estimated the electric dipole moment fluctuations at the surface of each of complex involved in this study, with similar trends to that observed in the interaction volume. Using fluctuations of the intrinsic volume and the number of flexible fragments of proteins using anisotropic and Gaussian elastic networks (ANM+GNM) suggests the complexes with ivermectin are more dynamic and flexible than the unbound monomer. In contrast, the binding of Paxlovid to mMpro shows that the mMpro-PF complex is the least structurally dynamic of all the species measured in this investigation. The results support a differential molecular mechanism of the ivermectin and PF homologues in the mMpro monomer. Finally, the results showed that Paxlovid despite beingbound in different sites through covalent or non-covalent forms behaves similarly in terms of its structural flexibility and volumetric behaviour.

Strain wars 2: Binding constants, enthalpies, entropies, Gibbs energies and rates of binding of SARS-CoV-2 variants.

SARS-CoV-2 virus is the cause of COVID-19 pandemic and belongs to RNA viruses, showing great tendency to mutate. Several dozens of mutations have been observed on the SARS-CoV-2 virus, during the last two years. Some of the mutated strains show a greater infectivity and are capable of suppressing the earlier strains, through interference. In this work, kinetic and thermodynamic properties were calculated for strains characterized by various numbers and locations of mutations. It was shown that mutations lead to changes in chemical composition, thermodynamic properties and infectivity. Through competition, the phenomenon of interference of various SARS-CoV-2 strains was explained, which results in suppression of the wild type by mutant strains. Standard Gibbs energy of binding and binding constant for the Omicron (B.1.1.529) strain were found to be ΔBG° = -45.96 kJ/mol and KB = 1.13 ∙ 10+8 M-1, respectively.

Virtual Screening of Natural Chemical Databases to Search for Potential ACE2 Inhibitors.

The angiotensin-converting enzyme II (ACE2) is a multifunctional protein in both health and disease conditions, which serves as a counterregulatory component of RAS function in a cardioprotective role. ACE2 modulation may also have relevance to ovarian cancer, diabetes, acute lung injury, fibrotic diseases, etc. Furthermore, since the outbreak of the coronavirus disease in 2019 (COVID-19), ACE2 has been recognized as the host receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The receptor binding domain of the SARS-CoV-2 S-protein has a strong interaction with ACE2, so ACE2 may be a potent drug target to prevent the virus from invading host cells for anti-COVID-19 drug discovery. In this study, structure- and property-based virtual screening methods were combined to filter natural product databases from ChemDiv, TargetMol, and InterBioScreen to find potential ACE2 inhibitors. The binding affinity between protein and ligands was predicted using both Glide SP and XP scoring functions and the MM-GBSA method. ADME properties were also calculated to evaluate chemical drug-likeness. Then, molecular dynamics (MD) simulations were performed to further explore the binding modes between the highest-potential compounds and ACE2. Results showed that the compounds 154-23-4 and STOCK1N-07141 possess potential ACE2 inhibition activities and deserve further study.

Modulation of the monomer-dimer equilibrium and catalytic activity of SARS-CoV-2 main protease by a transition-state analog inhibitor.

The role of dimer formation for the onset of catalytic activity of SARS-CoV-2 main protease (MProWT) was assessed using a predominantly monomeric mutant (MProM). Rates of MProWT and MProM catalyzed hydrolyses display substrate saturation kinetics and second-order dependency on the protein concentration. The addition of the prodrug GC376, an inhibitor of MProWT, to MProM leads to an increase in the dimer population and catalytic activity with increasing inhibitor concentration. The activity reaches a maximum corresponding to a dimer population in which one active site is occupied by the inhibitor and the other is available for catalytic activity. This phase is followed by a decrease in catalytic activity due to the inhibitor competing with the substrate. Detailed kinetics and equilibrium analyses are presented and a modified Michaelis-Menten equation accounts for the results. These observations provide conclusive evidence that dimer formation is coupled to catalytic activity represented by two equivalent active sites.

Specific length and structure rather than high thermodynamic stability enable regulatory mRNA stem-loops to pause translation.

Translating ribosomes unwind mRNA secondary structures by three basepairs each elongation cycle. Despite the ribosome helicase, certain mRNA stem-loops stimulate programmed ribosomal frameshift by inhibiting translation elongation. Here, using mutagenesis, biochemical and single-molecule experiments, we examine whether high stability of three basepairs, which are unwound by the translating ribosome, is critical for inducing ribosome pauses. We find that encountering frameshift-inducing mRNA stem-loops from the E. coli dnaX mRNA and the gag-pol transcript of Human Immunodeficiency Virus (HIV) hinders A-site tRNA binding and slows down ribosome translocation by 15-20 folds. By contrast, unwinding of first three basepairs adjacent to the mRNA entry channel slows down the translating ribosome by only 2-3 folds. Rather than high thermodynamic stability, specific length and structure enable regulatory mRNA stem-loops to stall translation by forming inhibitory interactions with the ribosome. Our data provide the basis for rationalizing transcriptome-wide studies of translation and searching for novel regulatory mRNA stem-loops.

Structures of the Omicron spike trimer with ACE2 and an anti-Omicron antibody.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has become the dominant infective strain. We report the structures of the Omicron spike trimer on its own and in complex with angiotensin-converting enzyme 2 (ACE2) or an anti-Omicron antibody. Most Omicron mutations are located on the surface of the spike protein and change binding epitopes to many current antibodies. In the ACE2-binding site, compensating mutations strengthen receptor binding domain (RBD) binding to ACE2. Both the RBD and the apo form of the Omicron spike trimer are thermodynamically unstable. An unusual RBD-RBD interaction in the ACE2-spike complex supports the open conformation and further reinforces ACE2 binding to the spike trimer. A broad-spectrum therapeutic antibody, JMB2002, which has completed a phase 1 clinical trial, maintains neutralizing activity against Omicron. JMB2002 binds to RBD differently from other characterized antibodies and inhibits ACE2 binding.

Systems analysis shows that thermodynamic physiological and pharmacological fundamentals drive COVID-19 and response to treatment.

Why a systems analysis view of this pandemic? The current pandemic has inflicted almost unimaginable grief, sorrow, loss, and terror at a global scale. One of the great ironies with the COVID-19 pandemic, particularly early on, is counter intuitive. The speed at which specialized basic and clinical sciences described the details of the damage to humans in COVID-19 disease has been impressive. Equally, the development of vaccines in an amazingly short time interval has been extraordinary. However, what has been less well understood has been the fundamental elements that underpin the progression of COVID-19 in an individual and in populations. We have used systems analysis approaches with human physiology and pharmacology to explore the fundamental underpinnings of COVID-19 disease. Pharmacology powerfully captures the thermodynamic characteristics of molecular binding with an exogenous entity such as a virus and its consequences on the living processes well described by human physiology. Thus, we have documented the passage of SARS-CoV-2 from infection of a single cell to species jump, to tropism, variant emergence and widespread population infection. During the course of this review, the recurrent observation was the efficiency and simplicity of one critical function of this virus. The lethality of SARS-CoV-2 is due primarily to its ability to possess and use a variable surface for binding to a specific human target with high affinity. This binding liberates Gibbs free energy (GFE) such that it satisfies the criteria for thermodynamic spontaneity. Its binding is the prelude to human host cellular entry and replication by the appropriation of host cell constituent molecules that have been produced with a prior energy investment by the host cell. It is also a binding that permits viral tropism to lead to high levels of distribution across populations with newly formed virions. This thermodynamic spontaneity is repeated endlessly as infection of a single host cell spreads to bystander cells, to tissues, to humans in close proximity and then to global populations. The principal antagonism of this process comes from SARS-CoV-2 itself, with its relentless changing of its viral surface configuration, associated with the inevitable emergence of variants better configured to resist immune sequestration and importantly with a greater affinity for the host target and higher infectivity. The great value of this physiological and pharmacological perspective is that it reveals the fundamental thermodynamic underpinnings of SARS-CoV-2 infection.

Predicting spike protein NTD mutations of SARS-CoV-2 causing immune evasion by molecular dynamics simulations.

The coronavirus disease 2019 (COVID-19) pandemic was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Among all the potential targets studied for developing drugs and antibodies, the spike (S) protein is the most striking one, which is on the surface of the virus. In contrast with the intensively investigated immunodominant receptor-binding domain (RBD) of the protein, little is known about the neutralizing antibody binding mechanisms of the N-terminal domain (NTD), let alone the effects of NTD mutations on antibody binding and thereby the risk of immune evasion. Based on 400 ns molecular dynamics simulation for 11 NTD-antibody complexes together with other computational approaches in this study, we investigated critical residues for NTD-antibody binding and their detailed mechanisms. The results show that 36 residues on the NTD including R246, Y144, K147, Y248, L249 and P251 are critically involved in the direct interaction of the NTD with many monoclonal antibodies (mAbs), indicating that the viruses harboring these residue mutations may have a high risk of immune evasion. Binding free energy calculations and an interaction mechanism study reveal that R246I, which is present in the Beta (B.1.351/501Y.V2) variant, may have various impacts on current NTD antibodies through abolishing the hydrogen bonds and electrostatic interaction with the antibodies or affecting other interface residues. Therefore, special attention should be paid to the mutations of these key residues in future antibody and vaccine design and development.