Change In Affinity Research Articles

Regulation of minimal spindle midzone organization by mitotic kinases

During cell division, the microtubule cytoskeleton undergoes dramatic cell cycle-driven reorganizations of its architecture. Coordinated by changes in the phosphorylation patterns of a multitude of microtubule associated proteins, the mitotic spindle first self-assembles to capture the chromosomes and then reorganizes in anaphase as the chromosomes are segregated. A key protein for this reorganization is PRC1 which is differentially phosphorylated by the mitotic kinases CDK1 and PLK1. How the phosphorylation state of PRC1 orchestrates spindle reorganization is not understood mechanistically. Here, we reconstitute in vitro the transition between metaphase and anaphase-like microtubule architectures triggered by the changes in PRC1 phosphorylation. We find that whereas PLK1 regulates its own recruitment by PRC1, CDK1 controls the affinity of PRC1 for antiparallel microtubule binding. Dephosphorylation of CDK1-phosphorylated PRC1 is required and sufficient to trigger the reorganization of a minimal anaphase midzone in the presence of the midzone length controlling kinesin KIF4A. These results demonstrate how phosphorylation-controlled affinity changes regulate the architecture of active microtubule networks, providing new insight into the mechanistic underpinnings of the cell cycle-driven reorganization of the central spindle during mitosis.

Graph masked self-distillation learning for prediction of mutation impact on protein–protein interactions

Assessing mutation impact on the binding affinity change (ΔΔG) of protein–protein interactions (PPIs) plays a crucial role in unraveling structural-functional intricacies of proteins and developing innovative protein designs. In this study, we present a deep learning framework, PIANO, for improved prediction of ΔΔG in PPIs. The PIANO framework leverages a graph masked self-distillation scheme for protein structural geometric representation pre-training, which effectively captures the structural context representations surrounding mutation sites, and makes predictions using a multi-branch network consisting of multiple encoders for amino acids, atoms, and protein sequences. Extensive experiments demonstrated its superior prediction performance and the capability of pre-trained encoder in capturing meaningful representations. Compared to previous methods, PIANO can be widely applied on both holo complex structures and apo monomer structures. Moreover, we illustrated the practical applicability of PIANO in highlighting pathogenic mutations and crucial proteins, and distinguishing de novo mutations in disease cases and controls in PPI systems. Overall, PIANO offers a powerful deep learning tool, which may provide valuable insights into the study of drug design, therapeutic intervention, and protein engineering.

Hemoglobin-oxygen affinity changes in neonatal blood transfusions: RBC selection insights.

Despite preterm newborns often requiring blood transfusions, we have an incomplete understanding of the impact of adult packed red blood cell (pRBC) transfusions on fetal red blood cell (RBC) oxygen affinity. We investigated the influence of adult pRBC on oxygen binding in fetal RBCs obtained from the umbilical cord of preterm newborns. This included exploring the influence of the biological age of adult pRBCs on the oxygen affinity of fetal blood. Cord blood samples from preterm infants were titrated with young (Y-RBC) and old (O-RBC) adult pRBCs using an in vitro transfusion model. Parameters, including oxygen affinity (p50), hemoglobin variants, and red blood cell indices, were measured. The titration of cord blood with adult pRBCs (n = 19) resulted in a concentration-dependent decrease in p50, indicating an increased oxygen affinity. Hemoglobin variant analysis revealed a shift in composition, with a decrease in fetal hemoglobin (HbF) and an increase in adult hemoglobin (HbA) following titration. The impact of biological age was evident, as O-RBCs had a more pronounced effect on p50 values compared to Y-RBCs. Understanding the physiologic implications of transfusing preterm infants with adult pRBCs is important for optimizing transfusion practices in newborns. In an in vitro model, titrating adult pRBC into fetal blood significantly affects oxygen binding Oxygen affinity of fetal blood is significantly increased after titration of adult pRBC. Compared to older RBC subpopulations of adult pRBC, oxygen-binding properties of younger RBC subpopulations of adult pRBC more closely approximate the oxygen affinity of fetal blood. This work highlights the importance of investigating the influence of adult pRBC transfusion on fetal RBC oxygen binding. This may have implications for morbidities in the newborn period related to oxygen physiology.

Controlled Protein-Membrane Interactions Modulate Self-Organization of Min Protein Patterns.

Self-organizing protein patterns are crucial for living systems, governing important cellular processes such as polarization and division. While the field of protein self-organization has reached a point where basic pattern-forming mechanisms can be reconstituted in vitro using purified proteins, understanding how cells can dynamically switch and modulate these patterns, especially when transiently needed, remains an interesting frontier. Here, we demonstrate the efficient regulation of self-organizing protein patterns through the modulation of simple biophysical membrane parameters. Our investigation focuses on the impact of membrane affinity changes on Min protein patterns at lipid membranes composed of Escherichia coli lipids or minimal lipid compositions, and we present three major results. First, we observed the emergence of a diverse array of pattern phenotypes, ranging from waves over flower-shaped patterns to snowflake-like structures. Second, we demonstrated the dependency of these patterns on the density of protein-membrane linkers. Finally, we demonstrate thatthe shape of snowflake-like patterns isfine-tuned by membrane charge. Our results demonstrate the significant influence of membrane linkage as a straightforward biophysical parameter governing protein pattern formation. Our research points towards a simple yet intriguing mechanism by which cells can adeptly tune and switch protein patterns on the mesoscale.

Hierarchical materials with interconnected pores from capillary suspensions for bone tissue engineering

AbstractThe increasing demand for bone grafts due to the aging population has opened new opportunities for the manufacture of porous ceramics to assist in bone reconstruction. In our study, we investigate a new, promising method to manufacture hierarchically porous structures in a straightforward and tunable way. It consists of combining the novel technology of capillary suspensions, formed by mixing solid particles and two immiscible liquids, one less than 5 vol%, with freeze casting. We have successfully achieved alumina and beta‐tricalcium phosphate (β‐TCP) materials with both <2 µm and 20–50 µm as the smallest and largest pore sizes, respectively. The microstructure exhibits fully open pores and high levels of porosity (>60%). The capillary suspensions’ rheological behavior indicates that silica nano‐suspensions as a secondary fluid creates a stronger internal particle network than sucrose for the alumina system. Conversely, the opposite was observed with the β‐TCP system. These differences were attributed to the change in affinity between the secondary fluids and the solid loading. In our study, both systems have served to deepen the knowledge about the new area of capillary suspensions and proved their use in hierarchical porous scaffolds for bone tissue engineering.

PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity

BackgroundMutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types.ResultsHere, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks.ConclusionsPNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA.

Impact of PCLNPG Nanopolymeric Additive on the Surface and Structural Properties of PPSU Ultrafiltration Membranes for Enhanced Protein Rejection

This research explored the use of a partially cross-linked graft copolymer (PCLNPG) as an innovative nanopolymer pore-forming agent to enhance polyphenylsulfone (PPSU) membranes for protein separation applications. The study systematically examined the impact of incorporating PCLNPG at varying concentrations on the morphological and surface properties of PPSU membranes. A thorough characterization of the resulting PPSU-PCLNPG membranes was performed, focusing on changes in morphology, water affinity, porosity, pore size, and pore size distribution. The experimental findings demonstrated that the use of PCLNPG led to a significantly more porous structure, as confirmed by SEM analysis, with notable increases in porosity and pore size (nearly double). Additionally, the hydrophilicity of the PPSU membrane was remarkably enhanced. Performance evaluations revealed a substantial improvement in pure water flux, with the flux nearly tripling. The BSA retention was directly correlated with the concentration of the PCLNPG pore former for a loading range of 0.25–0.75 wt.%. The incorporation of PCLNPG also reduced the membrane fouling propensity by reducing both cake layer resistance (Rc) and pore plugging resistance (Rp). These results underscore the potential of PCLNPG-PPSU membranes for wastewater reclamation and nutrient recovery applications.

In Vitro Spectroscopic Investigation of Losartan and Glipizide Competitive Binding to Glycated Albumin: A Comparative Study.

Understanding the interaction between pharmaceuticals and serum proteins is crucial for optimizing therapeutic strategies, especially in patients with coexisting chronic diseases. The primary goal of this study was to assess the potential changes in binding affinity and competition between glipizide (GLP, a second-generation sulfonylurea hypoglycemic drug) and losartan (LOS, a medication commonly prescribed for hypertension, particularly for patients with concurrent diabetes) with non-glycated (HSA) and glycated (gHSAGLC, gHSAFRC) human serum albumin using multiple spectroscopic techniques (fluorescence, UV-visible absorption, and circular dichroism spectroscopy). The results indicated that FRC is a more effective glycation agent for HSA than GLC, significantly altering the albumin structure and affecting the microenvironment around critical amino acid residues, Trp-214 and Tyr. These modifications reduce the binding affinity of LOS and GLP to gHSAGLC and gHSAFRC, compared to HSA, resulting in less stable drug-protein complexes. The study revealed that LOS and GLP interact nonspecifically with the hydrophobic regions of the albumin surface in both binary (ligand-albumin) and ternary systems (ligand-albumin-ligandconst) and specifically saturate the binding sites within the protein molecule. Furthermore, the presence of an additional drug (GLP in the LOS-albumin complex or LOS in the GLP-albumin complex) complicates the interactions, likely leading to competitive binding or displacement of the initially bound drug in both non-glycated and glycated albumins. Analysis of the CD spectra suggests mutual interactions between GLP and LOS, underscoring the importance of closely monitoring patients co-administered these drugs, to ensure optimal therapeutic efficacy and safety.

Molecular analysis of changes in DNA binding affinity and bending extent induced by the mutations on the aromatic amino residues in Cren7.

Proteins from Crenarchaeal organisms exhibit remarkable thermal stability. The aromatic amino acids in Cren7, a Crenarchaeal protein, regulate protein stability and further modulate DNA binding and its compaction. Specific aromatic amino acids were mutated, and using spectroscopic and theoretical approaches, we have examined the structure, DNA binding affinity, and DNA bending ability of mutants. and compared with wild-type (WT) Cren7. The reverse titration profiles were analysed by a noncooperativeMcGhee-von Hippel model to estimate affinity constant (Ka) and site size (n) associated with binding to the DNA. Biolayer interferometry (BLI) measurements showed that the binding affinity decreased at higher salt concentrations. For theoretical analysis of extent of DNA bending, radius of gyration and bending angle were compared for WT and mutants. Time evolution of order parameters based on translational and rotational motion of tryptophan residue (W26) was used for qualitative detection of stacking interactions between W26 of Cren7 and DNA nucleobases. It was observed that orientation of W26 in F41A favoredformation of a new lone pair-lone pair interaction between DNA and Cren7. Consequently, in thermostable proteins, the aromatic residues at the terminus maintain structural stability, whereas the residues at the core optimize the degree of DNA bending and compaction.

MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning.

Assessing changes in protein-protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism-aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS-CoV-2 variants and the ACE2 complex.

Cupric ion coordination-mediated molecularly imprinted electrochemical sensor for the recognition and ratiometric detection of lidocaine

Molecularly imprinted polymers (MIPs) have been widely used as artificial recognition elements in sensing applications. However, their electrochemical sensing performance is generally hampered by limited affinity and uncontrolled condition change. In this work, a novel MIP electrochemical sensor based on metal coordination interaction was prepared and used for the recognition and ratiometric detection of lidocaine (LC). The sensor was constructed by electrodepositing Cu-coordinated MIP on biomass carbon modified glassy carbon electrode. Herein, Cu2+ ions acted as anchor for the immobilization of LC during the synthesis process, enabling the orderly formation of molecular recognition sites. Reversely, the metal coordination between Cu2+ ions and LC molecules facilitated the recognition of LC. Moreover, the doped cupric ions in the polymer film could provide a reference signal for subsequent ratiometric strategy. Thus the resulting sensor exhibited high selectivity, sensitivity, satisfactory reproducibility, and anti-interference ability. Under the selected conditions, the peak current ratio of LC and cupric ion was linear to LC concentration in the range of 0.008–2.5 μmol L−1 (R2 = 0.9951), and the limit of detection was 1.9 nmol L−1 (S/N = 3). The practical feasibility of the sensor was evaluated by detecting human serum and pharmaceutical samples, and satisfactory outcomes were obtained.

Biochemical characterization of naturally occurring mutations in SARS-CoV-2 RNA-dependent RNA polymerase.

Since the emergence of SARS-CoV-2, mutations in all subunits of the RNA-dependent RNA polymerase (RdRp) of the virus have been repeatedly reported. Although RdRp represents a primary target for antiviral drugs, experimental studies exploring the phenotypic effect of these mutations have been limited. This study focuses on the phenotypic effects of substitutions in the three RdRp subunits: nsp7, nsp8, and nsp12, selected based on their occurrence rate and potential impact. We employed nano-differential scanning fluorimetry and microscale thermophoresis to examine the impact of these mutations on protein stability and RdRp complex assembly. We observed diverse impacts; notably, a single mutation in nsp8 significantly increased its stability as evidenced by a 13°C increase in melting temperature, whereas certain mutations in nsp7 and nsp8 reduced their binding affinity to nsp12 during RdRp complex formation. Using a fluorometric enzymatic assay, we assessed the overall effect on RNA polymerase activity. We found that most of the examined mutations altered the polymerase activity, often as a direct result of changes in stability or affinity to the other components of the RdRp complex. Intriguingly, a combination of nsp8 A21V and nsp12 P323L mutations resulted in a 50% increase in polymerase activity. To our knowledge, this is the first biochemical study to demonstrate the impact of amino acid mutations across all components constituting the RdRp complex in emerging SARS-CoV-2 subvariants.

Leptin/Ghrelin Axis and Antibodies Reactive to Both Hormones in Relation to Metabolic Syndrome Components in Mexican Children.

Background: Metabolic syndrome (MetS) in children is a rising health issue that is strongly associated with cardiovascular diseases and type 2 diabetes mellitus development. Low-affinity antibodies reactive to leptin and ghrelin are suggested to regulate hormone stability and function; nevertheless, the role of the leptin/ghrelin axis and antibodies reactive to both hormones in relation to MetS or its components in the pediatric population remains unknown. Methods: Fifty-eight children (7-12 years) were included and categorized according to the presence of one or more criteria for the diagnosis of MetS or according to body mass index. Body composition, biochemical variables, and metabolic risk indexes were determined. Antibodies reactive to leptin and ghrelin were quantified by an in-house enzyme-linked immunosorbent assay test. Ratios of leptin/ghrelin hormones and anti-leptin/anti-ghrelin immune complexes were obtained. Results: The biochemical variables glucose (P = 0.0009), insulin (P = 0.0001), leptin (P = 0.0036), HOMA-IR (homeostatic model assessment for insulin resistance) (P < 0.0001), and plasma atherogenic index (P < 0.0001) were significantly higher in children with two or three components of MetS (MetS 2-3) in comparison to children with none or one component (MetS 0-1). Ratios of leptin/ghrelin (P = 0.0307) and anti-leptin/anti-ghrelin immune complexes (P = 0.0338) were higher in MetS 2-3 group versus MetS 0-1 group. In MetS 2-3 group, both insulin (r = 0.4361, P = 0.0293) and HOMA-IR (r = 0.4761, P = 0.0161) were positively correlated with the leptin/ghrelin hormone ratio. Conclusions: The higher leptin/ghrelin hormone ratio scores observed in MetS 2-3 group, along with their correlation with insulin levels and HOMA-IR, highlight the role of leptin and ghrelin on insulin sensitivity and metabolic regulation. An increased ratio of anti-leptin/anti-ghrelin immune complexes suggests affinity changes in these antibodies that may lead to alterations in hormone function.

Non-Receptor Tyrosine Kinases: Their Structure and Mechanistic Role in Tumor Progression and Resistance.

Protein tyrosine kinases (PTKs) function as key molecules in the signaling pathways in addition to their impact as a therapeutic target for the treatment of many human diseases, including cancer. PTKs are characterized by their ability to phosphorylate serine, threonine, or tyrosine residues and can thereby rapidly and reversibly alter the function of their protein substrates in the form of significant changes in protein confirmation and affinity for their interaction with protein partners to drive cellular functions under normal and pathological conditions. PTKs are classified into two groups: one of which represents tyrosine kinases, while the other one includes the members of the serine/threonine kinases. The group of tyrosine kinases is subdivided into subgroups: one of them includes the member of receptor tyrosine kinases (RTKs), while the other subgroup includes the member of non-receptor tyrosine kinases (NRTKs). Both these kinase groups function as an "on" or "off" switch in many cellular functions. NRTKs are enzymes which are overexpressed and activated in many cancer types and regulate variable cellular functions in response to extracellular signaling-dependent mechanisms. NRTK-mediated different cellular functions are regulated by kinase-dependent and kinase-independent mechanisms either in the cytoplasm or in the nucleus. Thus, targeting NRTKs is of great interest to improve the treatment strategy of different tumor types. This review deals with the structure and mechanistic role of NRTKs in tumor progression and resistance and their importance as therapeutic targets in tumor therapy.

Evaluation of fluorous affinity using fluoroalkyl-modified silica gel and selective separation of poly-fluoroalkyl substances in organic solvents.

In this study, we focused on the fluorous affinity acting among fluorine compounds, and then developed a new separation medium and evaluated their performance. We prepared the stationary phases for a column using silica gel-modified alkyl fluoride and investigated the characteristics of fluorous affinity by comparing them with a typical stationary phase, which does not contain fluorine, using high-performance liquid chromatography (HPLC). In HPLC measurements, we confirmed that while all non-fluorine compounds were not retained, retention of fluorine compounds increased as the number of fluorine increased with the stationary phase. It also revealed that the strength of fluorous affinity changes depending on the types of the organic solvent; the more polar the solvent, the stronger the effect. Additionally, the stationary phase was employed to compare the efficiency of our column with that of a commercially available column, Fluofix-II. The retention selectivity was almost the same, but the absolute retention strength was slightly higher on our column, indicating that the column is available for practical use.

Fitness costs, realized heritability, and mechanism of resistance to tenvermectin B in Plutella xylostella.

Tenvermectin B (TVM-B) and five TVM-B analogs were produced by fermentation of a genetically engineered strain Streptomyces avermitilis HU02, and TVM-B is being developed as a new insecticide. Through 11 generations of resistance selection against TVM-B in the diamondback moth, Plutella xylostella, the median lethal concentration (LC50) was increased from 14.84 to 1213.73 mg L-1. The resistance to TVM-B in P. xylostella developed fast and its realized heritability was high (h2 = 0.2901 (F7), h2 = 0.4070 (F11)). However, the relative fitness was 0.6916 suggesting a fitness cost in the resistant strains. The fitness cost was partially explained by the upregulation of the detoxification enzyme activity by 2.15 folds in carboxylate esterase (CarE) and the gene expressions of ATP-binding cassette transporter gene (ABCC2) and the alpha subunit of the glutamate-gated chloride channel (GluCl) by 1.70- and 2.32 folds, respectively. The resistance was also explained by two points of mutations at the alpha subunit of the glutamate-gated chloride channel in the P. xylostella (PxGluClα) subunit in F11. However, there was little change in the binding affinity. These results provided helpful information for the mechanism study of TVM-B resistance and will be conducive to designing rational resistance management strategies in P. xylostella.

Multidecadal changes in coastal benthic species composition and ecosystem functioning occur independently of temperature-driven community shifts.

Rising global temperatures are often identified as the key driver impacting ecosystems and the services they provide by affecting biodiversity structure and function. A disproportionate amount of our understanding of biodiversity and function is from short-term experimental studies and static values of biodiversity indices, lacking the ability to monitor long-term trends and capture community dynamics. Here, we analyse a biennial dataset spanning 32 years of macroinvertebrate benthic communities and their functional response to increasing temperatures. We monitored changes in species' thermal affinities to examine warming-related shifts by selecting their mid-point global temperature distribution range and linking them to species' traits. We employed a novel weighted metric using Biological Trait Analysis (BTA) to gain better insights into the ecological potential of each species by incorporating species abundance and body size and selecting a subset of traits that represent five ecosystem functions: bioturbation activity, sediment stability, nutrient recycling and higher and lower trophic production. Using biodiversity indices (richness, Simpson's diversity and vulnerability) and functional indices (richness, Rao's Q and redundancy), the community structure showed no significant change over time with a narrow range of variation. However, we show shifts in species composition with warming and increases in the abundance of individuals, which altered ecosystem functioning positively and/or non-linearly. Yet, when higher taxonomic groupings than species were excluded from the analysis, there was only a weak increase in the measured change in community-weighted average thermal affinities, suggesting changes in ecosystem functions over time occur independently of temperature increase-related shifts in community composition. Other environmental factors driving species composition and abundance may be more important in these subtidal macrobenthic communities. This challenges the prevailing emphasis on temperature as the primary driver of ecological response to climate change and emphasises the necessity for a comprehensive understanding of the temporal dynamics of complex systems.

Machine learning assisted designing of Y-series small molecule acceptors: Library generation and property prediction

The designing of new small molecule acceptors (SMAs) for organic solar cells has been a prominent area of research for many decades. It is challenging to find unique materials due to expensive experimentation. Machine learning emerges as a promising tool for rapid and cost-effective prediction of properties. In the current study, electron affinity is predicted using machine learning models. Molecular descriptors are calculated for model training. Four machine learning models are used. A reasonably high predictive capability is obtained for random forest model (r-squared values of 0.92 and 0.82 for training and test set, respectively). Furthermore, the chemical database of small molecule acceptors is generated. Generated virtual space is anticipated by exploiting the t-distributed stochastic neighbor embedding (t-SNE) method. Structure Activity Landscape Index (SALI) analysis is conducted to examine how properties change with structural variations. A minimal change in electron affinity resulted from structural modifications. Additionally, clustering analysis is performed on selected small molecule acceptors for structural grouping of SMAs. Five SMAs with lowest synthetic accessibility (SA) score are selected for density functional theory (DFT) calculations. The introduced multiple dimensional framework has potential screened the materials in short-time and efficient way.

Emergence of SARS-CoV-2 omicron variant JN.1 in Tamil Nadu, India - Clinical characteristics and novel mutations

In December 2023, we observed a notable shift in the COVID-19 landscape, when JN.1 omicron emerged as the predominant SARS-CoV-2 variant with a 95% incidence. We characterized the clinical profile, and genetic changes in JN.1, an emerging SARS-CoV-2 variant of interest. Whole genome sequencing was performed on SARS-CoV-2 positive clinical specimens, followed by sequence analysis. Mutations within the spike protein sequences were analysed and compared with the previously reported lineages and sub-lineages, to identify the potential impact of the unique mutations on protein structure and possible alterations in the functionality. Several unique and dynamic mutations were identified herein. Molecular docking analysis showed changes in the binding affinity, and key interacting residues of wild-type and mutated structures with key host cell receptors of SARS-CoV-2 entry viz., ACE2, CD147, CD209L and AXL. Our data provides key insights on the emergence of newer variants and highlights the necessity for robust and sustained global genomic surveillance of SARS-CoV-2.

Impact of Missense Mutations on Spike Protein Stability and Binding Affinity in the Omicron Variant.

The global effort to combat the COVID-19 pandemic faces ongoing uncertainty with the emergence of Variants of Concern featuring numerous mutations on the Spike (S) protein. In particular, the Omicron Variant is distinguished by 32 mutations, including 10 within its receptor-binding domain (RBD). These mutations significantly impact viral infectivity and the efficacy of vaccines and antibodies currently in use for therapeutic purposes. In our study, we employed structure-based computational saturation mutagenesis approaches to predict the effects of Omicron missense mutations on RBD stability and binding affinity, comparing them to the original Wuhan-Hu-1 strain. Our results predict that mutations such as G431W and P507W induce the most substantial destabilizations in the Wuhan-Hu-1-S/Omicron-S RBD. Notably, we postulate that mutations in the Omicron-S exhibit a higher percentage of enhancing binding affinity compared to Wuhan-S. We found that the mutations at residue positions G447, Y449, F456, F486, and S496 led to significant changes in binding affinity. In summary, our findings may shed light on the widespread prevalence of Omicron mutations in human populations. The Omicron mutations that potentially enhance their affinity for human receptors may facilitate increased viral binding and internalization in infected cells, thereby enhancing infectivity. This informs the development of new neutralizing antibodies capable of targeting Omicron's immune-evading mutations, potentially aiding in the ongoing battle against the COVID-19 pandemic.