Structure Prediction Tools Research Articles

The Present State and Impact of AI-Driven Computational Tools for Predicting Plant Protein Structures.

Several key functions of plants, such as photosynthesis, nutrient transport, disease resistance, and abiotic tolerance, are manifested by several classes of proteins. Prediction of 3- dimensional (3-D) structures of proteins and their working mechanisms can have a profound impact on plant proteomics research and could help improve key agricultural traits in crop plants. This review aims to present the current status of plant protein structure determination and discuss the way forward. Most experimentally proven protein structures are available only for the model plant Arabidopsis thaliana. Most of the key crop plants have only a few hundred or fewer experimentally proven 3-D structures. Fewer than 1% of the protein sequences in the majority of plants have had their 3D structures experimentally determined, and A. thaliana is the sole plant with the highest percentage of 1.4 % of protein sequences with experimentally determined structures. AI-based protein structure prediction tool AlphaFold has predicted models of several thousand proteins for many crop plants. In AlphaFold predicted protein models, soybean has the highest percentage (65%) of its UniProt protein sequences with predicted models, and a few other crop plants have also a considerable percentage of its UniProt sequences with AlphaFold predicted models. AlphaFold might help predict models and bridge the gap in plant structure determination studies. Protein structure information might lead to engineering key residues to improve the agronomical performance of crop plants.

RDBSB: a database for catalytic bioparts with experimental evidence.

Catalytic bioparts are fundamental to the design, construction and optimization of biological systems for specific metabolic pathways. However, the functional characterization information of these bioparts is frequently dispersed across multiple databases and literature sources, posing significant challenges to the effective design and optimization of specific chassis or cell factories. We developed the Registry and Database of Bioparts for Synthetic Biology (RDBSB), a comprehensive resource encompassing 83 193 curated catalytic bioparts with experimental evidences. RDBSB offers their detailed qualitative and quantitative catalytic information, including critical parameters such as activities, substrates, optimal pH and temperature, and chassis specificity. The platform features an interactive search engine, visualization tools and analysis utilities such as biopart finder, structure prediction and pathway design tools. Additionally, RDBSB promotes community engagement through a catalytic bioparts submission system to facilitate rapid data sharing and utilization. To date, RDBSB has supported the contribution of >1000 catalytic bioparts. We anticipate that the database will significantly enhance the resources available for pathway design in synthetic biology and serve essential tools for researchers. RDBSB is freely available at https://www.biosino.org/rdbsb/.

An array of Zymoseptoria tritici effectors suppress plant immune responses.

Zymoseptoria tritici is the most economically significant fungal pathogen of wheat in Europe. However, despite the importance of this pathogen, the molecular interactions between pathogen and host during infection are not well understood. Herein, we describe the use of two libraries of cloned Z. tritici effectors that were screened to identify effector candidates with putative pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI)-suppressing activity. The effectors from each library were transiently expressed in Nicotiana benthamiana, and expressing leaves were treated with bacterial or fungal PAMPs to assess the effectors' ability to suppress reactive oxygen species (ROS) production. From these screens, numerous effectors were identified with PTI-suppressing activity. In addition, some effectors were able to suppress cell death responses induced by other Z. tritici secreted proteins. We used structural prediction tools to predict the putative structures of all of the Z. tritici effectors and used these predictions to examine whether there was enrichment of specific structural signatures among the PTI-suppressing effectors. From among the libraries, multiple members of the killer protein-like 4 (KP4) and killer protein-like 6 (KP6) effector families were identified as PTI suppressors. This observation is intriguing, as these protein families were previously associated with antimicrobial activity rather than virulence or host manipulation. This data provides mechanistic insight into immune suppression by Z. tritici during infection and suggests that, similar to biotrophic pathogens, this fungus relies on a battery of secreted effectors to suppress host immunity during early phases of colonization.

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase

Background The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Conclusion Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Computational Strategies to Enhance Cell-Free Protein Synthesis Efficiency

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements.

AlphaFold two years on: Validation and impact

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model’s capabilities and limitations.

6-Gingerol modulates miRNAs and PODXL gene expression via methyltransferase enzymes in NB4 cells: an in silico and in vitro study

This investigation delves into the influence of predicted microRNAs on DNA methyltransferases (DNMTs) and the PODXL gene within the NB4 cell line, aiming to elucidate their roles in the pathogenesis of acute myeloid leukemia (AML). A comprehensive methodological framework was adopted to explore the therapeutic implications of 6-gingerol on DNMTs. This encompassed a suite of bioinformatics tools for protein structure prediction, docking, molecular dynamics, and ADMET profiling, alongside empirical assessments of miRNA and PODXL expression levels. Such a multifaceted strategy facilitated an in-depth understanding of 6-gingerol’s potential efficacy in DNMT modulation. The findings indicate a nuanced interplay where 6-gingerol administration modulated miRNA expression levels, decreasing in DNMT1 and DNMT3A expression in NB4 cells. This alteration indirectly influenced PODXL expression, contributing to the manifestation of oncogenic phenotypes. The overexpression of DNMT1 and DNMT3A in NB4 cells may contribute to AML, which appears modulable via microRNAs such as miR-193a and miR-200c. Post-treatment with 6-gingerol, DNMT1 and DNMT3A expression alterations were observed, culminating in the upregulation of miR-193a and miR-200c. This cascade effect led to the dysregulation of tumor suppressor genes in cancer cells, including downregulation of PODXL, and the emergence of cancerous traits. These insights underscore the therapeutic promise of 6-gingerol in targeting DNMTs and microRNAs within the AML context.

Potential Transcriptional Enhancers in Coronaviruses: From Infectious Bronchitis Virus to SARS-CoV-2.

Coronaviruses constitute a global threat to human and animal health. It is essential to investigate the long-distance RNA-RNA interactions that approximate remote regulatory elements in strategies, including genome circularization, discontinuous transcription, and transcriptional enhancers, aimed at the rapid replication of their large genomes, pathogenicity, and immune evasion. Based on the primary sequences and modeled RNA-RNA interactions of two experimentally defined coronaviral enhancers, we detected via an in silico primary and secondary structural analysis potential enhancers in various coronaviruses, from the phylogenetically ancient avian infectious bronchitis virus (IBV) to the recently emerged SARS-CoV-2. These potential enhancers possess a core duplex-forming region that could transition between closed and open states, as molecular switches directed by viral or host factors. The duplex open state would pair with remote sequences in the viral genome and modulate the expression of downstream crucial genes involved in viral replication and host immune evasion. Consistently, variations in the predicted IBV enhancer region or its distant targets coincide with cases of viral attenuation, possibly driven by decreased open reading frame (ORF)3a immune evasion protein expression. If validated experimentally, the annotated enhancer sequences could inform structural prediction tools and antiviral interventions.

African Swine Fever Virus Protein-Protein Interaction Prediction.

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins

In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.

Evaluating the 3D structure prediction tools to identify optimal MEBPVC structure models

Multi-Epitope based Peptide Vaccines Candidates (MEBPVCs) are peptide sequences with the immunogenic epitopes interspersed with linkers, adjuvants and other components. The optimization of one of the components of MEBPVCs, epitope order, resulted in a unique epitope ordered MEBPVC showing improved vaccine potency. In addition to the above combinatorial optimization task, predicting the 3D structure of these MEBPVCs presents a significant challenge owing to the diverse modeling approaches employed by the modeling tools. Here we embarked on evaluating the performance of the threading based ab initio modeling tool: I-TASSER, Large Language Model (LLM) based tool: ESMFold and the MSA based artificial intelligence based tool: AlphaFold2. Our investigation employed RMSD, PEA, SASA, Solubility, Globularity, secondary structure analysis, Docking, MDS and MVP. I-TASSER emerged as the superior performer. The analysis revealed a strong correlation between predicted tertiary structure and secondary structure composition, especially the β-turns and random coils. It appears that accurate secondary structure identification to be a pivotal step, dictating the propagation of accuracy (or inaccuracy). This finding highlights the critical importance of secondary structure prediction/assignment algorithms for enhanced protein structure modeling accuracy especially to integrate predictions where evolutionary information is minimal and or lacking. Small sample size is attributed to the low significance of the parameters though Globularity and SASA have shown good p-values. Overall, the results support the need and utilization of the LLMs in biological research, underscoring their potential to revolutionize protein structure prediction and advance diverse biomolecular applications, accelerating Synthetic Biology research.

Excited-Ground-State Transition of the RNA Strand Slippage Mechanism Captured by the Base-Specific Force Field.

Excited-ground-state transition and strand slippage of RNA play key roles in transcription and translation of central dogma. Due to limitation of current experimental techniques, the dynamic structure ensembles of RNA remain inadequately understood. Molecular dynamics simulations offer a promising complementary approach, whose accuracy depends on the force field. Here, we develop the new version of RNA base-specific force field (BSFF2) to address underestimation of base pairing stability and artificial backbone conformations. Extensive evaluations on typical RNA systems have comprehensively confirmed the accuracy of BSFF2. Furthermore, BSFF2 demonstrates exceptional efficiency in de novo folding of tetraloops and reproducing base pair reshuffling transition between RNA excited and ground states. Then, we explored the RNA strand slippage mechanism with BSFF2. We conducted a comprehensive three-dimensional structural investigation into the strand slippage of the most complex r(G4C2)9 repeat element and presented the molecular details in the dynamic transition along with the underlying mechanism. Our results of capturing the strand slippage, excited-ground transition, de novo folding, and simulations for various typical RNA motifs indicate that BSFF2 should be one of valuable tools for dynamic conformation research and structure prediction of RNA, and a future contribution to RNA-targeted drug design as well as RNA therapy development.

SpSIZ1 from hyperaccumulator Sedum plumbizincicola orchestrates SpABI5 to fine-tune cadmium tolerance.

Sedum plumbizincicola is a renowned hyperaccumulator of cadmium (Cd), possesses significant potential for eco-friendly phytoremediation of soil contaminated with Cd. Nevertheless, comprehension of the mechanisms underpinning its Cd stress response remains constrained, primarily due to the absence of a comprehensive genome sequence and an established genetic transformation system. In this study, we successfully identified a novel protein that specifically responds to Cd stress through early comparative iTRAQ proteome and transcriptome analyses under Cd stress conditions. To further investigate its structure, we employed AlphaFold, a powerful tool for protein structure prediction, and found that this newly identified protein shares a similar structure with Arabidopsis AtSIZ1. Therefore, we named it Sedum plumbizincicola SIZ1 (SpSIZ1). Our study revealed that SpSIZ1 plays a crucial role in positively regulating Cd tolerance through its coordination with SpABI5. Overexpression of SpSIZ1 significantly enhanced plant resistance to Cd stress and reduced Cd accumulation. Expression pattern analysis revealed higher levels of SpSIZ1 expression in roots compared to stems and leaves, with up-regulation under Cd stress induction. Importantly, overexpressing SpSIZ1 resulted in lower Cd translocation factors (Tfs) but maintained relatively constant Cd levels in roots under Cd stress, leading to enhanced Cd stress resistance in plants. Protein interaction analysis revealed that SpSIZ1 interacts with SpABI5, and the expression of genes responsive to abscisic acid (ABA) through SpABI5-dependent signaling was significantly up-regulated in SpSIZ1-overexpressing plants with Cd stress treatment. Collectively, our results illustrate that SpSIZ1 interacts with SpABI5, enhancing the expression of ABA downstream stress-related genes through SpABI5, thereby increasing Cd tolerance in plants.

Cross-species molecular docking method to support predictions of species susceptibility to chemical effects

The advancement of protein structural prediction tools, exemplified by AlphaFold and Iterative Threading ASSEmbly Refinement, has enabled the prediction of protein structures across species based on available protein sequence and structural data. In this study, we introduce an innovative molecular docking method that capitalizes on this wealth of structural data to enhance predictions of chemical susceptibility across species. We demonstrated this method using the androgen receptor as a pertinent modulator of endocrine function. By using protein structures, this method contextualizes species susceptibility within a functional framework and helps to integrate molecular docking into the repertoire of New Approach Methodologies (NAMs) that support the Next-Generation Risk Assessment (NGRA) paradigm through the novel integration of various open-source tools.

The S-component fold: a link between bacterial transporters and receptors

The processes of nutrient uptake and signal sensing are crucial for microbial survival and adaptation. Membrane-embedded proteins involved in these functions (transporters and receptors) are commonly regarded as unrelated in terms of sequence, structure, mechanism of action and evolutionary history. Here, we analyze the protein structural universe using recently developed artificial intelligence-based structure prediction tools, and find an unexpected link between prominent groups of microbial transporters and receptors. The so-called S-components of Energy-Coupling Factor (ECF) transporters, and the membrane domains of sensor histidine kinases of the 5TMR cluster share a structural fold. The discovery of their relatedness manifests a widespread case of prokaryotic “transceptors” (related proteins with transport or receptor function), showcases how artificial intelligence-based structure predictions reveal unchartered evolutionary connections between proteins, and provides new avenues for engineering transport and signaling functions in bacteria.

The Functional Significance of High Cysteine Content in Eye Lens γ-Crystallins.

Cataract disease is strongly associated with progressively accumulating oxidative damage to the extremely long-lived crystallin proteins of the lens. Cysteine oxidation affects crystallin folding, interactions, and light-scattering aggregation especially strongly due to the formation of disulfide bridges. Minimizing crystallin aggregation is crucial for lifelong lens transparency, so one might expect the ubiquitous lens crystallin superfamilies (α and βγ) to contain little cysteine. Yet, the Cys content of γ-crystallins is well above the average for human proteins. We review literature relevant to this longstanding puzzle and take advantage of expanding genomic databases and improved machine learning tools for protein structure prediction to investigate it further. We observe remarkably low Cys conservation in the βγ-crystallin superfamily; however, in γ-crystallin, the spatial positioning of Cys residues is clearly fine-tuned by evolution. We propose that the requirements of long-term lens transparency and high lens optical power impose competing evolutionary pressures on lens βγ-crystallins, leading to distinct adaptations: high Cys content in γ-crystallins but low in βB-crystallins. Aquatic species need more powerful lenses than terrestrial ones, which explains the high methionine content of many fish γ- (and even β-) crystallins. Finally, we discuss synergies between sulfur-containing and aromatic residues in crystallins and suggest future experimental directions.

Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe – Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning.

It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary structures first. However, experimental protocols for solving RNA structures are unavailable for large-scale investigation due to their high costs and time-consuming nature. Various computational tools were thus developed to predict the RNA secondary structures from sequences. Recently, deep networks have been investigated to help predict RNA structures directly from their sequences. However, existing deep-learning-based tools are more or less suffering from model overfitting due to their complicated problem formulation and defective model training processes, limiting their applications across sequences from different structural families. In this research, we designed a two-stage RNA structure prediction strategy called DEBFold (deep ensemble boosting and folding) based on convolution encoding/decoding and self-attention mechanisms to enhance the existing thermodynamic structure models. Moreover, the model training process followed rigorous steps to achieve an acceptable prediction generalization. On the family-wise reserved test sets and the PDB-derived test set, DEBFold achieves better structure prediction performance over traditional tools and existing deep-learning methods. In summary, we obtained a cutting-edge deep-learning-based structure prediction tool with supreme across-family generalization performance. The DEBFold tool can be accessed at https://cobis.bme.ncku.edu.tw/DEBFold/.

Discovery and characterization of noncanonical E2-conjugating enzymes.

E2-conjugating enzymes (E2s) play a central role in the enzymatic cascade that leads to the attachment of ubiquitin to a substrate. This process, termed ubiquitylation, is required to maintain cellular homeostasis and affects almost all cellular process. By interacting with multiple E3 ligases, E2s dictate the ubiquitylation landscape within the cell. Since its discovery, ubiquitylation has been regarded as a posttranslational modification that specifically targets lysine side chains (canonical ubiquitylation). We used Matrix-Assisted Laser Desorption/Ionization-Time Of Flight Mass Spectrometry to identify and characterize a family of E2s that are instead able to conjugate ubiquitin to serine and/or threonine. We used structural modeling and prediction tools to identify the key activity determinants that these E2s use to interact with ubiquitin as well as their substrates. Our results unveil the missing E2s necessary for noncanonical ubiquitylation, underscoring the adaptability and versatility of ubiquitin modifications.

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple works had been done to study nonsynonymous mutations, which change protein sequences. However, there is little study on the effects of SARS-CoV-2 synonymous mutations, which may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. A total of 26645 SARS-CoV-2 genomic sequences retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database were aligned using MAFFT. Then, the mutations and their respective frequency were identified. Multiple RNA secondary structures prediction tools, namely RNAfold, IPknot++ and MXfold2 were applied to predict the effect of the mutations on RNA secondary structure and their base pair probabilities was estimated using MutaRNA. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a. Of these top 10 highest frequency synonymous mutations, C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. These four mutations show changes in base pair probabilities. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Synonymous mutations in SARS-CoV-2 genome may affect RNA secondary structure, changing base pair probabilities and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.