Wet-lab Approaches Research Articles

Investigating the Role of OsHDT701 and Other Blast-Associated Negative Regulatory Genes in Indica Rice Cultivar Ranjit Using Combined Wet Lab and Computational Approaches.

Rice blast, caused by Magnaporthe oryzae, severely impacts global rice production. Understanding the role of the host's blast negative regulatory genes is crucial for combating this disease. We studied the expression of seven rice blast negative regulatory genes (previously characterized in ssp. japonica) in susceptible (Ranjit and Mahsuri) and tolerant/resistant (Shahsarang 1 and IR64) indica rice cultivars during M. oryzae infection. The selected genes showed differential expression patterns, with OsHDT701 (histone H4 deacetylase) downregulated in tolerant/resistant and upregulated in susceptible cultivars. Promoter analysis of OsHDT701 in cv. Ranjit revealed biotic stress-responsive and M. oryzae effector binding motifs (MoHTR2 and MoSPAB1). Protein-DNA docking showed an interaction for both the effectors and promoter sequences. Furthermore, a strong interaction between the OsERF922 transcription factor (a negative regulator of rice blast) and GCC box at the promoter suggests OsERF922-mediated regulation of OsHDT701. Next, a comparative analysis of ssp. japonica (Nipponbare) OsHDT701 CDS to 11 indica rice cultivars, including Ranjit, detected an SNP (T-A) at position 591. Moreover, the structure modeling and protein-protein interaction reveal that OsHDT701 may form a complex with other histone deacetylases to regulate defense-related gene expression. Taken together, our study unveils new possibilities for OsHDT701 regulation mechanisms during M. oryzae infection.

When is an SNP not an SNP?

Genomic duplications are important sources of structural change and gene innovation. In humans, the most recent and highly identical sequences (>90% homology, >1 kb long) are known as segmental duplications (SDs). Single-nucleotide variants or single-nucleotide polymorphisms within SDs have not been systematically assessed due to limitations around mapping short-read sequencing data. Single-nucleotide variant rs62486260 was flagged in a study of familial renal stone disease but it was unclear whether it was real or an artifact resulting from the presence of a SD. We describe in silico and wet-lab approaches to investigate this, using segment-specific long-PCR assays, followed by short PCR for Sanger sequencing. Our conclusion was that rs62486260 is an artifact. Our approach can be generalized to deal with other such situations.

EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information

The identification of antibody-specific epitopes on virus proteins is crucial for vaccine development and drug design. Nonetheless, traditional wet-lab approaches for the identification of epitopes are both costly and labor-intensive, underscoring the need for the development of efficient and cost-effective computational tools. Here, EpiScan, an attention-based deep learning framework for predicting antibody-specific epitopes, is presented. EpiScan adopts a multi-input and single-output strategy by designing independent blocks for different parts of antibodies, including variable heavy chain (VH), variable light chain (VL), complementary determining regions (CDRs), and framework regions (FRs). The block predictions are weighted and integrated for the prediction of potential epitopes. Using multiple experimental data samples, we show that EpiScan, which only uses antibody sequence information, can accurately map epitopes on specific antigen structures. The antibody-specific epitopes on the receptor binding domain (RBD) of SARS coronavirus 2 (SARS-CoV-2) were located by EpiScan, and the potentially valuable vaccine epitope was identified. EpiScan can expedite the epitope mapping process for high-throughput antibody sequencing data, supporting vaccine design and drug development. Availability: For the convenience of related wet-experimental researchers, the source code and web server of EpiScan are publicly available at https://github.com/gzBiomedical/EpiScan.

STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer

Introduction: Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify these relationships through traditional wet lab approaches. Methods: We proposed an efficient computational model, STNMDA, that integrated a StructureAware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbedrug associations. The STNMDA began with a “random walk with a restart” approach to construct a heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated the probability of associations between microbes and drugs. Results: Extensive experimental results showed that STNMDA surpassed existing state-of-the-art models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of STNMDA in confirming associations between microbes and drugs was demonstrated through case validations. Conclusion: Hence, STNMDA showed promise as a valuable tool for future prediction of microbedrug associations.

There and back again: Discovering antiviral and antiphage defenses using deep homology

Two recent studies in Cell Host & Microbe (Cury etal. and van den Berg etal.) uncover cross-kingdom links between antiphage and antiviral immune defenses. Through reciprocal computational and wet lab approaches, they each discover and experimentally validate proteins used for host immunity.

A Viroinformatics Study: B-Cell Polytope Mapping of Envelope Protein to Develop Vaccine Candidate against Four DENV Serotype

Nowadays, dengue virus (DENV) is still become a global problem, even though the virus infection issues have reached half of the population in some countries each year. DENV belongs to the enveloped virus with positive-sense single-stranded RNA (+ssRNA) genus Flavivirus and belongs to the Flaviviridae family. DENV has structural proteins which consist of the envelope protein (E), capsid (C), and membrane (M). There are four serotypes of this virus which are DENV-1, 2, 3, and 4. These four serotypes are transmitted to humans through Aedes sp. The development of this vaccine is still in progress and the challenge of this DENV vaccine candidate design is to overcome the heterotypic infection and the expansion of coverage protection to all virus serotypes. This research uses design simulation for vaccine candidates using B cell epitope in all DENV’s serotypes envelope to trigger the antibody formation through bioinformatics method that consists of protein modeling, immunogenicity, toxicity, and immune stimulation. DENV envelope protein was predicted to have polytope that can be recognized by B cells and act as an antigen, have low similarity with the composing sequence of cell surface receptors on the body, and non-toxic, and then can trigger the population increase of B cell and IgM antibody production with high avidity to neutralize four of the DENV serotypes. We recommend the B cell polytype which consists of A, C, E, and G peptides be examined by the wet-lab approach.

CTD-Global (CTD-G): A novel composition, transition, and distribution based peptide sequence encoder for hormone peptide prediction

Hormone peptides are small signaling molecules that regulate key cellular processes such as cell growth, and differentiation. Hormone peptide identification is important for understanding their potential associations with certain diseases such as attention deficit hyperactivity disorder, diabetes, and psychiatric disorders. A comprehensive understanding of hormone peptides’ roles in cellular signaling, and immune regulation can provide insights into their therapeutic potential. Hormone peptides are identified through wet-lab approaches which are restricted by resource-intensive processes, limited scalability, and cost ineffectiveness. In an effort to substitute experimental approaches with computational predictors, researchers leveraged the capabilities of machine learning (ML) classifiers. These classifiers have inherent dependency over statistical vectors that are generated by extracting amino acids’ distinctive patterns from peptide sequences. Classifiers utilize these vectors for discriminating peptides into hormone and non-hormone classes. However, the performance of current predictors is constrained due to their inability to effectively extract discriminative amino acids patterns from peptide sequences. Following the need for a powerful predictor, the paper in hand presents a novel sequence encoder namely, CTD-G that transforms peptide sequences into statistical vectors by extracting 3 different types of amino acids patterns namely composition, transition, and distribution. Across public benchmark dataset, the proposed CTD-G encoder potential is compared with 56 existing encoders under two different evaluation strategies namely intrinsic and extrinsic. In Intrinsic evaluation, TSNE-based visualization demonstrates reduced overlap between clusters of hormone and non-hormone peptides with the proposed encoder’s statistical vectors compared to existing encoders. Extrinsic evaluation demonstrates the superiority of the proposed encoder, as 7 out of 11 ML classifiers achieve better performance with its statistical vectors compared to those from existing encoders. Furthermore, the proposed predictor outperforms existing hormone peptide classification predictors by 1.5% in accuracy, 5.36% in sensitivity, 1.80% in specificity, and 2.62% in MCC. To facilitate the scientific community, a web application is available at https://sds_genetic_analysis.opendfki.de/.

Building Representation Learning Models for Antibody Comprehension.

Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.

EphrinA5 regulates cell motility by modulating Snhg15/DNA triplex-dependent targeting of DNMT1 to the Ncam1 promoter

Cell–cell communication is mediated by membrane receptors and their ligands, such as the Eph/ephrin system, orchestrating cell migration during development and in diverse cancer types. Epigenetic mechanisms are key for integrating external “signals”, e.g., from neighboring cells, into the transcriptome in health and disease. Previously, we reported ephrinA5 to trigger transcriptional changes of lncRNAs and protein-coding genes in cerebellar granule cells, a cell model for medulloblastoma. LncRNAs represent important adaptors for epigenetic writers through which they regulate gene expression. Here, we investigate a lncRNA-mediated targeting of DNMT1 to specific gene loci by the combined power of in silico modeling of RNA/DNA interactions and wet lab approaches, in the context of the clinically relevant use case of ephrinA5-dependent regulation of cellular motility of cerebellar granule cells. We provide evidence that Snhg15, a cancer-related lncRNA, recruits DNMT1 to the Ncam1 promoter through RNA/DNA triplex structure formation and the interaction with DNMT1. This mediates DNA methylation-dependent silencing of Ncam1, being abolished by ephrinA5 stimulation-triggered reduction of Snhg15 expression. Hence, we here propose a triple helix recognition mechanism, underlying cell motility regulation via lncRNA-targeted DNA methylation in a clinically relevant context.Graphical

Insights into the Differential Composition of Stem-Loop Structures of Nanoviruses and Their Impacts.

Multipartite viruses package their genomic segments independently and mainly infect plants; few of them target animals. Nanoviridae is a family of multipartite single-stranded DNA (ssDNA) plant viruses that individually encapsidate ssDNAs of ~1 kb and transmit them through aphids without replication in aphid vectors, thereby causing important diseases in host plants, mainly leguminous crops. All of these components constitute an open reading frame to perform a specific role in nanovirus infection. All segments contain conserved inverted repeat sequences, potentially forming a stem-loop structure and a conserved nonanucleotide, TAGTATTAC, within a common region. This study investigated the variations in the stem-loop structure of nanovirus segments and their impact using molecular dynamics (MD) simulations and wet lab approaches. Although the accuracy of MD simulations is limited by force field approximations and simulation time scale, explicit solvent MD simulations were successfully used to analyze the important aspects of the stem-loop structure. This study involves the mutants' design, based on the variations in the stem-loop region and construction of infectious clones, followed by their inoculation and expression analysis, based on nanosecond dynamics of the stem-loop structure. The original stem-loop structures showed more conformational stability than mutant stem-loop structures. The mutant structures were expected to alter the neck region of the stem-loop by adding and switching nucleotides. Changes in conformational stability are suggested expression variations of the stem-loop structures found in host plants with nanovirus infection. However, our results can be a starting point for further structural and functional analysis of nanovirus infection. IMPORTANCE Nanoviruses comprise multiple segments, each with a single open reading frame to perform a specific function and an intergenic region with a conserved stem-loop region. The genome expression of a nanovirus has been an intriguing area but is still poorly understood. We attempted to investigate the variations in the stem-loop structure of nanovirus segments and their impact on viral expression. Our results show that the stem-loop composition is essential in controlling the virus segments' expression level.

Identification of conserved miRNAs and their targets in Jatropha curcas: an in silico approach

BackgroundMicroRNAs (miRNAs) are small endogenous RNAs with an approximate length of 18–22 nucleotides and involved in the regulation of gene expression in transcriptional or post-transcriptional levels. They were found to be associated with leaf morphogenesis, flowering time, vegetative phase change, and response to environmental cues in plants, where they act as a critical regulatory factor. The nature of high conservancy of plant miRNAs within the plant species made it possible to detect the conserved miRNAs by computational approaches. Expressed Sequence Tags (EST) based comparative genomic approaches provide advantages over wet lab approaches as it is convenient, easy to carry out and less time consuming. EST-based in silico approach can unravel new conserved miRNAs in plants, even when the complete genome sequence is not available. ResultsTo identify the novel miRNAs, a total of 46,865 ESTs from Jatropha curcas were searched for homology to all available 6746 mature miRNAs of plant eudicotyledons. Finally, we ended up with 12 novel miRNAs in Jatropha that range from 18 to 19 nucleotides where their respective precursor miRNAs had 54.11–71.76% (A + U) content. The putative miRNAs belong to 12 individual miRNA family and most of them have higher (A + U) content ranging from 47.36 to 77.77% than their respective miRNA homologs. Many of the target genes by the newly identified miRNAs were associated with plant growth and development, stress response, defense and hormone signaling, and oil synthesis pathways. ConclusionThese findings have the potential to speed up miRNA identification and expand our understanding of miRNA functions in J. curcas.

The BASIL cure: Using structure to predict function in protein biochemistry.

We have developed an undergraduate biochemistry lab curriculum based on authentic inquiry. This course-based undergraduate research experience (CURE) allows a large number of students to gain skills often learned only in the traditional one-on-one mentor-student research model by bringing a research project into the undergraduate teaching lab. The Biochemistry Authentic Scientific Inquiry Lab (BASIL) uses a combined computational and wet lab approach to study proteins of known structure but unknown function. Over 3800 structures in the Protein Data Bank (PDB) have unknown function. Students following the BASIL curriculum use a combination of sequence and structure alignment tools to study these structures with the goal of identifying possible enzymes. They then use molecular docking to predict what model substrates fit near a proposed active site. Students can produce the target enzymes in the lab using standard wet-lab biochemistry techniques for expression and purification, and they then perform kinetic assays with model substrates selected from their docking studies. We have successfully used this curriculum in biochemistry lab courses for majors and non-majors. The curriculum is modular and can be used as a whole or individual parts may be incorporated into an existing course - either lecture or lab. Curriculum materials are available free-of-charge at basilbiochem.org. We are currently investigating how this curriculum can be used in a variety of academic settings, and we welcome new collaborators. We can offer synchronous support via virtual meetings and asynchronous support via Slack. This project is supported in part by NSF IUSE 2141908.

Deciphering the Code of Pattern Formation: Integrating In Silico and Wet Lab Approaches in Synthetic Biology

Deciphering the Code of Pattern Formation: Integrating In Silico and Wet Lab Approaches in Synthetic Biology

Biochemistry authentic scientific inquiry lab (BASIL) provides flexible course-based undergraduate research experience with computational and wet-lab approaches to studying protein function.

Biochemistry authentic scientific inquiry lab (BASIL) provides flexible course-based undergraduate research experience with computational and wet-lab approaches to studying protein function.

Rhizosphere metagenome analysis and wet-lab approach to derive optimal strategy for lead remediation in situ

The Environmental Protection Agency (EPA) reports a significant number of heavy metal-contaminated sites across the United States. To address this public health concern, rhizoremediation using microbes has emerged as a promising solution, considering the limitations and inefficiencies of current approaches. We hypothesized that if Pseudomonas fluorescens, Burkholderia vietnamiensis, and Rhizobium leguminosarum are cultivated in the rhizospheres (area around the plant roots) of Brassica juncea, Oryza sativa, or Pisum sativum in lead-contaminated soil, then a significant reduction in soil-lead content should be observed. To determine the most effective soil microbe, we obtained raw sequences of 16s rRNA from 96 soil samples from the National Center for Biotechnology Information and processed the sequences through Qiime2 to examine bacterial taxonomy in lead-contaminated and uncontaminated rhizospheres. A combination of soil microbes – P. fluorescens, B. vietnamiensis, and R. leguminosarum – were inoculated in the rhizosphere of B. juncea, O. sativa, and P. sativum in soil contaminated with 500 parts per million (ppm) of lead. Soil lead content was measured at various stages of plant growth. After four weeks, we noticed a 70% decline in the lead content with P. fluorescens-B. juncea combination and a 90% decline with P. fluorescens-R. leguminosarum-B. vietnamiensis triple combination with B. juncea. Chlorophyll content analysis of the dried leaves of plant groups showed similar optical density to control leaves, indicating that lead decontamination in the soil did not negatively affect plant health. Therefore, rhizoremediation is an effective bioremediation strategy and may increase crop productivity by converting nonarable lands into arable lands.

The ChaC1 active site: Defining the residues and determining the role of ChaC1-exclusive residues in the structural and functional stability.

The glutathione degrading enzyme ChaC1 is highly upregulated in several cancers and viral infections making it a potential pharmacological target for cancer therapy. As an enzyme, however, ChaC1 has a relatively high Km (~2 mM) towards its natural substrate, and therefore finding its inhibitors becomes very difficult. Given this limitation, a careful mapping of the active site has become necessary. In the current study, the enzyme-substrate complex was generated by docking glutathione with the modeled hChaC1 structure. Using a combination of in silico and wet lab approaches, the active site residues forming direct interactions with the substrate glutathione were identified and validated. Furthermore, the role of residues exclusively conserved in the ChaC family and forming the surface of the active site were also explored for their putative role in active site stabilization. Mutants of these residues have been analysed for their structural stability and interaction with the substrate through MD simulations and MMGBSA binding energy calculations. These findings were experimentally validated by assessment of their function through in vivo assays in yeast. The experimental evidences along with the molecular modeling suggest that residues 38'YGSL'41, D68, R72, E115, and Y143 are responsible for high affinity binding of hChaC1 with the substrate/inhibitor, whereas the residues exclusive to the ChaC family are required for the structural stability of the enzyme and its active site. Such a characterization of essential active site and conserved residues is significant as a key step toward rational design of novel inhibitors of the ChaC1 enzyme.

M1ARegpred: Epitranscriptome Target Prediction of N1-methyladenosine (m1A) Regulators Based on Sequencing Features and Genomic Features.

N1-methyladenosine (m1A) is a reversible post-transcriptional modification in mRNA, which has been proved to play critical roles in various biological processes through interaction with different m1A regulators. There are several m1A regulators existing in the human genome, including YTHDF1-3 and YTHDC1. Several techniques have been developed to identify the substrates of m1A regulators, but their binding specificity and biological functions are not yet fully understood due to the limitations of wet-lab approaches. Here, we submitted the framework m1ARegpred (m1A regulators substrate prediction), which is based on machine learning and the combination of sequence-derived and genome-derived features. Our framework achieved area under the receiver operating characteristic (AUROC) scores of 0.92 in the full transcript model and 0.857 in the mature mRNA model, showing an improvement compared to the existing sequence-derived methods. In addition, motif search and gene ontology enrichment analysis were performed to explore the biological functions of each m1A regulator. Our work may facilitate the discovery of m1A regulators substrates of interest, and thereby provide new opportunities to understand their roles in human bodies.

Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network.

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin–proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery. The exponential growth in next-generation sequencing wet lab approaches have accelerated the accumulation of unannotated data in online databases, making the UPP characterization/analysis task more challenging. Thus, computational methods are used as an alternative for fast and accurate identification of UPPs. Aiming this, we develop a novel deep learning-based predictor named “2DCNN-UPP” for identifying UPPs with low error rate. In the proposed method, we used proposed algorithm with a two-dimensional convolutional neural network with dipeptide deviation features. To avoid the over fitting problem, genetic algorithm is employed to select the optimal features. Finally, the optimized attribute set are fed as input to the 2D-CNN learning engine for building the model. Empirical evidence or outcomes demonstrates that the proposed predictor achieved an overall accuracy and AUC (ROC) value using 10-fold cross validation test. Superior performance compared to other state-of-the art methods for discrimination the relations UPPs classification. Both on and independent test respectively was trained on 10-fold cross validation method and then evaluated through independent test. In the case where experimentally validated ubiquitination sites emerged, we must devise a proteomics-based predictor of ubiquitination. Meanwhile, we also evaluated the generalization power of our trained modal via independent test, and obtained remarkable performance in term of 0.862 accuracy, 0.921 sensitivity, 0.803 specificity 0.803, and 0.730 Matthews correlation coefficient (MCC) respectively. Four approaches were used in the sequences, and the physical properties were calculated combined. When used a 10-fold cross-validation, 2D-CNN-UPP obtained an AUC (ROC) value of 0.862 predicted score. We analyzed the relationship between UPP protein and non-UPP protein predicted score. Last but not least, this research could effectively analyze the large scale relationship between UPP proteins and non-UPP proteins in particular and other protein problems in general and our research work might improve computational biological research. Therefore, we could utilize the latest features in our model framework and Dipeptide Deviation from Expected Mean (DDE) -based protein structure features for the prediction of protein structure, functions, and different molecules, such as DNA and RNA.

Virtual Screening of Kaempferia galanga L. Bioactive Compounds as HPV-16 Antiviral Mechanism Through E6 Inhibitor Activity

Human papillomavirus (HPV) infection is caused by a virus with a type of DNA genetic material from the Papillomaviridae family, about 90% of HPV infections have no definite symptoms. HPV infection is spread through sexual intercourse, multiple sexual partners, anal sex, and from mother to child during pregnancy. HPV has oncoproteins E6 & E7 which affect the ability to trigger cancer cases. The role of E6 is very crucial and is able to cause cells to lose homeostasis in the cell cycle and transform into cancer, this shows that E6 has the potential to be a binding target for HPV drug candidate compounds. Kaempferia galanga L. is used as a cooking spice in Indonesia and known as kencur, this plant is often used by Javanese and Balinese people to make jamoe. The potential of Kaempferia galanga L. as an antiviral only has little scientific evidence as previously done, the plant has high protease inhibitory activity of HIV-1 and HCV viruses. This study aims to identify chemical compounds from Kaempferia galanga L. which have potential as HPV antivirals through inhibition of E6 activity. Chemical compounds from Kaempferia galanga L. are predicted to be antiviral candidates through the activity of Ethyl cinnamate and γ-Cadinene with an E6 inhibition mechanism in the Val69 & Leu57 domains, the results of this study must be further analyzed through a wetlab approach to strengthen scientific evidence.

The Multiple Roles of the Active Site Loop of Watermelon Glyoxysomal Malate Dehydrogenase

All malate dehydrogenases have a flexible loop that starts to close over the active site region of malate dehydrogenase when cofactor binds and locks down completing the active site when the dicarboxylic acid substrate binds. As such the active site loop appears to play a role in both substrate specificity and correct alignment of the substrate to allow hydride transfer and proton abstraction/donation depending on the direction of the reaction. Combined with available crystal structures, detailed analysis of the 1smk.pdb structure shows the loop in a series of microstates from fully open to a closed state induced by the inhibitor citrate binding (figure 1), sequence conservation suggests that the loop region contains a 19 amino acid stretch from P119 to N137 using the watermelon glyoxysomal enzyme as reference. Clustal Omega Analysis of malate dehydrogenases from prokaryotes to mammals shows 5 families of conservation of the loop region (figure 2) allowing a series of hypotheses about structure function relationships of specific residues within the sequence to be made which have been tested by a combination of computational and site directed mutagenesis wetlab approaches. In addition to the two canonical arginines involved in substrate binding, we have explored every other residue in the loop region. Overall, mutations in the first half of the loop had small impact on Specific Activity while mutations in the second half of the loop had larger impacts. R130 mutants to E or S had the most impact while mutation to A or Q had lesser effects. Three other residues in particular had striking effects on Oxaloacetate saturation, G121A, L133S and N137. L133 and N137, at the C terminal base of the loop, were hypothesized to play a role in the repulsive interactions between substrate and cofactor in the active site. Consistent with this hypothesis, creation of an L133S or D137A mutant showed that concomitant with a reduction in specific activity to 2% and 5% wildtype (respectively), Km for Oxaloacetate was dramatically increased while Km for NADH was decreased somewhat. Finally, a series of mutations of M128 (Q,I and A) were explored with varying effects on specific activity (Q>I>A) and significant effects on Km for Oxaloacetate but lesser or no effects on Km for NADH. The impact of M128 mutations in particular were further explored computationally using DeepDDG analysis and effects on stability correlated with impact on kinetic properties. Overall these studies provide a detailed picture of the complex interactions the flexible loop makes that contribute to both substrate binding and catalysis.