Biology subjects

Mardikoraem, M.

Publications and source records attributed to Mardikoraem, M..

5 recordsLinked to original sources

bioRxiv2024

EvoSeq-ML: Advancing Data-Centric Machine Learning with Evolutionary-Informed Protein Sequence Representation and Generation

From protein structure prediction to novel protein generation, challenging protein engineering tasks have been made possible by advancements in machine learning (ML). While largely driven by ML architecture refinements, these advancements in ML-based protein engineering campaigns have left the impact of data curation underexplored. In light of the growing wealth of labeled sequence data, data-centric advances (e.g. prioritizing improvements in ML protein engineering tools through the curation of high-quality, domain-specific training data) are increasingly preferred over model-centric advancements. Implementing datasets that accurately reflect biological complexity and diversity has been shown to improve the efficiency of training protein engineering ML tools. Here, we evaluate an ancestral sequence reconstruction (ASR)-informed data augmentation strategy for training generative and representation-learning models in protein engineering. Using ethylene-forming enzyme (EFE) as a model system, we show that variational autoencoder models trained on ancestral and near-ancestral sequence datasets generate variants with improved predicted and experimentally measured thermostability relative to variants generated from modern-sequence training data. All experimentally tested ancestral and ML-generated EFEs produced detectable ethylene, although ML-generated variants showed reduced activity relative to wild-type EFE, indicating that the approach more strongly captured stability-associated features than catalytic optimization. We further evaluated ASR-enriched sequence sets for fine-tuning ESM2 representations in endolysin and lysozyme C stability-classification tasks, where ancestral representations were competitive with modern-sequence fine-tuning in selected settings. Overall, this work supports ASR-informed data augmentation as a promising strategy for stability-oriented protein sequence generation and motivates future work to couple ancestral sequence diversity with explicit functional selection.

bioinformatics↗

bioRxiv2024

Predicting Inhibitors of OATP1B1 via Heterogeneous OATP-Ligand Interaction Graph Neural Network (HOLI-GNN)

Organic anion transporting polypeptides (OATPs) are membrane transporters crucial for drug uptake and distribution in the human body. OATPs can mediate drug-drug interactions (DDIs) in which the interaction of one drug with an OATP impairs the uptake of another drug, resulting in potentially fatal pharmacological effects. Predicting OATP-mediated DDIs is challenging, due to limited information on OATP inhibition mechanisms and inconsistent experimental OATP inhibition data across different studies. This study introduces Heterogeneous OATP-Ligand Interaction Graph Neural Network (HOLIgraph), a novel computational model that integrates molecular modeling with a graph neural network to enhance the prediction of drug-induced OATP inhibition. By combining ligand (i.e., drug) molecular features with protein-ligand interaction data from rigorous docking simulations, HOLIgraph outperforms traditional DDI prediction models which rely solely on ligand molecular features. HOLIgraph achieved a median balanced accuracy of over 90 percent when predicting inhibitors for OATP1B1, significantly outperforming purely ligand-based models. Beyond improving inhibition prediction, the data used to train HOLIgraph can enable the characterization of protein residues involved in inhibitory drug-OATP interactions. We identified certain OATP1B1 residues that preferentially interact with inhibitors, including I46 and K49. We anticipate such interaction information will be valuable to future structural and mechanistic investigations of OATP1B1. Scientific ContributionHOLIgraph introduces a new paradigm for DDI prediction by incorporating protein-ligand interactions derived from docking simulations into a graph neural net framework. This approach, enabled by recent structural breakthroughs for OATP1B1, represents a significant departure from traditional models that rely only on ligand features. By achieving high predictive accuracy and uncovering mechanistic insights, HOLIgraph sets a new trajectory for computational tools in drug design and DDI prediction.

bioengineering↗

bioRxiv2024

RecombiCraft Library construction: A novel method for DNA Library cloning and expansion using non-enzymatic single-step DNA recombination and liquid culture

In this study, we introduce RecombiCraft, an innovative, rapid, and cost-efficient method for constructing DNA libraries in E. coli. This method uses seamless ligation cloning extract (SLiCE) coupled with liquid culture amplification to effectively minimize sequence biases. The technique capitalizes on the natural homologous recombination capabilities of E. coli cell lysates, eliminating the need for multiple purified enzymes and reducing costs. We first synthesized the library backbone and inserts via PCR, employing high-fidelity polymerase to minimize sequence bias. The SLiCE technique was then used to assemble the DNA fragments introduced into E. coli through electroporation. To ensure the integrity of the library, we optimized culture times based on next-generation sequencing analysis which confirmed the minimal sequence bias. The RecombiCraft method demonstrates that this approach is economical and maintains the librarys uniformity. It is a promising tool for genetic research and biotechnological applications with a significantly shorter library generation period.

molecular biology↗

bioRxiv2023

AP-LASR: Automated Protein Libraries from Ancestral Sequence Reconstruction

BackgroundAncestral sequence reconstruction (ASR) provides an informative roadmap of evolutionary protein sequence space that benefits protein design and engineering in pursuit of high stability and diverse functionality. Using statistical and biological knowledge, ASR can determine the most probable ancestor among potential alternative amino acid states. However, the inherent uncertainty of ASR can be further leveraged to determine viable "nearby" ancestors with wide-ranging functionalities by sampling alternative amino acid states. ResultsHere we introduce AP-LASR which i) automates ASR and ii) leverages uncertainty in ASR to generate diverse protein sequence libraries that consist of ancestral sequences and near-ancestor sequences. In addition to automating pre-processing tasks (e.g., data cleaning, multiple sequence alignment, and software dependency management), AP-LASR offers several user-definable hyperparameters (e.g., input data size, ancestral probability cut-off, and sequence supplementation) to control the properties of the generated library. AP-LASR features an improved eLnP score (a metric for quantifying reconstructed ancestral sequence confidence) compared to FireProtASR, a well-established ASR workflow, for all four functionally diverse protein families studied. Furthermore, the rigorous statistical analysis undertaken in this study elucidates the influence of hyperparameters on ASR, enabling researchers to refine AP-LASR to their specific research. ConclusionAP-LASR offers an automated ASR experience that surpasses existing software by including a novel library design feature, powering curated protein libraries for wet-lab evaluation. We demonstrate how computational parameters impact the quality of ASR results, library composition, and the tradeoffs therein. AP-LASR offers a powerful tool for protein engineers to efficiently navigate the vast protein sequence landscape. Software available at: https://github.com/WoldringLabMSU/AP-LASR

bioinformatics↗

bioRxiv2023

Protein Fitness Prediction is Impacted by the Interplay of Language Models, Ensemble Learning, and Sampling Methods

Advances in machine learning (ML) and the availability of protein sequences via high-throughput sequencing techniques have transformed our ability to design novel diagnostic and therapeutic proteins. ML allows protein engineers to capture complex trends hidden within protein sequences that would otherwise be difficult to identify in the context of the immense and rugged protein fitness landscape. Despite this potential, there persists a need for guidance during the training and evaluation of ML methods over sequencing data. Two key challenges for training discriminative models and evaluating their performance include handling severely imbalanced datasets (e.g., few high-fitness proteins among an abundance of non-functional proteins) and selecting appropriate protein sequence representations. Here, we present a framework for applying ML over assay-labeled datasets to elucidate the capacity of sampling methods and protein representations to improve model performance in two different datasets with binding affinity and thermal stability prediction tasks. For protein sequence representations, we incorporate two widely used methods (One-Hot encoding, physiochemical encoding) and two language-based methods (next-token prediction, UniRep; masked-token prediction, ESM). Elaboration on performance is provided over protein fitness, length, data size, and sampling methods. In addition, an ensemble of representation methods is generated to discover the contribution of distinct representations to the final prediction score. Within the context of these datasets, the synthetic minority oversampling technique (SMOTE) outperformed undersampling while encoding sequences with One-Hot, UniRep, and ESM representations. In addition, ensemble learning increased the predictive performance of the affinity-based dataset by 4% compared to the best single encoding candidate (F1-score = 97%), while ESM alone was rigorous enough in stability prediction (F1-score = 92%).

bioinformatics↗