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Full-Text Articles in Other Biochemistry, Biophysics, and Structural Biology
Using Artificial (Ai) To Predict A Structure Of Protein Complex, Yiqing Zang
Using Artificial (Ai) To Predict A Structure Of Protein Complex, Yiqing Zang
SACAD: John Heinrichs Scholarly and Creative Activity Days
Proteins play pivotal roles in essential life processes and elucidating their three-dimensional (3D) structures is crucial for understanding their functions. AlphaFold2, an advanced artificial intelligence-based method developed by Google DeepMind, has emerged as a promising tool for predicting protein structures. In this study, we evaluated the predictive capabilities of AlphaFold2. Our findings highlight AlphaFold2's efficacy in providing valuable insights into protein structure prediction, albeit with certain limitations. While AlphaFold2 represents a significant advancement in the field, its utility is best realized when integrated with complementary experimental approaches. Consequently, combining the strengths of AlphaFold2 with experimental validation remains essential for achieving …
Computational Investigations Into Binding Dynamics Of Tau Protein Antibodies: Using Machine Learning And Biophysical Models To Build A Better Reality, Katherine Lee
University Scholar Projects
Misregulation of post-translational modifications of microtubule-associated protein tau is implicated in several neurodegenerative diseases including Alzheimer’s disease. Hyperphosphorylation of tau promotes aggregation of tau monomers into filaments which are common in tau-associated pathologies. Therefore, tau is a promising target for therapeutics and diagnostics. Recently, high-affinity, high-specificity single-chain variable fragment (scFv) antibodies against pThr-231 tau were generated and the most promising variant (scFv 3.24) displayed 20-fold increased binding affinity to pThr-231 tau compared to the wild-type. The scFv 3.24 variant contained five point mutations, and intriguingly none were in the tau binding site. The increased affinity was hypothesized to occur due …
Two Heads Are Better Than One: Current Landscape Of Integrating Qsp And Machine Learning, Tongli Zhang, Ioannis P. Androulakis, Peter Bonate, Limei Cheng, Tomáš Helikar, Jaimit Parikh, Christopher Rackauckas, Kalyanasundaram Subramanian, Carolyn R. Cho
Two Heads Are Better Than One: Current Landscape Of Integrating Qsp And Machine Learning, Tongli Zhang, Ioannis P. Androulakis, Peter Bonate, Limei Cheng, Tomáš Helikar, Jaimit Parikh, Christopher Rackauckas, Kalyanasundaram Subramanian, Carolyn R. Cho
Department of Biochemistry: Faculty Publications
Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about …
Recent Applications Of Quantitative Systems Pharmacology And Machine Learning Models Across Diseases, Sara Sadat Aghamiri1, Rada Amin, Tomáš Helikar
Recent Applications Of Quantitative Systems Pharmacology And Machine Learning Models Across Diseases, Sara Sadat Aghamiri1, Rada Amin, Tomáš Helikar
Department of Biochemistry: Faculty Publications
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019–2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review …
Cancerdiscover: An Integrative Pipeline For Cancer Biomarker And Cancer Class Prediction From High-Throughput Sequencing Data, Akram Mohammed, Greyson Biegert, Jiri Adamec, Tomáš Helikar
Cancerdiscover: An Integrative Pipeline For Cancer Biomarker And Cancer Class Prediction From High-Throughput Sequencing Data, Akram Mohammed, Greyson Biegert, Jiri Adamec, Tomáš Helikar
Department of Biochemistry: Faculty Publications
Accurate identification of cancer biomarkers and classification of cancer type and subtype from High Throughput Sequencing (HTS) data is a challenging problem because it requires manual processing of raw HTS data from various sequencing platforms, quality control, and normalization, which are both tedious and timeconsuming. Machine learning techniques for cancer class prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. To date, great research efforts have been taken for cancer biomarker identification and cancer class prediction. However, currently available tools and pipelines lack flexibility in data preprocessing, running multiple feature selection methods and learning algorithms, therefore, developing …