Biology subjects

Lai, P.-K.

Publications and source records attributed to Lai, P.-K..

4 recordsLinked to original sources

bioRxiv2024

DeepSP: Deep Learning-Based Spatial Properties to Predict Monoclonal Antibody Stability

Therapeutic antibody development, manufacturing, and administration face challenges due to high viscosities and aggregation tendencies often observed in highly concentrated antibody solutions. This poses a particular problem for subcutaneous administration, which requires low-volume and high-concentration formulations. The spatial charge map (SCM (mAbs, 8 (1) (2015), pp. 43-48)) and spatial aggregation propensity (SAP (PNAS. 2009; 106:11937-42) are two computational techniques proposed from previous studies to aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are known to be time-consuming and computationally demanding. DeepSCM (CSBJ. 2022, 20:2143-2152), a deep learning surrogate model to predict SCM scores in the entire variable region, was used to screen high-concentration antibody viscosity. DeepSCM is solely based on sequence information, which facilitates high throughput screening. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient (R) between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87 on the test set (N=2053). DeepSP was employed as features to build machine learning models to predict the aggregation rate of 21 antibodies. We observed remarkable results with R = 0.97 and a mean squared error (MSE) of 0.03 between the experimental and predicted aggregation rates, leave-one-out cross-validation (LOOCV) yielded R = 0.75 and MSE = 0.18, which is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody properties, such as viscosity, aggregation, or other properties that can influence their stability, using sequences only. The code and parameters are freely available at https://github.com/Lailabcode/DeepSP HighlightsO_LIDeep learning applied to develop a surrogate model (DeepSP) to rapidly predict 30 spatial properties of monoclonal antibodies that are usually calculated from MD simulations, using only sequences. C_LIO_LIThe DeepSP models achieved a linear correlation ranging between 0.76 and 0.96 with an average of 0.87, between the actual (MD simulation) and predicted score for all properties. C_LIO_LIDeepSP features were employed to build a model to predict aggregation rates of antibodies obtained from a previous study. A strong correlation of 0.97, and LOOCV correlation of 0.75 were achieved between the actual and predicted aggregation rates. C_LIO_LIDeepSP can be employed to generate antibody-specific features that can be used to train different machine learning models to predict antibody stability. C_LI

biophysics↗

bioRxiv2024

Investigating the mechanisms of antibody binding to alpha-synuclein for the treatment of Parkinson's Disease

Parkinsons Disease (PD) is an idiopathic neurodegenerative disorder with the second-highest prevalence rate behind Alzheimers Disease. The pathophysiological hallmarks of PD are both degeneration of dopaminergic neurons in the substantia nigra pars compacta and the inclusion of misfolded alpha-synuclein (-syn) aggregates known as Lewy bodies. Despite decades of research for potential PD treatments, none have been developed, and developing new therapeutic agents is a time-consuming and expensive process. Computational methods can be used to investigate the properties of drug candidates currently undergoing clinical trials to determine their theoretical efficiency at targeting -syn. Monoclonal antibodies (mAbs) are biological drugs with high specificity, and Prasinezumab (PRX002) is a mAb currently in Phase II, which targets the C-terminus (AA 118-126) of -syn. We utilized BioLuminate and PyMol for structure prediction and preparation of the fragment antigen-binding (Fab) region of PRX002 and 34 different conformations of -syn. Protein-protein docking simulations were performed using PIPER, and 3 of the docking poses were selected based on the best fit. Molecular dynamics simulations were conducted on the docked protein structures for 1000 ns, and hydrogen bonds, electrostatic, and hydrophobic interactions were analyzed using MDAnalysis to determine which residues were interacting and how often. Hydrogen bonds were shown to form frequently between the HCDR2 region of PRX002 and -syn. Free energy was calculated to determine binding affinity. The predicted binding affinity shows a strong antibody-antigen attraction between PRX002 and -syn. RMSD was calculated to determine the conformational change of these regions throughout the simulation. The mAbs developability was determined using computational screening methods. Our results demonstrate the efficiency and developability of this therapeutic agent.

biophysics↗

bioRxiv2023

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns

All life forms operate metabolism in constant flux. Metabolic fluxes offer a direct readout of cellular state, detailing the rates and driving forces of metabolic pathways. However, indirect, iterative solvers for mapping isotope patterns from tracing experiments onto metabolic fluxes leave much of cellular state uncharted. Here, we streamline metabolic flux quantitation by innovating a machine learning framework, ML-Flux, that deciphers complex isotope labeling patterns. We train neural networks using isotope pattern-flux pairs across central carbon metabolism from 26 key 13C-glucose, 2H-glucose, and 13C-glutamine tracers. ML-Flux takes variable-size isotope labeling patterns as input, imputes missing isotope patterns, and outputs mass-balanced metabolic fluxes. Computation of fluxes using ML-Flux is more accurate and faster than that of leading metabolic flux analysis software employing a least-squares method. Our biochemical networks and machine learning models constitute a curated and growing online knowledgebase of metabolic flux and free energy to democratize quantitative metabolic profiling.

systems biology↗

bioRxiv2022

DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers (mAbs. 2015, 8(1):43-48). SCM applies molecular dynamics simulations to calculate a score for the screening of antibody viscosity at high concentrations. However, molecular dynamics simulations are computationally costly and require structural information, a significant application bottleneck. In this work, high throughput computing was performed to calculate the SCM scores for 6596 nonredundant antibody variable regions. A convolutional neural network surrogate model, DeepSCM, requiring only sequence information, was then developed based on this dataset. The linear correlation coefficient of the DeepSCM and SCM scores achieved 0.9 on the test set (N=1320). The DeepSCM model was applied to screen the viscosity of 38 therapeutic antibodies that SCM correctly classified and resulted in only one misclassification. The DeepSCM model will facilitate high concentration antibody viscosity screening. The code and parameters are freely available at https://github.com/Lailabcode/DeepSCM.

biophysics↗