Computational Biology and Bioinformatics Lab (CBBio)

Computational Modeling of Nanodroplets on Digital Microfluidic Device

EWOD (ElectroWetting On Dielectric) is a microfluidic technique to transport fluid as discrete droplets on a surface. The movement of droplets on the EWOD surface is based on the buildup of charge on the dielectric material, which makes the surface more hydrophilic and decreases the contact angle of the droplet (see Loo Lab). Since wetting involves molecular rearrangement in the droplet, a complete picture cannot be obtained without knowing the structures and dynamics of molecules at the interfacial region. Here, our focus is to model and study in silico the atomic behavior of water and proteins on the solid surface of DMF. We will investigate the electrowetting and transport processes of a nanodroplet. The full-atomic model of the fluorocarbon surface on top of an electrode grid mimicking the experimental DMF configuration will be proposed and electric fields will be applied by charging electrodes. Via molecular dynamics simulations, molecular motions such as hydrogen bonding and dipole reorientation leading to the macroscopically observed processes will be elucidated. 

Studies of Protein Adsorption on Solid Surfaces: Structure Prediction and Simulations

Nonspecific adsorption of protein on the device surface is detrimental to digital microfluidic devices. It modifies the hydrophobicity of the surface, triggers further adhesion processes, and impairs the analytical performance of the device. While current experimental techniques for protein-surface adsorption studies provide estimates of the adsorbed amount, their sensitivities to the structure of the adsorbed proteins are still limited, and this structural information is the key in controlling or preventing protein adsorption on device surfaces.

In this project, protein adsorption on the hydrophobic surface of the DMF device will be investigated taking the protein bovine serum albumin (BSA) as the case study.  BSA is rarely studied in molecular simulations owing to its large size and lack of experimental structure. The high resolution X-ray structure of BSA resolved in last year provides an opportunity to explore its long-recognized highly adsorptive behavior on hydrophobic surfaces.  As the protein adsorption process - including diffusion, dehydration, and deformation – occurs in long time scales (from milliseconds to hours), it is a challenge to reduce the computational cost by sampling the informative molecular configurations pertaining to this process.  For this, computational biology methods to predict the preferred orientations of protein on surfaces will be exploited to select reasonable starting structures. Via large-scale molecular simulations of BSA surface adsorption, molecular interactions between the protein and the surface will be investigated from the energetic and structural points of view, and the influences of external electric fields on the protein conformations and surface affinity will be revealed.

Identification of Novel Natural Compound Inhibitors for Human Complement Component 5a Receptor by Homology Modeling and Virtual Screening

Neuropathic pain and inflammatory pain are two common types of pathological pain in human health problems. To date, normal painkillers are only partially effective in treating such pain, leading to a tremendous demand to develop new chemical entities to combat pain and inflammation. A promising pharmacological treatment is to control signal transduction via the inflammatory mediator-coupled receptor protein C5aR by finding antagonists to inhibit C5aR activation. Here, we report the first computational study on the identification of non-peptide natural compound inhibitors for C5aR by homology modeling and virtual screening. 

Our study revealed a novel natural compound inhibitor Acteoside with better docking scores than all four existing non-peptidic natural compounds. The MM-GBSA binding free energy calculations confirmed that Acteoside has a decrease of ~39 kcal/mol in the free energy of binding compared to the strongest-binding reference compound. Main contributions to the higher affinity of Acteoside to C5aR are the exceptionally strong lipophilic interaction, enhanced electrostatics and hydrogen bond interactions. Detail analysis on the physiochemical properties of Acteoside suggests further directions in lead optimization. Taken together, our study proposes that Acteoside is a potential lead molecule targeting the C5aR allosteric site and provides helpful information for further experimental studies.

Improving Protein-Ligand Docking by Particle Swarm Optimization Algorithms

Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurate predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combines the Particle Swarm Optimization (PSO) algorithm with the efficient BFGS local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a large and diverse data set of 201 protein-ligand complexes from the PDBbind database, we assessed the prediction performance of PSOVina in comparison to the original Vina program. Our docking simulations show that PSOVina has a remarkable speed-up of over 45% in terms of average docking time. With respect to docking accuracy, PSOVina predicted ligand conformations are on average 7% lower in RMSD compared to Vina with an increase of prediction success rate by 4%. Our work proves that PSO is superior to Monte Carlo search method in molecular docking applications and lays the foundation for future development of swarm-based algorithms in docking programs.

Improving Protein-Ligand Binding Affinity Prediction Using Simple Geometrical Features and Machine Learning 

Countings of protein-ligand contacts are popular geometrical features in scoring functions for structure-based drug design. When extracting features, cutoff values are used to define the range of distances within which a protein-ligand atom pair is considered as in contact. But effects of the number of ranges and the choice of cutoff values on the predictive ability of scoring functions are unclear. In this project, we compare five cutoff strategies (one-, two-, three-, six-range and soft boundary) with four machine learning methods. Prediction models are constructed using the latest PDBbind v2012 data sets and assessed by correlation coefficients. Our results show that the optimal one-range cutoff value lies between 6 and 8 angstrom instead of the customary choice of 12 angstrom. In general, two-range models have improved predictive performance in correlation coefficients by 3-5%, but introducing more cutoff ranges do not always help improving the prediction accuracy.

Lipid force fields development 

Collaborators: Prof. Rainer Boeckmann and Kristyna Pluhackova, Uni. Erlangen
Publication: Shirley W. I. Siu, Kristyna Pluhackova, Rainer A. Böckmann, J. Chem. Theor. Comput. 8, 1459-1470, 2012

The all-atom optimized potentials for liquid simulations (OPLS-AA) force field is a popular force field for simulating biomolecules. However, the current OPLS parameters for hydrocarbons developed using short alkanes cannot reproduce the liquid properties of long alkanes in molecular dynamics simulations. Therefore, the extension of OPLS-AA to (phospho)lipid molecules required for the study of biological membranes was hampered in the past. We optimized the OPLS-AA force field for both short and long hydrocarbons. Our results show that the optimized parameter set (L-OPLS) yields improved hydrocarbon diffusion coefficients, viscosities, and gauche−trans ratios. Moreover, its applicability for lipid bilayer simulations is shown for a GMO bilayer in its liquid-crystalline phase.