An Integrated Multi-scale Computational Modeling and Biological Experimental Research Program

Combine the strengths of experimental approaches and different computational methods: quantum mechanics, molecular dynamics and continuum method to study the complex biological phenomena with the goals 1) realize the multi-scale modeling the biological system to understand the function mechanism of the biological systems; 2) identify endogenous and exogenous small molecules, including reposition of FDA-approved drugs, to regulate the biomolecular interactions underlying important cellular activities in Alzheimer’s disease, cancer, cardiovascular disease, dermatopathology, regenerative medicine and precision medicine.

  • Molecular and structural mechanisms for the effect of Neurodegenerative Disease–Associated Variants in TREM2 on its immune function
    • Neurodegenerative Disease–Associated Variants in TREM2 Destabilize the Apical Ligand-Binding Region of the Immunoglobulin Domain.

Figure. Increased anti-correlated motion from FTD- and AD-associated variants of TREM2 is driven by free movement of CDR2. (A) Inter-residue distance maps (top left) and dynamic cross-correlation maps (DCCMs; bottom right) for seven isoforms of TREM2. In the distance maps, nearby residues are shown in red and more distant residues in blue. In the DCCM maps, 1 represents perfectly correlated motion (darkest red) and −1 represents perfectly anti-correlated motion (darkest blue). CDR2 is outlined in the dashed boxes. While subtle differences in motion appear between variants, particularly near the CDR, the maps reveal no large domain movements. (B,C) Essential dynamics using principal component analysis in each isoform of TREM2. (B) Magnitude of intrinsic motion (i.e., motion not driven by translation or rotation of the protein) described by the first fifty eigenvectors of each TREM2 isoform. (C) Porcupine plots representing the contributory components of the first eigenvectors for each isoform of TREM2.

Reference:
Hunter B Dean, Erik D Roberson* , Yuhua Song*. Neurodegenerative Disease–Associated Variants in TREM2 Destabilize the Apical Ligand-Binding Region of the Immunoglobulin Domain. Frontiers in Neurology, 2019, 10(1252).Published on November 26, 2019. doi: 10.3389/fneur.2019.01252

  • Protein interactions underlying death receptor-mediated death inducing signaling complex
    • Calmodulin and Death Receptor 5 Interaction in Triple-Negative and Estrogen Receptor Positive Breast Cancer Cells

Prediction and validation of the CaM-binding site in DR5 death domain and the key residues critical for CaM-DR5 binding.

Reference:
Romone M. Fancy, Lingyun Wang, Thomas Schmid, Qinghua Zeng, Hong Wang, Tong Zhou, Donald J. Buchsbaum, Yuhua Song*. Characterization of the Interactions between Calmodulin and Death Receptor 5 in Triple-Negative and Estrogen Receptor Positive Breast Cancer Cells: An Integrated Experimental and Computational Study. J Biol Chem. 2016, 291:12862-12870

  • Calmodulin Binding to Death Receptor 5-mediated Death-inducing Signaling Complex in Breast Cancer Cells

CaM recruited into DR5-mediated DISC in triple-negative and ER-positive breast cancer cells

Reference:
Romone M. Fancy, Harrison Kim, Tong Zhou, Kurt R. Zinn, Donald J. Buchsbaum, Yuhua Song*. Calmodulin Binding to Death Receptor 5-mediated Death-inducing Signaling Complex in Breast Cancer Cells. J Cell Biochem. 2017 Aug;118(8):2285-2294. doi: 10.1002/jcb.25882. Epub 2017 Apr 12.

  • Structural insight for roles of DR5 death domain mutations on oligomerization of DR5 death domain – FADD complex in the death-inducing signaling complex formation

Interfaces for DR5DD-FADD tetramer complex                   

Hydrogeon bond occupancy for H-bond formed between DR5 DD–FADD DD binding interface

Reference:
Hongyi Yang, Yuhua Song*. Structural insight for roles of DR5 death domain mutations on oligomerization of DR5 death domain – FADD complex in the death-inducing signaling complex formation. Journal of Molecular Modeling, 2016, 22 (4):89.

  • Characterization of calmodulin and Fas death domain interaction: an integrated experimental and computational study.

CaM-Fas DD WT binding isotherm from ITC experiment and effect of Fas V254N mutation on Fas DD conformatonal flexibility from MD simulations

Reference:
Romone Fancy, Lingyun Wang, Tiara Napier, Jiabei Lin, Gu Jing, Aaron Lucius, Jay M McDonald, Tong Zhou, Yuhua Song*. Characterization of calmodulin and Fas death domain interaction: an integrated experimental and computational study. Biochemistry, 2014, 53 (16), pp 2680–2688

  •  Trifluoperazine Regulation of Calmodulin Binding to Fas: A Computational Study.

Trifluoperazine regulation of calmodulin binding to Fas: A computational study

References:
Di Pan,Qi Yan, Yabing Chen, Jay M McDonald, Yuhua Song*. Trifluoperazine Regulation of Calmodulin Binding to Fas: A Computational Study. Proteins: Structure, Function, and Bioinformatics, 2011, 79(8):2543-56.

  • Conformation and Free Energy Analyses of the Complex of calcium-Bound Calmodulin and the Fas Death Domain

Calmodulin-Fas complex

Dynamical cross-correlation map of Fas upon binding to calmodulin

Reference:
Jonathan Suever, Yabing Chen, Jay M McDonald, Yuhua Song. Conformation and Free Energy Analyses of the Complex of Ca2+-Bound Calmodulin and the Fas Death Domain. Biophys J, 95 (12), 5913–5921, 2008

  • Regulation mechanisms for thrombospondin and calreticulin modulated signaling in cell adhesion disassembly
    • Effect of lipid bilayer environments on thrombospondin-1 and calreticulin interactions

Interaction of single CRT or TSP1-CRT complex with a model bilayer

Reference:
Lingyun Wang, Joanne E, Murphy-Ullrich, Yuhua Song*. Molecular insight for the effect of lipid bilayer environments on thrombospondin-1 and calreticulin interactions. Biochemistry. 2014, 53(40):6309-22

  • Molecular and Structural Insight for the Role of Thrombospondin-1 Binding to Calreticulin in Calreticulin-Induced Focal Adhesion Disassembly

Thrombospondin-calreticulin complex                                                           

Calreticulin before (blue) and after binding to thrombospondin (red)

References:
1. Qi Yan, Joanne E. Murphy-Ullrich, Yuhua Song. Structural Insight for the Role of Thrombospondin-1 Binding to Calreticulin in Calreticulin-InducedFocal Adhesion Disassembly. Biochemistry, 2010, 49 (17), pp 3685–3694
2. Qi Yan, Joanne E. Murphy-Ullrich, Yuhua Song*. Molecular and Structural Insight for the Role of Key Residues of Thrombospondin-1 and Calreticulin in Thrombospondin-1- Calreticulin Binding. Biochemistry 2011, 50, 566–573

  • Regulation mechanisms for integrin-mediated signaling in cell adhesion
    • Activation Mechanisms of αVβ3 Integrin by Binding to Fibronectin: A Computational Study
Principal dynamic modes for integrin αVβ3 without bound to fibronectin (A) and bound with fibronectin (B) from principle component analyses.
  • Role of Altered Sialylation of the I-like Domain of β1 Integrin in the Binding of Fibronectin to β1 Integrin  
Glycosylated beta1 I-like domain-fibronect complex
Hydrogen bond formations between the glycan and the beta1 I-like domain with (a) and without sailic acid (b)
  • References:
    1. Di Pan, Yuhua Song, Role of Altered Sialylation of the I-like Domain of β1 Integrin in the Binding of Fibronectin to β1 Integrin: Thermodynamics and Conformational Analyses.Biophys J, 99 (1), 208-217, 20102. Yuemin Liu, Di Pan, Susan L. Bellis, Yuhua Song. Effect of Altered Glycosylation on the Structure of the I-like Domain of beta1 Integrin: A Molecular Dynamics Study. Proteins: Structure, Function, and Bioinformatic, 73(4): 989-1000, 2008
      
  • A Naturally Occurring Extracellular α−β Clasp Contributes to Stabilization of β3 Integrins in a Bent, Resting Conformation

αvβ3 structure reveals a complex clasp interface.

References:
Anthony N. Vomund, Sarah Stuhlsatz-Krouper, Julie Dimitry, Yuhua Song and William A. Frazier. Breaking an Extracellular α−β Clasp Activates β3 Integrins. Biochemistry, 47(44), 11616-11624, 2008
 

  • Small molecules interactions with Acid-sensing Ion Channel-1
Psalmotoxin-1-Acid-sensing ion channel-1 complex
Amiloride-Acid-sensing Ion Channel-1 complex

References:
1. Yawar J. Qadri, Yuhua Song, Catherine M. Fuller and Dale J. Benos. Amiloride Docking to Acid-sensing Ion Channel-1. J Biol Chem. 2010 Mar 26;285(13):9627-35. Epub 2010 Jan.
2. Yawar J. Qadri, Bakhrom K. Berdiev, Yuhua Song, Howard L. Lippton, Catherine M. Fuller, and Dale J. Benos. Psalmotoxin-1 docking to human acid sensing ion channel-1.Journal of Biological Chemistry, 284(26), 17625-17633, 2009.

  • Peptide self-assembly into collagen-mimetic microfibers in biomaterials

References:
 Shyam Rele, Yuhua Song, Robert P. Apkarian, Zheng Qu, Vincent P. Conticello,and Elliot L. Chaikof. D-Periodic Collagen-Mimetic Microfibers. J Am Chem Soc. 2007 Nov 28;129(47):14780-14787

  • Effect of Drug on Micro-&mesoscopic Properties of A Lipid Bilayer with Molecular Dynamics Simulation
Drug interaction with lipid bilayer
Biomembrane system after 30 ns equilibration (with 150mM NaCl) (molecular dynamics simulations)

 References:
 Yuhua Song, Victor Guallar, Nathan A. Baker. Molecular dynamics simulation of salicylate effects on the micro- and mesoscopic properties of a dipalmitoylphosphatidylcholine bilayer. Biochemistry 44, 13425-13438, 2005.

  • Molecular Dynamics Simulations of Asymmetric NaCl and KCl Solutions Separated by Phosphatidylcholine Bilayers: Potential Drops and Structural Changes Induced by Strong Na+-Lipid Interactions and Finite Size Effects
Diagram of double-bilayer simulation geometry with approximate dimensions along the z axis labeled. Left and right boundaries are periodic (e.g., K is a single contiguous chamber in the simulation). As described in the text, the N chamber contains 48 Na+, 2 K+, and 50 Cl− ions while the K chamber contains 2 Na+ (blue), 48 K+ (green), and 50 Cl− (orange) ions

References:
Sun Joo Lee, Yuhua Song, Nathan A. Baker. Molecular dynamics simulations of asymmetric NaCl and KCl solutions separated by phosphatidylcholine bilayers: potential drops and structural changes induced by strong Na+-lipid interactions and finite size effects. Biophys J. 94:3565-3576, 2008

  • Substrate-Biological Macromolecule Diffusion

    Development of a 3-D adaptive finite element solver (SMOL) of solving Smoluchowski equation for ligand – biomolecular diffusion rate constant calculation that included the electrostatic potential effect. The developed software was applied to acetylcholinesterase biomolecular system to calculate the wild type and mutation rate constant. Softwares developed: SMOL finite element solver for diffusion distribution and rate constant calculation
Biomolecular diffusion, laying groundwork for integration of molecular-scale information into cellular-scale systems
Spherical reactive surface
Molecular reactive surface

References:
1. Yuhua Song, Yongjie Zhang, Chandrajit L. Bajaj, Nathan A. Baker. Continuum diffusion reaction rate calculations of wild type and mutant mouse acetylcholinesterase: adaptive finite element analysis. Biophys J. 2004 Sep.; 87(3):1558-1566
2. Yuhua Song, Yongjie Zhang , Tongye Shen , Chandrajit L. Bajaj , J. Andrew McCammon and Nathan A. Baker. Finite element solution of the steady-state Smoluchowski equation for rate constant calculations. Biophys J. 2004 Apr; 86(4):2017-29.
3. Deqiang Zhang, Jason Suen, Yongjie Zhang, Yuhua Song, Zoran Radic, Palmer Taylor, Michael J. Holst, Chandrajit Bajaj, Nathan A. Baker, J. Andrew McCammon. Tetrameric mouse acetylcholinesterase: continuum diffusion rate calculations by solving the steady-state smoluchowski equation using finite element methods. Biophys J. 2005 March; 88(3):1659-166

COMPUTATIONAL BIOMECHANICS

  • Computational Soft Tissue Biomechanics and Orthopedic Biomechanics

A combined experimental/computational approach was used for the development of the subject specific finite element model of the anterior cruciate ligament (ACL) (kinematics driven model) to more accurately to calculate the force and stress distribution in the ACL, and the finite element model of the knee joint (force driven model) to calculate both the knee kinematics and force, stress distribution in the ACL. The developed model has the potential to help design improved surgical procedures following ACL injuries.

Softwares used:
                • Geometry reconstruction from CT images:               MIMICS software
                • Mesh generation:                                                  MSC. Patran software
                • Finite element analysis:                                         MSC. Marc non-linear Finite Element software
Experimental tools:
                • Robotic/universal force sensor testing system 

1. Subject specific modeling of anterior cruciate ligament (Geometry model of ACL and bone reconstructed from CT images)

Subject specific finite element model of the ACL for a porcine right knee (anterior view)
ACL tibial insertion
ACL femur insertion
Stress distribution in ACL in response to kinematics under incremental anterial tibial load 

2. 3-D Finite element model of the knee joint

Displacement distribution in knee joint under anterial tibial load
3D knee joint under incremental anterior tibial load

References:
1. Yuhua Song, Richard E. Debski, Volker Musahl, Maribeth Thomas, Savio L-Y. Woo. A Three Dimensional Finite Element Model of the Human Anterior Cruciate Ligament – A Computational Analysis with Experimental Validation. J Biomech. 2004 March; 37(3):383-90.
2. Yuhua Song, Richard E. Debski, Jorge Gil, Savio L-Y. Woo. Development of a 3-D Non-Linear Finite Element Model of Human Knee Joint. BED-9C, Joint Biomechanics I, Advances in Bioengineering, American Society of Mechanical Engineers Meeting, New Orleans, Nov.22, 2002.
3. Song YH, et al. (2002) Development of a three dimensional nonlinear finite element model of the human knee joint ( under internal review in MSRC, University of Pittsburgh)

  • Cell Biomechanics:
Stress distribution within cell with the effect of focal adhesion

Softwares used:                     
Finite element analysis: ABAQUS non-linear Finite Element software

MATERIALS PROCESSING ENGINEERING

A combined computational/experimental approach was used to study the materials processing procedure, especially for rapid prototyping & rapid tooling, plastic sheet thermoforming and solidification process. The experimental results were used to validate the computational model, the results from computational model had been used to help optimize the parameters for the materials processing procedure to obtain the quality products with low cost and short production period.

  •  Rapid Prototyping & Rapid Tooling
              
      Softwares used:
                  • Finite element analysis:                 MSC. Marc non-linear Finite Element software

1. Coupled thermo-mechanical analysis of laminated object manufacturing (LOM)

Temperature distribution and thermal deformation during LOM procedure with dynamic thermal resource

Reference:
1. Yuhua Song, Yongnian Yan, Zhang Renji. Coupled Thermo-mechanical FEM Aanalysis of Laminated Object Manufacturing. China Mechanical Engineering. 2000; Vol. 11, Suppl.: 37-40.

2. Dimensional accuracy for casting dies of automobile deck part in rapid tooling: non-linear coupled thermo-mechanical finite element analysis

Mesh of the upper casting die and sand mold box
Upper casting die part
Temperature distribution
Thermal displacement distribution
Mesh of the bottom casting die and sand mold box
Bottom casting die part
Temperature distribution
Thermal displacement distribution

References:
1.Yuhua Song, Yongnian Yan, Renji Zhang etc. Boundary Model between Casting and Matrix and Its Influence on The Dimensional Accuracy Analysis Of Rapid Tooling. Proceeding of the institution of mechanical engineers Part B – Journal of Engineering Manufacture, 2002; 216 (8): 1123-1134.
2. Yuhua Song, Yongnian Yan, Renji Zhang Qingping Lu etc. 3-D Non-linear Coupled Thermo-Mechanical FEM Analysis of the Dimensional Accuracy for Casting Dies in Rapid Tooling. Finite Elements in Analysis and Design, 2001; 38 (1): 79-91.

  • Plastic Sheet Thermoforming: Coupled Thermomechanical Analysis

Softwares developed: A finite element software ARVIP-3D was developed to realize the coupled thermo-mechanical analysis of the plastic sheet thermoforming to predict the thickness, temperature, and thermal stress distribution, and analyze the warpage of the thermoforming plastic part.

Temperature distribution
Thickness distribution
Thermal stress distribution and warpage of the plastic part

References:
1. Yuhua Song, Kaifing Zhang, Zhongren Wang, Faxi Dao, Yongnian Yan, etc. Coupled thermal-mechanical Analysis of Plastics Thermalforming. Polymer Engineering and Science, Aug.; 40(8): 1736-1746.
2.Yuhua Song, Kaifeng Zhang, Zhongren Wang, Faxi Diao. Study on the Warpage of Plastics Vacuum- Forming Process. Journal of Reinforced Plastics and Composites, 1999; 18(10): 931-941.

  • Solidificaiton and Shrinkage Prediction of Ductile Ion Casing

Softwares developed: A numberical analysis software was developed to analyze the temperature distribution during solidification process and predict the second shrinkage of the ductile ion casting 

Reference:
1. Yuhua Song, S. P. Wu, F. Y. Qing, S. Z. Ren. Study On Searching For Isolated Region During Casting Solidification Process And Predicting Second Shrinkage Of Ductile Iron Casting. 3rd Pacific Rim International Conference on Modelling of Casting and Solidfication Processes, 1996 Dec, Beijing, China, International Academic Publishers, pp353-358.

MOLD AND EQUIPMENT DESIGN AND MANUFACTURING

  • Stamping mold design and manufacturing
    • Prestress wire-winding press: strength and stiffness finite element analysis (industry collaborated project)

Softwares used:
              • Finite element analysis:                 MSC. Marc non-linear Finite Element software

References:
1. Yuhua Song. Coupled thermo-mechanical finite element analysis for rapid prototyping and rapid tooling, and finite element analysis for prestress wire-winding press. Postdoctoral Research Report, Beijing: Tsinghua University, 2000.

Copyright ©Yuhua Song, Ph.D.