Tremendous progress has been made in computational protein design in the last 10 years. Though most work has focused on redesign of existing proteins to enhance their stability, specificity or new functionality, successful designs of novel protein folds (not naturally occurring) have emerged. However, to make protein design useful in practical applications, many challenges still exist in the design of protein-protein and protein- DNA interface and the design of dynamic properties of proteins, etc. To conquer those challenges, a practical solution is to develop more accurate scoring functions. It has been proved both experimentally and theoretically that intra and intermolecular signaling between distant sites within one or among many proteins plays a significant role in many biological processes, such as signal transmission and allosteric regulation, etc. The main research goal of this R21 grant is to develop new algorithms and strategies to incorporate the site-site couplings into a protein design procedure. Specifically, in Aim 1, we propose to study the site-site couplings and cooperative interactions of a protein or protein system using both the sequence-based and the physics-based approaches. Critical assessment of the existing approaches will be conducted and a novel algorithm will be developed using the residue-residue interaction energies.
In Aim 2, the best methods for site-site couplings prediction will be tailored and integrated to physical scoring functions of protein design. To guarantee the success of this research grant, we propose four schemes to incorporate/combine the site-site coupling into a protein design procedure. The first two schemes represent simple combinations of sequence-based and physics-based protein design approaches;on the other hand, the next two schemes represent a more advanced integration of site-site correlations and physical scoring functions. In the last protocol, we propose to develop a novel scheme and software package to conduct protein design using the residue-residue interaction energies as a template. This approach, which is referred to as correlation embedding (CE), is based on a hypothesis that the correlation information is intrinsically encoded in the interaction energy matrix. The success of this sub-aim will have a great impact on rational protein design, as it represents a perfect integration of site-site couplings into a physical scoring function and opens a new avenue to conduct protein design. All the designed promising PDZ proteins will be synthesized and tested in both folding and functional assays.

Public Health Relevance

Protein Design Using Physical Scoring Functions integrated with Site Couplings In this proposal, we intend to develop novel approaches to conquer the challenges in protein design. The new protein design strategies can facilitate us to engineer dynamical controls into a novel protein so that it can undertake a dynamic function. The novel approaches could be used to develop more effective biomedicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21GM097617-02
Application #
8320949
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Wehrle, Janna P
Project Start
2011-09-01
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2014-08-31
Support Year
2
Fiscal Year
2012
Total Cost
$198,750
Indirect Cost
$73,750
Name
University of Texas Sw Medical Center Dallas
Department
Pharmacology
Type
Schools of Medicine
DUNS #
800771545
City
Dallas
State
TX
Country
United States
Zip Code
75390
Wang, Junmei; Hou, Tingjun (2015) Advances in computationally modeling human oral bioavailability. Adv Drug Deliv Rev 86:11-6
Tian, Sheng; Li, Youyong; Wang, Junmei et al. (2013) Drug-likeness analysis of traditional Chinese medicines: 2. Characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from traditional Chinese medicines. J Cheminform 5:5
Tian, Sheng; Wang, Junmei; Li, Youyong et al. (2012) Drug-likeness analysis of traditional Chinese medicines: prediction of drug-likeness using machine learning approaches. Mol Pharm 9:2875-86
Cao, Dongyue; Wang, Junmei; Zhou, Rui et al. (2012) ADMET evaluation in drug discovery. 11. PharmacoKinetics Knowledge Base (PKKB): a comprehensive database of pharmacokinetic and toxic properties for drugs. J Chem Inf Model 52:1132-7
Wang, Sichao; Li, Youyong; Wang, Junmei et al. (2012) ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Mol Pharm 9:996-1010
Wang, Junmei; Hou, Tingjun (2012) Develop and test a solvent accessible surface area-based model in conformational entropy calculations. J Chem Inf Model 52:1199-212
Shen, Mingyun; Tian, Sheng; Li, Youyong et al. (2012) Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines. J Cheminform 4:31
Wang, Junmei; Cieplak, Piotr; Li, Jie et al. (2012) Development of polarizable models for molecular mechanical calculations. 4. van der Waals parametrization. J Phys Chem B 116:7088-101
Wang, Junmei; Tingjun, Hou (2011) Application of Molecular Dynamics Simulations in Molecular Property Prediction I: Density and Heat of Vaporization. J Chem Theory Comput 7:2151-2165
Wang, Junmei; Hou, Tingjun (2011) Application of molecular dynamics simulations in molecular property prediction II: diffusion coefficient. J Comput Chem 32:3505-19