REFINEMENT OF PREDICTED LOW-RESOLUTION PROTEIN MODELS TO HIGH-RESOLUTION ALL-AT

将预测的低分辨率蛋白质模型细化为高分辨率 All-AT

• 批准号: 7723173
• 负责人: JEFFREY SKOLNICK
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723173
• 关键词: Affect; Algorithms; Amino Acid Sequence; Amino Acids; Computer Retrieval of Information on Scientific Projects Database; Data; Distant; Drug Design; Environment; Failure; Free Energy; Funding; Generations; Goals; Grant; Institution; Laboratories; Ligands; Methodology; Modeling; Molecular Conformation; Pharmaceutical Preparations; Plant Roots; Proteins; Relative (related person); Research; Research Personnel; Resolution; Resources; Screening procedure; Shapes; Solvents; Source; Structure; United States National Institutes of Health; Vertebral column; improved; molecular mechanics; protein structure; protein structure prediction

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. A major challenge in protein structure prediction is the refinement of low-resolution predicted models (e.g. those with a backbone root-mean-square-deviation, RMSD, from the native structure of about 5 ), to high-resolution all-atom structures with a RMSD of less than 2 . To date, there have only been a few documented instances where structures at atomic detail have improved relative to their initial starting conformation. Even if low-resolution models with a RMSD from the native structure of about 5 were available for a target protein, the refinement to a high-resolution structure with a RMSD of less than 2 can be difficult to achieve. Given the increasing ability of protein structure prediction algorithms, such as TASSER developed in our laboratory, to assemble approximately correct native structures, the effort should be made to refine such models so that they can be used for ligand screening as well as more general types of functional annotation. High-resolution structures are useful in detailed studies of mechanisms as well as in the design of drug molecules in which the (atomic) local environment of the small (drug) molecule requires accurate characterization. While significant progress has been made in the generation of low-resolution structures and their selection, the final step of structural adjustment still poses significant difficulties. Despite considerable effort, structure refinement from proteins having near native structures has proven to be very difficult, especially if all atom models and atomic potentials are used. It is necessary for successful refinement of low-resolution models that the force field recognizes the native structure of a protein as the lowest free energy minimum, and it shows correlation between energy and the native-likeness of decoy structures. Current all-atom force fields in many cases fail to recognize the native structure as the lowest free energy minimum and energy of the decoys do not monotonically decreases as the native conformation is approached. The source of this inaccuracy may be: a). the mathematical form of the potential b). lack of important physical interactions in the potential, e.g. polarization or correlation effects (many body interactions), or c). inadequate parameterization of the force field, which does not include sufficient information about the global shape of the energy landscape. Most all-atom force fields rely on the assumption that the conformation of each residue is energetically insensitive to the conformation of its neighbors (i.e. they do not contain explicit correlation terms for the backbone conformation (torsional angles) of the neighboring residues). However, there are experimental and statistical data from solved protein structures, showing that the backbone conformation of a residue in the amino acid chain is correlated with the conformation and identity of the neighboring residues. These data reflect the influence of the environment of the protein, where the correlations of local effects with solvent and non-local effects are non-separable. Therefore, it is important to determine to what extent the local interactions (between residues close in amino acid sequence) alter the accessible conformational space and how much of this effect originates from non-local interactions (between residues close in space but distant in sequence) and solvation, and whether current extant force fields, that do not explicitly include backbone correlation and polarization can reproduce these effects. Conceivably, ignoring some important interactions could result in inaccuracies of the relative conformational energies thereby affecting the ability to identify the native structure as the lowest energy minimum. However, if the functional form of current molecular mechanics potentials is mathematically correct, then the failure to recognize the native structure as the global energy minimum could arise from incorrect details of the force field parameters that should be fixable by reparameterization. The long-term goal of this project is to evaluate and improve accuracy of current all-atom force fields and develop methodology to refine the low-resolution protein models, resulted from the protein structure prediction approaches developed by Skolnick and coworkers, to high-resolution structures in all-atom representation.

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