Ferrada Evandro, Vergara Ismael A, Melo Francisco
Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.
Cell Biochem Biophys. 2007;49(2):111-24. doi: 10.1007/s12013-007-0050-5.
The correct discrimination between native and near-native protein conformations is essential for achieving accurate computer-based protein structure prediction. However, this has proven to be a difficult task, since currently available physical energy functions, empirical potentials and statistical scoring functions are still limited in achieving this goal consistently. In this work, we assess and compare the ability of different full atom knowledge-based potentials to discriminate between native protein structures and near-native protein conformations generated by comparative modeling. Using a benchmark of 152 near-native protein models and their corresponding native structures that encompass several different folds, we demonstrate that the incorporation of close non-bonded pairwise atom terms improves the discriminating power of the empirical potentials. Since the direct and unbiased derivation of close non-bonded terms from current experimental data is not possible, we obtained and used those terms from the corresponding pseudo-energy functions of a non-local knowledge-based potential. It is shown that this methodology significantly improves the discrimination between native and near-native protein conformations, suggesting that a proper description of close non-bonded terms is important to achieve a more complete and accurate description of native protein conformations. Some external knowledge-based energy functions that are widely used in model assessment performed poorly, indicating that the benchmark of models and the specific discrimination task tested in this work constitutes a difficult challenge.
准确区分天然蛋白质构象和接近天然的蛋白质构象对于实现基于计算机的准确蛋白质结构预测至关重要。然而，事实证明这是一项艰巨的任务，因为目前可用的物理能量函数、经验势和统计评分函数在始终如一地实现这一目标方面仍然存在局限性。在这项工作中，我们评估并比较了不同的基于全原子知识的势区分天然蛋白质结构和通过比较建模生成的接近天然的蛋白质构象的能力。使用包含几种不同折叠的152个接近天然的蛋白质模型及其相应天然结构的基准，我们证明纳入近距离非键合成对原子项可提高经验势的区分能力。由于无法从当前实验数据直接且无偏地推导近距离非键合项，我们从基于非局部知识的势的相应伪能量函数中获取并使用了这些项。结果表明，该方法显著改善了天然蛋白质构象和接近天然的蛋白质构象之间的区分，这表明对近距离非键合项进行适当描述对于更完整、准确地描述天然蛋白质构象很重要。一些在模型评估中广泛使用的基于外部知识的能量函数表现不佳，表明这项工作中测试的模型基准和特定区分任务构成了一项艰巨挑战。
