Chen Brian Y, Bandyopadhyay Soutir
Department of Computer Science and Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, PA 18015, USA.
J Bioinform Comput Biol. 2012 Jun;10(3):1242004. doi: 10.1142/S0219720012420048.
Finding elements of proteins that influence ligand binding specificity is an essential aspect of research in many fields. To assist in this effort, this paper presents two statistical models, based on the same theoretical foundation, for evaluating structural similarity among binding cavities. The first model specializes in the "unified" comparison of whole cavities, enabling the selection of cavities that are too dissimilar to have similar binding specificity. The second model enables a "regionalized" comparison of cavities within a user-defined region, enabling the selection of cavities that are too dissimilar to bind the same molecular fragments in the given region. We applied these models to analyze the ligand binding cavities of the serine protease and enolase superfamilies. Next, we observed that our unified model correctly separated sets of cavities with identical binding preferences from other sets with varying binding preferences, and that our regionalized model correctly distinguished cavity regions that are too dissimilar to bind similar molecular fragments in the user-defined region. These observations point to applications of statistical modeling that can be used to examine and, more importantly, identify influential structural similarities within binding site structure in order to better detect influences on protein-ligand binding specificity.
寻找影响配体结合特异性的蛋白质元件是许多领域研究的重要方面。为助力这一研究工作，本文基于相同的理论基础，提出了两种统计模型，用于评估结合腔之间的结构相似性。第一种模型专门用于对整个腔进行“统一”比较，能够筛选出那些差异过大以至于不太可能具有相似结合特异性的腔。第二种模型能够对用户定义区域内的腔进行“区域化”比较，能够筛选出那些差异过大以至于无法在给定区域结合相同分子片段的腔。我们应用这些模型分析了丝氨酸蛋白酶和烯醇酶超家族的配体结合腔。接下来，我们观察到我们的统一模型能够正确地将具有相同结合偏好的腔组与具有不同结合偏好的其他腔组区分开来，并且我们的区域化模型能够正确地区分那些差异过大以至于无法在用户定义区域结合相似分子片段的腔区域。这些观察结果表明统计建模的应用可用于检查，更重要的是，识别结合位点结构内有影响的结构相似性，以便更好地检测对蛋白质 - 配体结合特异性的影响。
