A backbone-side-chain elastic network model (bsENM) is devised in this contribution to decipher the network of molecular interactions during protein dynamics. The chemical details in 5 $\mu$s all-atom molecular dynamics (MD) simulation are mapped onto the bsENM spring constants by self-consistent iterations. The elastic parameters obtained by this structure-mechanics statistical learning are then used to construct inter-residue rigidity graphs for the chemical components in protein amino acids. A key discovery is that the mechanical coupling strengths of both backbone and side chains exhibit heavy-tailed distributions and scale-free network properties. In both rat trypsin and PDZ3 proteins, the statistically prominent modes of rigidity graphs uncover the sequence-specific coupling patterns and mechanical hotspots. Based on the contributions to graphical modes, our residue rigidity scores in backbone and side chains are found to be very useful metrics for the biological significance. Most functional sites have high residue rigidity scores in side chains while the biologically important glycines are generally next to mechanical hotspots. Furthermore, prominent modes in the rigidity graphs involving side chains oftentimes coincide with the co-evolution patterns due to evolutionary restraints. The bsENM specifically devised to resolve the protein chemical character thus provides useful means for extracting functional information from all-atom MD.

在这一贡献中设计了主干侧链弹性网络模型 (bsENM)，以破译蛋白质动力学过程中的分子相互作用网络。5中的化学细节$\mu$s 全原子分子动力学 (MD) 模拟通过自洽迭代映射到 bsENM 弹簧常数。通过这种结构力学统计学习获得的弹性参数然后用于构建蛋白质氨基酸中化学成分的残基间刚度图。一个关键发现是主链和侧链的机械耦合强度都表现出重尾分布和无标度网络特性。在大鼠胰蛋白酶和 PDZ3 蛋白中，刚性图的统计显着模式揭示了序列特定的耦合模式和机械热点。基于对图形模式的贡献，我们发现我们在主链和侧链中的残基刚性分数是非常有用的生物学意义指标。大多数功能位点在侧链中具有较高的残基刚性分数，而生物学上重要的甘氨酸通常靠近机械热点。此外，由于进化限制，涉及侧链的刚性图中的突出模式通常与共同进化模式相吻合。bsENM 专门设计用于解析蛋白质化学特征，因此为从全原子 MD 中提取功能信息提供了有用的手段。