Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis

Easy Not Easy: Comparative Modeling with High-Sequence Identity Templates

Homology modeling is the most common technique to build structural models of a target protein based on the structure of proteins with high-sequence identity and available high-resolution structures. This technique is based on the idea that protein structure shows fewer changes than sequence through evolution. While in this scenario single mutations would minimally perturb the structure, experimental evidence shows otherwise: proteins with high conformational diversity impose a limit of the paradigm of comparative modeling as the same protein sequence can adopt dissimilar three-dimensional structures. These cases present challenges for modeling; at first glance, they may seem to be easy cases, but they have a complexity that is not evident at the sequence level. In this chapter, we address the following questions: Why should we care about conformational diversity? How to consider conformational diversity when doing template-based modeling in a practical way?

Analysis of changes of cavity volumes in predefined directions of protein motions and cavity flexibility

Dynamics of protein cavities associated with protein fluctuations and conformational plasticity is essential for their biological function. NMR ensembles, molecular dynamics (MD) simulations, and normal mode analysis (NMA) provide appropriate frameworks to explore functionally relevant protein dynamics and cavity changes relationships. Within this context, we have recently developed analysis of null areas (ANA), an efficient method to calculate cavity volumes. ANA is based on a combination of algorithms that guarantees its robustness against numerical differentiations. This is a unique feature with respect to other methods. Herein, we present an updated and improved version that expands it use to quantify changes in cavity features, like volume and flexibility, due to protein structural distortions performed on predefined biologically relevant directions, for example, directions of largest contribution to protein fluctuations (principal component analysis [PCA modes]) obtained by MD simulations or ensembles of NMR structures, collective NMA modes or any other direction of motion associated with specific conformational changes. A web page has been developed where its facilities are explained in detail. First, we show that ANA can be useful to explore gradual changes of cavity volume and flexibility associated with protein ligand binding. Secondly, we perform a comparison study of the extent of variability between protein backbone structural distortions, and changes in cavity volumes and flexibilities evaluated for an ensemble of NMR active and inactive conformers of the epidermal growth factor receptor structures. Finally, we compare changes in size and flexibility between sets of NMR structures for different homologous chains of dynein.

Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates

Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.