Mutual information analysis of mutation,nonlinearity, and triple interactions in proteins

Mutations are the cause of several diseases as well as the underlying force of evolution. A thorough understanding of their biophysical consequences is essential. We present a computational framework for evaluating different levels of mutual information (MI) and its dependence on mutation. We used molecular dynamics trajectories of the third PDZ domain and its different mutations. Nonlinear MI between all residue pairs are calculated by tensor Hermite polynomials up to the fifth order and compared with results from multivariate Gaussian distribution of joint probabilities. We show that MI is written as the sum of a Gaussian and a nonlinear component. Results for the PDZ domain show that the Gaussian term gives a sufficiently accurate representation of MI when compared with nonlinear terms up to the fifth order. Changes in MI between residue pairs show the characteristic patterns resulting from specific mutations. Emergence of new peaks in the MI versus residue index plots of mutated PDZ shows how mutation may change allosteric pathways. Triple correlations are characterized by evaluating MI between triplets of residues. We observed that certain triplets are strongly affected by mutation. Susceptibility of residues to perturbation is obtained by MI and discussed in terms of linear response theory.     

Universal features of fluctuations in globular proteins

Using data from 2000 non‐homologous protein crystal structures, we show that the distribution of residue B factors of proteins collapses onto a single master curve. We show by maximum entropy arguments that this curve is a Gamma function whose order and dispersion are obtained from experimental data. The distribution for any given specific protein can be generated from the master curve by a linear transformation. Any perturbation of the B factor distribution of a protein, imposed at constant energy, causes a decrease in the entropy of the protein relative to that of the reference state. Proteins 2016; 84:721–725. © 2016 Wiley Periodicals, Inc.     

Artificial proteins as allosteric modulators of PDZ3 and SH3 in two‐domain constructs: A computational characterization of novel chimeric proteins

Artificial multidomain proteins with enhanced structural and functional properties can be utilized in a broad spectrum of applications. The design of chimeric fusion proteins utilizing protein domains or one‐domain miniproteins as building blocks is an important advancement for the creation of new biomolecules for biotechnology and medical applications. However, computational studies to describe in detail the dynamics and geometry properties of two‐domain constructs made from structurally and functionally different proteins are lacking. Here, we tested an in silico design strategy using all‐atom explicit solvent molecular dynamics simulations. The well‐characterized PDZ3 and SH3 domains of human zonula occludens (ZO‐1) (3TSZ), along with 5 artificial domains and 2 types of molecular linkers, were selected to construct chimeric two‐domain molecules. The influence of the artificial domains on the structure and dynamics of the PDZ3 and SH3 domains was determined using a range of analyses. We conclude that the artificial domains can function as allosteric modulators of the PDZ3 and SH3 domains. Proteins 2016; 84:1358–1374. © 2016 Wiley Periodicals, Inc.     

Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques

Allostery is a phenomenon that the protein activity is regulated when a non-functional site on it is bounded. This phenomenon is important in life process and disease therapy. However, it is difficult to study allostery due to the lack of knowledge. Facing this demand, we have created Allosteric Database (ASD) 10 years before to collect numerous kinds of allosteric data. In this review, we will introduce the 4 categories of data in ASD. For each category, we further reviewed how researchers applied ASD data to conduct studies. We focused on their research topics, analytical methods and conclusions. Several discoveries of new drug targets and allosteric modulators driven by ASD are also summarized. We hope this review could inspire researchers with new utilities of ASD data.