Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor

The association of the receptor binding domain (RBD) of SARS-CoV-2 viral spike with human angiotensin converting enzyme (hACE2) represents the first required step for viral entry. Amino acid changes in the RBD have been implicated with increased infectivity and potential for immune evasion. Reliably predicting the effect of amino acid changes in the ability of the RBD to interact more strongly with the hACE2 receptor can help assess the public health implications and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born electrostatic solvation, hydrogen-bonding, π-π packing and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity using the decomposed energy terms as descriptors. The computational base achieves an accuracy of 82.2% in terms of correctly classifying single amino-acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient r of 0.69 between predicted and experimentally calculated binding affinities. Both metrics are calculated using a 5-fold cross validation test. Our method thus sets up a framework for effectively screening binding affinity change with unknown single and multiple amino-acid changes. This can be a very valuable tool to predict host adaptation and zoonotic spillover of current and future SARS-CoV-2 variants.

Robust inference of positive selection on regulatory sequences in the human brain

A longstanding hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory level adaptations than by protein sequence adaptations. This has especially been suggested for regulatory adaptations in the evolution of the human brain. We present a new method to detect positive selection on transcription factor binding sites on the basis of measuring predicted affinity change with a machine learning model of binding. Unlike other methods, this approach requires neither defining a priori neutral sites nor detecting accelerated evolution, thus removing major sources of bias. We scanned the signals of positive selection for CTCF binding sites in 29 human and 11 mouse tissues or cell types. We found that human brain–related cell types have the highest proportion of positive selection. This result is consistent with the view that adaptive evolution to gene regulation has played an important role in evolution of the human brain.

Conformational entropy and specific affinity of M-cholinolytics and ligands of µ-opioid and D2-dopamine receptors

The computation model for evaluation of conformational entropy changes upon binding ligands to receptors is described. Then, changes of conformational entropy component and of binding free energy are compared. Interest to conformational entropy arises from developing new drugs as it might be changed purposefully. It is shown that conformational entropy may be used for prediction of affinity to a certain receptor. Examples of directed affinity change under the modification of substances’ conformational flexibility are given. The specific role of the conformational entropy in the receptor’s protection from the irreversible inactivation is identified.

A Novel Thermo-Sensitive Polymer Film and its Imaging Properties

Three copolymers containing isobutyl p-styrenesulfonate (IBSS) and carboxyl units, i.e. methyl methacrylate (MMA) and methacrylic acid (MAA), were synthesized and their chemical structures and thermal behaviors were investigated by using TGA, DSC, FTIR and other methods. Films were prepared by using the synthesized copolymers in combination with a bisvinyl ether compound, i.e. 2,2-bis(4-(2-(vinyloxy)ethoxy)phenyl)propane (BVPP), to applied onto an aluminum plate. Crosslinking and de-crosslinking reactions would readily take place when the polymer films were baking treated at 100 oC and 200 oC for a short period of time, respectively. Along with the chemical structural changes during thermal treatments, an affinity change was achieved from being insoluble to completely soluble in neutral water. A positive-working and neutral water-developable imaging material was proposed and preliminary studies on the imaging properties were conducted.