Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Human microsomal triglyceride transfer protein (hMTP) plays an essential role in the assembly of apoB-containing lipoproteins, and has become an important drug target for the treatment of several disease states, such as abetalipoproteinemia, fat malabsorption and familial hypercholesterolemia. hMTP is a heterodimer composed of a larger hMTPα subunit and a smaller hMTPβ subunit (namely, protein disulfide isomerase, hPDI). hPDI can interact with 17β-estradiol (E₂), an endogenous female sex hormone. It has been reported that E₂ can significantly reduce the blood levels of low-density lipoprotein, cholesterol and triglyceride, and modulate liver lipid metabolism in vivo. However, some of the estrogen’s actions on lipid metabolism are not associated with estrogen receptors (ER), and the exact mechanism underlying estrogen’s ER-independent lipid-modulating action is still not clear at present. In this study, the potential influence of E₂ on the stability of the hMTP complex is investigated by jointly using multiple molecular dynamics analyses based on available experimental structures. The molecular dynamics analyses indicate that the hMTP complex in the presence of E₂ has reduced interface contacts and surface areas. A steered molecular dynamics analysis shows that the forces required to separate the two subunits (namely, hPDI and hMTPα subunit) of the hMTP complex in the absence of E₂ are significantly higher than the forces required to separate the complex in which its hPDI is already bound with E₂. E₂ makes the interface between hMTPα and hPDI subunits more flexible and less stable. The results of this study suggest that E₂-induced conformational changes of the hMTP complex might be a novel mechanism partly accounting for the ER-independent lipid-modulating effect of E₂.

Accurate estimation of the strength of the protein-ligand interaction is important in the field of drug discovery. The binding strength can be determined by using experimental binding affinity assays which are both time and labor consuming and costly. Predicting the binding affinity/energy in silico is an alternative approach, particularly for virtual screening of large data sets. In general, the distance-based terms such as electrostatic and van der Waals interactions are among the key determinants of binding energy. In this work, the distance-binding energy relationships, i.e., E ∝ -d^-k, are further explored, extended, and developed for protein-ligand binding affinity prediction. The contributions of different atom-type pairs were considered synthetically and jointly. Additionally, the contact number-energy relationships (E ∝ -n^k) were also explored for protein-ligand binding affinity prediction. Significantly, the power exponents of the distances or contact numbers in the energy functions are not restricted by the existing theories concerning van der Waals and electrostatic energies (expressed as ar⁶-br¹² and cr). The performances of the new distance-based or contact number-based models are better than the performances of those sophisticated non-machine-learning-based scoring functions developed before. The exploration and extension of the distance-energy and contact number-energy relationships may offer insights into the development of more effective methods for predicting the protein-ligand binding affinity accurately and analyzing the protein-ligand interactions rationally.

Insulin receptor plays an important role in regulation of energy metabolism. Dysfunction of insulin receptor (IR) can lead to many disease states, such as diabetes mellitus. Deciphering the complex dynamic structure of human IR and its mechanism of activation would greatly aid in understanding IR-mediated signaling pathways and in particular, in designing new drugs (including nonpeptidal insulin analogs) to treat diabetes mellitus. Experimental evidence about IR structure has been gradually obtained by biologists over the past three decades. Based on the available experimental structures of IR in different states, here we employ molecular modeling approach to construct the full-length IR structures in different states and model its structural and conformational changes during insulin-induced IR activation. Several key possible intermediate states are constructed based on structural alignment, rotation and computational modeling. Based on the structures of the full-length IR in different states, it appears that there are two possible conformational transition pathways: one is symmetric, and the other one is asymmetric. Structural changes and motions of different domains of the full-length IR along the pathways are analyzed. The role of insulin binding to IR in facilitating the conformational transition of the receptor is modeled. Information and insights derived from our present structural modeling analyses may aid in understanding the complex dynamic, structural and conformational changes during the process of IR activation.

It was recently postulated that neural microtubules (neuro-MTs), which are densely packed inside axons and dendrites, are vacuum cylindrical nanotubes that can mediate neuroelectrical transmission with a unique form of quasi-superconductivity. In this work, the behaviors of free electrons inside a theoretical neuro-MT are modeled using computational analysis and calculations. We reveal that neuro-MTs can function as nanosized physiological devices that mediate neuroelectrical transmission with a super-high energy efficiency in a quasi-superconducting manner. Under physiologically relevant conditions, the binding of cytosolic cations (e.g., K⁺ and Na⁺) to the surface residues of a neuro-MT triggers its transition from a resting state to an active state, and the rapid dissociation of these cations triggers the opposite. The dipole ring structures of a neuro-MT will help terminate the free electron conduction inside the vacuum tunnel with high efficiency. The proposed neuro-MT-mediated electrical transmission offers a potential mechanistic explanation for the saltatory conduction of action potentials along an axon or a dendrite. This theoretical exploration also offers unique insights into the rational design of biomimetic room-temperature quasi-superconducting materials, such as carbon or silicone-based quasi-superconducting nanotubes.