A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

<p>Abstract</p> <p>Background</p> <p>Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.</p> <p>Results</p> <p>In our study, an automatic algorithm was developed for summarizing key protein-ligand interactions as a pharmacophore model and identifying the reference complex with a maximal similarity to the query complex. Our KGS strategy was evaluated in combination with two scoring functions (X-Score and PLP) on three test sets, containing 112 HIV protease complexes, 44 carbonic anhydrase complexes, and 73 trypsin complexes, respectively. Our results obtained on crystal structures as well as computer-generated docking poses indicated that application of the KGS strategy produced more accurate predictions especially when X-Score or PLP alone did not perform well.</p> <p>Conclusions</p> <p>Compared to other targeted scoring functions, our KGS strategy does not require any re-parameterization or modification on current scoring methods, and its application is not tied to certain systems. The effectiveness of our KGS strategy is in theory proportional to the ever-increasing knowledge of experimental protein-ligand binding data. Our KGS strategy may serve as a more practical remedy for current scoring functions to improve their accuracy in binding affinity prediction.</p

Highly flexible, foldable, and rollable microsupercapacitors on an ultrathin polyimide substrate with high power density.

The design and functionality of extremely flexible, foldable, and rollable microsupercapacitors (MSCs) with in-plane interdigital electrodes that consist of single-walled carbon nanotube (SWCNT) networks on an ultrathin polyimide substrate are demonstrated through experiments and finite element simulations. The all-solid-state MSCs can be reversibly bent, folded, and rolled purely elastically without degradation of their electrical performance. The simulation results confirm that the deformation in bent, folded, and rolled MSCs is purely elastic. The high power density (1125 W cm-3) and small time constant (1 ms) of the present MSCs are comparable to those of aluminum electrolytic capacitors. The MSCs operate at scan rates of up to 1000 V s-1, are characterized by a volumetric capacitance of 18 F cm-3 and an energy density of 1.6 mWh cm-3, and exhibit superior electrochemical stability with 96% capacity retention even after 100,000 charge/discharge cycles. The developed MSCs demonstrate high potential for integration in flexible and wearable electronic systems

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes, X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42967/1/10822_2004_Article_405419.pd

Structure-based method for analyzing protein–protein interfaces

Hydrogen bond, hydrophobic and vdW interactions are the three major non-covalent interactions at protein–protein interfaces. We have developed a method that uses only these properties to describe interactions between proteins, which can qualitatively estimate the individual contribution of each interfacial residue to the binding and gives the results in a graphic display way. This method has been applied to analyze alanine mutation data at protein–protein interfaces. A dataset containing 13 protein–protein complexes with 250 alanine mutations of interfacial residues has been tested. For the 75 hot-spot residues (ΔΔ G ≥1.5 kcal mol -1 ), 66 can be predicted correctly with a success rate of 88%. In order to test the tolerance of this method to conformational changes upon binding, we utilize a set of 26 complexes with one or both of their components available in the unbound form. The difference of key residues exported by the program is 11% between the results using complexed proteins and those from unbound ones. As this method gives the characteristics of the binding partner for a particular protein, in-depth studies on protein–protein recognition can be carried out. Furthermore, this method can be used to compare the difference between protein–protein interactions and look for correlated mutation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47876/1/894_2003_Article_168.pd

Discovery of small-molecule inhibitors for the protein-protein interactions involving ATG5

The autophagy-related 12 (ATG12)–autophagy-related 5 (ATG5)–autophagy-related 16-like 1 (ATG16L1) ternary complex forms a dimer that facilitates the translocation of autophagy-related 8 (ATG8) proteins from autophagy-related 3 (ATG3) to phosphatidylethanolamine (PE). This event is fundamental for cargo sequestration and autophagy progression. Thus, one possible strategy for inhibiting autophagy is to disrupt the critical ATG5-ATG16L1 interaction during this process. So far very few known specific autophagy modulators can block autophagy effectively. We recently discovered a small-molecule compound, T1742, which is able to block the ATG5-ATG16L1 and ATG5-TECAIR interactions in vitro at the low-micromolar range (IC50 = 1~2 μM). Flow cytometry assay and western blot experiments indicated that T1742 can also effectively inhibit autophagy in living cells in a dose-dependent manner. To the best of our knowledge, T1742 represents the first small-molecule autophagy inhibitor that disrupts the protein-protein interactions involving ATG5. Such compounds may serve as a new chemical tool for deciphering the mechanism of autophagy or a potential candidate for therapeutic application

A new atom-additive method for calculating partition coefficients

A new method is presented for the calculation of partition coefficients of solutes in octanol/water. Our algorithm, XLOGP, is based on the summation of atomic contributions and includes correction factors for some intramolecular interactions. Using this method, we calculate the log P of 1831 organic compounds and analyze the derived parameters by multivariate regression to generate the final model. The correlation coefficient for fitting this training database is 0.968, and the standard deviation is 0.37. The result shows that our method for log P estimation is applicable to quantitative structure-activity relationship studies and gives better results than other more complicated atom-additive methods