Structural mining: self-consistent design on flexible protein-peptide docking and transferable binding affinity potential

A flexible protein-peptide docking method has been designed to consider not only ligand flexibility but also the flexibility of the protein. The method is based on a Monte Carlo annealing process. Simulations with a distance root-mean-square (dRMS) virtual energy function revealed that the flexibility of protein side chains was as important as ligand flexibility for successful protein-peptide docking. On the basis of mean field theory, a transferable potential was designed to evaluate distance-dependent protein-ligand interactions and atomic solvation energies. The potential parameters were developed using a self-consistent process based on only 10 known complex structures. The effectiveness of each intermediate potential was judged on the basis of a Z score, approximating the gap between the energy of the native complex and the average energy of a decoy set. The Z score was determined using experimentally determined native structures and decoys generated by docking with the intermediate potentials. Using 6600 generated decoys and the Z score optimization criterion proposed in this work, the developed potential yielded an acceptable correlation of R(2) = 0.77, with binding free energies determined for known MHC I complexes (Class I Major Histocompatibility protein HLA-A(*)0201) which were not present in the training set. Test docking on 25 complexes further revealed a significant correlation between energy and dRMS, important for identifying native-like conformations. The near-native structures always belonged to one of the conformational classes with lower predicted binding energy. The lowest energy docked conformations are generally associated with near-native conformations, less than 3.0 Angstrom dRMS (and in many cases less than 1.0 Angstrom) from the experimentally determined structures.     

Electronic structures and spectral and acid-base properties of pyrrol-, thiophen-, and furananthrones

The IR spectra, electronic absorption and luminescence spectra, solvatochromism, and basicities of compounds of the 6H-dibenz[cd,g]indol-6-one, 6H-anthra[9,1-bc] thiophen-6-one, and 6H-anthra[9,1-bc]furan-6-one groups were studied. The interrelationship between the indicated properties and such structural factors as the 1,10-anthraquinoid structure, the presence of a -surplus heteroring, and the presence of substituents was examined.Translated from Khimiya Geterotsiklicheskikh Soedinenii, No. 4, pp. 486–493, April, 1985.     

Synthesis of 3-substituted 2-hydroxybenz[f]indolequinones and benz[f]isatinquinone - A new π-acceptor system

Starting from 2,3-dichloro-1,4-naphthaquinone a series of 2-hydroxy-1-methylbenz[f]indolequinones bearing benzyl-, amino- and hydroxy substituents in position 3 , and benz[f]isatinquinone have been synthesised and their reactions have been studied.     

Freezing and microphase separation in random copolymers

Summary A two-letter random copolymer with attraction between similar monomers and repulsion between different ones is investigated using the replica method. This type of interactions favors microphase separation (MPS) in a compact state of a polymer or in a melt. Frustrations between interactions and polymeric bonds may lead to freezing transition in a phase where only a few conformations dominate and replica symmetry is broken. Our analysis reveals that stiff polymers have a frozen phase and do not undergo transition to a phase with microdomain structure. In flexible polymers the microphase transition may occur before freezing. A complete phase diagram showing the interplay between the two phase transitions is constructed for the two-letter random copolymer. Paper presented at the I International Conference on Scaling Concepts and Complex Fluids, Copanello, Italy, July 4–8, 1994.     

Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.

Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.     

Dimerization of 2-hydroxybenzindolequinones

It is shown that the decarboxylation of 2-hydroxybenz[f]indolequinone-3-carboxylic acids and the demethylation of 2-methoxybenz[f]indolequinone are accompanied by oxidative dimerization and lead to the 3,3-dimers of 2-hydroxybenz[f]indolequinones.Translated from Khimiya Geterotsiklicheskikh Soedinenii, No. 12, pp. 1636–1641, December, 1978.