Development and testing of a de novo drug-design algorithm

In this article we present an implementation of a de novo drug-design algorithm. The algorithm starts with a molecule placed in the binding site of a protein and then modifies it using a sequential growth approach. This involves successive cycles of suppression of randomly picked groups in the molecule and their replacement by other groups chosen from databanks of linear or cyclic fragments. The algorithm has been coupled with the Dynamo library which allows the simulation of macromolecules using molecular mechanical and quantum chemical methods. The main body of the article describes the methodologies we use to create, characterize and evaluate putative ligands. We also consider briefly an application of the algorithm to a protein of pharmacological interest, the neuraminidase of the influenza virus, and discuss the strengths and weaknesses of our approach.     

A very large diversity space of synthetically accessible compounds for use with drug design programs

We have constructed a very large virtual diversity space containing more than 10¹³ chemical compounds. The diversity space is built from about 400 combinatorial libraries, which have been expanded by choosing sizeable collections of suitable R-groups that can be attached to each link point of their scaffolds. These R-group collections have been created by selecting reagents that have drug-like properties from catalogs of available chemicals. As members of known combinatorial libraries, the compounds in the diversity space are in general synthetically accessible and useful as potential drug leads. Hence, the diversity space can be used as a vast source of compounds by a de novo drug design program. For example, we have used such a program to generate inhibitors of HIV integrase enzyme that exhibited activity in the micromolar range.     

Design of new selective inhibitors of cyclooxygenase-2 by dynamic assembly of molecular building blocks

A method of dynamically assembling molecular building blocks - DycoBlock - has been proposed and tested by Liu et al. This method is based on multiple-copy stochastic dynamics simulation in the presence of a receptor molecule. In this method, a novel algorithm was used to dynamically assemble the molecular building blocks to form candidate compounds. Currently, some new improvements have been incorporated into DycoBlock to make it more efficient. In the new version of DycoBlock, the binding energy and solvent accessible surface area (SASA) can be used to screen the resulting compounds. A simple clustering algorithm based on molecular similarity was developed and used to classify the remaining compounds. The revised DycoBlock was tested by breaking SC-558 - a selective inhibitor of cyclooxygenase-2 (COX-2) - into building blocks and reassembling them in the active site of the enzyme. The accuracy of recovery grew to 58.8% while it was only 16.7% in the previous version. Then, thirty-three kinds of molecular building blocks were used in the design of novel inhibitors and the investigation of diversity. As a result, a total of 1441 compounds was generated with high diversity. After the first screening procedure, there remained 864 reasonable compounds. The results from clustering indicate that the structural motifs in the diarylheterocycle class of COX-2-selective inhibitors have been generated using the revised DycoBlock, and their binding modes were investigated.     

The atom assignment problem in automated de novo drug design. 3. Algorithms for optimization of fragment placement onto 3D molecular graphs

Summary Atom assignment onto 3D molecular graphs is a combinatoric problem in discrete space. If atoms are to be placed efficiently on molecular graphs produced in drug binding sites, the assignment must be optimized. An algorithm, based on simulated annealing, is presented for efficient optimization of fragment placement. Extensive tests of the method have been performed on five ligands taken from the Protein Data Bank. The algorithm is presented with the ligand graph and the electrostatic potential as input. Self placement of molecular fragments was monitored as an objective test. A hydrogen-bond option was also included, to enable the user to highlight specific needs. The algorithm performed well in the optimization, with successful replications. In some cases, a modification was necessary to reduce the tendency to give multiple halogenated structures. This optimization procedure should prove useful for automated de novo drug design.     

Structure-based ligand design for flexible proteins: Application of new F-DycoBlock

A method of structure-based ligand design – DycoBlock – has been proposed and tested by Liu et al.[1]. It was further improved by Zhu et al. and applied to design new selective inhibitors of cyclooxygenase 2 [2]. In the current work, we present a new methodology – F-DycoBlock that allows for the incorporation of receptor flexibility. During the designing procedure, both the receptor and molecular building blocks are subjected to the multiple-copy stochastic molecular dynamics (MCSMD) simulation [1], while the protein moves in the mean field of all copies. It is tested for two enzymes studied previously – cyclooxygenase 2 (COX-2) and human immunodeficiency type 1 (HIV-1) protease. To identify the applicability of F-DycoBlock, the binding protein structure was used as starting point to explore the conformational space around the bound state. This method can be easily extended to accommodate the flexibility in different degree. Four types of treatment of the receptor flexibility – all-atom restrained, backbone restrained, intramolecular hydrogen-bond restrained and active-site flexible – were tested with or without the grid approximation. Two inhibitors, SC-558 for COX-2 and L700417 for HIV-1 protease, are used in this testing study for comparison with previous results. The accuracy of recovery, binding energy, solvent accessible surface area (SASA) and positional root-mean-square (RMS) deviation are used as criteria. The results indicate that F-DycoBlock is a robust methodology for flexible drug design. It is particularly notable that the protein flexibility has been perfectly associated with each stage of drug design – search for the binding sites, dynamic assembly and optimization of candidate compounds. When all protein atoms were restrained, F-DycoBlock yielded higher accuracy of recovery than DycoBlock (100%). If backbone atoms were restrained, the same ratio of accuracy was achieved. Moreover, with the intramolecular hydrogen bonds restrained, reasonable conformational changes were observed for HIV-1 protease during the long-time MCSMD simulation and L700417 was reassembled at the active site. It makes it possible to study the receptor motion in the binding process.     

Genetic algorithm for the design of molecules with desired properties

The design of molecules with desired properties is still a challenge because of the largely unpredictable end results. Computational methods can be used to assist and speed up this process. In particular, genetic algorithms have proved to be powerful tools with a wide range of applications, e.g. in the field of drug development. Here, we propose a new genetic algorithm that has been tailored to meet the demands of de novo drug design, i.e. efficient optimization based on small training sets that are analyzed in only a small number of design cycles. The efficiency of the design algorithm was demonstrated in the context of several different applications. First, RNA molecules were optimized with respect to folding energy. Second, a spinglass was optimized as a model system for the optimization of multiletter alphabet biopolymers such as peptides. Finally, the feasibility of the computer-assisted molecular design approach was demonstrated for the de novo construction of peptidic thrombin inhibitors using an iterative process of 4 design cycles of computer-guided optimization. Synthesis and experimental fitness determination of only 600 different compounds from a virtual library of more than 10¹⁷ molecules was necessary to achieve this goal.These authors contributed equally to the results presentedThese authors contributed equally to the results presentedThese authors contributed equally to the results presentedThese authors contributed equally to the results presented     

Bioactive peptides from the skin of ranid frogs: modern approaches to the mass spectrometric <Emphasis Type="Italic">de novo</Emphasis> sequencing

A mass spectrometric approach to the de novo sequencing of peptides containing up to 50 amino acid residues was proposed. Arising problems of the mass spectrometric sequencing and their solution were considered on specific examples. Published in Russian in Izvestiya Akademii Nauk. Seriya Khimicheskaya, No. 5, pp. 1061–1072, May, 2008.     

A molecular modelling study of the interaction of noradrenaline with theβ 2-adrenergic receptor

Summary A model of the ₂-adrenergic receptor binding site is built from the primary structure of the receptor, experimental evidence for key binding residues and analogy with a homologous protein of partially determined structure. It is suggested that residues Trp-109, Thr-110 and Asp-113 are involved in ligand binding. Noradrenaline is successfully docked into this model, and the results of an INDO molecular orbital calculation on the complex indicate that a charge transfer interaction between Trp-109 and noradrenaline is possible.     

Beyond labels: A review of the application of quantum dots as integrated components of assays, bioprobes, and biosensors utilizing optical transduction

A comprehensive review of the development of assays, bioprobes, and biosensors using quantum dots (QDs) as integrated components is presented. In contrast to a QD that is selectively introduced as a label, an integrated QD is one that is present in a system throughout a bioanalysis, and simultaneously has a role in transduction and as a scaffold for biorecognition. Through a diverse array of coatings and bioconjugation strategies, it is possible to use QDs as a scaffold for biorecognition events. The modulation of QD luminescence provides the opportunity for the transduction of these events via fluorescence resonance energy transfer (FRET), bioluminescence resonance energy transfer (BRET), charge transfer quenching, and electrochemiluminescence (ECL). An overview of the basic concepts and principles underlying the use of QDs with each of these transduction methods is provided, along with many examples of their application in biological sensing. The latter include: the detection of small molecules using enzyme-linked methods, or using aptamers as affinity probes; the detection of proteins via immunoassays or aptamers; nucleic acid hybridization assays; and assays for protease or nuclease activity. Strategies for multiplexed detection are highlighted among these examples. Although the majority of developments to date have been in vitro, QD-based methods for ex vivo biological sensing are emerging. Some special attention is given to the development of solid-phase assays, which offer certain advantages over their solution-phase counterparts.