Application of self-organizing maps in conformational analysis of lipids.

The characteristics of lipid assemblies are important for the functions of biological membranes. This has led to an increasing utilization of molecular dynamics simulations for the elucidation of the structural features of biomembranes. We have applied the self-organizing map (SOM) to the analysis of the complex conformational data from a 1-ns molecular dynamics simulation of PLPC phospholipids in a membrane assembly. Mapping of 1.44 million molecular conformations to a two-dimensional array of neurons revealed, without human intervention, the main conformational features in hours. Both the whole molecule and the characteristics of the unsaturated fatty acid chains were analyzed. All major structural features were easily distinguished, such as the orientational variability of the headgroup, the mainly trans state dihedral angles of the sn-1 chain, and both straight and bent conformations of the unsaturated sn-2 chain. Furthermore, presentation of the trajectory of an individual lipid molecule on the map provides information on conformational dynamics. The present results suggest that the SOM method provides a powerful tool for routinely gaining rapid insight to the main molecular conformations as well as to the conformational dynamics of any simulated molecular assembly without the requirement of a priori knowledge.     

PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritised for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.     

A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases

The discovery of various protein/receptor targets from genomic research is expanding rapidly. Along with the automation of organic synthesis and biochemical screening, this is bringing a major change in the whole field of drug discovery research. In the traditional drug discovery process, the industry tests compounds in the thousands. With automated synthesis, the number of compounds to be tested could be in the millions. This two-dimensional expansion will lead to a major demand for resources, unless the chemical libraries are made wisely. The objective of this work is to provide both quantitative and qualitative characterization of known drugs which will help to generate "drug-like" libraries. In this work we analyzed the Comprehensive Medicinal Chemistry (CMC) database and seven different subsets belonging to different classes of drug molecules. These include some central nervous system active drugs and cardiovascular, cancer, inflammation, and infection disease states. A quantitative characterization based on computed physicochemical property profiles such as log P, molar refractivity, molecular weight, and number of atoms as well as a qualitative characterization based on the occurrence of functional groups and important substructures are developed here. For the CMC database, the qualifying range (covering more than 80% of the compounds) of the calculated log P is between -0.4 and 5.6, with an average value of 2.52. For molecular weight, the qualifying range is between 160 and 480, with an average value of 357. For molar refractivity, the qualifying range is between 40 and 130, with an average value of 97. For the total number of atoms, the qualifying range is between 20 and 70, with an average value of 48. Benzene is by far the most abundant substructure in this drug database, slightly more abundant than all the heterocyclic rings combined. Nonaromatic heterocyclic rings are twice as abundant as the aromatic heterocycles. Tertiary aliphatic amines, alcoholic OH and carboxamides are the most abundant functional groups in the drug database. The effective range of physicochemical properties presented here can be used in the design of drug-like combinatorial libraries as well as in developing a more efficient corporate medicinal chemistry library.     

Automated accurate mass data processing using a gas chromatograph/time-of-flight mass spectrometer in drug discovery

A gas chromatograph/time-of-flight (GCT) mass spectrometer, with high mass measurement accuracy to within 5 ppm, has been used for the automated accurate mass analysis of multicomponent mixtures and drug discovery compounds. A multicomponent mixture was analyzed several times over the course of a week to assess the reproducibility and ruggedness of the automated method while operating the GCT in electron ionization mode. For example, the data for 31 radical cations generated via electron ionization was processed using automated software (i.e. OpenLynx) to provide for mass accuracies less than 5 ppm for nearly 100% of the ions from multiple injection data. Mass accuracies of the radical anions of polyaromatic hydrocarbons generated via negative chemical ionization, and protonated pyridines and quinolines generated via methane chemical ionization, were mainly less than 5 ppm from multiple injection data. In addition, the automated method has been used for the accurate mass analysis of drug discovery compounds.     

In vivo metabolite detection and identification in drug discovery via LC-MS/MS with data-dependent scanning and postacquisition data mining

An important aspect in drug discovery is the early structural identification of the metabolites of potential new drugs. This gives information on the metabolically labile points in the molecules under investigation, suggesting structural modifications to improve their metabolic stability, and allowing an early safety assessment via the identification of metabolic activation products. From an analytical point of view, metabolite identification still remains a challenging task, especially for in vivo samples, in which they occur at trace levels together with high amounts of endogenous compounds. Here we describe a method, based on LC-ion trap tandem MS, for the rapid in vivo metabolite identification. It is based on the automatic, data-dependent acquisition of multiple product ion MS/MS scans, followed by a postacquisition search, within the entire MS/MS data set obtained, for specific neutral losses or marker ions in the tandem mass spectra of parent molecule and putative metabolites. One advantage of the method is speed, since it requires minimum sample preparation and all the necessary data can be obtained in one chromatographic run. In addition, it is highly sensitive and selective, allowing detection of trace metabolites even in the presence of a complex matrix. As an example of application, we present the studies of the in vivo metabolism of the compound MEN 15916 (1). The method allowed identification of monohydroxy ([M + H](+) = m/z 655), dihydroxy ([M + H](+) = m/z 671), and trihydroxy ([M + H](+) = m/z 687) metabolites, as well as some unexpected biotransformation products such as a carboxylic acid ([M + H](+) = m/z 669), a N-dealkylated metabolite ([M + H](+) = m/z 541), and its hydroxy-analog ([M + H](+) = m/z 557).     

Active learning with support vector machines in the drug discovery process

We investigate the following data mining problem from computer-aided drug design: From a large collection of compounds, find those that bind to a target molecule in as few iterations of biochemical testing as possible. In each iteration a comparatively small batch of compounds is screened for binding activity toward this target. We employed the so-called "active learning paradigm" from Machine Learning for selecting the successive batches. Our main selection strategy is based on the maximum margin hyperplane-generated by "Support Vector Machines". This hyperplane separates the current set of active from the inactive compounds and has the largest possible distance from any labeled compound. We perform a thorough comparative study of various other selection strategies on data sets provided by DuPont Pharmaceuticals and show that the strategies based on the maximum margin hyperplane clearly outperform the simpler ones.     

Incorporating dynamics in E. coli dihydrofolate reductase enhances structure-based drug discovery

Escherichia coli dihydrofolate reductase (DHFR) is a long-standing target for enzyme studies. The influence of protein motion on its catalytic cycle is significant, and the conformation of the M20 loop is of particular interest. We present receptor-based pharmacophore models-an equivalent of solvent-mapping of binding hotspots-based on ensembles of protein conformations from molecular dynamics simulations of DHFR.NADPH in both the closed and open conformation of the M20 loop. The optimal models identify DHFR inhibitors over druglike non-inhibitors; furthermore, high-affinity inhibitors of E. coli DHFR are preferentially identified over general DHFR inhibitors. As expected, models resulting from simulations with DHFR in the productive conformation with a closed M20 loop have better performance than those from the open-loop simulations. Model performance improves with increased dynamic sampling, indicating that including a greater degree of protein flexibility can enhance the quest for potent inhibitors. This was compared to the limited conformational sampling seen in crystal structures, which were suboptimal for this application.     

Performance of multicomponent self-organizing regression (MCSOR) in QSAR, QSPR, and multivariate calibration: comparison with partial least-squares (PLS) and validation with large external data sets

A novel method for underdetermined regression problems, multicomponent self-organizing regression (MCSOR), has been recently introduced. Here, its performance is compared with partial least-squares (PLS), which is perhaps the most widely adopted multivariate method in chemometrics. A potpourri of models is presented, and MCSOR appears to provide highly predictive models that are comparable with or better than the corresponding PLS models in large internal (leave-one-out, LOO) and pseudo-external (leave-many-out, LMO) validation tests. The "blind" external predictive ability of MCSOR and PLS is demonstrated employing large melting point, factor Xa, log P and log S data sets. In a nutshell, MCSOR is fast, conceptually simple (employing multiple linear regression, MLR, as a statistical tool), and applicable to all kinds of multivariate problems with single Y-variable.     

Performance of multicomponent self-organizing regression (MCSOR) in QSAR,QSPR, and multivariate calibration: comparison with partial least-squares (PLS) and validation with large external data sets

A novel method for underdetermined regression problems, multicomponent self-organizing regression (MCSOR), has been recently introduced. Here, its performance is compared with partial least-squares (PLS), which is perhaps the most widely adopted multivariate method in chemometrics. A potpourri of models is presented, and MCSOR appears to provide highly predictive models that are comparable with or better than the corresponding PLS models in large internal (leave-one-out, LOO) and pseudo-external (leave-many-out, LMO) validation tests. The “blind” external predictive ability of MCSOR and PLS is demonstrated employing large melting point, factor Xa, log?P and log?S data sets. In a nutshell, MCSOR is fast, conceptually simple (employing multiple linear regression, MLR, as a statistical tool), and applicable to all kinds of multivariate problems with single Y-variable.     

Implications of matrix effects in ultra-fast gradient or fast isocratic liquid chromatography with mass spectrometry in drug discovery.

In the analysis of biological samples it is important to reduce the risk of interferences from the matrix itself, other analytes, the dosing vehicle (commonly PEG), and from the MS/MS transitions used for the analysis. Rapid analysis is essential for drug discovery, and even though the requirements for separation may be minimized for speed, the integrity of the analysis is still dependent on the separation. This paper focuses on the potential for interferences from various endogenous and exogenous matrix components commonly encountered in quantitation of analytes and their metabolites from biological matrices. We demonstrate that neither high organic isocratic nor ballistic gradient ultra-fast HPLC show a clearly defined advantage in regards to complex biological matrices. The critical factor in the resolution of matrix interferences still remains in sample preparation.     

The SHAPES strategy: an NMR-based approach for lead generation in drug discovery.

BACKGROUND: Recently, it has been shown that nuclear magnetic resonance (NMR) may be used to identify ligands that bind to low molecular weight protein drug targets. Recognizing the utility of NMR as a very sensitive method for detecting binding, we have focused on developing alternative approaches that are applicable to larger molecular weight drug targets and do not require isotopic labeling. RESULTS: A new method for lead generation (SHAPES) is described that uses NMR to detect binding of a limited but diverse library of small molecules to a potential drug target. The compound scaffolds are derived from shapes most commonly found in known therapeutic agents. NMR detection of low (microM-mM) affinity binding is achieved using either differential line broadening or transferred NOE (nuclear Overhauser effect) NMR techniques. CONCLUSIONS: The SHAPES method for lead generation by NMR is useful for identifying potential lead classes of drugs early in a drug design program, and is easily integrated with other discovery tools such as virtual screening, high-throughput screening and combinatorial chemistry.     

Incorporating protein flexibility in structure-based drug discovery: using HIV-1 protease as a test case

We have developed a receptor-based pharmacophore method which utilizes a collection of protein structures to account for inherent protein flexibility in structure-based drug design. Several procedures were systematically evaluated to derive the most general protocol for using multiple protein structures. Most notably, incorporating more protein flexibility improved the performance of the method. The pharmacophore models successfully discriminate known inhibitors from drug-like non-inhibitors. Furthermore, the models correctly identify the bound conformations of some ligands. We used unliganded HIV-1 protease to develop and validate this method. Drug design is always initiated with a protein-ligand structure, and such success with unbound protein structures is remarkable - particularly in the case of HIV-1 protease, which has a large conformational change upon binding. This technique holds the promise of successful computer-based drug design before bound crystal structures are even discovered, which can mean a jump-start of 1-3 years in tackling some medically relevant systems with computational methods.