BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level

Enumeration Method for Structural Isomers Containing User-Defined Structures Based on Breadth-First Search Approach

Enumeration of chemical structures satisfying given conditions is an important step in the discovery of new compounds and drugs, as well as the elucidation of the structure. One of the most frequently used conditions in the enumeration is the number of chemical elements that corresponds to the chemical formula. In this work, we propose a novel efficient enumeration algorithm, BfsStructEnum, which allows users to define desired cyclic structures and enumerates all nonredundant chemical compounds containing only defined structures as cyclic structures from a given chemical formula. To evaluate the performance, we confirm the number of enumerated structures of BfsStructEnum and MOLGEN 5.0, the latest version of a general-purpose structure generator. We also compare the computation time of BfsStructEnum with that of MOLGEN 5.0. The findings show that, given the same number of enumerated structures as MOLGEN 5.0, BfsStructEnum is significantly faster. By compressing a cyclic structure into a single node and representing chemical compounds by tree structures instead of normal graphs, the enumeration can be executed more efficiently.

CANDO and the infinite drug discovery frontier

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 'high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

Computational evaluation of factors governing catalytic 2-keto acid decarboxylation

Recent advances in computational approaches for creating pathways for novel biochemical reactions has motivated the development of approaches for identifying enzyme-substrate pairs that are attractive candidates for effecting catalysis. We present an improved structural-based strategy to probe and study enzyme-substrate binding based on binding geometry, energy, and molecule characteristics, which allows for in silico screening of structural features that imbue higher catalytic potential with specific substrates. The strategy is demonstrated using 2-keto acid decarboxylation with various pairs of 2-keto acids and enzymes. We show that this approach fitted experimental values for a wide range of 2-keto acid decarboxylases for different 2-keto acid substrates. In addition, we show that the structure-based methods can be used to select specific enzymes that may be promising candidates to catalyze decarboxylation of certain 2-keto acids. The key features and principles of the candidate enzymes evaluated by the strategy can be used to design novel biosynthesis pathways, to guide enzymatic mutation or to guide biomimetic catalyst design.

Cumulative risk assessment toolbox: methods and approaches for the practitioner

The historical approach to assessing health risks of environmental chemicals has been to evaluate them one at a time. In fact, we are exposed every day to a wide variety of chemicals and are increasingly aware of potential health implications. Although considerable progress has been made in the science underlying risk assessments for real-world exposures, implementation has lagged because many practitioners are unaware of methods and tools available to support these analyses. To address this issue, the US Environmental Protection Agency developed a toolbox of cumulative risk resources for contaminated sites, as part of a resource document that was published in 2007. This paper highlights information for nearly 80 resources from the toolbox and provides selected updates, with practical notes for cumulative risk applications. Resources are organized according to the main elements of the assessment process: (1) planning, scoping, and problem formulation; (2) environmental fate and transport; (3) exposure analysis extending to human factors; (4) toxicity analysis; and (5) risk and uncertainty characterization, including presentation of results. In addition to providing online access, plans for the toolbox include addressing nonchemical stressors and applications beyond contaminated sites and further strengthening resource accessibility to support evolving analyses for cumulative risk and sustainable communities.

A reaction network model for CYP2E1-mediated metabolism of toxicant mixtures

In this paper, we describe a modeling approach to predict the interlinked pathways and kinetics resulting from CYP2E1-mediated metabolism of both pure species and chemical mixtures. This approach is based on the concept of chemical reaction networks, an idea that has formed the basis for simulation tools that have shown good predictive capabilities in the petroleum industry, but also an idea that has heretofore seen minimal application in the biomedical research arena. Although the initial target for developing this reaction network approach was cytochrome P450 2E1 (CYP2E1) and its over 200 substrates, this technology has been used for other families of CYP enzymes and their substrates in our laboratory. Utilizing this approach, we have produced a modular 'predictive metabolomics' simulation framework comprising interdependent software components that perform such tasks as testing of substrate binding feasibility, performing virtual chemistry, formulating reaction-rate equations, computing reaction kinetics and predicting time-dependent species concentrations. As an illustrative example, we outline the application of this framework to the prediction of the reaction networks resulting from the Phase I metabolism of two compounds of important toxicological interest. The potential of this modeling technology is immense in providing a computer simulation platform for complex-chemical mixtures and complex-biological systems. It is possible that this technology will play an important role in formulating a 'Virtual Human'.

Developmental and pediatric pharmacogenomics

Children, as well as adults, should benefit from the discoveries of the genomic era. Many diseases with complex etiologies originate during childhood (e.g., asthma, autism, attention deficit/hyperactivity disorder, epilepsy and juvenile rheumatoid arthritis) and persist into adulthood. Attempts to better understand the genetic basis of age-specific disease processes requires an appreciation that the period of human development encompasses the prenatal period through adolescence, and is a rapidly changing, dynamic process. As a result, pharmacologic modulation of developing gene networks may have unintended and unanticipated consequences that do not become apparent or relevant until later in life. Thus, there is considerable potential for large-scale pharmacogenomic technologies to impact the development and utilization of new therapeutic strategies in children.

Application of biologically based computer modeling to simple or complex mixtures

The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures. This approach is promising for relatively simple chemical mixtures; the most complicated chemical mixtures studied so far using this approach contained five or fewer component chemicals. In this presentation we provide some examples of the utility of PBPK/PD modeling for toxicologic interactions in chemical mixtures. The probability of developing predictive tools for simple mixtures using PBPK/PD modeling is high. Unfortunately, relatively few attempts have been made to develop paradigms to consider the risks posed by very complex chemical mixtures such as gasoline, diesel, tobacco smoke, etc. However, recent collaboration between scientists at Colorado State University and engineers at Rutgers University attempting to use reaction network modeling has created hope for the possible development of a modeling approach with the potential of predicting the outcome of toxicology of complex chemical mixtures. We discuss the applications of reaction network modeling in the context of petroleum refining and its potential for elucidating toxic interactions with mixtures.