Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics

15 Years of molecular simulation of drug-binding kinetics

INTRODUCTION

Drug-binding kinetics has been increasingly recognized as an important factor to be considered in drug discovery. Long residence time could prolong the action of some drugs while produce toxicity on others. Early evaluation of the binding kinetics of drug candidates could reduce attrition rate late in the drug discovery process. Computational prediction of drug-binding kinetics is useful as compounds can be evaluated even before they are made. However, simulation of drug-binding kinetics is a challenging problem because of the long-time scale involved. Nevertheless, significant progress has been made.

AREAS COVERED

This review illustrates the rapid evolution of qualitative to quantitative molecular dynamics-based methods that have been developed over the last 15 years.

EXPERT OPINION

The development of new methods based on molecular dynamics simulations now enables computation of absolute association/dissociation rate constants. Cheaper methods capable of identifying candidates with fast or slow binding kinetics, or rank-ordering rate constants are also available. Together, these methods have generated useful insights into the molecular mechanisms of drug-binding kinetics, and the design of drug candidates with therapeutically favorable kinetics. Although predicting absolute rate constants is still expensive and challenging, rapid improvement is expected in the coming years with the continuing refinement of current technologies, development of new methodologies, and the utilization of machine learning.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (k_off) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the k_off value. We observed a high correlation among the simulated, predicted, and experimental -log(k_off) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our k_off predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental k_off values and are not in our ML training dataset. The predicted k_off values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict k_off of other proteins and will enhance the kinetics-based drug design endeavor.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five

In 1997, the 'Rule of Five' (Ro5) suggested physicochemical limitations for orally administered drugs, based on the analysis of chemical libraries from the early 1990s. In this review, we report on the trends in oral drug product development by analyzing products launched between 1994 and 1997 and between 2013 and 2019. Our analysis confirmed that most new oral drugs are within the Ro5 descriptors; however, the number of new drug products of drugs with molecular weight (MW) and calculated partition coefficient (clogP) beyond the Ro5 has slightly increased. Analysis revealed that there is no single scientific or technological reason for this trend, but that it likely results from incremental advances are being made in molecular biology, target diversity, drug design, medicinal chemistry, predictive modeling, drug metabolism and pharmacokinetics, and drug delivery.

Structural study of the uPA-nafamostat complex reveals a covalent inhibitory mechanism of nafamostat

Nafamostat mesylate (NM) is a synthetic compound that inhibits various serine proteases produced during the coagulation cascade and inflammation. Previous studies showed that NM was a highly safe drug for the treatment of different cancers, but the precise functions and mechanisms of NM are not clear. In this study, we determined a series of crystal structures of NM and its hydrolysates in complex with a serine protease (urokinase-type plasminogen activator [uPA]). These structures reveal that NM was cleaved by uPA and that a hydrolyzed product (4-guanidinobenzoic acid [GBA]) remained covalently linked to Ser195 of uPA, and the other hydrolyzed product (6-amidino-2-naphthol [6A2N]) released from uPA. Strikingly, in the inactive uPA (uPA-S195A):NM structure, the 6A2N side of intact NM binds to the specific pocket of uPA. Molecular dynamics simulations and end-point binding free-energy calculations show that the conf1 of NM (6A2N as P1 group) in the uPA-S195A:NM complex may be more stable than conf2 of NM (GBA as P1 group). Moreover, in the structure of uPA:NM complex, the imidazole group of His57 flips further away from Ser195 and disrupts the stable canonical catalytic triad conformation. These results not only reveal the inhibitory mechanism of NM as an efficient serine protease inhibitor but also might provide the structural basis for the further development of serine protease inhibitors.

Easy and Rapid Approach to Obtaining the Binding Affinity of Biomolecular Interactions Based on the Deep Learning Boost

Recently, the deep learning (DL) dimension of artificial intelligence has received much attention from biochemical researchers and thus has gradually become the key approach adopted in the area of biosensing applications. Studies have shown that the use of DL techniques for sensing can not only shorten the time of data analysis but also significantly increase the accuracy of data analysis and prediction, resulting in the performance improvement of biosensing systems in comparison to conventional methods. However, obtaining reliable equilibrium and rate constants of biomolecular interactions during the detection process remains difficult and time-consuming to date. In this study, we propose a transformed model based on the deep transfer learning and sequence-to-sequence autoencoder that can successfully transfer the SPR sensorgram to the protein-binding constants, that is, the association rate constant (k_a) and dissociation rate constant (k_d), which provide crucial information to understand the mechanisms of drug action and the functional structures of biomolecules. Experimentally, we first trained and tested the pre-trained model using the Langmuir model which generated ideal SPR sensorgrams and then we fine-tuned the pre-trained model through the augmented SPR sensorgrams which were synthesized by using the synthesized minority oversampling technique (SMOTE) through the moderate-scale experiment. Next, the fine-tuned model was inputted with a short experimental SPR sensorgram that only needs 110 s, and the sensorgram was directly transformed into a reconstructed ideal sensorgram. Finally, the binding kinetic constants, that is, k_a and k_d, as outputs, were obtained through fitting the reconstructed ideal sensorgram. The results showed that the prediction errors of k_a and k_d obtained by our model were less than 12 and 24%, respectively. Based on the convenience, accuracy, and reliability of the proposed DL approach, we believe our strategy significantly boosts the feasibility to monitor the binding affinity of antibodies online during production.

In Vitro and In Silico Analysis of the Residence Time of Serotonin 5-HT7 Receptor Ligands with Arylpiperazine Structure: A Structure-Kinetics Relationship Study

During the last decade, the kinetics of drug-target interaction has received increasing attention as an important pharmacological parameter in the drug development process. Several studies have suggested that the lipophilicity of a molecule can play an important role. To date, this aspect has been studied for several G protein-coupled receptors (GPCRs) ligands but not for the 5-HT₇ receptor (5-HT₇R), a GPCR proposed as a valid therapeutic target in neurodevelopmental and neuropsychiatric disorders associated with abnormal neuronal connectivity. In this study, we report on structure-kinetics relationships of a set of arylpiperazine-based 5-HT₇R ligands. We found that it is not the overall lipophilicity of the molecule that influences drug-target interaction kinetics but rather the position of polar groups within the molecule. Next, we performed a combination of molecular docking studies and molecular dynamics simulations to gain insights into structure-kinetics relationships. These studies did not suggest specific contact patterns between the ligands and the receptor-binding site as determinants for compounds kinetics. Finally, we compared the abilities of two 5-HT₇R agonists with similar receptor-binding affinities and different residence times to stimulate the 5-HT₇R-mediated neurite outgrowth in mouse neuronal primary cultures and found that the compounds induced the effect with different timing. This study provides the first insights into the binding kinetics of arylpiperazine-based 5-HT₇R ligands that can be helpful to design new 5-HT₇R ligands with fine-tuning of the kinetic profile.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the k_off values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

KOFFI and Anabel 2.0-a new binding kinetics database and its integration in an open-source binding analysis software

The kinetics of featured interactions (KOFFI) database is a novel tool and resource for binding kinetics data from biomolecular interactions. While binding kinetics data are abundant in literature, finding valuable information is a laborious task. We used text extraction methods to store binding rates (association, dissociation) as well as corresponding meta-information (e.g. methods, devices) in a novel database. To date, over 270 articles were manually curated and binding data on over 1705 interactions was collected and stored in the (KOFFI) database. Moreover, the KOFFI database application programming interface was implemented in Anabel (open-source software for the analysis of binding interactions), enabling users to directly compare their own binding data analyses with related experiments described in the database.

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Protein-ligand interaction fingerprints for accurate prediction of dissociation rates of p38 MAPK Type II inhibitors

Binding/unbinding kinetics are key determinants of drug potencies. However, there are still a lot of challenges in predicting kinetic properties during early-stage drug development. In this work, position-restrained molecular dynamics simulations combined with energy decomposition were applied to extract protein-ligand interaction (PLI) fingerprints along the unbinding pathway of 20 p38 mitogen-activated protein kinase (p38 MAPK) Type II inhibitors. The results showed that the electrostatic and/or van der Waals interaction fingerprints at three key positions can be used for accurate prediction of the dissociation rate constants (koff) of p38 MAPK Type II inhibitors. The strategy proposed in this paper can provide not only an efficient method of predicting the dissociation rates of the p38 MAPK Type II inhibitors, but also the atom-level mechanism of enthalpy-driven unbinding process.

Computational Study of Protein-Ligand Unbinding for Enzyme Engineering

The computational prediction of unbinding rate constants is presently an emerging topic in drug design. However, the importance of predicting kinetic rates is not restricted to pharmaceutical applications. Many biotechnologically relevant enzymes have their efficiency limited by the binding of the substrates or the release of products. While aiming at improving the ability of our model enzyme haloalkane dehalogenase DhaA to degrade the persistent anthropogenic pollutant 1,2,3-trichloropropane (TCP), the DhaA31 mutant was discovered. This variant had a 32-fold improvement of the catalytic rate toward TCP, but the catalysis became rate-limited by the release of the 2,3-dichloropropan-1-ol (DCP) product from its buried active site. Here we present a computational study to estimate the unbinding rates of the products from DhaA and DhaA31. The metadynamics and adaptive sampling methods were used to predict the relative order of kinetic rates in the different systems, while the absolute values depended significantly on the conditions used (method, force field, and water model). Free energy calculations provided the energetic landscape of the unbinding process. A detailed analysis of the structural and energetic bottlenecks allowed the identification of the residues playing a key role during the release of DCP from DhaA31 via the main access tunnel. Some of these hot-spots could also be identified by the fast CaverDock tool for predicting the transport of ligands through tunnels. Targeting those hot-spots by mutagenesis should improve the unbinding rates of the DCP product and the overall catalytic efficiency with TCP.

Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach

Efficient prediction and ranking of small molecule binders by their kinetic ( kon and koff) and thermodynamic ( Δ G) properties can be a valuable metric for drug lead optimization, as these quantities are often indicators of in vivo efficacy. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon's, koff's, and Δ G's. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, β-cyclodextrin, based on predicted kon's and koff's. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long time scale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and Δ G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish "best practices" for its future use.

Role of Molecular Interactions and Protein Rearrangement in the Dissociation Kinetics of p38α MAP Kinase Type-I/II/III Inhibitors

Understanding the governing factors of fast or slow inhibitor binding/unbinding assists in developing drugs with preferred kinetic properties. For inhibitors with the same binding affinity targeting different binding sites of the same protein, the kinetic behavior can profoundly differ. In this study, we investigated unbinding kinetics and mechanisms of fast (type-I) and slow (type-II/III) binders of p38α mitogen-activated protein kinase, where the crystal structures showed that type-I and type-II/III inhibitors bind to pockets with different conformations of the Asp-Phe-Gly (DFG) motif. The work used methods that combine conventional molecular dynamics (MD), accelerated molecular dynamics (AMD) simulations, and the newly developed pathway search guided by internal motions (PSIM) method to find dissociation pathways. The study focuses on revealing key interactions and molecular rearrangements that hinder ligand dissociation by using umbrella sampling and post-MD processing to examine changes in free energy during ligand unbinding. As anticipated, the initial dissociation steps all require breaking interactions that appeared in crystal structures of the bound complexes. Interestingly, for type-I inhibitors such as SB2, p38α keeps barrier-free conformational fluctuation in the ligand-bound complex and during ligand dissociation. In contrast, with a type-II/III inhibitor such as BIRB796, with the rearrangements of p38α in its bound state, ligand unbinding features energetically unfavorable protein-ligand concerted movement. Our results also show that the type-II/III inhibitors preferred dissociation pathways through the allosteric channel, which is consistent with an existing publication. The study suggests that the level of required protein rearrangement is one major determining factor of drug binding kinetics in p38α systems, providing useful information for development of inhibitors.

New approaches for computing ligand-receptor binding kinetics

The recent and growing evidence that the efficacy of a drug can be correlated to target binding kinetics has seeded the development of a multitude of novel methods aimed at computing rate constants for receptor-ligand binding processes, as well as gaining an understanding of the binding and unbinding pathways and the determinants of structure-kinetic relationships. These new approaches include various types of enhanced sampling molecular dynamics simulations and the combination of energy-based models with chemometric analysis. We assess these approaches in the light of the varying levels of complexity of protein-ligand binding processes.

Beta-catenin represses protein kinase D1 gene expression by non-canonical pathway through MYC/MAX transcription complex in prostate cancer

Down regulation of Protein Kinase D1 (PrKD1), a novel serine threonine kinase, in prostate, gastric, breast and colon cancers in humans leads to disease progression. While the down regulation of PrKD1 by DNA methylation in gastric cancer and by nuclear beta-catenin in colon cancer has been shown, the regulatory mechanisms in other cancers are unknown. Because we had demonstrated that PrKD1 is the only known kinase to phosphorylate threonine 120 (T120) of beta-catenin in prostate cancer resulting in increased nuclear beta-catenin, we explored the role of beta-catenin in gene regulation of PrKD1. An initial CHIP assay identified potential binding sites for beta-catenin in and downstream of PrKD1 promoter and sequencing confirmed recruitment of beta-catenin to a 166 base pairs sequence upstream of exon 2. Co-transfection studies with PrKD1-promoter-reporter suggested that beta-catenin represses PrKD1 promoter. Efforts to identify transcription factors that mediate the co-repressor effects of beta-catenin identified recruitment of both MYC and its obligate heterodimer MAX to the same binding site as beta-catenin on the PrKD1 promoter site. Moreover, treatment with MYC inhibitor rescued the co-repressor effect of beta-catenin on PrKD1 gene expression. Prostate specific knock out of PrKD1 in transgenic mice demonstrated increased nuclear expression of beta-catenin validating the in vitro studies. Functional studies showed that nuclear translocation of beta-catenin as a consequence of PrKD1 down regulation, increases AR transcriptional activity with attendant downstream effects on androgen responsive genes. In silico human gene expression analysis confirmed the down regulation of PrKD1 in metastatic prostate cancer correlated inversely with the expression of MAX, but not MYC, and positively with MXD1, a competing heterodimer of MAX, suggesting that the dimerization of MAX with either MYC or MXD1 regulates PrKD1 gene expression. The study has identified a novel auto-repressive loop that perpetuates PrKD1 down regulation through beta-catenin/MYC/MAX protein complex.

Harnessing Big Data for Systems Pharmacology

Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

Constructing Interconsistent, Reasonable, and Predictive Models for Both the Kinetic and Thermodynamic Properties of HIV-1 Protease Inhibitors

Accumulated evidence suggests that the in vivo biological potency of a ligand is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of the protein-ligand interaction (PLI). However, the existing experimental and computational techniques are largely insufficient and limited in large-scale measurements or accurate predictions of the kinetic properties of PLI. In this work, elaborate efforts have been made to develop interconsistent, reasonable, and predictive models of the association rate constant (k_on), dissociation rate constant (k_off), and equilibrium dissociation constant (K_D) of a series of HIV protease inhibitors with different structural skeletons. The results showed that nine Volsurf descriptors derived from water (OH2) and hydrophobic (DRY) probes are key molecular determinants for the kinetic and thermodynamic properties of HIV-1 protease inhibitors. To the best of our knowledge, this is the first time that interconsistent and reasonable models with strong prediction power have been established for both the kinetic and thermodynamic properties of HIV protease inhibitors.

Providing data science support for systems pharmacology and its implications to drug discovery

INTRODUCTION

The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology.

AREAS COVERED

This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction.

EXPERT OPINION

Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.