Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine

Evidence that the cold- and menthol-sensing functions of the human TRPM8 channel evolved separately

Transient receptor potential melastatin 8 (TRPM8) is a temperature- and menthol-sensitive ion channel that contributes to diverse physiological roles, including cold sensing and pain perception. Clinical trials targeting TRPM8 have faced repeated setbacks predominantly due to the knowledge gap in unraveling the molecular underpinnings governing polymodal activation. A better understanding of the molecular foundations between the TRPM8 activation modes may aid the development of mode-specific, thermal-neutral therapies. Ancestral sequence reconstruction was used to explore the origins of TRPM8 activation modes. By resurrecting key TRPM8 nodes along the human evolutionary trajectory, we gained valuable insights into the trafficking, stability, and function of these ancestral forms. Notably, this approach unveiled the differential emergence of cold and menthol sensitivity over evolutionary time, providing a fresh perspective on complex polymodal behavior. These studies provide a paradigm for understanding polymodal behavior in TRPM8 and other proteins with the potential to enhance our understanding of sensory receptor biology and pave the way for innovative therapeutic interventions.

Protein dynamics provide mechanistic insights about epistasis among common missense polymorphisms

Sequencing of the protein coding genome has revealed many different missense mutations of human proteins and different population frequencies of corresponding haplotypes, which consist of different sets of those mutations. Here, we present evidence for pairwise intramolecular epistasis (i.e., nonadditive interactions) between many such mutations through an analysis of protein dynamics. We suggest that functional compensation for conserving protein dynamics is a likely evolutionary mechanism that maintains high-frequency mutations that are individually nonneutral but epistatically compensating within proteins. This analysis is the first of its type to look at human proteins with specific high population frequency mutations and examine the relationship between mutations that make up that observed high-frequency protein haplotype. Importantly, protein dynamics revealed a separation between high and low frequency haplotypes within a target protein cytochrome P450 2A7, with the high-frequency haplotypes showing behavior closer to the wild-type protein. Common protein haplotypes containing two mutations display dynamic compensation in which one mutation can correct for the dynamic effects of the other. We also utilize a dynamics-based metric, EpiScore, that evaluates the epistatic interactions and allows us to see dynamic compensation within many other proteins.

Building Quantitative Bridges between Dynamics and Sequences of SARS-CoV-2 Main Protease and a Diverse Set of Thirty-Two Proteins

Proteases are major drug targets for many viral diseases. However, mutations can render several antiprotease drugs inefficient rapidly even though these mutations may not alter protein structures significantly. Understanding relations between quickly mutating residues, protease structures, and the dynamics of the proteases is crucial for designing potent drugs. Due to this reason, we studied relations between the evolutionary information on residues in the amino acid sequences and protein dynamics for SARS-CoV-2 main protease. More precisely, we analyzed three dynamical quantities (Schlitter entropy, root-mean-square fluctuations, and dynamical flexibility index) and their relation to the amino acid conservation extracted from multiple sequence alignments of the main protease. We showed that a quantifiable similarity can be built between a sequence-based quantity called Jensen-Shannon conservation and those three dynamical quantities. We validated this similarity for a diverse set of 32 different proteins, other than the SARS-CoV-2 main protease. We believe that establishing these kinds of quantitative bridges will have larger implications for all viral proteases as well as all proteins.

Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of β-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.

Dynamic Allostery Highlights the Evolutionary Differences between the CoV-1 and CoV-2 Main Proteases

The SARS-CoV-2 coronavirus has become one of the most immediate and widely-studied systems since its identification and subsequent global outbreak from 2019-2021. In an effort to understand the biophysical changes as a result of mutations, the mechanistic details of multiple different proteins within the SARS-CoV-2 virus have been studied and compared with SARS-CoV-1. Focusing on the main protease (mPro), we first explored the long-range dynamics using the Dynamic Coupling Index (DCI) to investigate the dynamic coupling between the catalytic site residues and the rest of the protein, both inter and intra chain, for the CoV-1 and CoV-2 mPro. We found that there is significant cross-chain coupling between these active sites and specific distal residues in the CoV-2 mPro not present in CoV-1. The enhanced long distance interactions, particularly between the two chains, suggest subsequently enhanced cooperativity for CoV-2. A further comparative analysis of the dynamic flexibility using the Dynamic Flexibility Index (DFI) between the CoV-1 and CoV-2 mPros shows that the inhibitor binding near active sites induces change in flexibility to a distal region of the protein, opposite in behavior between the two systems; this region becomes more flexible upon inhibitor binding in CoV-1 while it becomes less flexible in the CoV-2 mPro. Upon inspection, we show that, on average, the dynamic flexibility of the sites substituted from CoV-1 to CoV-2 changes significantly less than the average calculated across all residues within the structure, indicating that the differences in behaviors between the two systems is likely the result of allosteric influence, where the new substitutions in CoV-2 induce flexibility and dynamical changes elsewhere in the structure.

Design of novel cyanovirin-N variants by modulation of binding dynamics through distal mutations

We develop integrated co-evolution and dynamic coupling (ICDC) approach to identify, mutate, and assess distal sites to modulate function. We validate the approach first by analyzing the existing mutational fitness data of TEM-1 β-lactamase and show that allosteric positions co-evolved and dynamically coupled with the active site significantly modulate function. We further apply ICDC approach to identify positions and their mutations that can modulate binding affinity in a lectin, cyanovirin-N (CV-N), that selectively binds to dimannose, and predict binding energies of its variants through Adaptive BP-Dock. Computational and experimental analyses reveal that binding enhancing mutants identified by ICDC impact the dynamics of the binding pocket, and show that rigidification of the binding residues compensates for the entropic cost of binding. This work suggests a mechanism by which distal mutations modulate function through dynamic allostery and provides a blueprint to identify candidates for mutagenesis in order to optimize protein function.

The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.

Network Pharmacology-Based Study on the Mechanism of Scutellariae Radix for Hepatocellular Carcinoma Treatment

Hepatocellular carcinoma (HCC) is a malignant tumor without effective therapeutic drugs for most patients in advanced stages. Scutellariae Radix (SR) is a well-known anti-inflammatory and anticarcinogenic herbal medicine. However, the mechanism of SR against HCC remains to be clarified. In the present study, network pharmacology was utilized to characterize the mechanism of SR on HCC. The active components of SR and their targets were collected from the traditional Chinese medicine systems pharmacology database and the traditional Chinese medicine integrated database. HCC-related targets were acquired from the liver cancer databases OncoDB.HCC and Liverome. The gene ontology and the Kyoto Encyclopedia of Genes and Genomes pathway were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. Component-component target and protein-protein interaction networks were set up. A total of 143 components of SR were identified, and 37 of them were considered as candidate active components. Fifty targets corresponding to 29 components of SR were mapped with targets of HCC. Functional enrichment analysis indicated that SR exerted an antihepatocarcinoma effect by regulating pathways in cancer, hepatitis B, viral carcinogenesis, and PI3K-Akt signaling. The holistic approach of network pharmacology can provide novel insights into the mechanistic study and therapeutic drug development of SR for HCC treatment.

The Role of Conformational Dynamics and Allostery in Modulating Protein Evolution

Advances in sequencing techniques and statistical methods have made it possible not only to predict sequences of ancestral proteins but also to identify thousands of mutations in the human exome, some of which are disease associated. These developments have motivated numerous theories and raised many questions regarding the fundamental principles behind protein evolution, which have been traditionally investigated horizontally using the tip of the phylogenetic tree through comparative studies of extant proteins within a family. In this article, we review a vertical comparison of the modern and resurrected ancestral proteins. We focus mainly on the dynamical properties responsible for a protein's ability to adapt new functions in response to environmental changes. Using the Dynamic Flexibility Index and the Dynamic Coupling Index to quantify the relative flexibility and dynamic coupling at a site-specific, single-amino-acid level, we provide evidence that the migration of hinges, which are often functionally critical rigid sites, is a mechanism through which proteins can rapidly evolve. Additionally, we show that disease-associated mutations in proteins often result in flexibility changes even at positions distal from mutational sites, particularly in the modulation of active site dynamics.

Integrating Network Pharmacology and Pharmacological Evaluation for Deciphering the Action Mechanism of Herbal Formula Zuojin Pill in Suppressing Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a kind of complicated disease with an increasing incidence all over the world. A classic Chinese medicine formula, Zuojin pill (ZJP), was shown to exert therapeutic effects on HCC. However, its chemical and pharmacological profiles remain to be elucidated. In the current study, network pharmacology approach was applied to characterize the action mechanism of ZJP on HCC. All compounds were obtained from the corresponding databases, and active compounds were selected according to their oral bioavailability and drug-likeness index. The potential proteins of ZJP were obtained from the traditional Chinese medicine systems pharmacology (TCMSP) database and the traditional Chinese medicine integrated database (TCMID), whereas the potential genes of HCC were obtained from OncoDB.HCC and Liverome databases. The potential pathways related to genes were determined by gene ontology (GO) and pathway enrichment analyses. The compound-target and target-pathway networks were constructed. Subsequently, the potential underlying action mechanisms of ZJP on HCC predicted by the network pharmacology analyses were experimentally validated in HCC cellular and orthotopic HCC implantation murine models. A total of 224 components in ZJP were obtained, among which, 42 were chosen as bioactive components. The compound-target network included 32 compounds and 86 targets, whereas the target-pathway network included 70 proteins and 75 pathways. The in vitro and in vivo experiments validated that ZJP exhibited its prominent therapeutic effects on HCC mainly via the regulation of cell proliferation and survival though the EGFR/MAPK, PI3K/NF-κB, and CCND1 signaling pathways. In conclusion, our study suggested combination of network pharmacology prediction with experimental validation may offer a useful tool to characterize the molecular mechanism of traditional Chinese medicine (TCM) ZJP on HCC.

A Systems Pharmacology-Based Study of the Molecular Mechanisms of San Cao Decoction for Treating Hypertension

Traditional Chinese medicine (TCM) has a longstanding history and has gained widespread clinical applications. San Cao Decoction (SCD) is an experience prescription first formulated by Prof. Duzhou Liu. We previously demonstrated its antihypertensive effects; however, to systematically explain the underlying mechanisms of action, we employed a systems pharmacology approach for pharmacokinetic screening and target prediction by constructing protein-protein interaction networks of hypertension-related and putative SCD-related targets, and Database for Annotation, Visualization, and Integrated Discovery enrichment analysis. We identified 123 active compounds in SCD and 116 hypertension-related targets. Furthermore, the enrichment analysis of the drug-target network showed that SCD acts in a multidimensional manner to regulate PI3K-Akt-endothelial nitric oxide synthase signaling to maintain blood pressure. Our results highlighted the molecular mechanisms of antihypertensive actions of medicinal herbs at a systematic level.

Protein ensembles link genotype to phenotype

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

Mutations Utilize Dynamic Allostery to Confer Resistance in TEM-1 β-lactamase

β-lactamases are enzymes produced by bacteria to hydrolyze β-lactam antibiotics as a common mechanism of resistance. Evolution in such enzymes has been rendering a wide variety of antibiotics impotent, therefore posing a major threat. Clinical and in vitro studies of evolution in TEM-1 β-lactamase have revealed a large number of single point mutations that are responsible for driving resistance to antibiotics and/or inhibitors. The distal locations of these mutations from the active sites suggest that these allosterically modulate the antibiotic resistance. We investigated the effects of resistance driver mutations on the conformational dynamics of the enzyme to provide insights about the mechanism of their long-distance interactions. Through all-atom molecular dynamics (MD) simulations, we obtained the dynamic flexibility profiles of the variants and compared those with that of the wild type TEM-1. While the mutational sites in the variants did not have any direct van der Waals interactions with the active site position S70 and E166, we observed a change in the flexibility of these sites, which play a very critical role in hydrolysis. Such long distance dynamic interactions were further confirmed by dynamic coupling index (DCI) analysis as the sites involved in resistance driving mutations exhibited high dynamic coupling with the active sites. A more exhaustive dynamic analysis, using a selection pressure for ampicillin and cefotaxime resistance on all possible types of substitutions in the amino acid sequence of TEM-1, further demonstrated the observed mechanism. Mutational positions that play a crucial role for the emergence of resistance to new antibiotics exhibited high dynamic coupling with the active site irrespective of their locations. These dynamically coupled positions were neither particularly rigid nor particularly flexible, making them more evolvable positions. Nature utilizes these sites to modulate the dynamics of the catalytic sites instead of mutating the highly rigid positions around the catalytic site.

Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs

The conformational dynamics of proteins is rarely used in methodologies used to predict the impact of genetic mutations due to the paucity of three-dimensional protein structures as compared to the vast number of available sequences. Until now a three-dimensional (3D) structure has been required to predict the conformational dynamics of a protein. We introduce an approach that estimates the conformational dynamics of a protein, without relying on structural information. This de novo approach utilizes coevolving residues identified from a multiple sequence alignment (MSA) using Potts models. These coevolving residues are used as contacts in a Gaussian network model (GNM) to obtain protein dynamics. B-factors calculated using sequence-based GNM (Seq-GNM) are in agreement with crystallographic B-factors as well as theoretical B-factors from the original GNM that utilizes the 3D structure. Moreover, we demonstrate the ability of the calculated B-factors from the Seq-GNM approach to discriminate genomic variants according to their phenotypes for a wide range of proteins. These results suggest that protein dynamics can be approximated based on sequence information alone, making it possible to assess the phenotypes of nSNVs in cases where a 3D structure is unknown. We hope this work will promote the use of dynamics information in genetic disease prediction at scale by circumventing the need for 3D structures.

Proteins are dynamic machines that undergo atomic fluctuations, side chain rotations, and collective domain movements that are required for biological function. There is, therefore, a need for quantitative metrics that capture the dynamic fluctuations per position to understand the critical role of protein dynamics in shaping biological functions. A limiting factor in incorporating structural dynamics information in the classification of non-synonymous single nucleotide variants (nSNVs) is the limited number of known 3D structures compared to the vast number of available sequences. We have developed a new sequence-based GNM method, termed Seq-GNM, which uses co-evolving amino acid positions based on the multiple sequence alignment of a given query sequence to estimate the thermal motions of C-alpha atoms. In this paper, we have demonstrated that the predicted thermal motions using Seq-GNM are in reasonable agreement with experimental B-factors as well as B-factors computed using 3D crystal structures. We also provide evidence that B-factors predicted by Seq-GNM are capable of distinguishing between disease-associated and neutral nSNVs.

Ancient thioredoxins evolved to modern-day stability-function requirement by altering native state ensemble

Thioredoxins (THRXs)-small globular proteins that reduce other proteins-are ubiquitous in all forms of life, from Archaea to mammals. Although ancestral thioredoxins share sequential and structural similarity with the modern-day (extant) homologues, they exhibit significantly different functional activity and stability. We investigate this puzzle by comparative studies of their (ancient and modern-day THRXs') native state ensemble, as quantified by the dynamic flexibility index (DFI), a metric for the relative resilience of an amino acid to perturbations in the rest of the protein. Clustering proteins using DFI profiles strongly resemble an alternative classification scheme based on their activity and stability. The DFI profiles of the extant proteins are substantially different around the α3, α4 helices and catalytic regions. Likewise, allosteric coupling of the active site with the rest of the protein is different between ancient and extant THRXs, possibly explaining the decreased catalytic activity at low pH with evolution. At a global level, we note that the population of low-flexibility (called hinges) and high-flexibility sites increases with evolution. The heterogeneity (quantified by the variance) in DFI distribution increases with the decrease in the melting temperature typically associated with the evolution of ancient proteins to their modern-day counterparts.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

Biotechnological and protein-engineering implications of ancestral protein resurrection

Approximations to the sequences of ancestral proteins can be derived from the sequences of their modern descendants. Proteins encoded by such reconstructed sequences can be prepared in the laboratory and subjected to experimental scrutiny. These 'resurrected' ancestral proteins often display remarkable properties, reflecting ancestral adaptations to intra-cellular and extra-cellular environments that differed from the environments hosting modern/extant proteins. Recent experimental and computational work has specifically discussed high stability, substrate and catalytic promiscuity, conformational flexibility/diversity and altered patterns of interaction with other sub-cellular components. In this review, we discuss these remarkable properties as well as recent attempts to explore their biotechnological and protein-engineering potential.

Hinge-Shift Mechanism Modulates Allosteric Regulations in Human Pin1

Allostery, which is regulation from distant sites, plays a major role in biology. While traditional allostery is described in terms of conformational change upon ligand binding as an underlying principle, it is possible to have allosteric regulations without significant conformational change through modulating the conformational dynamics by altering the local effective elastic modulus of the protein upon ligand binding. Pin1 utilizes this dynamic allostery to regulate its function. It is a modular protein containing a WW domain and a larger peptidyl prolyl isomerase domain (PPIase) that isomerizes phosphoserine/threonine-proline (pS/TP) motifs. The WW domain serves as a docking module, whereas catalysis solely takes place within the PPIase domain. Here, we analyze the change in the dynamic flexibility profile of the PPIase domain upon ligand binding to the WW domain. Substrate binding to the WW domain induces the formation of a new rigid hinge site around the interface of the two domains and loosens the flexibility of a rigid site existing in the Apo form around the catalytic site. This hinge-shift mechanism enhances the dynamic coupling of the catalytic positions with the PPIase domain, where the rest of the domain can cooperatively respond to the local conformational changes around the catalytic site, leading to an increase in catalytic efficiency.

Piconewton-Scale Analysis of Ras-BRaf Signal Transduction with Single-Molecule Force Spectroscopy

Intermolecular interactions dominate the behavior of signal transduction in various physiological and pathological cell processes, yet assessing these interactions remains a challenging task. Here, this study reports a single-molecule force spectroscopic method that enables functional delineation of two interaction sites (≈35 pN and ≈90 pN) between signaling effectors Ras and BRaf in the canonical mitogen-activated protein kinase (MAPK) pathway. This analysis reveals mutations on BRaf at Q257 and A246, two sites frequently linked to cardio-faciocutaneous syndrome, result in ≈10-30 pN alterations in RasBRaf intermolecular binding force. The magnitude of changes in RasBRaf binding force correlates with the size of alterations in protein affinity and in α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-sensitive glutamate receptor (-R)-mediated synaptic transmission in neurons expressing replacement BRaf mutants, and predicts the extent of learning impairments in animals expressing replacement BRaf mutants. These results establish single-molecule force spectroscopy as an effective platform for evaluating the piconewton-level interaction of signaling molecules and predicting the behavior outcome of signal transduction.

Plant-expressed cocaine hydrolase variants of butyrylcholinesterase exhibit altered allosteric effects of cholinesterase activity and increased inhibitor sensitivity

Butyrylcholinesterase (BChE) is an enzyme with broad substrate and ligand specificities and may function as a generalized bioscavenger by binding and/or hydrolyzing various xenobiotic agents and toxicants, many of which target the central and peripheral nervous systems. Variants of BChE were rationally designed to increase the enzyme’s ability to hydrolyze the psychoactive enantiomer of cocaine. These variants were cloned, and then expressed using the magnICON transient expression system in plants and their enzymatic properties were investigated. In particular, we explored the effects that these site-directed mutations have over the enzyme kinetics with various substrates of BChE. We further compared the affinity of various anticholinesterases including organophosphorous nerve agents and pesticides toward these BChE variants relative to the wild type enzyme. In addition to serving as a therapy for cocaine addiction-related diseases, enhanced bioscavenging against other harmful agents could add to the practicality and versatility of the plant-derived recombinant enzyme as a multivalent therapeutic.

Protein-protein binding selectivity and network topology constrain global and local properties of interface binding networks

Protein-protein interactions networks (PPINs) are known to share a highly conserved structure across all organisms. What is poorly understood, however, is the structure of the child interface interaction networks (IINs), which map the binding sites proteins use for each interaction. In this study we analyze four independently constructed IINs from yeast and humans and find a conserved structure of these networks with a unique topology distinct from the parent PPIN. Using an IIN sampling algorithm and a fitness function trained on the manually curated PPINs, we show that IIN topology can be mostly explained as a balance between limits on interface diversity and a need for physico-chemical binding complementarity. This complementarity must be optimized both for functional interactions and against mis-interactions, and this selectivity is encoded in the IIN motifs. To test whether the parent PPIN shapes IINs, we compared optimal IINs in biological PPINs versus random PPINs. We found that the hubs in biological networks allow for selective binding with minimal interfaces, suggesting that binding specificity is an additional pressure for a scale-free-like PPIN. We confirm through phylogenetic analysis that hub interfaces are strongly conserved and rewiring of interactions between proteins involved in endocytosis preserves interface binding selectivity.

NiaoDuQing granules relieve chronic kidney disease symptoms by decreasing renal fibrosis and anemia

NiaoDuQing (NDQ) granules, a traditional Chinese medicine, has been clinically used in China for over fourteen years to treat chronic kidney disease (CKD). To elucidate the mechanisms underlying the therapeutic benefits of NDQ, we designed an approach incorporating chemoinformatics, bioinformatics, network biology methods, and cellular and molecular biology experiments. A total of 182 active compounds were identified in NDQ granules, and 397 putative targets associated with different diseases were derived through ADME modelling and target prediction tools. Protein-protein interaction networks of CKD-related and putative NDQ targets were constructed, and 219 candidate targets were identified based on topological features. Pathway enrichment analysis showed that the candidate targets were mostly related to the TGF-β, the p38MAPK, and the erythropoietin (EPO) receptor signaling pathways, which are known contributors to renal fibrosis and/or renal anemia. A rat model of CKD was established to validate the drug-target mechanisms predicted by the systems pharmacology analysis. Experimental results confirmed that NDQ granules exerted therapeutic effects on CKD and its comorbidities, including renal anemia, mainly by modulating the TGF-β and EPO signaling pathways. Thus, the pharmacological actions of NDQ on CKD symptoms correlated well with in silico predictions.

Role of Proteome Physical Chemistry in Cell Behavior

We review how major cell behaviors, such as bacterial growth laws, are derived from the physical chemistry of the cell’s proteins. On one hand, cell actions depend on the individual biological functionalities of their many genes and proteins. On the other hand, the common physics among proteins can be as important as the unique biology that distinguishes them. For example, bacterial growth rates depend strongly on temperature. This dependence can be explained by the folding stabilities across a cell’s proteome. Such modeling explains how thermophilic and mesophilic organisms differ, and how oxidative damage of highly charged proteins can lead to unfolding and aggregation in aging cells. Cells have characteristic time scales. For example, E. coli can duplicate as fast as 2–3 times per hour. These time scales can be explained by protein dynamics (the rates of synthesis and degradation, folding, and diffusional transport). It rationalizes how bacterial growth is slowed down by added salt. In the same way that the behaviors of inanimate materials can be expressed in terms of the statistical distributions of atoms and molecules, some cell behaviors can be expressed in terms of distributions of protein properties, giving insights into the microscopic basis of growth laws in simple cells.