Distribution patterns of small-molecule ligands in the protein universe and implications for origin of life and drug discovery

Advanced cancer targeting using aptamer functionalized nanocarriers for site-specific cargo delivery

The non-specificity of standard anticancer therapies has profound detrimental consequences in clinical treatment. Therapeutic specificity can be precisely achieved using cutting-edge ligands. Small synthetic oligonucleotide-ligands chosen through Systematic evolution of ligands by exponential enrichment (SELEX) would be an unceasing innovation in using nucleic acids as aptamers, frequently referred to as "chemical antibodies." Aptamers act as externally controlled switching materials that can attach to various substrates, for example, membrane proteins or nucleic acid structures. Aptamers pose excellent specificity and affinity for target molecules and can be used as medicines to suppress tumor cell growth directly. The creation of aptamer-conjugated nanoconstructs has recently opened up innovative options in cancer therapy that are more effective and target tumor cells with minor toxicity to healthy tissues. This review focuses on a comprehensive description of the most capable classes of aptamer-tethered nanocarriers for precise recognition of cancer cells with significant development in proficiency, selectivity, and targetability for cancer therapy. Existing theranostic applications with the problems and future directions are also highlighted.

Prebiotic Synthesis of ATP: A Terrestrial Volcanism-Dependent Pathway

Adenosine triphosphate (ATP) is a multifunctional small molecule, necessary for all modern Earth life, which must be a component of the last universal common ancestor (LUCA). However, the relatively complex structure of ATP causes doubts about its accessibility on prebiotic Earth. In this paper, based on previous studies on the synthesis of ATP components, a plausible prebiotic pathway yielding this key molecule is constructed, which relies on terrestrial volcanism to provide the required materials and suitable conditions.

The Legend of ATP: From Origin of Life to Precision Medicine

Adenosine triphosphate (ATP) may be the most important biological small molecule. Since it was discovered in 1929, ATP has been regarded as life’s energy reservoir. However, this compound means more to life. Its legend starts at the dawn of life and lasts to this day. ATP must be the basic component of ancient ribozymes and may facilitate the origin of structured proteins. In the existing organisms, ATP continues to construct ribonucleic acid (RNA) and work as a protein cofactor. ATP also functions as a biological hydrotrope, which may keep macromolecules soluble in the primitive environment and can regulate phase separation in modern cells. These functions are involved in the pathogenesis of aging-related diseases and breast cancer, providing clues to discovering anti-aging agents and precision medicine tactics for breast cancer.

Peptides before and during the nucleotide world: an origins story emphasizing cooperation between proteins and nucleic acids

Recent developments in Origins of Life research have focused on substantiating the narrative of an abiotic emergence of nucleic acids from organic molecules of low molecular weight, a paradigm that typically sidelines the roles of peptides. Nevertheless, the simple synthesis of amino acids, the facile nature of their activation and condensation, their ability to recognize metals and cofactors and their remarkable capacity to self-assemble make peptides (and their analogues) favourable candidates for one of the earliest functional polymers. In this mini-review, we explore the ramifications of this hypothesis. Diverse lines of research in molecular biology, bioinformatics, geochemistry, biophysics and astrobiology provide clues about the progression and early evolution of proteins, and lend credence to the idea that early peptides served many central prebiotic roles before they were encodable by a polynucleotide template, in a putative 'peptide-polynucleotide stage'. For example, early peptides and mini-proteins could have served as catalysts, compartments and structural hubs. In sum, we shed light on the role of early peptides and small proteins before and during the nucleotide world, in which nascent life fully grasped the potential of primordial proteins, and which has left an imprint on the idiosyncratic properties of extant proteins.

Short non-coded peptides interacting with cofactors facilitated the integration of early chemical networks

Independently developed iron-sulphur/thioester- and phosphate-driven chemical reactions would have set up two distinct reaction networks prior to coupling in a proto-metabolic system supporting a minimal organisation closure. Each chemical system assisted initially by simple catalysts and then by more complex cofactors would have provided the precursors of the small metabolites and monomer units along with their respective polymers through dehydrating template-independent assemblies. For example, acylation reactions mediated by activated thioester groups produced peptides, fatty acids and polyhydroxyalkanoates, while phosphorylation reactions by phosphorylating agents allowed the synthesis of polysaccharides, polyribonucleotides and polyphosphates. Here, we address how these independent chemical systems might fit together and shaped a proto-metabolic system, focusing specifically on cofactors as molecular fossils of metabolism. As a result, the proposed overview suggests that non-coded peptides capable of binding a variety of ligands, but in particular with a redox active versatility and/or group transfer potential could have facilitated the chemical connections that led to a minimal closure with a proto-metabolism. Later developments would have made it possible to establish a cellular organisation with more complex and interdependent metabolic pathways.

NUDT5 as a novel drug target and prognostic biomarker for ER-positive breast cancer

Breast cancer (BRCA) is the most common malignant tumor in women. The estrogen receptor-positive (ER⁺) subtype accounts for ∼70% of BRCA cases. Estrogen is a crucial hormone that directly stimulates the growth and development of mammary glands. Recent studies revealed that, as an estrogen cofactor, ATP has an important role in determining the action of estrogen by mediating phase separation. NUDT5 has been recognized as a key factor for ATP production in the nucleus of BRCA cells and, therefore, could represent a novel drug target for ER⁺ BRCA. Based on a survival analysis of patients with BRCA documented in The Cancer Genome Atlas (TGCA) database, we show that NUDT5 is also a potential prognostic biomarker for ER⁺ BRCA.

Cofactors as Molecular Fossils To Trace the Origin and Evolution of Proteins

Due to their early origin and extreme conservation, cofactors are valuable molecular fossils for tracing the origin and evolution of proteins. First, as the order of protein folds binding with cofactors roughly coincides with protein-fold chronology, cofactors are considered to have facilitated the origin of primitive proteins by selecting them from pools of random amino acid sequences. Second, in the subsequent evolution of proteins, cofactors still played an important role. More interestingly, as metallic cofactors evolved with geochemical variations, some geochemical events left imprints in the chronology of protein architecture; this provides further evidence supporting the coevolution of biochemistry and geochemistry. In this paper, we attempt to review the molecular fossils used in tracing the origin and evolution of proteins, with a special focus on cofactors.

Ligand Binding Site Structure Influences the Evolution of Protein Complex Function and Topology

It has been suggested that the evolution of protein complexes is significantly influenced by stochastic, non-adaptive processes. Using ligand binding as a proxy of function, we show that the structure of ligand-binding sites significantly influences the evolution of protein complexes. We show that homomers with multi-chain binding sites (MBSs) evolve new functions slower than monomers or other homomers, and those binding cofactors and metals have more conserved quaternary structure than other homomers. Moreover, the ligands and ligand-binding pockets of homologous MBS homomers are more similar than monomers and other homomers. Our results suggest strong evolutionary selection for quaternary structure in cofactor-binding MBS homomers, whereas neutral processes are more important in complexes with single-chain binding sites. They also have pharmacological implications, suggesting that complexes with single-chain binding sites are better targets for selective drugs, whereas MBS homomers are good candidates for broad-spectrum antibiotic and multitarget drug design.

•

Ligand binding site structure significantly influences protein function evolution

•

MBS homomers have more similar ligand binding pockets than monomers and other homomers

•

Cofactor and metal-binding MBS homomers have more conserved QS than other homomers

•

MBS homomers are promising targets for developing antibiotics and multitarget drugs

Reduced alphabet of prebiotic amino acids optimally encodes the conformational space of diverse extant protein folds

Background

There is wide agreement that only a subset of the twenty standard amino acids existed prebiotically in sufficient concentrations to form functional polypeptides. We ask how this subset, postulated as {A,D,E,G,I,L,P,S,T,V}, could have formed structures stable enough to found metabolic pathways. Inspired by alphabet reduction experiments, we undertook a computational analysis to measure the structural coding behavior of sequences simplified by reduced alphabets. We sought to discern characteristics of the prebiotic set that would endow it with unique properties relevant to structure, stability, and folding.

Results

Drawing on a large dataset of single-domain proteins, we employed an information-theoretic measure to assess how well the prebiotic amino acid set preserves fold information against all other possible ten-amino acid sets. An extensive virtual mutagenesis procedure revealed that the prebiotic set excellently preserves sequence-dependent information regarding both backbone conformation and tertiary contact matrix of proteins. We observed that information retention is fold-class dependent: the prebiotic set sufficiently encodes the structure space of α/β and α + β folds, and to a lesser extent, of all-α and all-β folds. The prebiotic set appeared insufficient to encode the small proteins. Assessing how well the prebiotic set discriminates native vs. incorrect sequence-structure matches, we found that α/β and α + β folds exhibit more pronounced energy gaps with the prebiotic set than with nearly all alternatives.

Conclusions

The prebiotic set optimally encodes local backbone structures that appear in the folded environment and near-optimally encodes the tertiary contact matrix of extant proteins. The fold-class-specific patterns observed from our structural analysis confirm the postulated timeline of fold appearance in proteogenesis derived from proteomic sequence analyses. Polypeptides arising in a prebiotic environment will likely form α/β and α + β-like folds if any at all. We infer that the progressive expansion of the alphabet allowed the increased conformational stability and functional specificity of later folds, including all-α, all-β, and small proteins. Our results suggest that prebiotic sequences are amenable to mutations that significantly lower native conformational energies and increase discrimination amidst incorrect folds. This property may have assisted the genesis of functional proto-enzymes prior to the expansion of the full amino acid alphabet.

Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin: Application of MARSplines and Descriptors Encoded in SMILES String

The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).

An Augmented Pocketome: Detection and Analysis of Small-Molecule Binding Pockets in Proteins of Known 3D Structure

Protein-ligand interactions form the basis of most cellular events. Identifying ligand binding pockets in proteins will greatly facilitate rationalizing and predicting protein function. Ligand binding sites are unknown for many proteins of known three-dimensional (3D) structure, creating a gap in our understanding of protein structure-function relationships. To bridge this gap, we detect pockets in proteins of known 3D structures, using computational techniques. This augmented pocketome (PocketDB) consists of 249,096 pockets, which is about seven times larger than what is currently known. We deduce possible ligand associations for about 46% of the newly identified pockets. The augmented pocketome, when subjected to clustering based on similarities among pockets, yielded 2,161 site types, which are associated with 1,037 ligand types, together providing fold-site-type-ligand-type associations. The PocketDB resource facilitates a structure-based function annotation, delineation of the structural basis of ligand recognition, and provides functional clues for domains of unknown functions, allosteric proteins, and druggable pockets.

Piecemeal Buildup of the Genetic Code, Ribosomes, and Genomes from Primordial tRNA Building Blocks

The origin of biomolecular machinery likely centered around an ancient and central molecule capable of interacting with emergent macromolecular complexity. tRNA is the oldest and most central nucleic acid molecule of the cell. Its co-evolutionary interactions with aminoacyl-tRNA synthetase protein enzymes define the specificities of the genetic code and those with the ribosome their accurate biosynthetic interpretation. Phylogenetic approaches that focus on molecular structure allow reconstruction of evolutionary timelines that describe the history of RNA and protein structural domains. Here we review phylogenomic analyses that reconstruct the early history of the synthetase enzymes and the ribosome, their interactions with RNA, and the inception of amino acid charging and codon specificities in tRNA that are responsible for the genetic code. We also trace the age of domains and tRNA onto ancient tRNA homologies that were recently identified in rRNA. Our findings reveal a timeline of recruitment of tRNA building blocks for the formation of a functional ribosome, which holds both the biocatalytic functions of protein biosynthesis and the ability to store genetic memory in primordial RNA genomic templates.

NIMS: a database on nucleobase compounds and their interactions in macromolecular structures

The intense exploration of nucleotide-binding protein structures has created a whirlwind in the field of structural biology and bioinformatics. This has led to the conception and birth of NIMS. This database is a collection of detailed data on the nucleobases, nucleosides and nucleotides, along with their analogues as well as the protein structures to which they bind. Interaction details such as the interacting residues and all associated values have been made available. As a pioneering step, the diffraction precision index for protein structures, the atomic uncertainty for each atom, and the computed errors on the interatomic distances and angles are available in the database. Apart from the above, provision has been made to visualize the three-dimensional structures of both ligands and protein–ligand structures and their interactions inJmolas well asJSmol. One of the salient features of NIMS is that it has been interfaced with a user-friendly and query-based efficient search engine. It was conceived and developed with the aim of serving a significant section of researchers working in the area of protein and nucleobase complexes. NIMS is freely available online at http://iris.physics.iisc.ernet.in/nims and it is hoped that it will prove to be an invaluable asset.

ATP selection in a random peptide library consisting of prebiotic amino acids

Based upon many theoretical findings on protein evolution, we proposed a ligand-selection model for the origin of proteins, in which the most ancient proteins originated from ATP selection in a pool of random peptides. To test this ligand-selection model, we constructed a random peptide library consisting of 15 types of prebiotic amino acids and then used cDNA display to perform six rounds of in vitro selection with ATP. By means of next-generation sequencing, the most prevalent sequence was defined. Biochemical and biophysical characterization of the selected peptide showed that it was stable and foldable and had ATP-hydrolysis activity as well.

The universal statistical distributions of the affinity, equilibrium constants, kinetics and specificity in biomolecular recognition

We uncovered the universal statistical laws for the biomolecular recognition/binding process. We quantified the statistical energy landscapes for binding, from which we can characterize the distributions of the binding free energy (affinity), the equilibrium constants, the kinetics and the specificity by exploring the different ligands binding with a particular receptor. The results of the analytical studies are confirmed by the microscopic flexible docking simulations. The distribution of binding affinity is Gaussian around the mean and becomes exponential near the tail. The equilibrium constants of the binding follow a log-normal distribution around the mean and a power law distribution in the tail. The intrinsic specificity for biomolecular recognition measures the degree of discrimination of native versus non-native binding and the optimization of which becomes the maximization of the ratio of the free energy gap between the native state and the average of non-native states versus the roughness measured by the variance of the free energy landscape around its mean. The intrinsic specificity obeys a Gaussian distribution near the mean and an exponential distribution near the tail. Furthermore, the kinetics of binding follows a log-normal distribution near the mean and a power law distribution at the tail. Our study provides new insights into the statistical nature of thermodynamics, kinetics and function from different ligands binding with a specific receptor or equivalently specific ligand binding with different receptors. The elucidation of distributions of the kinetics and free energy has guiding roles in studying biomolecular recognition and function through small-molecule evolution and chemical genetics.

Uncovering the principles and underlying mechanisms of biomolecular recognition and molecular binding process is crucial for understanding the function and evolution, yet challenging. We meet the challenge by quantifying the statistical natures of the relevant physical variables of biomolecular recognition using the analytical model combined with microscopic flexible docking simulation methods. We uncovered the universal statistical laws obeyed by the affinity, equilibrium constant, intrinsic specificity and kinetics for biomolecular recognition. The general statistical laws based on energy landscape theory can serve as a conceptual framework for molecular recognition in biological repertoires. They can be applied to molecular selection, in vitro evolution process, high throughput screening and virtual screening for drug discovery. The statistical laws in combinations with experiments provide quantitative signatures of a specific ligand binding to a specific receptor, these resultant laws as a guideline will contribute to drug design against a specific target. Our developed statistical methodology is general and applicable for all other biomolecular recognitions.

A network pharmacology study of Sendeng-4, a Mongolian medicine

We collected the data on the Sendeng-4 chemical composition corresponding targets through the literature and from DrugBank, SuperTarget, TTD (Therapeutic Targets Database) and other databases and the relevant signaling pathways from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database and established models of the chemical composition-target network and chemical composition-target-disease network using Cytoscape software, the analysis indicated that the chemical composition had at least nine different types of targets that acted together to exert effects on the diseases, suggesting a "multi-component, multi-target" feature of the traditional Mongolian medicine. We also employed the rat model of rheumatoid arthritis induced by Collgen Type II to validate the key targets of the chemical components of Sendeng-4, and three of the key targets were validated through laboratory experiments, further confirming the anti-inflammatory effects of Sendeng-4. In all, this study predicted the active ingredients and targets of Sendeng-4, and explored its mechanism of action, which provided new strategies and methods for further research and development of Sendeng-4 and other traditional Mongolian medicines as well.

Origination of the Protein Fold Repertoire from Oily Pluripotent Peptides

While the repertoire of protein folds that exists today underlies most of life’s capabilities, our mechanistic picture of protein fold origination is incomplete. This paper discusses a hypothetical mechanism for the emergence of the protein fold repertoire from highly dynamic and collapsed peptides, exemplified by peptides with high oil content or hydrophobicity. These peptides are called pluripotent to emphasize their capacity to evolve into numerous folds transiently available to them. As evidence, the paper will discuss previous simulation work on the superior fold evolvability of oily peptides, trace (“fossil”) evidence within proteomes seen today, and a general relationship between protein dynamism and evolvability. Aside from implications on the origination of protein folds, the hypothesis implies that the vanishing utility of a random peptide in protein origination may be relatively exaggerated, as some random peptides with a certain composition (e.g., oily) may fare better than others. In later sections, the hypothesis is discussed in the context of existing discussions regarding the spontaneous origination of biomolecules.

Nucleos: a web server for the identification of nucleotide-binding sites in protein structures

Nucleos is a web server for the identification of nucleotide-binding sites in protein structures. Nucleos compares the structure of a query protein against a set of known template 3D binding sites representing nucleotide modules, namely the nucleobase, carbohydrate and phosphate. Structural features, clustering and conservation are used to filter and score the predictions. The predicted nucleotide modules are then joined to build whole nucleotide-binding sites, which are ranked by their score. The server takes as input either the PDB code of the query protein structure or a user-submitted structure in PDB format. The output of Nucleos is composed of ranked lists of predicted nucleotide-binding sites divided by nucleotide type (e.g. ATP-like). For each ranked prediction, Nucleos provides detailed information about the score, the template structure and the structural match for each nucleotide module composing the nucleotide-binding site. The predictions on the query structure and the template-binding sites can be viewed directly on the web through a graphical applet. In 98% of the cases, the modules composing correct predictions belong to proteins with no homology relationship between each other, meaning that the identification of brand-new nucleotide-binding sites is possible using information from non-homologous proteins. Nucleos is available at http://nucleos.bio.uniroma2.it/nucleos/.

Drug target central

BACKGROUND

One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets.

OBJECTIVE

Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development.

CONCLUSION

Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways.

Identification of nucleotide-binding sites in protein structures: a novel approach based on nucleotide modularity

Nucleotides are involved in several cellular processes, ranging from the transmission of genetic information, to energy transfer and storage. Both sequence and structure based methods have been developed to predict the location of nucleotide-binding sites in proteins. Here we propose a novel methodology that leverages the observation that nucleotide-binding sites have a modular structure. Nucleotides are composed of identifiable fragments, i.e. the phosphate, the nucleobase and the carbohydrate moieties. These fragments are bound by specific structural motifs that recur in proteins of different fold. Moreover these motifs behave as modules and are found in different combinations across fold space. Our method predicts binding sites for each nucleotide fragment by comparing a query protein with a database of templates extracted from proteins of known structure. Whenever a similarity is found the fragment bound by the template is transferred on the query protein, thus identifying a putative binding site. Predictions falling inside the surface of the protein are discarded, and the remaining ones are scored using clustering and conservation. The method is able to rank as first a correct prediction in the 48%, 48% and 68% of the analyzed proteins for the nucleobase, carbohydrate and phosphate respectively, while considering the first five predictions the performances change to 71%, 65% and 86% respectively. Furthermore we attempted to reconstruct the full structure of the binding site, starting from the predicted positions of the fragments. We calculated that in the 59% of the analyzed proteins the method ranks as first a reconstructed binding site or a part of it. Finally we tested the reliability of our method in a real world case in which it has to predict nucleotide-binding sites in unbound proteins. We analyzed proteins whose structure has been solved with and without the nucleotide and observed only little variations in the method performance.

Protein domain structure uncovers the origin of aerobic metabolism and the rise of planetary oxygen

The origin and evolution of modern biochemistry remain a mystery despite advances in evolutionary bioinformatics. Here, we use a structural census in nearly 1,000 genomes and a molecular clock of folds to define a timeline of appearance of protein families linked to single-domain enzymes. The timeline sorts out enzymatic recruitment, validates patterns in metabolic history, and reveals that the most ancient reaction of aerobic metabolism involved the synthesis of pyridoxal 5'-phosphate or pyridoxal and appeared 2.9 Gyr ago. The oxygen source for this primordial reaction was probably Mn catalase, which appeared at the same time and could have generated oxygen as a side product of hydrogen peroxide detoxification. Finally, evolutionary analysis of transferred groups and metabolite fragments revealed that oxidized sulfur did not participate in metabolism until the rise of oxygen. The evolutionary patterns we uncover in molecules and chemistries provide strong support for the coevolution of biochemistry and geochemistry.

The phylogenomic roots of modern biochemistry: origins of proteins, cofactors and protein biosynthesis

The complexity of modern biochemistry developed gradually on early Earth as new molecules and structures populated the emerging cellular systems. Here, we generate a historical account of the gradual discovery of primordial proteins, cofactors, and molecular functions using phylogenomic information in the sequence of 420 genomes. We focus on structural and functional annotations of the 54 most ancient protein domains. We show how primordial functions are linked to folded structures and how their interaction with cofactors expanded the functional repertoire. We also reveal protocell membranes played a crucial role in early protein evolution and show translation started with RNA and thioester cofactor-mediated aminoacylation. Our findings allow elaboration of an evolutionary model of early biochemistry that is firmly grounded in phylogenomic information and biochemical, biophysical, and structural knowledge. The model describes how primordial α-helical bundles stabilized membranes, how these were decorated by layered arrangements of β-sheets and α-helices, and how these arrangements became globular. Ancient forms of aminoacyl-tRNA synthetase (aaRS) catalytic domains and ancient non-ribosomal protein synthetase (NRPS) modules gave rise to primordial protein synthesis and the ability to generate a code for specificity in their active sites. These structures diversified producing cofactor-binding molecular switches and barrel structures. Accretion of domains and molecules gave rise to modern aaRSs, NRPS, and ribosomal ensembles, first organized around novel emerging cofactors (tRNA and carrier proteins) and then more complex cofactor structures (rRNA). The model explains how the generation of protein structures acted as scaffold for nucleic acids and resulted in crystallization of modern translation.

Novel computational approaches to polypharmacology as a means to define responses to individual drugs

Polypharmacology, which focuses on designing therapeutics to target multiple receptors, has emerged as a new paradigm in drug discovery. Polypharmacological effects are an attribute of most, if not all, drug molecules. The efficacy and toxicity of drugs, whether designed as single- or multitarget therapeutics, result from complex interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, and environmental factors. Ultimately, to predict a drug response phenotype, it is necessary to understand the change in information flow through cellular networks resulting from dynamic drug-target interactions and the impact that this has on the complete biological system. Although such is a future objective, we review recent progress and challenges in computational techniques that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes.

Small cofactors may assist protein emergence from RNA world: clues from RNA-protein complexes

It is now widely accepted that at an early stage in the evolution of life an RNA world arose, in which RNAs both served as the genetic material and catalyzed diverse biochemical reactions. Then, proteins have gradually replaced RNAs because of their superior catalytic properties in catalysis over time. Therefore, it is important to investigate how primitive functional proteins emerged from RNA world, which can shed light on the evolutionary pathway of life from RNA world to the modern world. In this work, we proposed that the emergence of most primitive functional proteins are assisted by the early primitive nucleotide cofactors, while only a minority are induced directly by RNAs based on the analysis of RNA-protein complexes. Furthermore, the present findings have significant implication for exploring the composition of primitive RNA, i.e., adenine base as principal building blocks.

Phosfinder: a web server for the identification of phosphate-binding sites on protein structures

Phosfinder is a web server for the identification of phosphate binding sites in protein structures. Phosfinder uses a structural comparison algorithm to scan a query structure against a set of known 3D phosphate binding motifs. Whenever a structural similarity between the query protein and a phosphate binding motif is detected, the phosphate bound by the known motif is added to the protein structure thus representing a putative phosphate binding site. Predicted binding sites are then evaluated according to (i) their position with respect to the query protein solvent-excluded surface and (ii) the conservation of the binding residues in the protein family. The server accepts as input either the PDB code of the protein to be analyzed or a user-submitted structure in PDB format. All the search parameters are user modifiable. Phosfinder outputs a list of predicted binding sites with detailed information about their structural similarity with known phosphate binding motifs, and the conservation of the residues involved. A graphical applet allows the user to visualize the predicted binding sites on the query protein structure. The results on a set of 52 apo/holo structure pairs show that the performance of our method is largely unaffected by ligand-induced conformational changes. Phosfinder is available at http://phosfinder.bio.uniroma2.it.

Proteome evolution and the metabolic origins of translation and cellular life

The origin of life has puzzled molecular scientists for over half a century. Yet fundamental questions remain unanswered, including which came first, the metabolic machinery or the encoding nucleic acids. In this study we take a protein-centric view and explore the ancestral origins of proteins. Protein domain structures in proteomes are highly conserved and embody molecular functions and interactions that are needed for cellular and organismal processes. Here we use domain structure to study the evolution of molecular function in the protein world. Timelines describing the age and function of protein domains at fold, fold superfamily, and fold family levels of structural complexity were derived from a structural phylogenomic census in hundreds of fully sequenced genomes. These timelines unfold congruent hourglass patterns in rates of appearance of domain structures and functions, functional diversity, and hierarchical complexity, and revealed a gradual build up of protein repertoires associated with metabolism, translation and DNA, in that order. The most ancient domain architectures were hydrolase enzymes and the first translation domains had catalytic functions for the aminoacylation and the molecular switch-driven transport of RNA. Remarkably, the most ancient domains had metabolic roles, did not interact with RNA, and preceded the gradual build-up of translation. In fact, the first translation domains had also a metabolic origin and were only later followed by specialized translation machinery. Our results explain how the generation of structure in the protein world and the concurrent crystallization of translation and diversified cellular life created further opportunities for proteomic diversification.

The Mycobacterium tuberculosis drugome and its polypharmacological implications

We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale. The approach has been applied to the genome of Mycobacterium tuberculosis (M.tb), the causative agent of one of today's most widely spread infectious diseases. The resulting drug-target interaction network for all structurally characterized approved drugs bound to putative M.tb receptors, we refer to as the ‘TB-drugome’. The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets. Furthermore, a detailed analysis of the TB-drugome has shed new light on the controversial issues surrounding drug-target networks [1]–[3]. Indeed, our results support the idea that drug-target networks are inherently modular, and further that any observed randomness is mainly caused by biased target coverage. The TB-drugome (http://funsite.sdsc.edu/drugome/TB) has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics.

The worldwide increase in multi-drug resistant TB poses a great threat to human health and highlights the need to identify new anti-tubercular agents. We have developed a computational strategy to link the structural proteome of Mycobacterium tuberculosis, the causative agent of tuberculosis, to all structurally characterized approved drugs, and hence construct a proteome-wide drug-target network – the TB-drugome. The TB-drugome has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally, the proteome-wide and multi-scale view of target and drug space may facilitate a systematic drug discovery process, which concurrently takes into account the disease mechanism and druggability of targets, the drug-likeness and ADMET properties of chemical compounds, and the genetic dispositions of individuals. Ultimately it may help to reduce the high attrition rate in drug development through a better understanding of drug-receptor interactions on a large scale.

Phosphate binding sites identification in protein structures

Nearly half of known protein structures interact with phosphate-containing ligands, such as nucleotides and other cofactors. Many methods have been developed for the identification of metal ions-binding sites and some for bigger ligands such as carbohydrates, but none is yet available for the prediction of phosphate-binding sites. Here we describe Pfinder, a method that predicts binding sites for phosphate groups, both in the form of ions or as parts of other non-peptide ligands, in proteins of known structure. Pfinder uses the Query3D local structural comparison algorithm to scan a protein structure for the presence of a number of structural motifs identified for their ability to bind the phosphate chemical group. Pfinder has been tested on a data set of 52 proteins for which both the apo and holo forms were available. We obtained at least one correct prediction in 63% of the holo structures and in 62% of the apo. The ability of Pfinder to recognize a phosphate-binding site in unbound protein structures makes it an ideal tool for functional annotation and for complementing docking and drug design methods. The Pfinder program is available at http://pdbfun.uniroma2.it/pfinder.

Emergence and evolution of modern molecular functions inferred from phylogenomic analysis of ontological data

The biological processes that characterize the phenotypes of a living system are embodied in the function of molecules and hold the key to evolutionary history, delimiting natural selection and change. These processes and functions provide direct insight into the emergence, development, and organization of cellular life. However, detailed molecular functions make up a network-like hierarchy of relationships that tells little of evolutionary links between structure and function in biology. For example, Gene Ontology terms represent widely-used vocabularies of processes and functions with evolutionary relationships that are implicit but not defined. Here, we uncover patterns of global evolutionary history in ontological terms associated with the sequence of 38 genomes. These patterns unfold the metabolic origins of modern molecular functions and major biological transitions in evolution toward complex life. Phylogenies reveal the primordial appearance of hydrolases and transferases, with ATPase, GTPase, and helicase activities being the most ancient. This indicates that ancient catalysts were crucial for binding and transport, the emergence of nucleic acids and protein biopolymers, and the communication of primordial cells with the environment. Finally, the history of biological processes showed that cellular biopolymer metabolic processes preceded biopolymer biosynthesis and essential processes related to macromolecular formation, directly challenging the existence of an RNA world. Phylogenomic systematization of biological function takes the structure and function paradigm to a completely new level of abstraction, demonstrating a "metabolic first" origin of life. The approach uncovers patterns in the morphing of function that are unprecedented and necessary for systematic views in biology.

Modular architecture of nucleotide-binding pockets

Recently, modularity has emerged as a general attribute of complex biological systems. This is probably because modular systems lend themselves readily to optimization via random mutation followed by natural selection. Although they are not traditionally considered to evolve by this process, biological ligands are also modular, being composed of recurring chemical fragments, and moreover they exhibit similarities reminiscent of mutations (e.g. the few atoms differentiating adenine and guanine). Many ligands are also promiscuous in the sense that they bind to many different protein folds. Here, we investigated whether ligand chemical modularity is reflected in an underlying modularity of binding sites across unrelated proteins. We chose nucleotides as paradigmatic ligands, because they can be described as composed of well-defined fragments (nucleobase, ribose and phosphates) and are quite abundant both in nature and in protein structure databases. We found that nucleotide-binding sites do indeed show a modular organization and are composed of fragment-specific protein structural motifs, which parallel the modular structure of their ligands. Through an analysis of the distribution of these motifs in different proteins and in different folds, we discuss the evolutionary implications of these findings and argue that the structural features we observed can arise both as a result of divergence from a common ancestor or convergent evolution.

Alignment-free ultra-high-throughput comparison of druggable protein-ligand binding sites

Inferring the biological function of a protein from its three-dimensional structure as well as explaining why a drug may bind to various targets is of crucial importance to modern drug discovery. Here we present a generic 4833-integer vector describing druggable protein-ligand binding sites that can be applied to any protein and any binding cavity. The fingerprint registers counts of pharmacophoric triplets from the Calpha atomic coordinates of binding-site-lining residues. Starting from a customized data set of diverse protein-ligand binding site pairs, the most appropriate metric and a similarity threshold could be defined for similar binding sites. The method (FuzCav) has been used in various scenarios: (i) screening a collection of 6000 binding sites for similarity to different queries; (ii) classifying protein families (serine endopeptidases, protein kinases) by binding site diversity; (iii) discriminating adenine-binding cavities from decoys. The fingerprint generation and comparison supports ultra-high throughput (ca. 1000 measures/s), does not require prior alignment of protein binding sites, and is able to detect local similarity among subpockets. It is thus particularly well suited to the functional annotation of novel genomic structures with low sequence identity to known X-ray templates.

Evolutionary formation of new protein folds is linked to metallic cofactor recruitment

To explore whether the generation of new protein folds could be linked to metallic cofactor recruitment, we identified the oldest examples of folds for manganese, iron, zinc, and copper proteins by analyzing their fold-domain mapping patterns. We discovered that the generation of these folds was tightly coupled to corresponding metals. We found that the emerging order for these folds, i.e., manganese and iron protein folds appeared earlier than zinc and copper counterparts, coincides with the putative bioavailability of the corresponding metals in the ancient anoxic ocean. Therefore, we conclude that metallic cofactors, like organic cofactors, play an evolutionary role in the formation of new protein folds. This link could be explained by the emergence of protein structures with novel folds that could fulfill the new protein functions introduced by the metallic cofactors. These findings not only have important implications for understanding the evolutionary mechanisms of protein architectures, but also provide a further interpretation for the evolutionary story of superoxide dismutases.

The origin, evolution and structure of the protein world

Contemporary protein architectures can be regarded as molecular fossils, historical imprints that mark important milestones in the history of life. Whereas sequences change at a considerable pace, higher-order structures are constrained by the energetic landscape of protein folding, the exploration of sequence and structure space, and complex interactions mediated by the proteostasis and proteolytic machineries of the cell. The survey of architectures in the living world that was fuelled by recent structural genomic initiatives has been summarized in protein classification schemes, and the overall structure of fold space explored with novel bioinformatic approaches. However, metrics of general structural comparison have not yet unified architectural complexity using the 'shared and derived' tenet of evolutionary analysis. In contrast, a shift of focus from molecules to proteomes and a census of protein structure in fully sequenced genomes were able to uncover global evolutionary patterns in the structure of proteins. Timelines of discovery of architectures and functions unfolded episodes of specialization, reductive evolutionary tendencies of architectural repertoires in proteomes and the rise of modularity in the protein world. They revealed a biologically complex ancestral proteome and the early origin of the archaeal lineage. Studies also identified an origin of the protein world in enzymes of nucleotide metabolism harbouring the P-loop-containing triphosphate hydrolase fold and the explosive discovery of metabolic functions that recapitulated well-defined prebiotic shells and involved the recruitment of structures and functions. These observations have important implications for origins of modern biochemistry and diversification of life.

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

Knowledgebase for addiction-related genes: is it possible an extrapolation to rational multi-target drug design?

In recent years the single-probe-single-target approach in drug design has started to be smoothly replaced by the single-probe-multiple-target (or multi-target) one, where a single drug is able to tackle different, but disease-related targets in a selective manner. However, the design of multi-target drugs has been hindered by a lack of a systematic network of disease-related common pathways. The recent development of the knowledgebase of addiction-related genes (KARG) has provided important hints on how to rationally design multi-target probes by connecting experimental techniques with available network models. In this perspective, the use of KARG as a template to build knowledgebases of disease-related genes for the rational multi-target drug design is suggested. Moreover, it is proposed that building knowledgebases of disease-related genes will become a necessary and ubiquitous tool in the rational design of multi-target drugs.

Organic cofactors participated more frequently than transition metals in redox reactions of primitive proteins

Protein redox reactions are one of the most basic and important biochemical actions. As amino acids are weak redox mediators, most protein redox functions are undertaken by protein cofactors, which include organic ligands and transition metal ions. Since both kinds of redox cofactors were available in the pre-protein RNA world, it is challenging to explore which one was more involved in redox processes of primitive proteins? In this paper, using an examination of the redox cofactor usage of putative ancient proteins, we infer that organic ligands participated more frequently than transition metals in redox reactions of primitive proteins, at least as protein cofactors. This is further supported by the relative abundance of amino acids in the primordial world. Supplementary material for this article can be found on the BioEssays website.

Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments

Here, a scalable, accurate, reliable, and robust protein functional site comparison algorithm is presented. The key components of the algorithm consist of a reduced representation of the protein structure and a sequence order-independent profile-profile alignment (SOIPPA). We show that SOIPPA is able to detect distant evolutionary relationships in cases where both a global sequence and structure relationship remains obscure. Results suggest evolutionary relationships across several previously evolutionary distinct protein structure superfamilies. SOIPPA, along with an increased coverage of protein fold space afforded by the structural genomics initiative, can be used to further test the notion that fold space is continuous rather than discrete.

Characters of very ancient proteins

Tracing the characters of very ancient proteins represents one of the biggest challenges in the study of origin of life. Although there are no primitive protein fossils remaining, the characters of very ancient proteins can be traced by molecular fossils embedded in modern proteins. In this paper, first the prior findings in this area are outlined and then a new strategy is proposed to address the intriguing issue. It is interesting to find that various molecular fossils and different protein datasets lead to similar conclusions on the features of very ancient proteins, which can be summarized as follows: (i) the architectures of very ancient proteins belong to the following folds: P-loop containing nucleoside triphosphate hydrolases (c.37), TIM beta/alpha-barrel (c.1), NAD(P)-binding Rossmann-fold domains (c.2), Ferredoxin-like (d.58), Flavodoxin-like (c.23) and Ribonuclease H-like motif (c.55); (ii) the functions of very ancient proteins are related to the metabolisms of purine, pyrimidine, porphyrin, chlorophyll and carbohydrates; (iii) a certain part of very ancient proteins need cofactors (such as ATP, NADH or NADPH) to work normally.