Ligand-Binding-Site Structure Shapes Allosteric Signal Transduction and the Evolution of Allostery in Protein Complexes

Cellular location shapes quaternary structure of enzymes

The main forces driving protein complex evolution are currently not well understood, especially in homomers, where quaternary structure might frequently evolve neutrally. Here we examine the factors determining oligomerisation by analysing the evolution of enzymes in circumstances where homomers rarely evolve. We show that 1) In extracellular environments, most enzymes with known structure are monomers, while in the cytoplasm homomers, indicating that the evolution of oligomers is cellular environment dependent; 2) The evolution of quaternary structure within protein orthogroups is more consistent with the predictions of constructive neutral evolution than an adaptive process: quaternary structure is gained easier than it is lost, and most extracellular monomers evolved from proteins that were monomers also in their ancestral state, without the loss of interfaces. Our results indicate that oligomerisation is context-dependent, and even when adaptive, in many cases it is probably not driven by the intrinsic properties of enzymes, like their biochemical function, but rather the properties of the environment where the enzyme is active. These factors might be macromolecular crowding and excluded volume effects facilitating the evolution of interfaces, and the maintenance of cellular homeostasis through shaping cytoplasm fluidity, protein degradation, or diffusion rates.

Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases

In this work, we investigated the lactate dehydrogenase (LDH) from Selenomonas ruminantium (S. rum), an enzyme that differs at key amino acid positions from canonical allosteric LDHs. The wild type (Wt) of this enzyme recognises pyuvate as all LDHs. However, introducing a single point mutation in the active site loop (I85R) allows S. Rum LDH to recognize the oxaloacetate substrate as a typical malate dehydrogenase (MalDH), whilst maintaining homotropic activation as an LDH. We report the tertiary structure of the Wt and I85RLDH mutant. The Wt S. rum enzyme structure binds NADH and malonate, whilst also resembling the typical compact R-active state of canonical LDHs. The structure of the mutant with I85R was solved in the Apo State (without ligand), and shows no large conformational reorganization such as that observed with canonical allosteric LDHs in Apo state. This is due to a local structural feature typical of S. rum LDH that prevents large-scale conformational reorganization. The S. rum LDH was also studied using Molecular Dynamics simulations, probing specific local deformations of the active site that allow the S. rum LDH to sample the T-inactive state. We propose that, with respect to the LDH/MalDH superfamily, the S. rum enzyme possesses a specificstructural and dynamical way to ensure homotropic activation.

Unusual Evolution of Cephalopod Tryptophan Indole-Lyases

Tryptophan indole-lyase (TIL), a pyridoxal-5-phosphate-dependent enzyme, catalyzes the hydrolysis of L-tryptophan (L-Trp) to indole and ammonium pyruvate. TIL is widely distributed among bacteria and bacterial TILs consist of a D2-symmetric homotetramer. On the other hand, TIL genes are also present in several metazoans. Cephalopods have two TILs, TILα and TILβ, which are believed to be derived from a gene duplication that occurred before octopus and squid diverged. However, both TILα and TILβ individually contain disruptive amino acid substitutions for TIL activity, and neither was active when expressed alone. When TILα and TILβ were coexpressed, however, they formed a heterotetramer that exhibited low TIL activity. The loss of TIL activity of the heterotetramer following site-directed mutagenesis strongly suggests that the active heterotetramer contains the TILα/TILβ heterodimer. Metazoan TILs generally have lower kcat values for L-Trp than those of bacterial TILs, but such low TIL activity may be rather suitable for metazoan physiology, where L-Trp is in high demand. Therefore, reduced activity may have been a less likely target for purifying selection in the evolution of cephalopod TILs. Meanwhile, the unusual evolution of cephalopod TILs may indicate the difficulty of post-gene duplication evolution of enzymes with catalytic sites contributed by multiple subunits, such as TIL.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

Binding Affinity and Mechanisms of Potential Antidepressants Targeting Human NMDA Receptors

Depression, a mental disorder that plagues the world, is a burden on many families. There is a great need for new, fast-acting antidepressants to be developed. N-methyl-D-aspartic acid (NMDA) is an ionotropic glutamate receptor that plays an important role in learning and memory processes and its TMD region is considered as a potential target to treat depression. However, due to the unclear binding sites and pathways, the mechanism of drug binding lacks basic explanation, which brings great complexity to the development of new drugs. In this study, we investigated the binding affinity and mechanisms of an FDA-approved antidepressant (S-ketamine) and seven potential antidepressants (R-ketamine, memantine, lanicemine, dextromethorphan, Ro 25-6981, ifenprodil, and traxoprodil) targeting the NMDA receptor by ligand-protein docking and molecular dynamics simulations. The results indicated that Ro 25-6981 has the strongest binding affinity to the TMD region of the NMDA receptor among the eight selected drugs, suggesting its potential effective inhibitory effect. We also calculated the critical binding-site residues at the active site and found that residues Leu124 and Met63 contributed the most to the binding energy by decomposing the free energy contributions on a per-residue basis. We further compared S-ketamine and its chiral molecule, R-ketamine, and found that R-ketamine had a stronger binding capacity to the NMDA receptor. This study provides a computational reference for the treatment of depression targeting NMDA receptors, and the proposed results will provide potential strategies for further antidepressant development and is a useful resource for the future discovery of fast-acting antidepressant candidates.

It's ok to be outnumbered - sub-stoichiometric modulation of homomeric protein complexes

An arsenal of molecular tools with increasingly diversified mechanisms of action is being developed by the scientific community to enable biological interrogation and pharmaceutical modulation of targets and pathways of ever increasing complexity. While most small molecules interact with the target of interest in a 1 : 1 relationship, a noteworthy number of recent examples were reported to bind in a sub-stoichiometric manner to a homomeric protein complex. This approach requires molecular understanding of the physiologically relevant protein assemblies and in-depth characterization of the compound's mechanism of action. The recent literature examples summarized here were selected to illustrate methods used to identify and characterize molecules with such mechanisms. The concept of one small molecule targeting a homomeric protein assembly is not new but the subject deserves renewed inspection in light of emerging technologies and increasingly diverse target biology, to ensure relevant in vitro systems are used and valuable compounds with potentially novel sub-stoichiometric mechanisms of action aren't overlooked.

ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure-function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.

How gene duplication diversifies the landscape of protein oligomeric state and function

Oligomeric proteins are central to cellular life and the duplication and divergence of their genes is a key driver of evolutionary innovations. The duplication of a gene coding for an oligomeric protein has numerous possible outcomes, which motivates questions on the relationship between structural and functional divergence. How do protein oligomeric states diversify after gene duplication? In the simple case of duplication of a homo-oligomeric protein gene, what properties can influence the fate of descendant paralogs toward forming independent homomers or maintaining their interaction as a complex? Furthermore, how are functional innovations associated with the diversification of oligomeric states? Here, we review recent literature and present specific examples in an attempt to illustrate and answer these questions.

Non-adaptive complexity and biochemical function

Intricate biochemical structures are usually thought to be useful, because natural selection preserves them from degradation by a constant hail of destructive mutations. Biochemists therefore often deliberately disrupt them to understand how complexity improves protein function or fitness. However, evolutionary theory suggests that even useless complexity that never improved fitness can become completely essential if a simple set of evolutionary conditions is fulfilled. We review evidence that stable protein complexes, protein-chaperone interactions, and complexes consisting of several paralogs all fulfill these conditions. This makes reverse genetics or destructive mutagenesis unsuitable for assigning functions to these kinds of complexity. Instead, we advocate that incorporating evolutionary approaches into biochemistry overcomes this difficulty and allows us to distinguish useless from useful biochemical complexity.

Known allosteric proteins have central roles in genetic disease

Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, contribute to genetic disease and comorbidities much more than non-allosteric proteins, and there is an association between being allosteric and involvement in disease; 2) they are enriched in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system; 3) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 4) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and less due to their dynamical properties.

On Monomeric and Multimeric Structures-Based Protein-Ligand Interactions

Many ligands simultaneously interact with multiple protein chains in quaternary structure (QS). However, a significant number of previous studies on template-based modeling of protein-ligand interactions were based on monomeric structure (MS), which may suffer from incomplete binding information. The defects of using MS rather than QS have not been systematically studied before. In this work, based on molecular docking experiments and binding free energy estimations, we performed a large-scale comparison of the protein-ligand interactions in both forms of structures. We found that 1) about 18.6 percent biologically relevant ligands bind multiple chains in QS simultaneously. 2) For more than 95 percent complexes with multiple chains involved in the interactions, the binding free energy is lower for the QS form than the MS form. 3) For over 70 percent complexes with multi-chain binding pockets, docking with QS yields more accurate ligand conformations than with MS. While for about 1.82 percent complexes, accurate docking conformations were obtained by MS. Based on this work, it is encouraged to make use of QS rather than MS in future studies on protein-ligand interactions.

Towards the Idea of Molecular Brains

How can single cells without nervous systems perform complex behaviours such as habituation, associative learning and decision making, which are considered the hallmark of animals with a brain? Are there molecular systems that underlie cognitive properties equivalent to those of the brain? This review follows the development of the idea of molecular brains from Darwin’s “root brain hypothesis”, through bacterial chemotaxis, to the recent discovery of neuron-like r-protein networks in the ribosome. By combining a structural biology view with a Bayesian brain approach, this review explores the evolutionary labyrinth of information processing systems across scales. Ribosomal protein networks open a window into what were probably the earliest signalling systems to emerge before the radiation of the three kingdoms. While ribosomal networks are characterised by long-lasting interactions between their protein nodes, cell signalling networks are essentially based on transient interactions. As a corollary, while signals propagated in persistent networks may be ephemeral, networks whose interactions are transient constrain signals diffusing into the cytoplasm to be durable in time, such as post-translational modifications of proteins or second messenger synthesis. The duration and nature of the signals, in turn, implies different mechanisms for the integration of multiple signals and decision making. Evolution then reinvented networks with persistent interactions with the development of nervous systems in metazoans. Ribosomal protein networks and simple nervous systems display architectural and functional analogies whose comparison could suggest scale invariance in information processing. At the molecular level, the significant complexification of eukaryotic ribosomal protein networks is associated with a burst in the acquisition of new conserved aromatic amino acids. Knowing that aromatic residues play a critical role in allosteric receptors and channels, this observation suggests a general role of π systems and their interactions with charged amino acids in multiple signal integration and information processing. We think that these findings may provide the molecular basis for designing future computers with organic processors.

CoeViz 2: Protein Graphs Derived From Amino Acid Covariance

Proteins by and large carry out their molecular functions in a folded state when residues, distant in sequence, assemble together in 3D space to bind a ligand, catalyze a reaction, form a channel, or exert another concerted macromolecular interaction. It has been long recognized that covariance of amino acids between distant positions within a protein sequence allows for the inference of long range contacts to facilitate 3D structure modeling. In this work, we investigated whether covariance analysis may reveal residues involved in the same molecular function. Building upon our previous work, CoeViz, we have conducted a large scale covariance analysis among 7,595 non-redundant proteins with resolved 3D structures to assess 1) whether the residues with the same function coevolve, 2) which covariance metric captures such couplings better, and 3) how different molecular functions compare in this context. We found that the chi-squared metric is the most informative for the identification of coevolving functional sites, followed by the Pearson correlation-based, whereas mutual information is the least informative. Of the seven categories of the most common natural ligands, including coenzyme A, dinucleotide, DNA/RNA, heme, metal, nucleoside, and sugar, the trace metal binding residues display the most prominent coupling, followed by the sugar binding sites. We also developed a web-based tool, CoeViz 2, that enables the interactive visualization of covarying residues as cliques from a larger protein graph. CoeViz 2 is publicly available at https://research.cchmc.org/CoevLab/.

Analyzing In Silico the Relationship Between the Activation of the Edema Factor and Its Interaction With Calmodulin

Molecular dynamics (MD) simulations have been recorded on the complex between the edema factor (EF) of Bacilllus anthracis and calmodulin (CaM), starting from a structure with the orthosteric inhibitor adefovir bound in the EF catalytic site. The starting structure has been destabilized by alternately suppressing different co-factors, such as adefovir ligand or ions, revealing several long-distance correlations between the conformation of CaM, the geometry of the CaM/EF interface, the enzymatic site and the overall organization of the complex. An allosteric communication between CaM/EF interface and the EF catalytic site, highlighted by these correlations, was confirmed by several bioinformatics approaches from the literature. A network of hydrogen bonds and stacking interactions extending from the helix V of of CaM, and the residues of the switches A, B and C, and connecting to catalytic site residues, is a plausible candidate for the mediation of allosteric communication. The greatest variability in volume between the different MD conditions was also found for cavities present at the EF/CaM interface and in the EF catalytic site. The similarity between the predictions from literature and the volume variability might introduce the volume variability as new descriptor of allostery.

Determining the interaction status and evolutionary fate of duplicated homomeric proteins

Oligomeric proteins are central to life. Duplication and divergence of their genes is a key evolutionary driver, also because duplications can yield very different outcomes. Given a homomeric ancestor, duplication can yield two paralogs that form two distinct homomeric complexes, or a heteromeric complex comprising both paralogs. Alternatively, one paralog remains a homomer while the other acquires a new partner. However, so far, conflicting trends have been noted with respect to which fate dominates, primarily because different methods and criteria are being used to assign the interaction status of paralogs. Here, we systematically analyzed all Saccharomyces cerevisiae and Escherichia coli oligomeric complexes that include paralogous proteins. We found that the proportions of homo-hetero duplication fates strongly depend on a variety of factors, yet that nonetheless, rigorous filtering gives a consistent picture. In E. coli about 50%, of the paralogous pairs appear to have retained the ancestral homomeric interaction, whereas in S. cerevisiae only ~10% retained a homomeric state. This difference was also observed when unique complexes were counted instead of paralogous gene pairs. We further show that this difference is accounted for by multiple cases of heteromeric yeast complexes that share common ancestry with homomeric bacterial complexes. Our analysis settles contradicting trends and conflicting previous analyses, and provides a systematic and rigorous pipeline for delineating the fate of duplicated oligomers in any organism for which protein-protein interaction data are available.

About half of all proteins assemble as oligomers, either by self-interaction (homomers) or via interaction with another protein (heteromers). The latter can be unrelated, yet, quite commonly, the interacting proteins are paralogs, namely two genes that arose by gene duplication. Indeed, while a homomer is encoded by a single gene, heteromers demand two genes as a minimum. Duplication can therefore yield two discrete homomeric complexes or a single heteromer. Do paralogs tend to retain the ancestral homomeric interaction, or do they mostly diverge into heteromeric complexes? Despite several studies addressing this question, to date, we lack a systematic, rigorous approach for delineating the oligomeric fates of paralogs on an organism scale. To this end, we developed a new pipeline for analysis of molecular interaction databases that includes various filtering steps and unambiguous definitions of all possible oligomeric fates. Applying this method to Escherichia coli and Saccharomyces cerevisiae we noted that paralogous pairs tend to remain homomeric in the former while in the latter heteromeric complexes dominate. We consequently note a systematic trend of homomeric bacterial proteins diverging into heteromeric complexes in eukaryotes.

Selective Modulation of Dynamic Protein Complexes

Dynamic proteins perform critical roles in cellular machines, including those that control proteostasis, transcription, translation, and signaling. Thus, dynamic proteins are prime candidates for chemical probe and drug discovery but difficult targets because they do not conform to classical rules of design and screening. Selectivity is pivotal for candidate probe molecules due to the extensive interaction network of these dynamic hubs. Recognition that the traditional rules of probe discovery are not necessarily applicable to dynamic proteins and their complexes, as well as technological advances in screening, have produced remarkable results in the last 2-4 years. Particularly notable are the improvements in target selectivity for small-molecule modulators of dynamic proteins, especially with techniques that increase the discovery likelihood of allosteric regulatory mechanisms. We focus on approaches to small-molecule screening that appear to be more suitable for highly dynamic targets and have the potential to streamline identification of selective modulators.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

BACKGROUND

Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

OBJECTIVE

The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

METHOD

This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

RESULTS

This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

CONCLUSION

The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

Solvent Effect on the Regulation of Urea Hydrolysis Reactions by Copper Complexes

Abiotic allosterism is most commonly observed in hetero-bimetallic supramolecular complexes and less frequently in homo-bimetallic complexes. The use of hemilabile ligands with high synthetic complexity enables the catalytic center by the addition or removal of allosteric effectors and simplicity is unusually seen in these systems. Here we describe a simpler approach to achieve kinetic regulation by the use of dimeric Schiff base copper complexes connected by a chlorido ligand bridge. The chlorido ligand acts as a weak link between monomers, generating homo-bimetallic self-aggregating supramolecular complexes that generate monomeric species in different reaction rates depending on the solvent and on the radical moiety of the ligand. The ligand exchange was observed by electron paramagnetic resonance (EPR) and conductivity measurements, indicating that complexes with ligands bearing methoxyl (CuIIL2) and ethoxyl (CuIIL5) radicals were more prone to form dimeric complexes in comparison to ligands bearing hydrogen (CuIIL1), methyl (CuIIL3), or t-butyl (CuIIL4) radicals. The equilibrium between dimer and monomer afforded different reactivities of the complexes in acetonitrile/water and methanol/water mixtures toward urea hydrolysis as a model reaction. It was evident that the dimeric species were inactive and that by increasing the water concentration in the reaction medium, the dimeric structures dissociated to form the active monomeric structures. This behavior was more pronounced when methanol/water mixtures were employed due to a slower displacement of the chlorido bridge in this medium than in the acetonitrile/water mixtures, enabling the reaction kinetics to be evaluated. This effect was attributed to the preferential solvation shell by the organic solvents and in essence, an upregulation behavior was observed due to the intrinsic nature of the complexes to form dimeric structures in solution that could be dismantled in the presence of water, indicating their possible use as water-sensors in organic solvents.

Biophysical insight into the interaction of levocabastine with human serum albumin: spectroscopy and molecular docking approach

Interaction of levocabastine with human serum albumin (HSA) is investigated by applying fluorescence spectroscopy, circular dichroism spectroscopy and molecular docking methods. Levocabastine is an important drug in treatment of allergy and currently a target drug for drug repurposing to treat other diseases like vernal keratoconjuctivitis. Fluorescence quenching data revealed that levocabastine bind weakly to protein with binding constant in the order of 10³ M^-1. Förster resonance energy transfer results indicated the binding distance of 2.28 nm for levocabastine. Synchronous fluorescence result suggest slight blue shift for tryptophan upon levocabastine binding, binding of levocabastine impelled rise in α-helical structure in protein, while there are minimal changes in tertiary structure in protein. Moreover, docking results indicate levocabastine binds to pocket near to the drug site-I in HSA via hydrogen bonding and hydrophobic interactions. Understanding the interaction of levocabastine with HSA is significant for the advancement of therapeutic and diagnostic strategies for optimal treatment results.Communicated by Ramaswamy H. Sarma.

Recent advances suggest increased influence of selective pressure in allostery

Allosteric regulation of protein functions is ubiquitous in organismal biology, but the principles governing its evolution are not well understood. Here we discuss recent studies supporting the large-scale existence of latent allostery in ancestor proteins of superfamilies. As suggested, the evolution of allostery could be driven by the need for specificity in paralogs of slow evolving protein complexes with conserved active sites. The same slow evolution is displayed by purifying selection exhibited in allosteric proteins with somatic mutations involved in cancer, where disease-associated mutations are enriched in both orthosteric and allosteric sites. Consequently, disease-associated variants can be used to identify druggable allosteric sites that are specific for paralogs in protein superfamilies with otherwise similar functions.

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.

Intrinsic dynamics is evolutionarily optimized to enable allosteric behavior

Allosteric behavior is central to the function of many proteins, enabling molecular machinery, metabolism, signaling and regulation. Recent years have shown that the intrinsic dynamics of allosteric proteins defined by their 3-dimensional architecture or by the topology of inter-residue contacts favors cooperative motions that bear close similarity to structural changes they undergo during their allosteric actions. These conformational motions are usually driven by energetically favorable or soft modes at the low frequency end of the mode spectrum, and they are evolutionarily conserved among orthologs. These observations brought into light evolutionary adaptation mechanisms that help maintain, optimize or regulate allosteric behavior as the evolution from bacterial to higher organisms introduces sequential heterogeneities and structural complexities.

Ligands and Receptors with Broad Binding Capabilities Have Common Structural Characteristics: An Antibiotic Design Perspective

The spread of antibiotic resistance is one of the most serious global public-health problems. Here we show that a particular class of homomers with binding sites spanning multiple protein chains is particularly suitable for targeting by broad-spectrum antibacterial agents because due to the slow evolutionary change of such binding pockets, ligands of such homomers are much more likely to bind their homologs than ligands of monomers, or homomers with a single-chain binding site. Additionally, using de novo ligand design and deep learning, we show that the chemical compounds that can bind several different receptors have common structural characteristics and that halogens and fragments similar to the building blocks existing antimicrobials are overrepresented in them. Finally, we show that binding multiple receptors selects for flexible compounds, which are less likely to accumulate in Gram-negative bacteria; thus there is trade-off between reducing the emergence of resistance by multitargeting and broad-spectrum antibacterial activity.

Ligand Binding Site Structure Shapes Folding, Assembly and Degradation of Homomeric Protein Complexes

Ligand binding site structure has profound consequences for the evolution of function of protein complexes, particularly in homomers—complexes comprising multiple copies of the same protein. Previously, we have shown that homomers with multichain binding sites (MBSs) are characterized by more conserved binding sites and quaternary structure, and qualitatively different allosteric pathways than homomers with single-chain binding sites (SBSs) or monomers. Here, using computational methods, we show that the folds of single-domain MBS and SBS homomers are different, and SBS homomers are likely to be folded cotranslationally, while MBS homomers are more likely to form post-translationally and rely on more advanced folding-assistance and quality control mechanisms, which include chaperonins. In addition, our findings demonstrate that MBS homomers are qualitatively different from monomers, while SBS homomers are much less distinct, supporting the hypothesis that the evolution of quaternary structure in SBS homomers is significantly influenced by stochastic processes.