Ligand binding to proteins: the binding landscape model

Sigma-Class Glutathione Transferases (GSTσ): A New Target with Potential for Helminth Control

Glutathione transferases (GSTs EC 2.5.1.18) are critical components of phase II metabolism, instrumental in xenobiotics' metabolism. Their primary function involves conjugating glutathione to both endogenous and exogenous toxic compounds, which increases their solubility and enables their ejection from cells. They also play a role in the transport of non-substrate compounds and immunomodulation, aiding in parasite establishment within its host. The cytosolic GST subfamily is the most abundant and diverse in helminths, and sigma-class GST (GSTσ) belongs to it. This review focuses on three key functions of GSTσ: serving as a detoxifying agent that provides drug resistance, functioning as an immune system modulator through its involvement in prostaglandins synthesis, and acting as a vaccine antigen.

Systematic Evaluation of Protein-Small Molecule Hybrids on the Yeast Surface

Protein-small molecule hybrids are structures that have the potential to combine the inhibitory properties of small molecules and the specificities of binding proteins. However, achieving such synergies is a substantial engineering challenge with fundamental principles yet to be elucidated. Recent work has demonstrated the power of the yeast display-based discovery of hybrids using a combination of fibronectin-binding domains and thiol-mediated conjugations to introduce small-molecule warheads. Here, we systematically study the effects of expanding the chemical diversity of these hybrids on the yeast surface by investigating a combinatorial set of fibronectins, noncanonical amino acid (ncAA) substitutions, and small-molecule pharmacophores. Our results show that previously discovered thiol-fibronectin hybrids are generally tolerant of a range of ncAA substitutions and retain binding functions to carbonic anhydrases following click chemistry-mediated assembly of hybrids with diverse linker structures. Most surprisingly, we identified several cases where replacement of a potent acetazolamide warhead with a substantially weaker benzenesulfonamide warhead still resulted in the assembly of multiple functional hybrids. In addition to these unexpected findings, we expanded the throughput of our system by validating a 96-well plate-based format to produce yeast-displayed hybrid conjugates in parallel. These efficient explorations of hybrid chemical diversity demonstrate that there are abundant opportunities to expand the functions of protein-small molecule hybrids and elucidate principles that dictate their efficient discovery and design.

Prediction of protein-ligand binding affinity via deep learning models

Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.

RNA-dependent proteome solubility maintenance in Escherichia coli lysates analysed by quantitative mass spectrometry: Proteomic characterization in terms of isoelectric point, structural disorder, functional hub, and chaperone network

Protein aggregation, a consequence of misfolding and impaired proteostasis, can lead to cellular malfunctions such as various proteinopathies. The mechanisms protecting proteins from aggregation in complex cellular environments have long been investigated, often from a protein-centric viewpoint. However, our study provides insights into a crucial, yet overlooked actor: RNA. We found that depleting RNAs from Escherichia coli lysates induces global protein aggregation. Our quantitative mass spectrometry analysis identified over 900 statistically significant proteins from the Escherichia coli proteome whose solubility depends on RNAs. Proteome-wide characterization showed that the RNA dependency is particularly enriched among acidic proteins, intrinsically disordered proteins, and structural hub proteins. Moreover, we observed distinct differences in RNA-binding mode and Gene Ontology categories between RNA-dependent acidic and basic proteins. Notably, the solubility of key molecular chaperones [Trigger factor, DnaJ, and GroES] is largely dependent on RNAs, suggesting a yet-to-be-explored hierarchical relationship between RNA-based chaperone (termed as chaperna) and protein-based chaperones, both of which constitute the whole chaperone network. These findings provide new insights into the RNA-centric role in maintaining healthy proteome solubility in vivo, where proteins associate with a variety of RNAs, either stably or transiently.

Binding interactions of hydrophobically-modified flavonols with β-glucosidase: fluorescence spectroscopy and molecular modelling study

Natural flavonoids are capable of inhibiting glucosidase activity, so they can be used for treating diabetes mellitus and hypertension. However, molecular-level details of their interactions with glucosidase enzymes remain poorly understood. This paper describes the synthesis and spectral characterization of a series of fluorescent flavonols and their interaction with the β-glucosidase enzyme. To tune flavonol-enzyme interaction modes and affinity, we introduced different polar halogen-containing groups or bulky aromatic/alkyl substituents in the peripheral 2-aryl ring of a flavonol moiety. Using fluorescence spectroscopy methods in combination with molecular docking and molecular dynamics simulations, we examined the binding affinity and identified probe binding patterns, which are critical for steric blockage of the key catalytic residues of the enzyme. Using a fluorescent assay, we demonstrated that the binding of flavonol 2e to β-glucosidase decreased its enzymatic activity up to 3.5 times. In addition, our molecular docking and all-atom molecular dynamics simulations suggest that the probe binding is driven by hydrophobic interactions with aromatic Trp and Tyr residues within the catalytic glycone binding pockets of β-glucosidase. Our study provides a new insight into structure-property relations for flavonol-protein interactions, which govern their enzyme binding, and outlines a framework for a rational design of new flavonol-based potent inhibitors for β-glucosidases.

The Energetic Landscape of Catch Bonds in TCR Interfaces

Recognition of peptide/MHC complexes by αβ TCRs has traditionally been viewed through the lens of conventional receptor-ligand theory. Recent work, however, has shown that TCR recognition and T cell signaling can be profoundly influenced and tuned by mechanical forces. One outcome of applied force is the catch bond, where TCR dissociation rates decrease (half-lives increase) when limited force is applied. Although catch bond behavior is believed to be widespread in biology, its counterintuitive nature coupled with the difficulties of describing mechanisms at the structural level have resulted in considerable mystique. In this review, we demonstrate that viewing catch bonds through the lens of energy landscapes, barriers, and the ensuing reaction rates can help demystify catch bonding and provide a foundation on which atomic-level TCR catch bond mechanisms can be built.

Ligand binding free energy evaluation by Monte Carlo Recursion

The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: (i) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; (ii) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called 'end-point' methods; (iii) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and (iv) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W(b,T), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N = 75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa/MCLiBELa project (https://github.com/alessandronascimento/LiBELa).

Identifying Protein-Ligand Interactions via a Novel Distance Self-Feedback Biomolecular Interaction Network

Efficient and accurate characterizations of protein-ligand interactions are key to understanding biology at the molecular level. They are particularly useful in pharmaceutical industry applications. They are usually computationally demanding for those widely applied dynamics-based methods in identifying important residues or calculating ligand binding free energy. In this work, we proposed a graph deep learning (DL) framework, namely, the distance self-feedback biomolecular interaction network (DSBIN), in which the relationship between the complex structure and binding affinity can be established by means of a carefully designed distance self-feedback module and interaction layer. Our model can directly provide a quantitative evaluation of inhibitor binding affinities (pK_d). More importantly, the DSBIN model efficiently identifies key interactions for inhibitor binding and thus intrinsically bears the interpretability. Its generalization performance was further verified using 1405 unseen structures. The predicted binding free energies' deviations were calculated to be less than 1.37 kcal/mol for more than 55% structures. Moreover, we also compared the DSBIN model with a commonly used theoretical method in calculating the substrate binding free energy, MM/GBSA. Our results show that the current DL model has generally better performance in predicting the binding free energy. For a specific complex system, mannopentaose/TmCBM27, the DSBIN predicted binding free energy is -8.21 kcal/mol, which is very close to experimentally measured -7.76 kcal/mol and MM/GBSA calculated -7.16 kcal/mol. Meanwhile, all important aromatic residues around the binding pocket can be identified by our DL model. Considering the accuracy and efficiency of the newly developed DL model, it may be very helpful in the field of drug design and molecular recognition.

Peptide-dependent tuning of major histocompatibility complex motional properties and the consequences for cellular immunity

T cell receptors (TCRs) and other receptors of the immune system recognize peptides presented by class I or class II major histocompatibility complex (MHC) proteins. Although we generally distinguish between the MHC protein and its peptide, at an atomic level the two form a structural composite, which allows peptides to influence MHC properties and vice versa. One consequence is the peptide-dependent tuning of MHC structural dynamics, which contributes to protein structural adaptability and influences how receptors identify and bind targets. Peptide-dependent tuning of MHC protein dynamics can impact processes such as antigenicity, TCR cross-reactivity, and T cell repertoire selection. Motional tuning extends beyond the binding groove, influencing peptide selection and exchange, as well as interactions with other immune receptors. Here, we review recent findings showing how peptides can affect the dynamic and adaptable nature of MHC proteins. We highlight consequences for immunity and demonstrate how MHC proteins have evolved to be highly sensitive dynamic reporters, with broad immunological consequences.

Prototypical Allosterism in a Simple Ditopic Ligand: Gas-Phase Topologies of Cucurbit[n]uril·n-Alkylammonium Complexes Controlled by Binding in the Second Site

We have employed mass spectrometry, ion mobility, and computational techniques to characterize complexes of n-alkylammonium ions with cucurbit[5]uril (CB[5]) and cucurbit[6]uril (CB[6]) ligands in the gas phase. Nonrotaxane structures are energetically preferred and experimentally observed for all CB[5] complexes. Pseudorotaxane structures are computationally favored and experimentally observed for [CB[6]·n-alkylammonium]⁺ complexes, but the addition of a second cation (proton, alkali metal ion, another alkylammonium ion, or guanidinium) on the opposite rim of CB[6] causes sufficiently unfavorable steric interactions that n-pentylammonium and longer chains no longer remain threaded through the CB[6] cavity; nonrotaxane topologies are then favored. This provides a very simple example of negative allosteric interactions and molecular structure switching in these complexes.

Long-range electrostatic interactions significantly modulate the affinity of dynein for microtubules

The dynein family of microtubule minus-end-directed motor proteins drives diverse functions in eukaryotic cells, including cell division, intracellular transport, and flagellar beating. Motor protein processivity, which characterizes how far a motor walks before detaching from its filament, depends on the interaction between its microtubule-binding domain (MTBD) and the microtubule. Dynein's MTBD switches between high- and low-binding affinity states as it steps. Significant structural and functional data show that specific salt bridges within the MTBD and between the MTBD and the microtubule govern these affinity state shifts. However, recent computational work suggests that nonspecific, long-range electrostatic interactions between the MTBD and the microtubule may also play an important role in the processivity of dynein. To investigate this hypothesis, we mutated negatively charged amino acids remote from the dynein MTBD-microtubule-binding interface to neutral residues and measured the binding affinity using microscale thermophoresis and optical tweezers. We found a significant increase in the binding affinity of the mutated MTBDs for microtubules. Furthermore, we found that charge screening by free ions in solution differentially affected the binding and unbinding rates of MTBDs to microtubules. Together, these results demonstrate a significant role for long-range electrostatic interactions in regulating dynein-microtubule affinity. Moreover, these results provide insight into the principles that potentially underlie the biophysical differences between molecular motors with various processivities and protein-protein interactions more generally.

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

Polyanions Cause Protein Destabilization Similar to That in Live Cells

The structural stability of proteins is found to markedly change upon their transfer to the crowded interior of live cells. For some proteins, the stability increases, while for others, it decreases, depending on both the sequence composition and the type of host cell. The mechanism seems to be linked to the strength and conformational bias of the diffusive in-cell interactions, where protein charge is found to play a decisive role. Because most proteins, nucleotides, and membranes carry a net-negative charge, the intracellular environment behaves like a polyanionic (Z:1) system with electrostatic interactions different from those of standard 1:1 ion solutes. To determine how such polyanion conditions influence protein stability, we use negatively charged polyacetate ions to mimic the net-negatively charged cellular environment. The results show that, per Na+ equivalent, polyacetate destabilizes the model protein SOD1barrel significantly more than monoacetate or NaCl. At an equivalent of 100 mM Na+, the polyacetate destabilization of SOD1barrel is similar to that observed in live cells. By the combined use of equilibrium thermal denaturation, folding kinetics, and high-resolution nuclear magnetic resonance, this destabilization is primarily assigned to preferential interaction between polyacetate and the globally unfolded protein. This interaction is relatively weak and involves mainly the outermost N-terminal region of unfolded SOD1barrel. Our findings point thus to a generic influence of polyanions on protein stability, which adds to the sequence-specific contributions and needs to be considered in the evaluation of in vivo data.

Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

The designing of drugs that can simultaneously affect different protein targets is one novel and promising way to treat complex diseases. Multitarget drugs act on multiple protein receptors each implicated in the same disease state, and may be considered to be more beneficial than conventional drug therapies. For example, these drugs can have improved therapeutic potency due to synergistic effects on multiple targets, as well as improved safety and resistance profiles due to the combined regulation of potential primary therapeutic targets and compensatory elements and lower dosage typically required. This review analyzes in-silico methods that facilitate multitarget drug design that facilitate the discovery and development of novel therapeutic agents. Here presented is a summary of the progress in structure-based drug discovery techniques that study the process of molecular recognition of targets and ligands, moving from static molecular docking to improved molecular dynamics approaches in multitarget drug design, and the advantages and limitations of each.

DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation

Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design. In recent years, several deep learning models were developed to learn important physical–chemical and spatial information to predict ligand-binding pockets in a protein. However, ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for computational molecular biologists using a single web-based tool. Hence, we believe, by using closer to real application data set as training and by providing ligand information, an enhanced model to identify accurate pockets can be obtained. In this article, we propose a new deep learning method called DeepBindPoc for identifying and ranking ligand-binding pockets in proteins. The model is built by using information about the binding pocket and associated ligand. We take advantage of the mol2vec tool to represent both the given ligand and pocket as vectors to construct a densely fully connected layer model. During the training, important features for pocket-ligand binding are automatically extracted and high-level information is preserved appropriately. DeepBindPoc demonstrated a strong complementary advantage for the detection of native-like pockets when combined with traditional popular methods, such as fpocket and P2Rank. The proposed method is extensively tested and validated with standard procedures on multiple datasets, including a dataset with G-protein Coupled receptors. The systematic testing and validation of our method suggest that DeepBindPoc is a valuable tool to rank near-native pockets for theoretically modeled protein with unknown experimental active site but have known ligand. The DeepBindPoc model described in this article is available at GitHub (https://github.com/haiping1010/DeepBindPoc) and the webserver is available at (http://cbblab.siat.ac.cn/DeepBindPoc/index.php).

Parsimonious evolutionary scenario for the origin of allostery and coevolution patterns in proteins

Proteins display generic properties that are challenging to explain by direct selection, notably allostery, the capacity to be regulated through long-range effects, and evolvability, the capacity to adapt to new selective pressures. An evolutionary scenario is proposed where proteins acquire these two features indirectly as a by-product of their selection for a more fundamental property, exquisite discrimination, the capacity to bind discriminatively very similar ligands. Achieving this task is shown to typically require proteins to undergo a conformational change. We argue that physical and evolutionary constraints impel this change to be controlled by a group of sites extending from the binding site. Proteins can thus acquire a latent potential for allosteric regulation and evolutionary adaptation because of long-range effects that initially arise as evolutionary spandrels. This scenario accounts for the groups of conserved and coevolving residues observed in multiple sequence alignments. However, we propose that most pairs of coevolving and contacting residues inferred from such alignments have a different origin, related to thermal stability. A physical model is presented that illustrates this evolutionary scenario and its implications. The scenario can be implemented in experiments of protein evolution to directly test its predictions.

Geometry and Flexibility of Optimal Catalysts in a Minimal Elastic Model

We have general knowledge of the principles by which catalysts accelerate the rate of chemical reactions but no precise understanding of the geometrical and physical constraints to which their design is subject. To analyze these constraints, we introduce a minimal model of catalysis based on elastic networks where the implications of the geometry and flexibility of a catalyst can be studied systematically. The model demonstrates the relevance and limitations of the principle of transition-state stabilization: optimal catalysts are found to have a geometry complementary to the transition state but a degree of flexibility that nontrivially depends on the parameters of the reaction as well as on external parameters such as the concentrations of reactants and products. The results illustrate how simple physical models can provide valuable insights into the design of catalysts.

The pressure and temperature perturbation approach reveals a whole variety of conformational substates of amyloidogenic hIAPP monitored by 2D NMR spectroscopy

The intrinsically disordered human islet amyloid polypeptide (hIAPP) is a 37 amino acid peptide hormone that is secreted by pancreatic beta cells along with glucagon and insulin. The glucose metabolism of humans is regulated by a balanced ratio of insulin and hIAPP. The disturbance of this balance can result in the development of type-2 diabetes mellitus (T2DM), whose pathogeny is associated by self-assembly induced aggregation and amyloid deposits of hIAPP into nanofibrils. Here, we report pressure- and temperature-induced changes of NMR chemical shifts of monomeric hIAPP in bulk solution to elucidate the contribution of conformational substates in a residue-specific manner in their role as molecular determinants for the initial self-assembly. The comparison with a similar peptide, the Alzheimer peptide Aβ(1-40), which is leading to self-assembly induced aggregation and amyloid deposits as well, reveals that in both peptides highly homologous areas exist (Q10-‍L16 and N21-L27 in hIAPP and Q15-A21 and S26-I32 in Aβ). The N-terminal area of hIAPP around amino acid residues 3-20 displays large differences in pressure sensitivity compared to Aβ, pinpointing to a different structural ensemble in this sequence element which is of helical origin in hIAPP. Knowledge of the structural nature of the highly amyloidogenic hIAPP and the differences with respect to the conformational ensemble of Aβ(1-40) will help to identify molecular determinants of self-assembly as well as cross-seeded assembly initiated aggregation and help facilitate the rational design of drugs for therapeutic use.

Protein-protein binding pathways and calculations of rate constants using fully-continuous, explicit-solvent simulations

A grand challenge in the field of biophysics has been the complete characterization of protein-protein binding processes at atomic resolution. This characterization requires the direct simulation of binding pathways starting from the initial, unbound state and proceeding through states that are too transient to be captured by experiment. Here, we applied the weighted ensemble path sampling strategy to orchestrate atomistic simulation of protein-protein binding pathways. Our simulation generated 203 fully-continuous and independent pathways along with rate constants for the binding process involving the barnase and barstar proteins. Results reveal multiple binding pathways along a "funnel-like" free energy landscape in which the formation of the "encounter complex" intermediate is rate-limiting followed by a relatively rapid rearrangement of the encounter complex to the bound state. Among all diffusional collisions, only ∼11% were productive. In the most probable binding pathways, the proteins rotated to a large extent (likely via electrostatic steering) in order to collide productively followed by "rolling" of the proteins along each other's binding interfaces to reach the bound state. Consistent with experiment, R59 was identified as the most kinetically important barnase residue for the binding process. Furthermore, protein desolvation occurs late in the binding process during the rearrangement of the encounter complex to the bound state. Notably, the positions of crystallographic water molecules that bridge hydrogen bonds between barnase and barstar are occupied in the bound-state ensemble. Our simulation was completed in a month using 1600 CPU cores at a time, demonstrating that it is now practical to carry out atomistic simulations of protein-protein binding.

RNA as a key factor in driving or preventing self-assembly of the TAR DNA-binding protein 43

Amyotrophic lateral sclerosis and frontotemporal lobar degeneration are incurable motor neuron diseases associated with muscle weakness, paralysis and respiratory failure. Accumulation of TAR DNA-binding protein 43 (TDP-43) as toxic cytoplasmic inclusions is one of the hallmarks of these pathologies. TDP-43 is an RNA-binding protein responsible for regulating RNA transcription, splicing, transport and translation. Aggregated TDP-43 does not retain its physiological function.

Here, we exploit the ability of TDP-43 to bind specific RNA sequences to validate our hypothesis that the native partners of a protein can be used to interfere with its ability to self-assemble into aggregates. We propose that binding of TDP-43 to specific RNA can compete with protein aggregation. This study provides a solid proof of concept to the hypothesis that natural interactions can be exploited to increase protein solubility and could be adopted as a more general rational therapeutic strategy.

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We found that binding of the RRM domains of TDP-43 to specific RNA competes with protein aggregation.

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This study provides a solid proof of concept to the hypothesis that natural interactions can be exploited to increase protein solubility.

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The concept could be adopted as a more general rationale for protein-specific drug design.

Emergent Concepts of Receptor Pharmacology

Pharmacology, the chemical control of physiology, emerged as an offshoot of physiology when the physiologists using chemicals to probe physiological systems became more interested in the probes than the systems. Pharmacologists were always, and in many ways still are, bound to study drugs in systems they do not fully understand. Under these circumstances, null methods were the main ways in which conclusions about biologically active molecules were made. However, as understanding of the basic mechanisms of cellular function and biochemical systems were elucidated, so too did the understanding of how drugs affected these systems. Over the past 20 years, new ideas have emerged in the field that have completely changed and revitalized it; these are described herein. It will be seen how null methods in isolated tissues gave way to, first biochemical radioligand binding studies, and then to a wide array of functional assay technologies that can measure the effects of molecules on drug targets. In addition, the introduction of molecular dynamics, the appreciation of the allosteric nature of receptors, protein X-ray crystal structures, genetic manipulations in the form of knock-out and knock-in systems and Designer Receptors Exclusively Activated by Designer Drugs have revolutionized pharmacology.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.

Effect of ligand binding on a protein with a complex folding landscape

Ligand binding to a protein can stabilize it significantly against unfolding. The variation of the folding free energy, ΔΔG⁰, due to ligand binding can be derived from a simple reaction scheme involving exclusive binding to the native state. One obtains the following expression: , where K_d is the ligand dissociation constant and L is its concentration, R is the universal gas constant and T is the temperature. This expression has been shown to correctly describe experimental results on multiple proteins. In the current work we studied the effect of ligand binding on the stability of the multi-domain protein adenylate kinase from E. coli (AKE). Unfolding experiments were conducted using single-molecule FRET spectroscopy, which allowed us to directly obtain the fraction of unfolded protein in a model-free way from FRET efficiency histograms. Surprisingly, it was found that the effect of two inhibitors (Ap₅A and AMPPNP) and a substrate (AMP) on the stability of AKE was much smaller than expected based on K_d values obtained independently using microscale thermophoresis. To shed light on this issue, we measured the K_d for Ap₅A over a range of chemical denaturant concentrations where the protein is still folded. It was found that K_d increases dramatically over this range, likely due to the population of folding intermediates, whose binding to the ligand is much weaker than that of the native state. We propose that binding to folding intermediates may dominate the effect of ligands on the stability of multi-domain proteins, and could therefore have a strong impact on protein homeostasis in vivo.

An integrated perspective on RNA aptamer ligand-recognition models: clearing muddy waters

Riboswitches are short RNA motifs that sensitively and selectively bind cognate ligands to modulate gene expression. Like protein receptor-ligand pairs, their binding dynamics are traditionally categorized as following one of two paradigmatic mechanisms: conformational selection and induced fit. In conformational selection, ligand binding stabilizes a particular state already present in the receptor's dynamic ensemble. In induced fit, ligand-receptor interactions enable the system to overcome the energetic barrier into a previously inaccessible state. In this article, we question whether a polarized division of RNA binding mechanisms truly meets the conceptual needs of the field. We will review the history behind this classification of RNA-ligand interactions, and the way induced fit in particular has been rehabilitated by single-molecule studies of RNA aptamers. We will highlight several recent results from single-molecule experimental studies of riboswitches that reveal gaps or even contradictions between common definitions of the two terms, and we will conclude by proposing a more robust framework that considers the range of RNA behaviors unveiled in recent years as a reality to be described, rather than an increasingly unwieldy set of exceptions to the traditional models.

Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations

BACKGROUND

Predicting protein structure from amino acid sequence is a prominent problem in computational biology. The long range interactions (or non-local interactions) are known as the main source of complexity for protein folding and dynamics and play the dominant role in the compact architecture. Some simple but exact model, such as HP model, captures the pain point for this difficult problem and has important implications to understand the mapping between protein sequence and structure.

RESULTS

In this paper, we formulate the biological problem into optimization model to study the hydrophobic-hydrophilic model on 3D square lattice. This is a combinatorial optimization problem and known as NP-hard. Particle swarm optimization is utilized as the heuristic framework to solve the hard problem. To avoid premature in computation, we incorporated the Tabu search strategy. In addition, a pulling strategy was designed to accelerate the convergence of algorithm based on the characteristic of native protein structure. Together a novel hybrid method combining particle swarm optimization, Tabu strategy, and pulling strategy can fold the amino acid sequences on 3D square lattice efficiently. Promising results are reported in several examples by comparing with existing methods. This allows us to use this tool to study the protein stability upon amino acid mutation on 3D lattice. In particular, we evaluate the effect of single amino acid mutation and double amino acids mutation via 3D HP lattice model and some useful insights are derived.

CONCLUSION

We propose a novel hybrid method to combine several heuristic strategies to study HP model on 3D lattice. The results indicate that our hybrid method can predict protein structure more accurately and efficiently. Furthermore, it serves as a useful tools to probe the protein stability on 3D lattice and provides some biological insights.

Stilbene Boronic Acids Form a Covalent Bond with Human Transthyretin and Inhibit Its Aggregation

Transthyretin (TTR) is a homotetrameric protein. Its dissociation into monomers leads to the formation of fibrils that underlie human amyloidogenic diseases. The binding of small molecules to the thyroxin-binding sites in TTR stabilizes the homotetramer and attenuates TTR amyloidosis. Herein, we report on boronic acid-substituted stilbenes that limit TTR amyloidosis in vitro. Assays of affinity for TTR and inhibition of its tendency to form fibrils were coupled with X-ray crystallographic analysis of nine TTR·ligand complexes. The ensuing structure-function data led to a symmetrical diboronic acid that forms a boronic ester reversibly with serine 117. This diboronic acid inhibits fibril formation by both wild-type TTR and a common disease-related variant, V30M TTR, as effectively as does tafamidis, a small-molecule drug used to treat TTR-related amyloidosis in the clinic. These findings establish a new modality for covalent inhibition of fibril formation and illuminate a path for future optimization.

Biophysical Models of Protein Evolution: Understanding the Patterns of Evolutionary Sequence Divergence

For decades, rates of protein evolution have been interpreted in terms of the vague concept of functional importance. Slowly evolving proteins or sites within proteins were assumed to be more functionally important and thus subject to stronger selection pressure. More recently, biophysical models of protein evolution, which combine evolutionary theory with protein biophysics, have completely revolutionized our view of the forces that shape sequence divergence. Slowly evolving proteins have been found to evolve slowly because of selection against toxic misfolding and misinteractions, linking their rate of evolution primarily to their abundance. Similarly, most slowly evolving sites in proteins are not directly involved in function, but mutating these sites has a large impact on protein structure and stability. In this article, we review the studies in the emerging field of biophysical protein evolution that have shaped our current understanding of sequence divergence patterns. We also propose future research directions to develop this nascent field.

Protein-ligand (un)binding kinetics as a new paradigm for drug discovery at the crossroad between experiments and modelling

In the last three decades, protein and nucleic acid structure determination and comprehension of the mechanisms, leading to their physiological and pathological functions, have become a cornerstone of biomedical sciences. A deep understanding of the principles governing the fates of cells and tissue at the molecular level has been gained over the years, offering a solid basis for the rational design of drugs aimed at the pharmacological treatment of numerous diseases. Historically, affinity indicators (i.e. Kd and IC50/EC50) have been assumed to be valid indicators of the in vivo efficacy of a drug. However, recent studies pointed out that the kinetics of the drug-receptor binding process could be as important or even more important than affinity in determining the drug efficacy. This eventually led to a growing interest in the characterisation and prediction of the rate constants of protein-ligand association and dissociation. For instance, a drug with a longer residence time can kinetically select a given receptor over another, even if the affinity for both receptors is comparable, thus increasing its therapeutic index. Therefore, understanding the molecular features underlying binding and unbinding processes is of central interest towards the rational control of drug binding kinetics. In this review, we report the theoretical framework behind protein-ligand association and highlight the latest advances in the experimental and computational approaches exploited to investigate the binding kinetics.

Accelerated flexible protein-ligand docking using Hamiltonian replica exchange with a repulsive biasing potential

A molecular dynamics replica exchange based method has been developed that allows rapid identification of putative ligand binding sites on the surface of biomolecules. The approach employs a set of ambiguity restraints in replica simulations between receptor and ligand that allow close contacts in the reference replica but promotes transient dissociation in higher replicas. This avoids long-lived trapping of the ligand or partner proteins at nonspecific, sticky, sites on the receptor molecule and results in accelerated exploration of the possible binding regions. In contrast to common docking methods that require knowledge of the binding site, exclude solvent and often keep parts of receptor and ligand rigid the approach allows for full flexibility of binding partners. Application to peptide-protein, protein-protein and a drug-receptor system indicate rapid sampling of near-native binding regions even in case of starting far away from the native binding site outperforming continuous MD simulations. An application on a DNA minor groove binding ligand in complex with DNA demonstrates that it can also be used in explicit solvent simulations.

Scoring Functions of Protein-Ligand Interactions

Scoring function of protein-ligand interactions is used to recognize the “native” binding pose of a ligand on the protein and to predict the binding affinity, so that the active small molecules can be discriminated from the non-active ones. Scoring function is widely used in computationally molecular docking and structure-based drug discovery. The development and improvement of scoring functions have broad implications in pharmaceutical industry and academic research. During the past three decades, much progress have been made in methodology and accuracy for scoring functions, and many successful cases have be witnessed in virtual database screening. In this chapter, the authors introduced the basic types of scoring functions and their derivations, the commonly-used evaluation methods and benchmarks, as well as the underlying challenges and current solutions. Finally, the authors discussed the promising directions to improve and develop scoring functions for future molecular docking-based drug discovery.

Long-Range Communication between Different Functional Sites in the Picornaviral 3C Protein

The 3C protein is a master regulator of the picornaviral infection cycle, responsible for both cleaving viral and host proteins, and interacting with genomic RNA replication elements. Here we use nuclear magnetic resonance spectroscopy and molecular dynamics simulations to show that 3C is conformationally dynamic across multiple timescales. Binding of peptide and RNA lead to structural dynamics changes at both the protease active site and the RNA-binding site, consistent with these sites being dynamically coupled. Indeed, binding of RNA influences protease activity, and likewise, interactions at the active site affect RNA binding. We propose that RNA and peptide binding re-shapes the conformational energy landscape of 3C to regulate subsequent functions, including formation of complexes with other viral proteins. The observed channeling of the 3C energy landscape may be important for regulation of the viral infection cycle.

Distributions of experimental protein structures on coarse-grained free energy landscapes

Predicting conformational changes of proteins is needed in order to fully comprehend functional mechanisms. With the large number of available structures in sets of related proteins, it is now possible to directly visualize the clusters of conformations and their conformational transitions through the use of principal component analysis. The most striking observation about the distributions of the structures along the principal components is their highly non-uniform distributions. In this work, we use principal component analysis of experimental structures of 50 diverse proteins to extract the most important directions of their motions, sample structures along these directions, and estimate their free energy landscapes by combining knowledge-based potentials and entropy computed from elastic network models. When these resulting motions are visualized upon their coarse-grained free energy landscapes, the basis for conformational pathways becomes readily apparent. Using three well-studied proteins, T4 lysozyme, serum albumin, and sarco-endoplasmic reticular Ca(2+) adenosine triphosphatase (SERCA), as examples, we show that such free energy landscapes of conformational changes provide meaningful insights into the functional dynamics and suggest transition pathways between different conformational states. As a further example, we also show that Monte Carlo simulations on the coarse-grained landscape of HIV-1 protease can directly yield pathways for force-driven conformational changes.

Identification of protein-ligand binding sites by the level-set variational implicit-solvent approach

Protein–ligand binding is a key biological process at the molecular level. The identification and characterization of small-molecule binding sites on therapeutically relevant proteins have tremendous implications for target evaluation and rational drug design. In this work, we used the recently developed level-set variational implicit-solvent model (VISM) with the Coulomb field approximation (CFA) to locate and characterize potential protein–small-molecule binding sites. We applied our method to a data set of 515 protein–ligand complexes and found that 96.9% of the cocrystallized ligands bind to the VISM-CFA-identified pockets and that 71.8% of the identified pockets are occupied by cocrystallized ligands. For 228 tight-binding protein–ligand complexes (i.e, complexes with experimental pK_d values larger than 6), 99.1% of the cocrystallized ligands are in the VISM-CFA-identified pockets. In addition, it was found that the ligand binding orientations are consistent with the hydrophilic and hydrophobic descriptions provided by VISM. Quantitative characterization of binding pockets with topological and physicochemical parameters was used to assess the “ligandability” of the pockets. The results illustrate the key interactions between ligands and receptors and can be very informative for rational drug design.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Mechanism of allosteric propagation across a β-sheet structure investigated by molecular dynamics simulations

The bacterial adhesin FimH consists of an allosterically regulated mannose-binding lectin domain and a covalently linked inhibitory pilin domain. Under normal conditions, the two domains are bound to each other, and FimH interacts weakly with mannose. However, under tensile force, the domains separate and the lectin domain undergoes conformational changes that strengthen its bond with mannose. Comparison of the crystallographic structures of the low and the high affinity state of the lectin domain reveals conformational changes mainly in the regulatory inter-domain region, the mannose binding site and a large β sheet that connects the two distally located regions. Here, molecular dynamics simulations investigated how conformational changes are propagated within and between different regions of the lectin domain. It was found that the inter-domain region moves towards the high affinity conformation as it becomes more compact and buries exposed hydrophobic surface after separation of the pilin domain. The mannose binding site was more rigid in the high affinity state, which prevented water penetration into the pocket. The large central β sheet demonstrated a soft spring-like twisting. Its twisting motion was moderately correlated to fluctuations in both the regulatory and the binding region, whereas a weak correlation was seen in a direct comparison of these two distal sites. The results suggest a so called "population shift" model whereby binding of the lectin domain to either the pilin domain or mannose locks the β sheet in a rather twisted or flat conformation, stabilizing the low or the high affinity state, respectively. Proteins 2016; 84:990-1008. © 2016 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Finite lattice model for molecular aggregation equilibria. Boolean statistics, analytical approximations, and the macroscopic limit

Molecular processes, ranging from hydrophobic aggregation and protein binding to mesoscopic self-assembly, are typically driven by a delicate balance of energetic and entropic non-covalent interactions. Here, we focus on a broad class of such processes in which multiple ligands bind to a central solute molecule as a result of solute-ligand (direct) and/or ligand-ligand (cooperative) interaction energies. Previously, we described a weighted random mixing (WRM) mean-field model for such processes and compared the resulting adsorption isotherms and aggregate size distributions with exact finite lattice (FL) predictions, for lattices with up to n = 20 binding sites. Here, we compare FL predictions obtained using both Bethe-Guggenheim (BG) and WRM approximations, and find that the latter two approximations are complementary, as they are each most accurate in different aggregation regimes. Moreover, we describe a computationally efficient method for exhaustively counting nearest neighbors in FL configurations, thus making it feasible to obtain FL predictions for systems with up n = 48 binding sites, whose properties approach the thermodynamic (infinite lattice) limit. We further illustrate the applicability of our results by comparing lattice model and molecular dynamics simulation predictions pertaining to the aggregation of methane around neopentane.

The Noncompacted Folding of Proteins by Modified Elastic Net Algorithm

In this article, a protein sequence of length n is embedded in a two-dimensional lattice with m (>n) points. Thus the obtained minimal energy configurations are expected to have flexible shapes in contrast to the compact rectangular conformations. To fulfill this extension, the elastic net algorithm is modified to deal with the difficulty brought by the unsymmetrical relationship between amino acids and lattice points. New set partition strategy in the embedding phase is introduced, and two local search methods are applied to overcome the multimapping phenomena. Several HP benchmark examples with up to 48 amino acids are tested to verify the effectiveness of the proposed approach.

Collisional unfolding of multiprotein complexes reveals cooperative stabilization upon ligand binding

Cooperative binding mechanisms are a common feature in biology, enabling a diverse range of protein-based molecular machines to regulate activities ranging from oxygen uptake to cellular membrane transport. Much, however, is not known about such cooperative binding mechanisms, including how such events typically add to the overall stability of such protein systems. Measurements of such cooperative stabilization events are challenging, as they require the separation and resolution of individual protein complex bound states within a mixture of potential stoichiometries to individually assess protein stabilities. Here, we report ion mobility-mass spectrometry results for the concanavalin A tetramer bound to a range of polysaccharide ligands. We use collision induced unfolding, a relatively new methodology that functions as a gas-phase analog of calorimetry experiments in solution, to individually assess the stabilities of concanavalin A bound states. By comparing the differences in activation voltage required to unfold different concanavalin A-ligand stoichiometries, we find evidence suggesting a cooperative stabilization of concanavalin A occurs upon binding most carbohydrate ligands. We critically evaluate this observation by assessing a broad range of ligands, evaluating the unfolding properties of multiple protein charge states, and by comparing our gas-phase results with those obtained from calorimetry experiments carried out in solution.

PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking

Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

Protein–ligand docking using fitness learning-based artificial bee colony with proximity stimuli

A novel optimization algorithm FlABCps was introduced for protein–ligand docking. The performance of FlABCps was analyzed in terms of the energy landscape and the binding pocket structure of the target protein.

Seeking structural specificity: direct modulation of pentameric ligand-gated ion channels by alcohols and general anesthetics

Alcohols and other anesthetic agents dramatically alter neurologic function in a wide range of organisms, yet their molecular sites of action remain poorly characterized. Pentameric ligand-gated ion channels, long implicated in important direct effects of alcohol and anesthetic binding, have recently been illuminated in renewed detail thanks to the determination of atomic-resolution structures of several family members from lower organisms. These structures provide valuable models for understanding and developing anesthetic agents and for allosteric modulation in general. This review surveys progress in this field from function to structure and back again, outlining early evidence for relevant modulation of pentameric ligand-gated ion channels and the development of early structural models for ion channel function and modulation. We highlight insights and challenges provided by recent crystal structures and resulting simulations, as well as opportunities for translation of these newly detailed models back to behavior and therapy.

Receptor-ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein-ligand applications. We summarise the main topics and recent computational and methodological advances in protein-ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.

Ligand binding and self-association cooperativity of β-lactoglobulin

Unlike most small globular proteins, lipocalins lack a compact hydrophobic core. Instead, they present a large central cavity that functions as the primary binding site for hydrophobic molecules. Not surprisingly, these proteins typically exhibit complex structural dynamics in solution, which is intricately modified by intermolecular recognition events. Although many lipocalins are monomeric, an increasing number of them have been proven to form oligomers. The coupling effects between self-association and ligand binding in these proteins are largely unknown. To address this issue, we have calorimetrically characterized the recognition of dodecyl sulfate by bovine β-lactoglobulin, which forms weak homodimers at neutral pH. A thermodynamic analysis based on coupled-equilibria revealed that dimerization exerts disparate effects on the ligand-binding capacity of β-lactoglobulin. Protein dimerization decreases ligand affinity (or, reciprocally, ligand binding promotes dimer dissociation). The two subunits in the dimer exhibit a positive, entropically driven cooperativity. To investigate the structural determinants of the interaction, the crystal structure of β-lactoglobulin bound to dodecyl sulfate was solved at 1.64 Å resolution.

Optimizing scoring function of protein-nucleic acid interactions with both affinity and specificity

Protein-nucleic acid (protein-DNA and protein-RNA) recognition is fundamental to the regulation of gene expression. Determination of the structures of the protein-nucleic acid recognition and insight into their interactions at molecular level are vital to understanding the regulation function. Recently, quantitative computational approach has been becoming an alternative of experimental technique for predicting the structures and interactions of biomolecular recognition. However, the progress of protein-nucleic acid structure prediction, especially protein-RNA, is far behind that of the protein-ligand and protein-protein structure predictions due to the lack of reliable and accurate scoring function for quantifying the protein-nucleic acid interactions. In this work, we developed an accurate scoring function (named as SPA-PN, SPecificity and Affinity of the Protein-Nucleic acid interactions) for protein-nucleic acid interactions by incorporating both the specificity and affinity into the optimization strategy. Specificity and affinity are two requirements of highly efficient and specific biomolecular recognition. Previous quantitative descriptions of the biomolecular interactions considered the affinity, but often ignored the specificity owing to the challenge of specificity quantification. We applied our concept of intrinsic specificity to connect the conventional specificity, which circumvents the challenge of specificity quantification. In addition to the affinity optimization, we incorporated the quantified intrinsic specificity into the optimization strategy of SPA-PN. The testing results and comparisons with other scoring functions validated that SPA-PN performs well on both the prediction of binding affinity and identification of native conformation. In terms of its performance, SPA-PN can be widely used to predict the protein-nucleic acid structures and quantify their interactions.