On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Measurement of Low Concentration of Micro-Plastics by Detection of Bioaffinity-Induced Particle Retention Using Surface Plasmon Resonance Biosensors

The issue of micro-plastics is becoming more and more important due to their ubiquity and the harm they cause to the human body. Therefore, evaluating the biological–physical interaction of micro-plastics with health cells has become the focus of many research efforts. This study focuses on the movement mode and low concentration detection development for micro-plastics in surface plasmon resonance (SPR). Firstly, 20-micrometer micro-plastics were prepared by grinding and filtering, and the movement mode was explored; then, the characteristics were investigated by SPR. Chromatographic analysis showed that the surface charge of micro-plastics dominated the elution time, and estrogen receptors (ERs) played a supporting role. A difference of micro-plastics in SPR sensorgram was observed, inferring the micro-plastics’ movement in rolling mode on the ERs. Characteristics analysis indicated that the low particle number of micro-plastics on SPR showed a linear relationship with the response unit (RU). When ERs were immobilized on the biosensor, the force of the binding of micro-plastics to ERs under an ultra-low background was equivalent to the dissociation rate constant shown as follows: PS (0.05 nM) > PVC (0.09 nM) > PE (0.14 nM). The ELISA-like magnetic beads experiment verified the specificity between ERs and micro-plastics. Therefore, by using the SPR technique, a biological-derived over-occupation of PS was found via higher binding force with ERs and longer retention time. In the future, there will be considerable potential for micro-plastics issues, such as identification in natural samples, biomarking, real-time detection in specific environments/regions and human health subject.

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be supported with computational methods. Most of the available computational prediction techniques depend upon protein structures that bound their applicability to only protein complexes with recognized 3D structures. In this work, we explore the sequence-based prediction of change in protein binding affinity upon mutation and question the effectiveness of [Formula: see text]-fold cross-validation (CV) across mutations adopted in previous studies to assess the generalization ability of such predictors with no known mutation during training. We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the change in protein binding affinity upon mutation. Our proposed sequence-based novel change in protein binding affinity predictor called PANDA performs comparably to the existing methods gauged through an appropriate CV scheme and an external independent test dataset. On an external test dataset, our proposed method gives a maximum Pearson correlation coefficient of 0.52 in comparison to the state-of-the-art existing protein structure-based method called MutaBind which gives a maximum Pearson correlation coefficient of 0.59. Our proposed protein sequence-based method, to predict a change in binding affinity upon mutations, has wide applicability and comparable performance in comparison to existing protein structure-based methods. We made PANDA easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/panda, respectively.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

Fungal enzymes for the degradation of polyethylene: Molecular docking simulation and biodegradation pathway proposal

Polyethylene (PE) is one of the most highly consumed petroleum-based polymers and its accumulation as waste causes environmental pollution. In this sense, the use of microorganisms and their enzymes represents the most ecofriendly and effective decontamination approach. In this work, molecular docking simulation for catalytic enzyme degradation of PE was carried out using individual enzymes: laccase (Lac), manganese peroxidase (MnP), lignin peroxidase (LiP) and unspecific peroxygenase (UnP). PE-binding energy, PE-binding affinity and dimensions of PE-binding sites in the enzyme cavity were calculated in each case. Four hypothetical PE biodegradation pathways were proposed using individual enzymes, and one pathway was proposed using a synergic enzyme combination. These results show that in nature, enzymes act in a synergic manner, using their specific features to undertake an extraordinarily effective sequential catalytic process for organopollutants degradation. In this process, Lac (oxidase) is crucial to provide hydrogen peroxide to the medium to ensure pollutant breakdown. UnP is a versatile enzyme that offers a promising practical application for the degradation of PE and other pollutants due to its cavity features. This is the first in silico report of PE enzymatic degradation, showing the mode of interaction of PE with enzymes as well as the degradation mechanism.

Modified Snake α-Neurotoxin Averts β-Amyloid Binding to α7 Nicotinic Acetylcholine Receptor and Reverses Cognitive Deficits in Alzheimer's Disease Mice

Alzheimer's disease (AD) is the most common cause of senile dementia and one of the greatest medical, social, and economic challenges. According to a dominant theory, amyloid-β (Aβ) peptide is a key AD pathogenic factor. Aβ-soluble species interfere with synaptic functions, aggregate gradually, form plaques, and trigger neurodegeneration. The AD-associated pathology affects numerous systems, though the substantial loss of cholinergic neurons and α7 nicotinic receptors (α7AChR) is critical for the gradual cognitive decline. Aβ binds to α7AChR under various experimental settings; nevertheless, the functional significance of this interaction is ambiguous. Whereas the capability of low Aβ concentrations to activate α7AChR is functionally beneficial, extensive brain exposure to high Aβ concentrations diminishes α7AChR activity, contributes to the cholinergic deficits that characterize AD. Aβ and snake α-neurotoxins competitively bind to α7AChR. Accordingly, we designed a chemically modified α-cobratoxin (mToxin) to inhibit the interaction between Aβ and α7AChR. Subsequently, we examined mToxin in a set of original in silico, in vitro, ex vivo experiments, and in a murine AD model. We report that mToxin reversibly inhibits α7AChR, though it attenuates Aβ-induced synaptic transmission abnormalities, and upregulates pathways supporting long-term potentiation and reducing apoptosis. Remarkably, mToxin demonstrates no toxicity in brain slices and mice. Moreover, its chronic intracerebroventricular administration improves memory in AD-model animals. Our results point to unique mToxin neuroprotective properties, which might be tailored for the treatment of AD. Our methodology bridges the gaps in understanding Aβ-α7AChR interaction and represents a promising direction for further investigations and clinical development.

Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor

Severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) has caused substantially more infections, deaths, and economic disruptions than the 2002‐2003 SARS‐CoV. The key to understanding SARS‐CoV‐2's higher infectivity lies partly in its host receptor recognition mechanism. Experiments show that the human angiotensin converting enzyme 2 (ACE2) protein, which serves as the primary receptor for both CoVs, binds to the receptor binding domain (RBD) of CoV‐2's spike protein stronger than SARS‐CoV's spike RBD. The molecular basis for this difference in binding affinity, however, remains unexplained from X‐ray structures. To go beyond insights gained from X‐ray structures and investigate the role of thermal fluctuations in structure, we employ all‐atom molecular dynamics simulations. Microseconds‐long simulations reveal that while CoV and CoV‐2 spike‐ACE2 interfaces have similar conformational binding modes, CoV‐2 spike interacts with ACE2 via a larger combinatorics of polar contacts, and on average, makes 45% more polar contacts. Correlation analysis and thermodynamic calculations indicate that these differences in the density and dynamics of polar contacts arise from differences in spatial arrangements of interfacial residues, and dynamical coupling between interfacial and non‐interfacial residues. These results recommend that ongoing efforts to design spike‐ACE2 peptide blockers will benefit from incorporating dynamical information as well as allosteric coupling effects.

Effect of molecular properties of the protein-ligand complex on the prediction accuracy of AutoDock

Molecular docking approach has been extensively used to predict the ligand's binding conformation in the binding pocket of protein. However, its prediction accuracy is still limited and highly dependent on target protein-ligand complexes. In this study, we investigated the effects of ligand torsion number, ligand hydrophobicity, and binding-site hydrophobicity on the prediction accuracy of Autodock, a popular molecular docking tool, combinatorially as well as respectively. A clear understanding of how these properties affect the prediction accuracy was observed when these properties were studied combinatorially rather than individually. The combination of low ligand torsion number-hydrophilic ligand-hydrophobic binding site provided the best prediction accuracy while the high ligand torsion number-hydrophilic ligand-hydrophobic binding pocket combination showed the least prediction accuracy. This study allowed us to determine the molecular properties of complex, showing relatively higher or low prediction accuracy and can be employed as a reference in the molecular docking studies using Autodock.

COVID-19: insights into virus-receptor interactions

The recent outbreak of Coronavirus Disease 2019 (COVID-19) calls for rapid mobilization of scientists to probe and explore solutions to this deadly disease. A limited understanding of the high transmissibility of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) relative to other coronavirus strains guides a deeper investigation into the virus/receptor interactions. The cutting-edge studies in thermodynamic and kinetic properties of interactions such as protein-protein interplays have been reviewed in many modeling and analysis studies. Highlighting the thermodynamic assessments of biological interactions and emphasizing the boosted transmissibility of SARS-CoV-2 despite its high similarity in structure and sequence with other coronavirus strains is an important and highly valuable investigation that can lead scientists to discover analytical and fundamental approaches in studying virus's interactions. Accordingly, we have attempted to describe the crucial factors such as conformational changes and hydrophobicity particularities that influence on thermodynamic potentials in the SARS-COV-2 S-protein adsorption process. Discussing the thermodynamic potentials and the kinetics of the SARS-CoV-2 S-protein in its interaction with the ACE2 receptors of the host cell is a fundamental approach that would be extremely valuable in designing candidate pharmaceutical agents or exploring alternative treatments.

Computational Assessment of Protein-Protein Binding Affinity by Reverse Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.

Quantitative Yeast-Yeast Two Hybrid for the Discovery and Binding Affinity Estimation of Protein-Protein Interactions

Quantifying the binding affinity of protein-protein interactions is important for elucidating connections within biochemical signaling pathways, as well as characterization of binding proteins isolated from combinatorial libraries. We describe a quantitative yeast-yeast two-hybrid (qYY2H) system that not only enables the discovery of specific protein-protein interactions but also efficient, quantitative estimation of their binding affinities (K_D). In qYY2H, the bait and prey proteins are expressed as yeast cell surface fusions using yeast surface display. We developed a semiempirical framework for estimating the K_D of monovalent bait-prey interactions, using measurements of bait-prey yeast-yeast binding, which is mediated by multivalent interactions between yeast-displayed bait and prey. Using qYY2H, we identified interaction partners of SMAD3 and the tandem WW domains of YAP from a cDNA library and characterized their binding affinities. Finally, we showed that qYY2H could also quantitatively evaluate binding interactions mediated by post-translational modifications on the bait protein.

Composition-dependent multivalency of peptide-peptide interactions revealed by tryptophan-scanning mutagenesis

We have examined in this contribution the composition dependence of binding characteristics in peptide-peptide interactions between an oligopeptide octa-glycine and a series of tryptophan-containing octapeptides. The binding energy associated with tryptophan-glycine interactions manifests pronounced stepwise binding characteristics as the number of tryptophan increases from 0 to 8 in the octapeptides consisting only of glycine and can be attributed to mono-, di-, and tri-valent peptide-peptide interactions. At the same time, only weak fluctuations in binding energy were observed as the number of tryptophan increases from 2 to 7. Such distinctive nonlinearity of composition-dependent tryptophan-glycine binding energy characteristics due to continuously varying tryptophan compositions in the octapeptides could be considered as a reflection of combinatorial contributions due to the hydrogen bonds originated from the indole moieties of tryptophan with the main chains of octapeptide of glycine containing N-H and C=O moieties and the van der Waals interactions (including π-π and π-CH interactions) between peptides.

Comparison of Huggins Coefficients and Osmotic Second Virial Coefficients of Buffered Solutions of Monoclonal Antibodies

The Huggins coefficient kH is a well-known metric for quantifying the increase in solution viscosity arising from intermolecular interactions in relatively dilute macromolecular solutions, and there has been much interest in this solution property in connection with developing improved antibody therapeutics. While numerous kH measurements have been reported for select monoclonal antibodies (mAbs) solutions, there has been limited study of kH in terms of the fundamental molecular interactions that determine this property. In this paper, we compare measurements of the osmotic second virial coefficient B22, a common metric of intermolecular and interparticle interaction strength, to measurements of kH for model antibody solutions. This comparison is motivated by the seminal work of Russel for hard sphere particles having a short-range "sticky" interparticle interaction, and we also compare our data with known results for uncharged flexible polymers having variable excluded volume interactions because proteins are polypeptide chains. Our observations indicate that neither the adhesive hard sphere model, a common colloidal model of globular proteins, nor the familiar uncharged flexible polymer model, an excellent model of intrinsically disordered proteins, describes the dependence of kH of these antibodies on B22. Clearly, an improved understanding of protein and ion solvation by water as well as dipole-dipole and charge-dipole effects is required to understand the significance of kH from the standpoint of fundamental protein-protein interactions. Despite shortcomings in our theoretical understanding of kH for antibody solutions, this quantity provides a useful practical measure of the strength of interprotein interactions at elevated protein concentrations that is of direct significance for the development of antibody formulations that minimize the solution viscosity.

Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction

Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.

Recent developments in engineering protein-protein interactions using phage display

Targeted inhibition of misregulated protein-protein interactions (PPIs) has been a promising area of investigation in drug discovery and development for human diseases. However, many constraints remain, including shallow binding surfaces and dynamic conformation changes upon interaction. A particularly challenging aspect is the undesirable off-target effects caused by inherent structural similarity among the protein families. To tackle this problem, phage display has been used to engineer PPIs for high-specificity binders with improved binding affinity and greatly reduced undesirable interactions with closely related proteins. Although general steps of phage display are standardized, library design is highly variable depending on experimental contexts. Here in this review, we examined recent advances in the structure-based combinatorial library design and the advantages and limitations of different approaches. The strategies described here can be explored for other protein-protein interactions and aid in designing new libraries or improving on previous libraries.

Predicted 'wiring landscape' of Ras-effector interactions in 29 human tissues

Ras is a plasma membrane (PM)-associated signaling hub protein that interacts with its partners (effectors) in a mutually exclusive fashion. We have shown earlier that competition for binding and hence the occurrence of specific binding events at a hub protein can modulate the activation of downstream pathways. Here, using a mechanistic modeling approach that incorporates high-quality proteomic data of Ras and 56 effectors in 29 (healthy) human tissues, we quantified the amount of individual Ras-effector complexes, and characterized the (stationary) Ras "wiring landscape" specific to each tissue. We identified nine effectors that are in significant amount in complex with Ras in at least one of the 29 tissues. We simulated both mutant- and stimulus-induced network re-configurations, and assessed their divergence from the reference scenario, specifically discussing a case study for two stimuli in three epithelial tissues. These analyses pointed to 32 effectors that are in significant amount in complex with Ras only if they are additionally recruited to the PM, e.g. via membrane-binding domains or domains binding to activated receptors at the PM. Altogether, our data emphasize the importance of tissue context for binding events at the Ras signaling hub.

Unveiling the Targets Involved in the Quest of Antileishmanial Leads Using In silico Methods

BACKGROUND

Leishmaniasis is a neglected tropical disease associated with several clinical manifestations, including cutaneous, mucocutaneous, and visceral forms. As currently available drugs have some limitations (toxicity, resistance, among others), the target-based identification has been an important approach to develop new leads against leishmaniasis. The present study aims to identify targets involved in the pharmacological action of potent antileishmanial compounds.

METHODS

The literature information regarding molecular interactions of antileishmanial compounds studied over the past half-decade is discussed. The information was obtained from databases such as Wiley, SciFinder, Science Direct, National Library of Medicine, American Chemical Society, Scientific Electronic Library Online, Scopus, Springer, Google Scholar, Web of Science, etc. Results: Numerous in vitro antileishmanial compounds showed affinity and selective interactions with enzymes such as arginase, pteridine reductase 1, trypanothione reductase, pyruvate kinase, among others, which are crucial for the survival and virulence of the Leishmania parasite.

CONCLUSION

The in-silico activity of small molecules (enzymes, proteins, among others) might be used as pharmacological tools to develop candidate compounds for the treatment of leishmaniasis. As some pharmacologically active compounds may act on more than one target, additional studies of the mechanism (s) of action of potent antileishmanial compounds might help to better understand their pharmacological action. Also, the optimization of promising antileishmanial compounds might improve their biological activity.

N-Terminal Protein Labeling with N-Hydroxysuccinimide Esters and Microscale Thermophoresis Measurements of Protein-Protein Interactions Using Labeled Protein

Protein labeling strategies have been explored for decades to study protein structure, function, and regulation. Fluorescent labeling of a protein enables the study of protein-protein interactions through biophysical methods such as microscale thermophoresis (MST). MST measures the directed motion of a fluorescently labeled protein in response to microscopic temperature gradients, and the protein's thermal mobility can be used to determine binding affinity. However, the stoichiometry and site specificity of fluorescent labeling are hard to control, and heterogeneous labeling can generate inaccuracies in binding measurements. Here, we describe an easy-to-apply protocol for high-stoichiometric, site-specific labeling of a protein at its N-terminus with N-hydroxysuccinimide (NHS) esters as a means to measure protein-protein interaction affinity by MST. This protocol includes guidelines for NHS ester labeling, fluorescent-labeled protein purification, and MST measurement using a labeled protein. As an example of the entire workflow, we additionally provide a protocol for labeling a ubiquitin E3 enzyme and testing ubiquitin E2-E3 enzyme binding affinity. These methods are highly adaptable and can be extended for protein interaction studies in various biological and biochemical circumstances. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Labeling a protein of interest at its N-terminus with NHS esters through stepwise reaction Alternate Protocol: Labeling a protein of interest at its N-terminus with NHS esters through a one-pot reaction Basic Protocol 2: Purifying the N-terminal fluorescent-labeled protein and determining its concentration and labeling efficiency Basic Protocol 3: Using MST to determine the binding affinity of an N-terminal fluorescent-labeled protein to a binding partner. Basic Protocol 4: NHS ester labeling of ubiquitin E3 ligase WWP2 and measurement of the binding affinity between WWP2 and an E2 conjugating enzyme by the MST binding assay.

Visualizing thrombosis to improve thrombus resolution

The severity, course, and outcomes of thrombosis are determined mainly by the size and location of the thrombus, but studying thrombus structure and composition has been an important but challenging task. The substantial progress in determination of thrombus morphology has become possible due to new intravital imaging methodologies in combination with mechanical thrombectomy, which allows extraction of a fresh thrombus from a patient followed by microscopy. Thrombi have been found to contain various structural forms of fibrin along with platelet aggregates, leukocytes, and red blood cells, many of which acquire a polyhedral shape (polyhedrocytes) as a result of intravital platelet-driven contraction. The relative volume fractions of thrombus components and their structural forms vary substantially, depending on the clinical and pathogenic characteristics. This review summarizes recent research that describes quantitative and qualitative morphologic characteristics of arterial and venous thrombi that are relevant for the pathogenesis, prophylaxis, diagnosis, and treatment of thrombosis.

ISLAND: in-silico proteins binding affinity prediction using sequence information

BACKGROUND

Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning.

METHOD

We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity.

RESULTS

We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software .

CONCLUSION

This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

Effects of Biotin-Avidin Interactions on Hydrogel Swelling

Affinity interactions between the small molecule biotin and the protein avidin have been used extensively to functionalize biomaterials. More recently, researchers have leveraged the changes in biotin-avidin affinity that occur upon biotin conjugation to larger molecules to control the release of biotinylated drugs and proteins. However, the effects of biotin-avidin interactions on hydrogel properties have not been thoroughly investigated. The objective of this study was to evaluate the effect of increasing biotin and avidin concentrations on hydrogel swelling properties, as an indicator of crosslinking. Gelatin, selected as a model hydrogel material, was biotinylated at increasing fold molar excesses of biotin with a PEG linker using N-hydroxysuccinimide chemistry. Afterwards, biotinylated gelatin was formed into hydrogels and stabilized with glutaraldehyde. Swelling properties of the biotinylated hydrogels were investigated by conducting swelling studies in different avidin solutions. Increasing the degree of biotinylation caused significant decreases in swelling ratios of the hydrogels in a dose-dependent manner, suggesting increases in crosslinking of the hydrogels. However, increasing avidin concentrations in excess of biotin content did not significantly affect swelling ratios. Moving hydrogels to phosphate-buffered saline following avidin incorporation resulted in increased swelling ratios for hydrogels prepared with a lower concentration of biotin. However, hydrogels prepared with the highest concentration of biotin did not experience increased swelling ratios, implying that the stability of biotin-avidin-mediated crosslinking depends on the number of biotin molecules available for binding. Collectively, these results demonstrate that biotin-avidin interactions control hydrogel swelling properties, and that the magnitude and stability of the effects depend on the biotin concentration. These results have important implications for affinity-based controlled release of biotinylated drugs or proteins from biotin-avidin-crosslinked hydrogels.

Effects of SARS-CoV-2 mutations on protein structures and intraviral protein-protein interactions

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) causing coronavirus disease 2019 (COVID‐19) has infected 10 millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to a change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 20 nonsynonymous mutations in the SARS‐CoV‐2 genome in which incidence rates were all ≥ 1% as of September 1st, 2020, and then modeled and analyzed the mutant protein structures. The results showed that four types of mutations caused dramatic changes in protein structures (RMSD ≥ 5.0 Å), which were Q57H and G251V in open‐reading frames 3a (ORF3a), S194L, and R203K/G204R in nucleocapsid (N). Next, we found that these mutations also affected the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking mutant complexes were altered, among which the mutation Q57H was involved in both Orf3a–S and Orf3a–Orf8 protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS‐CoV‐2 mutations could change viral protein structure, binding affinity, and hot spots of the interface, thereby might have impacts on SARS‐CoV‐2 transmission, diagnosis, and treatment of COVID‐19.

Inhibition of pancreatic elastase in silico and in vitro by Rubus rosifolius leaves extract and its constituents

Objective:

Elastases are protease enzymes, which mainly hydrolyze proteins of the connective tissue, so they have a significant impact on human disease. Rubus rosifolius is one of the Rubus species found in Indonesian mountains, and it has potential as an elastase inhibitor. The objective of this research was to examine the in vitro elastase inhibitor activity of R. rosifolius leaves and to dock different ligands of its constituents against target protein of Porcine Pancreatic Elastase (PPE) receptor.

Method:

Dried leaves powder of R. rosifolius was extracted using Soxhlet apparatus with n-hexane, ethyl acetate, and methanol. The extract was evaporated, and in vitro elastase inhibitor activity was determined using PPE with the quercetin used as control positive. Selected nine constituents of R. rosifolius were evaluated on the docking behavior of elastase receptor using Protein–Ligand ANT System (PLANTS) computational software with PPE enzyme with Protein Data Bank (PDB) file 1BRU.

Result:

The methanol extract showed significantly inhibited elastase with IC50 186.13 μg/mL, but ethyl acetate extract showed weak activity, and n-hexane extract did not show any activity. Docking studies and binding free energy calculations and hydrogen bonding with some amino acids revealed that ellagic acid showed the least binding energy for the target enzyme.

Conclusion:

This research has opened new insights into understanding that constituents of R. rosifolius methanol extract are potential inhibitors against elastase, and suggested the active compound is ellagic acid.

Molecular dynamics of the histamine H3 membrane receptor reveals different mechanisms of GPCR signal transduction

In this work, we studied the mechanisms of classical activation and inactivation of signal transduction by the histamine H3 receptor, a 7-helix transmembrane bundle G-Protein Coupled Receptor through long-time-scale atomistic molecular dynamics simulations of the receptor embedded in a hydrated double layer of dipalmitoyl phosphatidyl choline, a zwitterionic polysaturated ordered lipid. Three systems were prepared: the apo receptor, representing the constitutively active receptor; and two holo-receptors-the receptor coupled to the antagonist/inverse agonist ciproxifan, representing the inactive state of the receptor, and the receptor coupled to the endogenous agonist histamine and representing the active state of the receptor. An extensive analysis of the simulation showed that the three states of H3R present significant structural and dynamical differences as well as a complex behavior given that the measured properties interact in multiple and interdependent ways. In addition, the simulations described an unexpected escape of histamine from the orthosteric binding site, in agreement with the experimental modest affinities and rapid off-rates of agonists.

Quantitative mechanistic model for ultrasmall nanoparticle-protein interactions

To date, extensive effort has been devoted toward the characterization of protein interactions with synthetic nanostructures. However, much remains to be understood, particularly concerning microscopic mechanisms of interactions. Here, we have conducted a detailed investigation of the kinetics of nanoparticle-protein complexation to gain deeper insights into the elementary steps and molecular events along the pathway for complex formation. Toward that end, the binding kinetics between p-mercaptobenzoic acid-coated ultrasmall gold nanoparticles (AuMBA) and fluorescently-labeled ubiquitin was investigated at millisecond time resolution using stopped-flow spectroscopy. It was found that both the association and dissociation kinetics consisted of multiple exponential phases, hence suggesting a complex, multi-step reaction mechanism. The results fit into a picture where complexation proceeds through the formation of a weakly-bound first-encounter complex with an apparent binding affinity (KD) of ∼9 μM. Encounter complex formation is followed by unimolecular tightening steps of partial desolvation/ion removal and conformational rearrangement, which, collectively, achieve an almost 100-fold increase in affinity of the final bound state (apparent KD ∼0.1 μM). The final state is found to be weakly stabilized, displaying an average lifetime in the range of seconds. Screening of the electrostatic forces at high ionic strength weakens the AuMBA-ubiquitin interactions by destabilizing the encounter complex, whereas the average lifetime of the final bound state remains largely unchanged. Overall, our rapid kinetics investigation has revealed novel quantitative insights into the molecular-level mechanisms of ultrasmall nanoparticle-protein interactions.

A novel strategy for molecular interfaces optimization: The case of Ferritin-Transferrin receptor interaction

Protein-protein interactions regulate almost all cellular functions and rely on a fine tune of surface amino acids properties involved on both molecular partners. The disruption of a molecular association can be caused even by a single residue mutation, often leading to a pathological modification of a biochemical pathway. Therefore the evaluation of the effects of amino acid substitutions on binding, and the ad hoc design of protein-protein interfaces, is one of the biggest challenges in computational biology. Here, we present a novel strategy for computational mutation and optimization of protein-protein interfaces. Modeling the interaction surface properties using the Zernike polynomials, we describe the shape and electrostatics of binding sites with an ordered set of descriptors, making possible the evaluation of complementarity between interacting surfaces. With a Monte Carlo approach, we obtain protein mutants with controlled molecular complementarities. Applying this strategy to the relevant case of the interaction between Ferritin and Transferrin Receptor, we obtain a set of Ferritin mutants with increased or decreased complementarity. The extensive molecular dynamics validation of the method results confirms its efficacy, showing that this strategy represents a very promising approach in designing correct molecular interfaces.

A large accessory protein interactome is rewired across environments

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

Interaction of the maturation protein of the bacteriophage MS2 and the sex pilus of the Escherichia coli F plasmid

One promising strategy to combat antimicrobial resistance is to use bacteriophages that attach to the sex pili produced by transmissible antimicrobial resistance (AMR) plasmids, infect AMR bacteria and select for loss of the AMR plasmids, prolonging the life of existing antimicrobials. The maturation protein of the bacteriophage MS2 attaches to the pili produced by Incompatibility group F plasmid-containing bacteria. This interaction initiates delivery of the viral genetic material into the bacteria. Using protein-protein docking we constructed a model of the F pilus comprising a trimer of subunits binding to the maturation protein. Interactions between the maturation protein and the F pilus were investigated using molecular dynamics simulations. In silico alanine scanning and in silico single-point mutations were explored, with the longer term aim of increasing the affinity of the maturation protein to other Incompatibility group pili, without reducing the strength of binding to F pilin. We report our computational findings on which residues are required for the maturation protein and F pilin to interact, those which had no effect on the interaction and the mutations which led to a stronger interaction.

The inhibitory effect of a coronavirus spike protein fragment with ACE2

In this article, we investigate the binding processes of a fragment of the coronavirus spike protein receptor binding domain (RBD), the hexapeptide YKYRYL on the angiotensin-converting enzyme 2 (ACE2) receptor, and its inhibitory effect on the binding and activation of the coronavirus-2 spike protein CoV-2 RBD at ACE2. In agreement with an experimental study, we find a high affinity of the hexapeptide to the binding interface between CoV-2 RBD and ACE2, which we investigate using 20 independent equilibrium molecular dynamics (MD) simulations over a total of 1 μs and a 200-ns enhanced correlation guided MD simulation. We then evaluate the effect of the hexapeptide on the assembly process of the CoV-2 RBD to ACE2 in long-time enhanced correlation guided MD simulations. In that set of simulations, we find that CoV-2 RBD does not bind to ACE2 with the binding motif shown in experiments, but it rotates because of an electrostatic repulsion and forms a hydrophobic interface with ACE2. Surprisingly, we observe that the hexapeptide binds to CoV-2 RBD, which has the effect that this protein only weakly attaches to ACE2 so that the activation of CoV-2 RBD might be inhibited in this case. Our results indicate that the hexapeptide might be a possible treatment option that prevents the viral activation through the inhibition of the interaction between ACE2 and CoV-2 RBD.

Plant glucose transporter structure and function

The carbohydrate D-glucose is the main source of energy in living organisms. In contrast to animals, as well as most fungi, bacteria, and archaea, plants are capable to synthesize a surplus of sugars characterizing them as autothrophic organisms. Thus, plants are de facto the source of all food on earth, either directly or indirectly via feed to livestock. Glucose is stored as polymeric glucan, in animals as glycogen and in plants as starch. Despite serving a general source for metabolic energy and energy storage, glucose is the main building block for cellulose synthesis and represents the metabolic starting point of carboxylate- and amino acid synthesis. Finally yet importantly, glucose functions as signalling molecule conveying the plant metabolic status for adjustment of growth, development, and survival. Therefore, cell-to-cell and long-distance transport of photoassimilates/sugars throughout the plant body require the fine-tuned activity of sugar transporters facilitating the transport across membranes. The functional plant counterparts of the animal sodium/glucose transporters (SGLTs) are represented by the proton-coupled sugar transport proteins (STPs) of the plant monosaccharide transporter(-like) family (MST). In the framework of this special issue on “Glucose Transporters in Health and Disease,” this review gives an overview of the function and structure of plant STPs in comparison to the respective knowledge obtained with the animal Na⁺-coupled glucose transporters (SGLTs).

Genetic variability of human angiotensin-converting enzyme 2 (hACE2) among various ethnic populations

BACKGROUND

There appears to be large regional variation for susceptibility, severity, and mortality for COVID-19 infections. Numerous potential factors could explain the wide variability in the number of infections and death among the countries. We examined genetic differences in the human angiotensin-converting enzyme 2 (hACE2) gene, as its receptor serves as a cellular entry for SARS-CoV-2. At present, there is a paucity of data regarding the differences for ACE2 polymorphisms and expression levels between ethnicities.

METHODS

We compared the allele frequency of mutations between European and East Asians. Molecular dynamic simulation were performed to investigate the influences of significant mutant on protein structure. The binding free energies were calculated between S protein and hACE2. We also examined hACE2 gene expression in eight global populations from HapMap3.

RESULTS

Four missense mutations showed significant minor allele frequency difference between Asians and Caucasians. Molecular dynamic demonstrated that two of these variants (K26R and I468V) may affect binding characteristics between S protein of the virus and hACE2 receptor. We also noted marginal differences in gene expression for some populations in HapMap3 as compared to the Chinese population.

CONCLUSION

Our studies reveal subtle changes in the genetics of hACE2 between human populations, but the magnitude of the difference was small and the significance is not clear in the absence of further in vitro and functional studies.

Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

Effects of Ethylene Glycol on the Structure and Stability of Myoglobin Using Spectroscopic, Interaction, and In Silico Approaches: Monomer Is Different from Those of Its Polymers

Investigation of changes in thermal stabilities and structures of proteins in the presence of different co-solutes (ligands) is an integral part in the basic research, discovery, and development of drugs. Ethylene glycol (EG) is known to be toxic and causes teratogenic, inducing primarily skeletal and external malformations and other diseases. The effect of EG on the structure and thermal stability of myoglobin (Mb) was studied using various spectroscopic techniques at pH 7.0 and two different temperatures. As revealed by circular dichroism, Trp fluorescence, nano-DSF, and absorption (UV and visible) measurements, EG (i) has no significant effect on secondary and tertiary structures of Mb at 25 °C, and (ii) it decreases the thermal stability of the protein, which increases with increasing concentration of EG. As revealed by ANS (8-anilino-1-naphthalene sulfonic acid) fluorescence measurements, heat-induced denatured protein has newly exposed hydrophobic patches that bind to ANS. Isothermal titration calorimetry revealed that the interaction between EG and Mb is temperature dependent; the preferential interaction of EG is entropy driven at low temperature, 298 K (25 °C), and it is enthalpy driven at higher temperature, 343 K (70 °C). Molecular docking study showed that EG interacts with side chains of amino acid residues of Mb through van der Waals interactions and hydrogen bonding.

Quantifying Protein-Protein Interactions in Molecular Simulations

Interactions among proteins, nucleic acids, and other macromolecules are essential for their biological functions and shape the physicochemcial properties of the crowded environments inside living cells. Binding interactions are commonly quantified by dissociation constants K_d, and both binding and nonbinding interactions are quantified by second osmotic virial coefficients B₂. As a measure of nonspecific binding and stickiness, B₂ is receiving renewed attention in the context of so-called liquid–liquid phase separation in protein and nucleic acid solutions. We show that K_d is fully determined by B₂ and the fraction of the dimer observed in molecular simulations of two proteins in a box. We derive two methods to calculate B₂. From molecular dynamics or Monte Carlo simulations using implicit solvents, we can determine B₂ from insertion and removal energies by applying Bennett’s acceptance ratio (BAR) method or the (binless) weighted histogram analysis method (WHAM). From simulations using implicit or explicit solvents, one can estimate B₂ from the probability that the two molecules are within a volume large enough to cover their range of interactions. We validate these methods for coarse-grained Monte Carlo simulations of three weakly binding proteins. Our estimates for K_d and B₂ allow us to separate out the contributions of nonbinding interactions to B₂. Comparison of calculated and measured values of K_d and B₂ can be used to (re-)parameterize and improve molecular force fields by calibrating specific affinities, overall stickiness, and nonbinding interactions. The accuracy and efficiency of K_d and B₂ calculations make them well suited for high-throughput studies of large interactomes.

Mechanistic study of PDIA1-catalyzed TFF3 dimerization during sepsis

AIMS

Trefoil factor 3 (TFF3) is a gut mucosal protective molecule that is secreted by intestinal goblet cells. The dimeric structure of TFF3 enables it to function in intestinal mucosal repair and to maintain its own stability. Protein disulfide isomerase a1 (PDIA1) can directly catalyze the formation, isomerization and reduction of disulfide bonds in proteins and may play an important role in the formation of TFF3 dimer. In this study, we focused on the specific molecular mechanism of TFF3 dimerization by PDIA1 and the changes during sepsis.

METHODS

We examined the changes of PDIA1 and TFF3 in sepsis rats and cell models and used a variety of experimental techniques to investigate the specific molecular mechanism of PDIA1-catalyzed TFF3 dimerization.

KEY FINDINGS

We found that PDIA1 can directly catalyze the dimerization of TFF3. Our MD model proposed that two TFF3 monomers form hydrogen bonds with the region b' of PDIA1 through two stepwise reactions. Furthermore, we propose that the Cys24-Cys27 active site at the region a' of PDIA1 mediates disulfide bond formation between the Cys79 residues of each of the two TFF3 monomers via deprotonation and nucleophilic attack. During sepsis, PDIA1 is downregulated and the excessive release of nitric oxide (NO) promoted PDIA1 nitrosylation. This modification reduced PDIA1 activity, which resulted in the corresponding decrease of TFF3 dimerization and compromised TFF3 dimer function.

SIGNIFICANCE

Our study revealed a novel mechanism for the inhibition of intestinal mucosal repair during sepsis and revealed novel targets for the prevention and treatment of sepsis.

Interaction of a Model Hydrophobic Drug Dimethylcurcumin with Albumin Nanoparticles

The aim of present study was to investigate the binding interactions of a model hydrophobic molecule, dimethylcurcumin (DMC) with nanoparticle form of bovine serum albumin (BSA) using fluorescence spectroscopy techniques. For this, BSA nanoparticles (size = 62.0 ± 3.5 nm, molecular weight = 11,243 ± 3445 kD) prepared by thermal denaturation method was mixed with DMC in solution and monitored for fluorescence emission of tryptophan (Trp) residue as well as DMC separately. The emission maximum of DMC in nanoparticles form exhibited more blue sift and quenched the excited state of tryptophan (Trp) by six fold higher than in the native form of BSA. By analyzing Trp fluorescence, the mean binding constant (K) estimated for the interaction of DMC with native and nanoparticles forms of BSA was 2.7 ± 0.4 × 10⁴ M^-1 and 1.5 ± 0.5 × 10⁵ M^-1 respectively. Together these results suggested that DMC experienced a more rigid environment in nanoparticles than in native form of BSA. Additionally the above determined K values were in agreement with those reported previously by absorption techniques. Further direct energy transfer was observed between Trp and DMC, using which the distance (r) calculated between them was 28.25 ± 0.27 Ǻ in BSA native. Similar analysis involving BSA nanoparticle and DMC revealed a distance of 24.25 ± 1.05 Ǻ between the hydrophobic core and the ligand. Finally interaction of DMC with BSA was validated through molecular docking studies, which indicated sub-domain IIA as the binding site of DMC. Thus it is concluded that intrinsic fluorescence of protein can be utilized to study the interaction of its different physical forms with any hydrophobic ligand.

A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna

Over the past two decades, substantial amount of work has been conducted to characterize different odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized, and the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined. Identity and concentration, two key characteristics of the space of odorants, are encoded by the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model odorant identity and concentration using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the mechanics of the molecular ligand binding/dissociation of the OTP. We model the BSG as a Connor-Stevens point neuron. The resulting spatio-temporal encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the spatio-temporal level of complexity of the transformation taking place in the antennae. The concentration-dependent spatio-temporal code at the output of the antenna circuits determines the level of complexity of olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiological recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Identity and concentration, intrinsically embedded in the odorant space, are two key characteristics of olfactory coding that define the level of complexity of neural processing throughout the olfactory system in the fruit fly. In this paper we advance a theoretical foundation for understanding these two characteristics by quantifying mathematically the odorant space and devising a biophysical model of the olfactory sensory neurons (OSNs). To validate our modeling approach, we propose and apply an algorithm to estimate the affinity value and the dissociation rate, the two characteristics that define odorant identity, of multiple odorant-receptor pairs. We then evaluate our model with a multitude of odorant waveforms and demonstrate that the model output reproduces the temporal responses of OSNs obtained from in vivo electrophysiology recordings. Furthermore, we evaluate the model at the OSN population level and quantify on the molecular level the spatio-temporal level of complexity of the transformation taking place between the odorant space and the OSNs. The resulting concentration-dependent spatio-temporal code determines the level of complexity of the input space driving olfactory processing in the downstream neuropils. Lastly, our model demonstrates that the currently available data for OSN responses only enables estimation of affinity value. This calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Gibbs Free Energy Calculation of Mutation in PncA and RpsA Associated With Pyrazinamide Resistance

A central approach for better understanding the forces involved in maintaining protein structures is to investigate the protein folding and thermodynamic properties. The effect of the folding process is often disturbed in mutated states. To explore the dynamic properties behind mutations, molecular dynamic (MD) simulations have been widely performed, especially in unveiling the mechanism of drug failure behind mutation. When comparing wild type (WT) and mutants (MTs), the structural changes along with solvation free energy (SFE), and Gibbs free energy (GFE) are calculated after the MD simulation, to measure the effect of mutations on protein structure. Pyrazinamide (PZA) is one of the first-line drugs, effective against latent Mycobacterium tuberculosis isolates, affecting the global TB control program 2030. Resistance to this drug emerges due to mutations in pncA and rpsA genes, encoding pyrazinamidase (PZase) and ribosomal protein S1 (RpsA) respectively. The question of how the GFE may be a measure of PZase and RpsA stabilities, has been addressed in the current review. The GFE and SFE of MTs have been compared with WT, which were already found to be PZA-resistant. WT structures attained a more stable state in comparison with MTs. The physiological effect of a mutation in PZase and RpsA may be due to the difference in energies. This difference between WT and MTs, depicted through GFE plots, might be useful in predicting the stability and PZA-resistance behind mutation. This study provides useful information for better management of drug resistance, to control the global TB problem.

Combinatorial Effect of Ligand and Ligand-Binding Site Hydrophobicities on Binding Affinity

Computational methods to study protein-ligand interactions at a molecular level have been successful to a certain extent in predicting the pose, atomic interactions, and so forth, but poor efficiency in estimating a protein-ligand binding affinity is still a crucial problem to be solved. Analyzing the protein-ligand interactions quantitatively is one primary concern for understanding. Qualitative analysis of these interactions may lead to better insights about protein-ligand interactions. To perform such an analysis, the macroscopic molecular properties of the protein and ligand can be studied in detail and should be correlated with the ligand-binding affinity. This detailed study can be helpful in designing the ligands and the ligand-binding site as well. In this study, we attempted to identify the hydrophobic/hydrophilic features of a ligand and ligand-binding site and check their correlation with the experimental affinity of the protein-ligand complexes. This combinatorial analysis of ligand log P and binding site hydrophobicity on data set distribution and binding affinity suggested two critical findings. The hydrophobic ligands bind to hydrophilic and hydrophobic pockets equally, whereas hydrophilic ligands are specific to hydrophilic pockets. The combination of the hydrophobic ligand-hydrophobic pocket prefers high-affinity values compared to other combinations. Although these results cannot be used for atomic-level design of ligands or binding sites, they are expected to be used as a reference for screening the ligands for a given target binding site.

Interactions of Whey Proteins with Metal Ions

Whey proteins tend to interact with metal ions, which have implications in different fields related to human life quality. There are two impacts of such interactions: they can provide opportunities for applications in food and nutraceuticals, but may lead to analytical challenges related to their study and outcomes for food processing, storage, and food interactions. Moreover, interactions of whey proteins with metal ions are complicated, requiring deep understanding, leading to consequences, such as metalloproteins, metallocomplexes, nanoparticles, or aggregates, creating a biologically active system. To understand the phenomena of metal-protein interactions, it is important to develop analytical approaches combined with studies of changes in the biological activity and to analyze the impact of such interactions on different fields. The aim of this review was to discuss chemistry of β-lactoglobulin, α-lactalbumin, and lactotransferrin, their interactions with different metal ions, analytical techniques used to study them and the implications for food and nutraceuticals.

Molecular Recognition and Advances in Antibody Design and Antigenic Peptide Targeting

Molecular recognition, the specific interaction between molecules by a combination of physical forces, has been a subject of scientific investigation for decades [...].

Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation

Calculating free energies of binding (ΔG_bind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔG_bind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔG_bind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein-ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.