Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

High-Throughput Interactome Determination via Sulfur Anomalous Scattering

We propose a novel approach for detecting the binding between proteins making use of the anomalous diffraction of natively present heavy elements, e.g., sulfurs, inside molecular three-dimensional structures. In particular, we analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus, the differences in the patterns produced by bound proteins with respect to their nonbonded states can be exploited to rapidly assess protein complex formation. On the basis of our results, we suggest a possible experimental procedure for detecting protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale, thus paving the way for the development of a novel field of research based on a synchrotron light source.

In silico investigation of potential phytoconstituents against ligand- and voltage-gated ion channels as antiepileptic agents

The most promising anticonvulsant phytocompounds were explored in this work using docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) approaches. A total of 70 phytochemicals were screened against α-amino-3-hydroxyl-5-methyl-4-isoxazole propionic acid (AMPA), N-methyl-d-aspartate (NMDA), voltage-gated sodium ion channels (VGSC), and carbonic anhydrase enzyme II (CA II) receptors, and the docking results were compared to the reference drug phenytoin. Amentoflavone displayed the highest affinity for AMPA and VGSC receptors, with docking scores of - 10.4 and - 10.1 kcal/mol, respectively. Oliganthin H-NMDA and epigallocatechin-3-gallate-CA II complexes showed docking scores of - 10.9 and - 6.9 kcal/mol, respectively. All four complexes depicted a high dock score compared to the phenytoin complex at the binding site of the corresponding proteins. The MD simulation investigated the stabilities and favorable conformation of apoproteins and ligand/reference-bound complexes. The results revealed that proteins AMPA, VGSC, and CA II were more efficiently stabilized by lead phytochemicals than phenytoin binding. Additionally, principal component analysis and MM-PBSA results suggested that these lead phytocompounds have good compactness and strong binding free energy. Further, physicochemical and pharmacokinetic studies revealed that these final lead phytochemicals would be suitable for oral intake, have sufficient intestinal permeability, and have the ability to cross the blood-brain barrier (BBB). Comprehensively, this study predicted amentoflavone as the best lead phytochemical out of the 70 anticonvulsant phytocompounds that can be used to treat epilepsy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03948-1.

13 Cβ-Valine and 13 Cγ-Leucine Methine Labeling To Probe Protein Ligand Interaction

Precise information regarding the interaction between proteins and ligands at molecular resolution is crucial for effectively guiding the optimization process from initial hits to lead compounds in early stages of drug development. In this study, we introduce a novel aliphatic side chain isotope-labeling scheme to directly probe interactions between ligands and aliphatic sidechains using NMR techniques. To demonstrate the applicability of this method, we selected a set of Brd4-BD1 binders and analyzed 1 H chemical shift perturbation resulting from CH-π interaction of Hβ -Val and Hγ -Leu as CH donors with corresponding ligand aromatic moieties as π acceptors.

Charge cluster occurrence in land plants' mitochondrial proteomes with functional and structural insights

The Charge Clusters (CCs) are involved in key functions and are distributed according to the organism, the protein's type, and the charge of amino acids. In the present study, we have explored the occurrence, position, and annotation as a first large-scale study of the CCs in land plants mitochondrial proteomes. A new python script was used for data curation. The Finding Clusters Charge in Protein Sequences Program was performed after adjusting the reading window size. A 44316 protein sequences belonging to 52 species of land plants were analysed. The occurrence of Negative Charge Clusters (NCCs) (1.2%) is two times more frequent than the Positive Charge Clusters (PCCs) (0.64%). Moreover, 39 and 30 NCCs were conserved in 88 and 41 proteins in intra and in inter proteomes respectively, while 14 and 21 PCCs were conserved in 53 and 85 protein sequences in intra and inter proteomes consecutively. Sequences carrying mixed CCs are rare (0.12%). Despite this low abundance, CCs play a crucial role in protein function. The CCs tend to be located mainly in the terminal regions of proteins which guarantees specific protein targeting and import into the mitochondria. In addition, the functional annotation of CCs according to Gene Ontology shows that CCs are involved in binding functions of either proteins or macromolecules which are deployed in different metabolic and cellular processes such as RNA editing and transcription. This study may provide valuable information while considering the CCs in understanding the environmental adaptation of plants.Communicated by Ramaswamy H. Sarma.

Biological evaluation of carbohydrate-based aprepitant analogs for neuroblastoma treatment

Different studies using Aprepitant, a NK1R antagonist currently used as a clinical drug for treating chemotherapy-related nausea and vomiting, have demonstrated that pharmacological inhibition of NK1R effectively reduces the growth of several tumor types such as neuroblastoma (NB). In a previous work, we demonstrated that a series of carbohydrate-based Aprepitant analogs, derived from either d-galactose or l-arabinose, have shown high affinity and NK1R antagonistic activity with a broad-spectrum anticancer activity and an important selectivity. In this new study, we explore the selective cytotoxic effects of these derivatives for the treatment of NB. Furthermore, we describe the design and stereoselective synthesis of a new generation of d-glucose derivatives as Aprepitant analogs, supported by docking studies. This approach showed that most of our carbohydrate-based analogs are significantly more selective than Aprepitant. The galactosyl derivative 2α, has demonstrated a marked in vitro selective cytotoxic activity against NB, with IC50 values in the same range as those of Aprepitant and its prodrug Fosaprepitant. Interestingly, the derivative 2α has shown similar apoptotic effect to that of Aprepitant. Moreover, we can select the glucosyl amino derivative 10α as an interesting hit exhibiting higher in vitro cytotoxic activity against NB than Aprepitant, being 1.2 times more selective.

Design of protein-binding peptides with controlled binding affinity: the case of SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 derived peptides

The development of methods able to modulate the binding affinity between proteins and peptides is of paramount biotechnological interest in view of a vast range of applications that imply designed polypeptides capable to impair or favour Protein-Protein Interactions. Here, we applied a peptide design algorithm based on shape complementarity optimization and electrostatic compatibility and provided the first experimental in vitro proof of the efficacy of the design algorithm. Focusing on the interaction between the SARS-CoV-2 Spike Receptor-Binding Domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) receptor, we extracted a 23-residues long peptide that structurally mimics the major interacting portion of the ACE2 receptor and designed in silico five mutants of such a peptide with a modulated affinity. Remarkably, experimental KD measurements, conducted using biolayer interferometry, matched the in silico predictions. Moreover, we investigated the molecular determinants that govern the variation in binding affinity through molecular dynamics simulation, by identifying the mechanisms driving the different values of binding affinity at a single residue level. Finally, the peptide sequence with the highest affinity, in comparison with the wild type peptide, was expressed as a fusion protein with human H ferritin (HFt) 24-mer. Solution measurements performed on the latter constructs confirmed that peptides still exhibited the expected trend, thereby enhancing their efficacy in RBD binding. Altogether, these results indicate the high potentiality of this general method in developing potent high-affinity vectors for hindering/enhancing protein-protein associations.

Protocatechuate hydroxylase is a novel group A flavoprotein monooxygenase with a unique substrate recognition mechanism

Para-hydroxybenzoate hydroxylase (PHBH) is a group A flavoprotein monooxygenase that hydroxylates p-hydroxybenzoate to protocatechuate (PCA). Despite intensive studies of Pseudomonas aeruginosa p-hydroxybenzoate hydroxylase (PaPobA), the catalytic reactions of extremely diverse putative PHBH isozymes remain unresolved. We analyzed the phylogenetic relationships of known and predicted PHBHs and identified eight divergent clades. Clade F contains a protein that lacks the critical amino acid residues required for PaPobA to generate PHBH activity. Among proteins in this clade, Xylophilus ampelinus PobA (XaPobA) preferred PCA as a substrate and is the first known natural PCA 5-hydroxylase (PCAH). Crystal structures and kinetic properties revealed similar mechanisms of substrate carboxy group recognition between XaPobA and PaPobA. The unique Ile75, Met72, Val199, Trp201, and Phe385 residues of XaPobA form the bottom of a hydrophobic cavity with a shape that complements the 3-and 4-hydroxy groups of PCA and its binding site configuration. An interaction between the δ-sulfur atom of Met210 and the aromatic ring of PCA is likely to stabilize XaPobA-PCA complexes. The 4-hydroxy group of PCA forms a hydrogen bond with the main chain carbonyl of Thr294. These modes of binding constitute a novel substrate recognition mechanism that PaPobA lacks. This mechanism characterizes XaPobA and sheds light on the diversity of catalytic mechanisms of PobA-type PHBHs and group A flavoprotein monooxygenases.

Distinct or Overlapping Areas of Mitochondrial Thioredoxin 2 May Be Used for Its Covalent and Strong Non-Covalent Interactions with Protein Ligands

In silico approaches were employed to examine the characteristics of interactions between human mitochondrial thioredoxin 2 (HsTrx2) and its 38 previously identified mitochondrial protein ligands. All interactions appeared driven mainly by electrostatic forces. The statistically significant residues of HsTrx2 for interactions were characterized as "contact hot spots". Since these were identical/adjacent to putative thermodynamic hot spots, an energy network approach identified their neighbors to highlight possible contact interfaces. Three distinct areas for binding emerged: (i) one around the active site for covalent interactions, (ii) another antipodal to the active site for strong non-covalent interactions, and (iii) a third area involved in both kinds of interactions. The contact interfaces of HsTrx2 were projected as respective interfaces for Escherichia coli Trx1 (EcoTrx1), 2, and HsTrx1. Comparison of the interfaces and contact hot spots of HsTrx2 to the contact residues of EcoTx1 and HsTrx1 from existing crystal complexes with protein ligands supported the hypothesis, except for a part of the cleft/groove adjacent to Trp30 preceding the active site. The outcomes of this study raise the possibility for the rational design of selective inhibitors for the interactions of HsTrx2 with specific protein ligands without affecting the entirety of the functions of the Trx system.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors

The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein is of paramount importance. Recently, two DNA aptamers were designed with the aim to inhibit the interaction between the ACE2 receptor and the spike protein of SARS-CoV-2. Indeed, the two molecules interact with the ACE2 receptor in the region around the K353 residue, preventing its binding of the spike protein. If on the one hand this inhibition process hinders the entry of the virus into the host cell, it could lead to a series of side effects, both in physiological and pathological conditions, preventing the correct functioning of the ACE2 receptor. Here, we discuss through a computational study the possible effect of these two very promising DNA aptamers, investigating all possible interactions between ACE2 and its experimentally known molecular partners. Our in silico predictions show that some of the 10 known molecular partners of ACE2 could interact, physiologically or pathologically, in a region adjacent to the K353 residue. Thus, the curative action of the proposed DNA aptamers could recruit ACE2 from its biological functions.

CMPK2 restricts Zika virus replication by inhibiting viral translation

Flaviviruses continue to emerge as global health threats. There are currently no Food and Drug Administration (FDA) approved antiviral treatments for flaviviral infections. Therefore, there is a pressing need to identify host and viral factors that can be targeted for effective therapeutic intervention. Type I interferon (IFN-I) production in response to microbial products is one of the host's first line of defense against invading pathogens. Cytidine/uridine monophosphate kinase 2 (CMPK2) is a type I interferon-stimulated gene (ISG) that exerts antiviral effects. However, the molecular mechanism by which CMPK2 inhibits viral replication is unclear. Here, we report that CMPK2 expression restricts Zika virus (ZIKV) replication by specifically inhibiting viral translation and that IFN-I- induced CMPK2 contributes significantly to the overall antiviral response against ZIKV. We demonstrate that expression of CMPK2 results in a significant decrease in the replication of other pathogenic flaviviruses including dengue virus (DENV-2), Kunjin virus (KUNV) and yellow fever virus (YFV). Importantly, we determine that the N-terminal domain (NTD) of CMPK2, which lacks kinase activity, is sufficient to restrict viral translation. Thus, its kinase function is not required for CMPK2's antiviral activity. Furthermore, we identify seven conserved cysteine residues within the NTD as critical for CMPK2 antiviral activity. Thus, these residues may form an unknown functional site in the NTD of CMPK2 contributing to its antiviral function. Finally, we show that mitochondrial localization of CMPK2 is required for its antiviral effects. Given its broad antiviral activity against flaviviruses, CMPK2 is a promising potential pan-flavivirus inhibitor.

ProtInteract: A deep learning framework for predicting protein-protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.