Solvent-accessible surface area: How well can be applied to hot-spot detection?

Protective efficacy of nafronyl in diabetic retinopathy through targeted inhibition of key enzymes

Diabetic retinopathy is governed by abnormal apoptosis, increased capillary pressure, and other linked pathology that needs an efficient treatment by multitargeted approaches. Thus, the current study aimed to explore the potential of inhibition of targeted enzymes (DPP4, ACE-2, and aldose reductase) and free radical scavenging capabilities of selected compounds (nafronyl or naftidrofuryl) through in silico and in vivo investigations. Significant binding energies were observed in complexes of aldolase reductase, angiotensin type 1 receptor, and DPP4 against the nafronyl and sitagliptin more than -7.5 kcal/mol. Further validation of free energy was confirmed by calculations of molecular mechanics Poisson-Boltzmann surface area (MMPBSA), and configurational stabilities examined by PCA (principal component analysis). Additionally, drug-likeness was examined by the Swiss ADME web tool, which showed significant findings. Consequently, in vivo experimentations showed significant inflammation and alterations in retinal layers of inner plexiform (inner limiting membrane, nerve fibers, and ganglionic cells), inner nuclear layer (bipolar cells and horizontal cells), and photoreceptors cells. Whereas the treatments (nafronyl and sitagliptin) caused significant improvements in the histoarchitecture of the retina. Additionally, the HOMA indices (IR-insulin resistance, sensitivity, and β cells functioning) and levels of free radicals were significantly altered in the diabetic control group in comparison to intact control. Nafronyl administration showed significant ameliorations in HOMA indices as well as antioxidant levels. Based on the results, it can be concluded that nafronyl efficiently interacts with target enzymes, which may result in potent inhibition and ameliorations in retinal histology as well as glucose homeostasis and antioxidants.

Dual-Capped Helical Interface Mimics

Disruption of protein-protein interactions is medicinally important. Interface helices may be mimicked in helical probes featuring enhanced rigidities, binding to protein targets, stabilities in serum, and cell uptake. This form of mimicry is dominated by stapling between side chains of helical residues: there has been less progress on helical N-caps, and there were no generalizable C-caps. Conversely, in natural proteins, helicities are stabilized and terminated by C- and N-caps but not staples. Bicyclic caps previously introduced by us enable interface helical mimicry featuring rigid synthetic caps at both termini in this work. An unambiguously helical dual-capped system proved to be conformationally stable, binding cyclins A and E, and showed impressive cellular uptake. In addition, the dual-capped mimic was completely resistant to proteolysis in serum over an extended period when compared with "gold standard" hydrocarbon-stapled controls. Dual-capped peptidomimetics are a new, generalizable paradigm for helical interface probe design.

Streamlined Protein-Protein Interface Loop Mimicry

Cyclic peptides comprising endocyclic organic fragments, "cyclo-organopeptides", can be probes for perturbing protein-protein interactions (PPIs). Finding loop mimics is difficult because of high conformational variability amongst targets. Backbone Matching (BM), introduced here, helps solve this problem in the illustrative cases by facilitating efficient evaluation of virtual cyclo-organopeptide core-structure libraries. Thus, 86 rigid organic fragments were selected to build a library of 602 cyclo-organopeptides comprising Ala and organic parts: "cyclo-{-(Ala)n -organo-}". The central hypothesis is "hit" library members have accessible low energy conformers corresponding to backbone structures of target protein loops, while library members which cannot attain this conformation are probably unworthy of further evaluation. BM thereby prioritizes candidate loop mimics, so that less than 10 cyclo-organopeptides are needed to be prepared to find leads for two illustrative PPIs: iNOS ⋅ SPSB2, and uPA ⋅ uPAR.

MUG: A mutation overview of GPCR subfamily A17 receptors

G protein-coupled receptors (GPCRs) mediate several signaling pathways through a general mechanism that involves their activation, upholding a chain of events that lead to the release of molecules responsible for cytoplasmic action and further regulation. These physiological functions can be severely altered by mutations in GPCR genes. GPCRs subfamily A17 (dopamine, serotonin, adrenergic and trace amine receptors) are directly related with neurodegenerative diseases, and as such it is crucial to explore known mutations on these systems and their impact in structure and function. A comprehensive and detailed computational framework - MUG (Mutations Understanding GPCRs) - was constructed, illustrating key reported mutations and their effect on receptors of the subfamily A17 of GPCRs. We explored the type of mutations occurring overall and in the different families of subfamily A17, as well their localization within the receptor and potential effects on receptor functionality. The mutated residues were further analyzed considering their pathogenicity. The results reveal a high diversity of mutations in the GPCR subfamily A17 structures, drawing attention to the considerable number of mutations in conserved residues and domains. Mutated residues were typically hydrophobic residues enriched at the ligand binding pocket and known activating microdomains, which may lead to disruption of receptor function. MUG as an interactive web application is available for the management and visualization of this dataset. We expect that this interactive database helps the exploration of GPCR mutations, their influence, and their familywise and receptor-specific effects, constituting the first step in elucidating their structures and molecules at the atomic level.

Screening of novel and selective inhibitors for neuronal nitric oxide synthase (nNOS) via structure-based drug design techniques

NO, or nitric oxide, is produced by a family of enzymes called nitric oxide synthase (NOS) from L-arginine. NO regulates many physiological functions such as smooth muscle relaxation, immune defense, and memory function. The overproduction of NO by the neuronal isoform of nitric oxide synthase (nNOS) is implicated in neurodegeneration and neuropathic pain, making nNOS inhibition a promising therapeutic approach. Many developed nNOS inhibitors, generally L-arginine mimetics, have some issues in selectivity and bioavailability. According to earlier studies, targeting nNOS has the advantage of decreasing excess NO in the brain while avoiding the negative consequences of inhibiting the two isozymes: endothelial NOS (eNOS) and inducible NOS (iNOS). This study applied structure-based virtual screening, molecular docking, and molecular dynamics simulations to design potent and selective inhibitors against nNOS over related isoforms (eNOS and iNOS) using human X-ray crystal structures of the NOS isoforms. It was discovered that some compounds displayed a very good inhibitory potency for hnNOS and moderate selectivity for the other isozymes, eNOS and iNOS, in addition to good solubility and desirable physiochemical properties. The compounds which showed good stability and selectivity with nNOS, such as ZINC000013485422, can be interesting and informative guidance for designing more potent human nNOS inhibitors.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2's most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2.

MENSAdb: a thorough structural analysis of membrane protein dimers

Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration-approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π-π stacking, T-stacking and cation-π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.

Developing a machine learning model to identify protein–protein interaction hotspots to facilitate drug discovery

Throughout the history of drug discovery, an enzymatic-based approach for identifying new drug molecules has been primarily utilized. Recently, protein–protein interfaces that can be disrupted to identify small molecules that could be viable targets for certain diseases, such as cancer and the human immunodeficiency virus, have been identified. Existing studies computationally identify hotspots on these interfaces, with most models attaining accuracies of ~70%. Many studies do not effectively integrate information relating to amino acid chains and other structural information relating to the complex. Herein, (1) a machine learning model has been created and (2) its ability to integrate multiple features, such as those associated with amino-acid chains, has been evaluated to enhance the ability to predict protein–protein interface hotspots. Virtual drug screening analysis of a set of hotspots determined on the EphB2-ephrinB2 complex has also been performed. The predictive capabilities of this model offer an AUROC of 0.842, sensitivity/recall of 0.833, and specificity of 0.850. Virtual screening of a set of hotspots identified by the machine learning model developed in this study has identified potential medications to treat diseases caused by the overexpression of the EphB2-ephrinB2 complex, including prostate, gastric, colorectal and melanoma cancers which are linked to EphB2 mutations. The efficacy of this model has been demonstrated through its successful ability to predict drug-disease associations previously identified in literature, including cimetidine, idarubicin, pralatrexate for these conditions. In addition, nadolol, a beta blocker, has also been identified in this study to bind to the EphB2-ephrinB2 complex, and the possibility of this drug treating multiple cancers is still relatively unexplored.

SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features

Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein–protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array of protein sequences in comparison to determined tree-dimensional structures indicates that a sequence-based HS predictor has the potential to be more useful for the scientific community. Herein, we present SPOTONE, a new ML predictor able to accurately classify protein HS via sequence-only features. This algorithm shows accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, on an independent testing set. The algorithm is deployed within a free-to-use webserver, only requiring the user to submit a FASTA file with one or more protein sequences.

Node-Weighted Amino Acid Network Strategy for Characterization and Identification of Protein Functional Residues

The study of functional residues (FRs) is essential for understanding protein functions and biological processes. The amino acid network (AAN) has become an emerging paradigm for studying FRs during the past decade. Current AAN models ignore the heterogeneity of nodes and treat amino acids in the AAN as the same. However, the properties of each amino acid node are of fundamental importance. We here proposed a node-weighted AAN strategy termed the node-weighted amino acid contact energy network (NACEN) to characterize and predict three types of FRs, namely, hot spots, catalytic residues, and allosteric residues. We first constructed NACENs with their nodes weighted based on structural, sequence, physicochemical, and dynamical properties of the amino acids and then characterized the FRs with the NACEN parameters. We finally built machine learning predictors to identify each type of FR. The results revealed that residues characterized with NACEN parameters are more distinguishable between FRs and non-FRs than those with unweighted network ones. With few features for classification, NACEN yields comparable performance for FR identification and provides residue level prediction for allosteric regulation. The proposed strategy can be easily implemented to other functional residue identification. An R package is also provided for NACEN construction and analysis at http://sysbio.suda.edu.cn/NACEN/index.html .

SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots

We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/.

A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Perspectives for the structure-based design of acetylcholinesterase reactivators

Rational design of active molecules through structure-based methods has been gaining adepts during the last decades due to the wider availability of protein structures, most of them conjugated with relevant ligands. Acetylcholinesterase (AChE) is a molecular target with a considerable amount of data related to its sequence and 3-dimensional structure. In addition, there are structural insights about the mechanism of action of the natural substrate and drugs used in Alzheimer's disease, organophosphorus compounds, among others. We looked for AChE structural data useful for in silico design of potential interacting molecules. In particular, we focused on information regarding the design of ligands aimed to reactivate AChE catalytic activity. The structures of 178 AChE were annotated and categorized on different subsets according to the nature of the ligand, source organisms and experimental details. We compared sequence homology among the active site from Torpedo californica, Mus musculus and Homo sapiens with the latter two species having the closest relationship (88.9% identity). In addition, the mechanism of organophosphorus binding and the design of effective reactivators are reviewed. A curated data collection obtained with information from several sources was included for researchers working on the field. Finally, a molecular dynamics simulation with human AChE indicated that the catalytic pocket volume stabilizes around 600 Å(3), providing additional clues for drug design.

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein-membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein-membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein-protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org .

Solvent accessible surface area-based hot-spot detection methods for protein-protein and protein-nucleic acid interfaces

Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein-protein systems) and a more complex multi-objective genetic algorithm-support vector machine algorithms (protein-nucleic acid systems). The best models for these interactions have been implemented in two free Web tools.

A new scoring function for protein-protein docking that identifies native structures with unprecedented accuracy

Protein-protein (P-P) 3D structures are fundamental to structural biology and drug discovery. However, most of them have never been determined. Many docking algorithms were developed for that purpose, but they have a very limited accuracy in generating native-like structures and identifying the most correct one, in particular when a single answer is asked for. With such a low success rate it is difficult to point out one docked structure as being native-like. Here we present a new, high accuracy, scoring method to identify the 3D structure of P-P complexes among a set of trial poses. It incorporates alanine scanning mutagenesis experimental data that need to be obtained a priori. The scoring scheme works by matching the computational and the experimental alanine scanning mutagenesis results. The size of the trial P-P interface area is also taken into account. We show that the method ranks the trial structures and identifies the native-like structures with unprecedented accuracy (∼94%), providing the correct P-P 3D structures that biochemists and molecular biologists need to pursue their studies. With such a success rate, the bottleneck of protein-protein docking moves from the scoring to searching algorithms.

Integrating water exclusion theory into β contacts to predict binding free energy changes and binding hot spots

BACKGROUND

Binding free energy and binding hot spots at protein-protein interfaces are two important research areas for understanding protein interactions. Computational methods have been developed previously for accurate prediction of binding free energy change upon mutation for interfacial residues. However, a large number of interrupted and unimportant atomic contacts are used in the training phase which caused accuracy loss.

RESULTS

This work proposes a new method, βACVASA, to predict the change of binding free energy after alanine mutations. βACVASA integrates accessible surface area (ASA) and our newly defined β contacts together into an atomic contact vector (ACV). A β contact between two atoms is a direct contact without being interrupted by any other atom between them. A β contact's potential contribution to protein binding is also supposed to be inversely proportional to its ASA to follow the water exclusion hypothesis of binding hot spots. Tested on a dataset of 396 alanine mutations, our method is found to be superior in classification performance to many other methods, including Robetta, FoldX, HotPOINT, an ACV method of β contacts without ASA integration, and ACVASA methods (similar to βACVASA but based on distance-cutoff contacts). Based on our data analysis and results, we can draw conclusions that: (i) our method is powerful in the prediction of binding free energy change after alanine mutation; (ii) β contacts are better than distance-cutoff contacts for modeling the well-organized protein-binding interfaces; (iii) β contacts usually are only a small fraction number of the distance-based contacts; and (iv) water exclusion is a necessary condition for a residue to become a binding hot spot.

CONCLUSIONS

βACVASA is designed using the advantages of both β contacts and water exclusion. It is an excellent tool to predict binding free energy changes and binding hot spots after alanine mutation.