Investigation of atomic level patterns in protein--small ligand interactions

Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania donovani: An Update and Literature Review

Leishmaniasis, a disease caused by Leishmania parasites and transmitted via sandflies, presents in two main forms: cutaneous and visceral, the latter being more severe. With 0.7 to 1 million new cases each year, primarily in Brazil, diagnosing remains challenging due to diverse disease manifestations. Traditionally, the identification of Leishmania species is inferred from clinical and epidemiological data. Advances in disease management depend on technological progress and the improvement of parasite identification programs. Current treatments, despite the high incidence, show limited efficacy due to factors like cost, toxicity, and lengthy regimens causing poor adherence and resistance development. Diagnostic techniques have improved but a significant gap remains between scientific progress and application in endemic areas. Complete genomic sequence knowledge of Leishmania allows for the identification of therapeutic targets. With the aid of computational tools, testing, searching, and detecting affinity in molecular docking are optimized, and strategies that assess advantages among different options are developed. The review focuses on the use of molecular docking and molecular dynamics (MD) simulation for drug development. It also discusses the limitations and advancements of current treatments, emphasizing the importance of new techniques in improving disease management.

Homology modeling of human BAP1 and analysis of its binding properties through molecular docking and molecular dynamics simulation

BRCA1-associated protein 1 (BAP1) is a nuclear-localized Ubiquitin C-terminal hydrolase (UCH) that functions as a tumour suppressor, and although BAP1 has been linked to cancer, the molecular mechanism by which BAP1 regulates cancer and its crystal structure have not been elucidated. In this study, computational approaches were used to identify the protein model of BAP1 and its potential inhibitors. The structure of the BAP1 model was constructed through homology modeling and the generated BAP1 model was observed to exhibit good quality protein model as the distribution of its amino acids in the Ramachandran's plot corresponded to 87.7% in the most favoured regions. Docking and simulating of the ubiquitin on the BAP1 model structure revealed the rearrangement of F228, F50, and H169 residues of the BAP1 and switching of BAP1's conformation into a productive state. Our screening results of potential BAP1 inhibitors against the FDA approved drugs shortlisted two potential inhibitors, which are FDA1065 and FDA755. We then performed molecular dynamics simulations and Molecular mechanics Poisson-Boltzmann surface area (MMPBSA) analysis on both inhibitors and found that only the BAP1-FDA755 formed a stable complex and the FDA755 ligand remained its position inside the active site of the BAP1 with a total binding energy of (-51.77 ± 3.49 kcal/mol). We speculate that the presence of methyl group in FDA755 play an important role in stabilizing the BAP1-FDA755 complex.Communicated by Ramaswamy H. Sarma.

Environmental health risks induced by interaction between phthalic acid esters (PAEs) and biological macromolecules: A review

As a kind of compounds abused in industry productions, phthalic acid esters (PAEs) cause serious problems in natural environment. PAEs pollution has penetrated into environmental media and human food chain. This review consolidates the updated information to assess the occurrence and distribution of PAEs in each transmission section. It is found that micrograms per kilogram of PAEs are exposed to humans through daily diets. After entering the human body, PAEs often undergo the metabolic process of hydrolysis to monoesters phthalates and conjugation process. Unfortunately, in the process of systemic circulation, PAEs will interact with biological macromolecules in vivo under the action of non-covalent binding, which is also the essence of biological toxicity. The interactions usually operate in the following pathways: (a) competitive binding; (b) functional interference; and (c) abnormal signal transduction. While the non-covalent binding forces mainly contain hydrophobic interaction, hydrogen bond, electrostatic interaction, and π interaction. As a typical endocrine disruptor, the health risks of PAEs often start with endocrine disorder, further leading to metabolic disruption, reproductive disorders, and nerve injury. Besides, genotoxicity and carcinogenicity are also attributed to the interaction between PAEs and genetic materials. This review also pointed out that the molecular mechanism study on biological toxicity of PAEs are deficient. Future toxicological research should pay more attention to the intermolecular interactions. This will be beneficial for evaluating and predicting the biological toxicity of pollutants at molecular scale.

In vitro and in silico analysis of the Anopheles anticholinesterase activity of terpenoids

Anopheles gambiae, An. coluzzii, An. arabiensis, and An. funestus are major vectors in high malaria endemic African regions. Various terpenoid classes form the main chemical constituent repository of essential oils, many of which have been shown to possess insecticidal effects against Anopheles species. The current study aimed to assess the bioactivity of terpenoids including four sesquiterpene alcohols, farnesol, (-)-α-bisabolol, cis-nerolidol, and trans-nerolidol; a phenylpropanoid, methyleugenol, and a monoterpene, (R)-(+)-limonene, using the larvicidal screening assay against the four Anopheles species. The mechanism of action was investigated through in vitro acetylcholinesterase inhibition assay and in silico molecular modelling. All six terpenoids showed potent larvicidal activity against the four Anopheles species. Insights into the mechanism of action revealed that the six terpenoids are strong AChE inhibitors against An. funestus and An. arabiensis, while there was a moderate inhibitory activity against An. gambiae AChE, but very weak activity against An. coluzzii. Interestingly, in the in silico study, farnesol established a favourable hydrogen bonding interaction with a conserved amino acid residue, Cys⁴⁴⁷, at the entrance to the active site gorge. While (-)-α-bisabolol and methyleugenol displayed a strong interaction with the catalytic Ser³⁶⁰ and adjacent amino acid residues; but sparing the mutable Gly²⁸⁰ residue that confers resistance to the current anticholinesterase insecticides. As a result, this study identified farnesol, (-)-α-bisabolol, and methyleugenol as selective bioinsecticidal agents with potent Anopheles AChE inhibition. These terpenoids present as natural compounds for further development as anticholinesterase bioinsecticides.

Identification of natural compounds as SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation

Background

Base mutations increase the contagiousness and transmissibility of the Delta and Lambda strains and lead to the severity of the COVID-19 pandemic. Molecular docking and molecular dynamics (MD) simulations are frequently used for drug discovery and relocation. Small molecular compounds from Chinese herbs have an inhibitory effect on the virus. Therefore, this study used computational simulations to investigate the effects of small molecular compounds on the spike (S) protein and the binding between them and angiotensin-converting enzyme 2 (ACE2) receptors.

Methods

In this study, molecular docking, MD simulation, and protein-protein analysis were used to explore the medicinal target inhibition of Chinese herbal medicinal plant chemicals on SARS-CoV-2. 12,978 phytochemicals were screened against S proteins of SARS-CoV-2 Lambda and Delta mutants.

Results

Molecular docking showed that 65.61% and 65.28% of the compounds had the relatively stable binding ability to the S protein of Lambda and Delta mutants (docking score ≤ -6). The top five compounds with binding energy with Lambda and Delta mutants were clematichinenoside AR2 (-9.7), atratoglaucoside,b (-9.5), physalin b (-9.5), atratoglaucoside, a (-9.4), Ochnaflavone (-9.3) and neo-przewaquinone a (-10), Wikstrosin (-9.7), xilingsaponin A (-9.6), ardisianoside G (-9.6), and 23-epi-26-deoxyactein (-9.6), respectively. Four compounds (Casuarictin, Heterophylliin D, Protohypericin, and Glansrin B) could interact with S protein mutation sites of Lambda and Delta mutants, respectively, and MD simulation results showed that four plant chemicals and spike protein have good energy stable complex formation ability. In addition, protein-protein docking was carried out to evaluate the changes in ACE2 binding ability caused by the formation of four plant chemicals and S protein complexes. The analysis showed that the binding of four plant chemicals to the S protein could reduce the stability of the binding to ACE2, thereby reducing the replication ability of the virus.

Conclusion

To sum up, the study concluded that four phytochemicals (Casuarictin, Heterophylliin D, Protohypericin, and Glansrin B) had significant effects on the binding sites of the SARS-CoV-2 S protein. This study needs further in vitro and in vivo experimental validation of these major phytochemicals to assess their potential anti-SARS-CoV-2. Graphical abstract.

Spectroscopic details on the molecular structure of pyrimidine‑2‑thiones heterocyclic compounds: computational and antiviral activity against the main protease enzyme of SARS-CoV-2

Computational tools in investigating of spectral heterocyclic compounds ranges based on pyrimidine‑2‑thiones, take some importance in identifying their molecular and electronic behavior. Some charcoal heterocyclic compounds were previously synthesized in our laboratory and their experimental results were compared with the computational evaluation. Computational spectroscopic analytical items (IR, NMR and UV–Vis) were calculated using the more popular DFT methods and the predicted results were compared with the reported experimental ones. Quantum and chemical parameters were calculated and molecular electrostatic surface potential (MEP) was studied which predicted the highly electronic sites around the compounds. Some molecular properties (ionization energy, electron affinity, energy gap, hardness, electronegativity, electrophilicity index, static dipole moment and average linear polarizability) of these Schiff bases which were computed at B3LYP/6-31G(d,p) level in aqueous phase. Benchmark analysis was performed for three ab initio functionals such B3LYP, BPV86 and B3PW91 methods to explain the data resulted from NMR spectra. The docking study of some selected previously synthesized compounds was performed using the viral Mpro enzyme protein in compared to a k36 reference ligand inhibitor. The study indicated the ability of the synthesized compounds to form H-bond and hydrophobic (VDW, π-alkyl and π-sulfur) interactions with Mpro enzyme receptor with high inhibition effect of compound L2.

Structural Bioinformatic Survey of Protein-Small Molecule Interfaces Delineates the Role of Glycine in Surface Pocket Formation

With a structural bioinformatic approach, we have explored amino acid compositions at PISA defined interfaces between small molecules and proteins that are contained in an optimized subset of 11,351 PDB files. The use of a series of restrictions, to prevent redundancy and biases from interactions between amino acids with charged side chains and ions, yielded a final data set of 45,230 protein-small molecule interfaces. We have compared occurrences of natural amino acids in surface exposed regions and binding sites for all the proteins of our data set. From our structural bioinformatic survey, the most relevant signal arose from the unexpected Gly abundance at enzyme catalytic sites. This finding suggested that Gly must have a fundamental role in stabilizing concave protein surface moieties. Subsequently, we have tried to predict the effect of in silico Gly mutations in hen egg white lysozyme to optimize those conditions that can reshape the protein surface with the appearance of new pockets. Replacing amino acids having bulky side chains with Gly in specific protein regions seems a feasible way for designing proteins with additional surface pockets, which can alter protein surface dynamics, therefore, representing controllable switches for protein activity.

Hierarchical representation for PPI sites prediction

Background

Protein–protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection.

Results

We define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar.

Conclusions

The hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions.

Computational details of molecular structure, spectroscopic properties, topological studies and SARS-Cov-2 enzyme molecular docking simulation of substituted triazolo pyrimidine thione heterocycles

Investigation the molecular structure of the system requires a detailed experience in dealing with theoretical computational guides to highlight its important role. Molecular structure of three heterocyclic compounds 8,10-diphenylpyrido[3,2-e][1,2,4]triazolo[4,3-c]pyrimidine-3(2H)-thione (HL), 8-phenyl-10-(p-tolyl)pyrido[3,2-e][1,2,4]triazolo[4,3-c]pyrimidine-3(2H)-thione (CH3L) and10-(4-nitrophenyl)-8-phenylpyrido[3,2-e][1,2,4]triazolo[4,3-c]pyrimidine-3(2H)-thione (NO2L) was studied at DFT/B3LYP/6-31G (d,p) level in ethanol solvent. Spectroscopic properties such Infrared (IR, 1H NMR and 13C NMR) and ultraviolet-visible (UV-VIS) analyses were computed. Some quantum and reactivity parameters (HOMO energy, LUMO energy, energy gap, ionization potential, electron affinity, chemical potential, global softness, lipophelicity) were studied, also molecular electrostatic potential (MEP) was performed to indicate the reactive nucleophilic and electrophilic sites. The effects of H-, CH3- and NO2- substituents on heterocyclic ligands were studied and it was found that the electron donation sites concerned with hydrogen and methyl substituents over nitro substituent. Topological analysis using reduced density gradient (RDG) was discussed in details. To predict the relevant antiviral activity of the reported heterocyclic compounds, molecular docking simulation was applied to the crystal structure of SARS-Cov-2 viral Mpro enzyme with 6WTT code and PLpro with 7JRN code. The enzymatic viral protein gives an image about the binding affinity between the target protein receptor and the heterocyclic ligands entitled. The hydrogen bonding interactions were evaluated from molecular docking with different strength for each ligand compound to discuss the efficiency of heterocyclic ligands toward viral inhibition.

Type II c-Met inhibitors: molecular insight into crucial interactions for effective inhibition

The c-Met tyrosine kinase plays an important role in human cancers. Preclinical studies demonstrated that c-Met is over-expressed, mutated and amplified in a variety of human tumor types and design of more potent c-Met inhibitors is a priority. In this study, 14 molecular dynamics simulations of potent type II c-Met inhibitors were run to resolve the critical interactions responsible for high affinity of ligands towards c-Met considering the essential flexibility of protein-ligand interactions. Residues Phe1223 and Tyr1159, involved in pi-pi interactions were recognized as the most effective residues in the ligand binding in terms of binding free energies. Hydrogen bond interaction with Met1160 was also found necessary for effective type II ligand binding to c-Met.

Characterization of amino acid residues of T-cell receptors interacting with HLA-A*02-restricted antigen peptides

Background

The present study aimed to explore residues’ properties interacting with HLA-A*02-restricted peptides on T-cell receptors (TCRs) and their effects on bond types of interaction and binding free energy.

Methods

We searched the crystal structures of HLA-A*02-restricted peptide-TCR complexes from the Protein Data Bank (PDB) database and subsequently collected relevant parameters. We then employed Schrodinger to analyze the bond types of interaction and Gromacs 2019 to evaluate the TCR-antigen peptide complex’s molecular dynamics simulation. Finally, we compared the changes of bond types of interaction and binding free energy before and after residue substitution to ensure consistency of the conditions before and after residue substitution.

Results

The main sites on the antigen peptides that formed the intermolecular interaction [hydrogen bond (HB) and pi stack] with TCRs were P4, P8, P2, and P6. The hydrophobicity of the amino acids inside or outside the disulfide bond of TCRs may be related to the intermolecular interaction and binding free energy between TCRs and peptides. Residues located outside the disulfide bond of TCR α or β chains and forming pi stack force played favorable roles in the complex intermolecular interaction and binding free energy. The residues of the TCR α or β chains that interacted with peptides were replaced by alanine (Ala) or glycine (Gly), and their intermolecular binding free energy of the complex had been improved. However, it had nothing to do with the formation of HB.

Conclusions

The findings of this study suggest that the hydrophobic nature of the amino acids inside or outside the disulfide bonds on the TCR may be associated with the intermolecular interaction and binding between the TCR and polypeptide. The residues located outside the TCR α or β single-chain disulfide bond and forming the pi-stack force showed a beneficial effect on the intermolecular interaction and binding of the complex. In addition, the part of the residues on the TCR α or β single chain that produced bond types of interaction with the polypeptide after being replaced by Ala or Gly, the intermolecular binding free energy of the complex was increased, regardless of whether HB was formed.

Promising phytochemicals of traditional Indian herbal steam inhalation therapy to combat COVID-19 - An in silico study

Background

COVID-19, the presently prevailing global public health emergency has culminated in international instability in economy. This unprecedented pandemic outbreak pressingly necessitated the trans-disciplinary approach in developing novel/new anti-COVID-19 drugs especially, small molecule inhibitors targeting the seminal proteins of viral etiological agent, SARS-CoV-2.

Methods

Based on the traditional medicinal knowledge, we made an attempt through molecular docking analysis to explore the phytochemical constituents of three most commonly used Indian herbs in ‘steam inhalation therapy’ against well recognized viral receptor proteins.

Results

A total of 57 phytochemicals were scrutinized virtually against four structural protein targets of SARS-CoV-2 viz. 3CL^pro, ACE-2, spike glycoprotein and RdRp. Providentially, two bioactives from each of the three plants i.e. apigenin-o-7-glucuronide and ellagic acid from Eucalyptus globulus; eudesmol and viridiflorene from Vitex negundo and; vasicolinone and anisotine from Justicia adhatoda were identified to be the best hit lead molecules based on interaction energies, conventional hydrogen bonding numbers and other non-covalent interactions. On comparison with the known SARS-CoV-2 protease inhibitor –lopinavir and RdRp inhibitor –remdesivir, apigenin-o-7-glucuronide was found to be a phenomenal inhibitor of both protease and polymerase, as it strongly interacts with their active sites and exhibited remarkably high binding affinity. Furthermore, in silico drug-likeness and ADMET prediction analyses clearly evidenced the usability of the identified bioactives to develop as drug against COVID-19.

Conclusion

Overall, the data of the present study exemplifies that the phytochemicals from selected traditional herbs having significance in steam inhalation therapy would be promising in combating COVID-19.

Ethnomedicines of Indian origin for combating COVID-19 infection by hampering the viral replication: using structure-based drug discovery approach

In the present study, we have explored the interaction of the active components from 10 different medicinal plants of Indian origin that are commonly used for treating cold and respiratory-related disorders, through molecular docking analysis. In the current scenario, COVID-19 patients experience severe respiratory syndromes, hence it is envisaged from our study that these traditional medicines are very likely to provide a favourable effect on COVID-19 infections. The active ingredients identified from these natural products are previously reported for antiviral activities against large group of viruses. Totally 47 bioactives identified from the medicinal plants were investigated against the structural targets of SARS-CoV-2 (Mpro and spike protein) and human ACE2 receptor. The top leads were identified based on interaction energies, number of hydrogen bond and other parameters that explain their potency to inhibit SARS-CoV-2. The bioactive ligands such as Cucurbitacin E, Orientin, Bis-andrographolide, Cucurbitacin B, Isocucurbitacin B, Vitexin, Berberine, Bryonolic acid, Piperine and Magnoflorine targeted the hotspot residues of SARS-CoV-2 main protease. In fact, this protease enzyme has an essential role in mediating the viral replication and therefore compounds targeting this key enzyme are expected to block the viral replication and transcription. The top scoring conformations identified through docking analysis were further demonstrated with molecular dynamics simulation. Besides, the stability of the conformation was studied in detail by investigating the binding free energy using MM-PBSA method. Overall, the study emphasized that the proposed hit Cucurbitacin E and orientin could serve as a promising scaffold for developing anti-COVID-19 drug.

Communicated by Ramaswamy H. Sarma

SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

Motivation

Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.

Results

We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.

Availability and implementation

SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

2H-Azirine-Based Reagents for Chemoselective Bioconjugation at Carboxyl Residues Inside Live Cells

Protein modification by chemical reagents has played an essential role in the treatment of human diseases. However, the reagents currently used are limited to the covalent modification of cysteine and lysine residues. It is thus desirable to develop novel methods that can covalently modify other residues. Despite the fact that the carboxyl residues are crucial for maintaining the protein function, few selective labeling reactions are currently available. Here, we describe a novel reactive probe, 3-phenyl-2H-azirine, that enables chemoselective modification of carboxyl groups in proteins under both in vitro and in situ conditions with excellent efficiency. Furthermore, proteome-wide profiling of reactive carboxyl residues was performed with a quantitative chemoproteomic platform.

On the evolution of protein-adenine binding

How proteins evolved to recognize and bind their ligands is a key mystery in protein function evolution. To explore this mystery, we study how proteins bind adenine, an ancient fragment. We characterize physicochemical patterns of protein–adenine interactions and link these to proteins’ evolutionary origins. In conflict with previous findings, we see that all of adenine’s hydrogen donors and acceptors have been used to bind proteins, and that adenine binding is likely to have emerged multiple times in evolution. To identify adenine-binding sites of shared origin, we use “themes”: short amino acid segments suggested to constitute evolutionary building blocks. We detect specific themes that are engaged in adenine binding; the detection of these in a protein’s sequence might reveal its function.

Proteins’ interactions with ancient ligands may reveal how molecular recognition emerged and evolved. We explore how proteins recognize adenine: a planar rigid fragment found in the most common and ancient ligands. We have developed a computational pipeline that extracts protein–adenine complexes from the Protein Data Bank, structurally superimposes their adenine fragments, and detects the hydrogen bonds mediating the interaction. Our analysis extends the known motifs of protein–adenine interactions in the Watson–Crick edge of adenine and shows that all of adenine’s edges may contribute to molecular recognition. We further show that, on the proteins' side, binding is often mediated by specific amino acid segments (“themes”) that recur across different proteins, such that different proteins use the same themes when binding the same adenine-containing ligands. We identify numerous proteins that feature these themes and are thus likely to bind adenine-containing ligands. Our analysis suggests that adenine binding has emerged multiple times in evolution.

Glycine-induced formation and druggability score prediction of protein surface pockets

Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry. CRISPR technology adds new alternatives to cure diseases by removing DNA defects responsible of genome-related pathologies. In principle, the same technology, however, could also be exploited to induce protein mutations whose effects are controlled by the presence of suitable ligands. In this paper, a new idea is proposed for the realization of mutated proteins, on the surface of which more spacious transient pockets are formed and, therefore, are more suitable for hosting drugs. In particular, new allosteric sites are obtained by replacing amino-acids with bulky side chains with glycine, Gly, the smallest natural amino-acid. We also present a machine learning approach to evaluate the druggability score of new (or enlarged) pockets. Preliminary experimental results are very promising, showing that 10% of the sites created by the Gly-pipe software are druggable.

Functionalization of gold nanoparticles with two aminoalcohol-based quinoxaline derivatives for targeting phosphoinositide 3-kinases (PI3Kα)

Quinoxaline derivatives have attracted considerable attention due to their vast range of applications that includes electroluminescence and biomedicine.

Arylfluorosulfate-Based Electrophiles for Covalent Protein Labeling: A New Addition to the Arsenal

Selective covalent modification of a targeted protein is a powerful tool in chemical biology and drug discovery, with applications ranging from identification and characterization of proteins and their functions to the development of targeted covalent inhibitors. Most covalent ligands contain an affinity motif and an electrophilic warhead that reacts with a nucleophilic residue of the targeted protein. Because the electrophilic warhead is prone to react and modify off‐target nucleophiles, its reactivity should be balanced carefully to maximize target selectivity. Arylfluorosulfates have recently emerged as latent electrophiles for selective labeling of context‐specific tyrosine and lysine residues in protein pockets. Here, we review the recent but intense introduction of arylfluorosulfates into the arsenal of available warheads for selective covalent modification of proteins. We highlight the untapped potential of this functional group for use in chemical biology and drug discovery.

Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database

Background

Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.

Results

In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. The method is based on a functional site and physicochemical interaction-annotated domain profile database, called fiDPD, which was built using protein domains found in the Protein Data Bank. This method was applied to 13 target proteins from the very recent Critical Assessment of Structure Prediction (CASP10/11), and our calculations gave a Matthews correlation coefficient (MCC) value of 0.66 for PFS prediction and an 80% recall in the prediction of the associated physicochemical interactions.

Conclusions

Our results show that, in addition to the PFSs, the physical interactions at these sites are also conserved in the evolution of proteins. This work provides a valuable sequence-based tool for rational drug design and side-effect assessment. The method is freely available and can be accessed at http://202.119.249.49.

Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains

Proteins interact with a variety of molecules including proteins and nucleic acids. We review a comprehensive collection of over 50 studies that analyze and/or predict these interactions. While majority of these studies address either solely protein–DNA or protein–RNA binding, only a few have a wider scope that covers both protein–protein and protein–nucleic acid binding. Our analysis reveals that binding residues are typically characterized with three hallmarks: relative solvent accessibility (RSA), evolutionary conservation and propensity of amino acids (AAs) for binding. Motivated by drawbacks of the prior studies, we perform a large-scale analysis to quantify and contrast the three hallmarks for residues that bind DNA-, RNA-, protein- and (for the first time) multi-ligand-binding residues that interact with DNA and proteins, and with RNA and proteins. Results generated on a well-annotated data set of over 23 000 proteins show that conservation of binding residues is higher for nucleic acid- than protein-binding residues. Multi-ligand-binding residues are more conserved and have higher RSA than single-ligand-binding residues. We empirically show that each hallmark discriminates between binding and nonbinding residues, even predicted RSA, and that combining them improves discriminatory power for each of the five types of interactions. Linear scoring functions that combine these hallmarks offer good predictive performance of residue-level propensity for binding and provide intuitive interpretation of predictions. Better understanding of these residue-level interactions will facilitate development of methods that accurately predict binding in the exponentially growing databases of protein sequences.

FLIP: An assisting software in structure based drug design using fingerprint of protein-ligand interaction profiles

With the growing number of labor-intensive data in the pharmaceutical industries and public domain for protein-ligand complexes, a significant challenge is still remaining in managing and leveraging this vast information. Here, a standalone application is presented for analysis, organization, and illustration of structural data and molecular interactions for exploiting 3D-structures into simple 1D fingerprints. The utility of the approach was shown in unraveling a feasible solution for post-processing of docking results in parallel with providing fruitful analysis for users in order to investigate molecular interactions. Remarkably, all interaction possibilities including (hydrogen bond, water-bridged, electrostatic, and hydrophobic as well as π- π and cation-π interactions) are supported both in the form of fingerprints and compelling reports. These investigations are mainly considered based on right orientation, location, and geometry of the interacting pairs rather than the acquisition of the energy terms. The reasonable efficiency of our application in different models was comparable to recent methods It is clearly presented that FLIP provides a faster way to generate usable fingerprints for ligand and protein binding modes. FLIP is free for academic use and is available at: http://zistrayan.com/development/download/flip/package.zip.

Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions

The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.

Global vision of druggability issues: applications and perspectives

During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.

Composition of Overlapping Protein-Protein and Protein-Ligand Interfaces

Protein-protein interactions (PPIs) play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP) complexes, and 161 protein-ligand (PL) complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar.

PDB-Explorer: a web-based interactive map of the protein data bank in shape space

Background

The RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited.

Methods

A 136-dimensional atom pair 3D-fingerprint for proteins (3DP) counting categorized atom pairs at increasing through-space distances was designed to represent the molecular shape of PDB-entries. Nearest neighbor searches examples were reported exemplifying the ability of 3DP-similarity to identify closely related biomolecules from small peptides to enzyme and large multiprotein complexes such as virus particles. The principle component analysis was used to obtain the visualization of PDB in 3DP-space.

Results

The 3DP property space groups proteins and protein assemblies according to their 3D-shape similarity, yet shows exquisite ability to distinguish between closely related structures. An interactive website called PDB-Explorer is presented featuring a color-coded interactive map of PDB in 3DP-space. Each pixel of the map contains one or more PDB-entries which are directly visualized as ribbon diagrams when the pixel is selected. The PDB-Explorer website allows performing 3DP-nearest neighbor searches of any PDB-entry or of any structure uploaded as protein-type PDB file. All functionalities on the website are implemented in JavaScript in a platform-independent manner and draw data from a server that is updated daily with the latest PDB additions, ensuring complete and up-to-date coverage. The essentially instantaneous 3DP-similarity search with the PDB-Explorer provides results comparable to those of much slower 3D-alignment algorithms, and automatically clusters proteins from the same superfamilies in tight groups.

Conclusion

A chemical space classification of PDB based on molecular shape was obtained using a new atom-pair 3D-fingerprint for proteins and implemented in a web-based database exploration tool comprising an interactive color-coded map of the PDB chemical space and a nearest neighbor search tool. The PDB-Explorer website is freely available at www.cheminfo.org/pdbexplorer and represents an unprecedented opportunity to interactively visualize and explore the structural diversity of the PDB.

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Graphical abstract

Systematic investigation of sequence and structural motifs that recognize ATP

Interaction between ATP, a multifunctional and ubiquitous nucleotide, and proteins initializes phosphorylation, polypeptide synthesis and ATP hydrolysis which supplies energy for metabolism. However, current knowledge concerning the mechanisms through which ATP is recognized by proteins is incomplete, scattered, and inaccurate. We systemically investigate sequence and structural motifs of proteins that recognize ATP. We identified three novel motifs and refined the known p-loop and class II aminoacyl-tRNA synthetase motifs. The five motifs define five distinct ATP-protein interaction modes which concern over 5% of known protein structures. We demonstrate that although these motifs share a common GXG tripeptide they recognize ATP through different functional groups. The p-loop motif recognizes ATP through phosphates, class II aminoacyl-tRNA synthetase motif targets adenosine and the other three motifs recognize both phosphates and adenosine. We show that some motifs are shared by different enzyme types. Statistical tests demonstrate that the five sequence motifs are significantly associated with the nucleotide binding proteins. Large-scale test on PDB reveals that about 98% of proteins that include one of the structural motifs are confirmed to bind ATP.

Identification of a potential anticancer target of danshensu by inverse docking

OBJECTIVE

To study potential targets of Danshensu via dual inverse docking.

METHOD

PharmMapper and idTarget servers were used as tools, and the results were checked with the molecular docking program autodock vina in PyRx 0.8.

RESULT

The disease-related target HRas was rated top, with a pharmacophore model matching well the molecular features of Danshensu. In addition, docking results indicated that the complex was also matched in terms of structure, H-bonds, and hydrophobicity.

CONCLUSION

Dual inverse docking indicates that HRas may be a potential anticancer target of Danshensu. This approach can provide useful information for studying pharmacological effects of agents of interest.

Sequence-based predictor of ATP-binding residues using random forest and mRMR-IFS feature selection

We develop a computational and statistical approach (ATPBR) for predicting ATP-binding residues in proteins from amino acid sequences by using random forests with a novel hybrid feature. The hybrid feature incorporates a new feature called PSSMPP, the predicted secondary structure and orthogonal binary vectors. The mRMR-IFS feature selection method is utilized to construct the best prediction model. At last, ATPBR achieves significantly improved performance over existing methods, with 87.53% accuracy and a Matthew׳s correlation coefficient of 0.554. In addition, our further analysis demonstrates that PSSMPP distinguishes more effectively between ATP-binding and non-binding residues. Besides, the optimal features selected by the mRMR-IFS method improve the prediction performance and may provide useful insights for revealing the mechanisms of ATP and proteins interactions.

Structural significance of the β1K396 residue found in the Porphyromonas gingivalis sialidase β-propeller domain: a computational study with implications for novel therapeutics against periodontal disease

Porphyromonas gingivalis sialidase activity is associated with virulence and initiated by sialic acid (SA) binding to the β-propeller domain (BPD). Sialidase BPD is structurally conserved in various bacterial species and the protein binding interfaces have the tendency to form salt bridges, whereas uncommitted charged residues may affect binding and protein structure. However, it is not clear whether the sialidase BPD of varying strains of the same bacterial species differ, particularly with regards to salt bridge formation. Here, we determined the P. gingivalis ATCC 33277 and W50 sialidase homology models and sialidase activities, while the putative salt bridge residues found in the sialidase BPDs were compared. We established that both ATCC 33277 and W50 have different sialidase homology models and activities, whereas, the BPD (β1-6) is structurally conserved with most salt bridge-forming residues following a common orientation. Moreover, β2D444-β6K338 distance measurement in ATCC 33277 (5.99 Å) and W50 (3.09 Å) differ, while β1K396A substitution alters the β2D444-β6K338 distance measurements in ATCC 33277 (3.09 Å) and W50 (3.01 Å) consequentially affecting each model. P. gingivalis plays a major role in periodontitis induction and its virulence is greatly influenced by the sialidase enzyme wherein the sialidase BPD is highly conserved. Our results suggest that alterations in the salt bridge formation within the BPD interface may affect the P. gingivalis sialidase structure. This would imply that disrupting the salt bridge formation within the P. gingivalis sialidase BPD could serve as a potential therapeutic strategy for the treatment of P. gingivalis-related periodontitis.

Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment

Detection of remote binding site similarity in proteins plays an important role for drug repositioning and off-target effect prediction. Various non-covalent interactions such as hydrogen bonds and van-der-Waals forces drive ligands' molecular recognition by binding sites in proteins. The increasing amount of available structures of protein-small molecule complexes enabled the development of comparative approaches. Several methods have been developed to characterize and compare protein-ligand interaction patterns. Usually implemented as fingerprints, these are mainly used for post processing docking scores and (off-)target prediction. In the latter application, interaction profiles detect similarities in the bound interactions of different ligands and thus identify essential interactions between a protein and its small molecule ligands. Interaction pattern similarity correlates with binding site similarity and is thus contributing to a higher precision in binding site similarity assessment of proteins with distinct global structure. This renders it valuable for existing drug repositioning approaches in structural bioinformatics. Current methods to characterize and compare structure-based interaction patterns - both for protein-small-molecule and protein-protein interactions - as well as their potential in target prediction will be reviewed in this article. The question of how the set of interaction types, flexibility or water-mediated interactions, influence the comparison of interaction patterns will be discussed. Due to the wealth of protein-ligand structures available today, predicted targets can be ranked by comparing their ligand interaction pattern to patterns of the known target. Such knowledge-based methods offer high precision in comparison to methods comparing whole binding sites based on shape and amino acid physicochemical similarity.

Protein pocket and ligand shape comparison and its application in virtual screening

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions

Inverse ligand binding prediction utilizes a few protein-ligand (drug) complexes to predict other secondary therapeutic and off-targets of a given drug molecule on a proteomic scale. We adapt two binding site predictors, FINDSITE and SMAP, to perform the inverse predictions and evaluate them on over 30 representative ligands. Use of just one complex allows the identification of other protein targets; the availability of additional complexes improves the results. Both methods offer comparable quality when using three complexes with diverse proteins. SMAP is better when fewer complexes are available, while FINDSITE provides stronger predictions for smaller ligands. We propose a consensus that combines (and outperforms) the two complementary approaches implemented by FINDSITE and SMAP. Most importantly, we demonstrate that these methods successfully find distant targets that belong to structurally different folds compared to the proteins in the input complexes.

Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors

MOTIVATION

Nucleotides are multifunctional molecules that are essential for numerous biological processes. They serve as sources for chemical energy, participate in the cellular signaling and they are involved in the enzymatic reactions. The knowledge of the nucleotide-protein interactions helps with annotation of protein functions and finds applications in drug design.

RESULTS

We propose a novel ensemble of accurate high-throughput predictors of binding residues from the protein sequence for ATP, ADP, AMP, GTP and GDP. Empirical tests show that our NsitePred method significantly outperforms existing predictors and approaches based on sequence alignment and residue conservation scoring. The NsitePred accurately finds more binding residues and binding sites and it performs particularly well for the sites with residues that are clustered close together in the sequence. The high predictive quality stems from the usage of novel, comprehensive and custom-designed inputs that utilize information extracted from the sequence, evolutionary profiles, several sequence-predicted structural descriptors and sequence alignment. Analysis of the predictive model reveals several sequence-derived hallmarks of nucleotide-binding residues; they are usually conserved and flanked by less conserved residues, and they are associated with certain arrangements of secondary structures and amino acid pairs in the specific neighboring positions in the sequence.

AVAILABILITY

http://biomine.ece.ualberta.ca/nSITEpred/

CONTACT

lkurgan@ece.ualberta.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ATPsite: sequence-based prediction of ATP-binding residues

Background

ATP is a ubiquitous nucleotide that provides energy for cellular activities, catalyzes chemical reactions, and is involved in cellular signalling. The knowledge of the ATP-protein interactions helps with annotation of protein functions and finds applications in drug design. The sequence to structure annotation gap motivates development of high-throughput sequence-based predictors of the ATP-binding residues. Moreover, our empirical tests show that the only existing predictor, ATPint, is characterized by relatively low predictive quality.

Methods

We propose a novel, high-throughput machine learning-based predictor, ATPsite, which identifies ATP-binding residues from protein sequences. Our predictor utilizes Support Vector Machine classifier and a comprehensive set of input features that are based on the sequence, evolutionary profiles, and the sequence-predicted structural descriptors including secondary structure, solvent accessibility, and dihedral angles.

Results

The ATPsite achieves significantly higher Mathews Correlation Coefficient (MCC) and Area Under the ROC Curve (AUC) values when compared with the existing methods including the ATPint, conservation-based rate4site, and alignment-based BLAST predictors. We also assessed the effectiveness of individual input types. The PSSM profile, the conservation scores, and certain features based on amino acid groups are shown to be more effective in predicting the ATP-binding residues than the remaining feature groups.

Conclusions

Statistical tests show that ATPsite significantly outperforms existing solutions. The consensus of the ATPsite with the sequence-alignment based predictor is shown to give further improvements.

A critical comparative assessment of predictions of protein-binding sites for biologically relevant organic compounds

Protein function annotation and rational drug discovery rely on the knowledge of binding sites for small organic compounds, and yet the quality of existing binding site predictors was never systematically evaluated. We assess predictions of ten representative geometry-, energy-, threading-, and consensus-based methods on a new benchmark data set that considers apo and holo protein structures with multiple binding sites for biologically relevant ligands. Statistical tests show that threading-based Findsite outperforms other predictors when its templates have high similarity with the input protein. However, Findsite is equivalent or inferior to some geometry-, energy-, and consensus-based methods when the similarity is lower. We demonstrate that geometry-, energy-, and consensus-based predictors benefit from the usage of holo structures and that the top four methods, Findsite, Q-SiteFinder, ConCavity, and MetaPocket, perform better for larger binding sites. Predictions from these four methods are complementary, and our simple meta-predictor improves over the best single predictor.