A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction

Atomistic mechanisms of the regulation of small-conductance Ca2+-activated K+ channel (SK2) by PIP2

Small-conductance Ca2+-activated K+ channels (SK, KCa2) are gated solely by intracellular microdomain Ca2+. The channel has emerged as a therapeutic target for cardiac arrhythmias. Calmodulin (CaM) interacts with the CaM binding domain (CaMBD) of the SK channels, serving as the obligatory Ca2+ sensor to gate the channels. In heterologous expression systems, phosphatidylinositol 4,5-bisphosphate (PIP2) coordinates with CaM in regulating SK channels. However, the roles and mechanisms of PIP2 in regulating SK channels in cardiomyocytes remain unknown. Here, optogenetics, magnetic nanoparticles, combined with Rosetta structural modeling, and molecular dynamics (MD) simulations revealed the atomistic mechanisms of how PIP2 works in concert with Ca2+-CaM in the SK channel activation. Our computational study affords evidence for the critical role of the amino acid residue R395 in the S6 transmembrane segment, which is localized in propinquity to the intracellular hydrophobic gate. This residue forms a salt bridge with residue E398 in the S6 transmembrane segment from the adjacent subunit. Both R395 and E398 are conserved in all known isoforms of SK channels. Our findings suggest that the binding of PIP2 to R395 residue disrupts the R395:E398 salt bridge, increasing the flexibility of the transmembrane segment S6 and the activation of the channel. Importantly, our findings serve as a platform for testing of structural-based drug designs for therapeutic inhibitors and activators of the SK channel family. The study is timely since inhibitors of SK channels are currently in clinical trials to treat atrial arrhythmias.

Computational insights into rational design and virtual screening of pyrazolopyrimidine derivatives targeting Janus kinase 3 (JAK3)

The Janus kinase 3 (JAK3) family, particularly JAK3, is pivotal in initiating autoimmune diseases such as rheumatoid arthritis. Recent advancements have focused on developing antirheumatic drugs targeting JAK3, leading to the discovery of novel pyrazolopyrimidine-based compounds as potential inhibitors. This research employed covalent docking, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) analysis, molecular dynamics modeling, and MM/GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy techniques to screen 41 in silico-designed pyrazolopyrimidine derivatives. Initially, 3D structures of the JAK3 enzyme were generated using SWISS-MODEL, followed by virtual screening and covalent docking via AutoDock4 (AD4). The selection process involved the AMES test, binding affinity assessment, and ADMET analysis, narrowing down the candidates to 27 compounds that passed the toxicity test. Further covalent docking identified compounds 21 and 41 as the most promising due to their high affinity and favourable ADMET profiles. Subsequent development led to the creation of nine potent molecules, with derivatives 43 and 46 showing exceptional affinity upon evaluation through molecular dynamics simulation and MM/GBSA calculations over 300 nanoseconds, comparable to tofacitinib, an approved RA drug. However, compounds L21 and L46 demonstrated stable performance, suggesting their effectiveness in treating rheumatoid arthritis and other autoimmune conditions associated with JAK3 inhibition.

Rational Design of Far-Red Archaerhodopsin-3-Based Fluorescent Genetically Encoded Voltage Indicators: from Elucidation of the Fluorescence Mechanism in Archers to Novel Red-Shifted Variants

Genetically encoded voltage indicators (GEVIs) have found wide applications as molecular tools for visualization of changes in cell membrane potential. Among others, several classes of archaerhodopsin-3-based GEVIs have been developed and have proved themselves promising in various molecular imaging studies. To expand the application range for this type of GEVIs, new variants with absorption band maxima shifted toward the first biological window and enhanced fluorescence signal are required. Here, we integrate computational and experimental strategies to reveal structural factors that distinguish far-red bright archaerhodopsin-3-based GEVIs, Archers, obtained by directed evolution in a previous study (McIsaac et al., PNAS, 2014) and the wild-type archaerhodopsin-3 with an extremely dim fluorescence signal, aiming to use the obtained information in subsequent rational design. We found that the fluorescence can be enhanced by stabilization of a certain conformation of the protein, which, in turn, can be achieved by tuning the pK a value of two titratable residues. These findings were supported further by introducing mutations into wild-type archeorhodopsin-3 and detecting the enhancement of the fluorescence signal. Finally, we came up with a rational design and proposed previously unknown Archers variants with red-shifted absorption bands (λmax up to 640 nm) and potential-dependent bright fluorescence (quantum yield up to 0.97%).

HTLV-1 reverse transcriptase homology model provides structural basis for sensitivity to existing nucleoside/nucleotide reverse transcriptase inhibitors

The human T-lymphotropic virus type 1 (HTLV-1) infects millions of people globally and is endemic to various resource-limited regions. Infections persist for life and are associated with increased susceptibility to opportunistic infections and severe diseases including adult T cell leukemia/lymphoma and HTLV-1-associated myelopathy-tropical spastic paraparesis. No HTLV-1-specific anti-retrovirals have been developed and it is unclear whether existing anti-retrovirals developed for treatment of human immunodeficiency virus (HIV) have efficacy against HTLV-1. To understand the structural basis for therapeutic binding, homology modelling and machine learning were used to develop a structural model of the HTLV-1 reverse transcriptase. With this, molecular docking experiments using a panel of FDA-approved inhibitors of viral reverse transcriptases to assess their capacity for binding, and in turn, inhibition. Importantly, nucleoside/nucleotide reverse transcriptase inhibitor but not non-nucleoside reverse transcriptase inhibitors were predicted to bind the HTLV-1 reverse transcriptase, with similar affinity to HIV-1 reverse transcriptase. By strengthening the rationale for clinical testing of therapies such as tenofovir alafenamide, zidovudine, lamivudine, and azvudine for treatment of HTLV-1, this study has demonstrated the power of in silico structural biology approaches in drug design and therapeutic testing.

Fluorescence of the Retinal Chromophore in Microbial and Animal Rhodopsins

Fluorescence of the vast majority of natural opsin-based photoactive proteins is extremely low, in accordance with their functions that depend on efficient transduction of absorbed light energy. However, several recently proposed classes of engineered rhodopsins with enhanced fluorescence, along with the discovery of a new natural highly fluorescent rhodopsin, NeoR, opened a way to exploit these transmembrane proteins as fluorescent sensors and draw more attention to studies on this untypical rhodopsin property. Here, we review the available data on the fluorescence of the retinal chromophore in microbial and animal rhodopsins and their photocycle intermediates, as well as different isomers of the protonated retinal Schiff base in various solvents and the gas phase.

High-throughput virtual screening approach of natural compounds as target inhibitors of plasmepsin-II

Plasmepsin II is a key enzyme in the life cycle of the Plasmodium falciparum parasite responsible for malaria, a disease that is causing deaths on a worldwide scale. Recently, plasmepsin II enzyme has gained much importance as an attractive drug target for the investigation of antimalarial drugs. In this sense, structure-based virtual screening have been utilized as tools in the process of discovering novel natural compounds based on quinoline as potential plasmepsin II inhibitors. Among the 58 quinoline derivatives isolated from different plants was screened by utilizing docking molecular, ADMET approaches, molecular dynamics simulation and MM-PBSA binding free energy. The first step in this work is building the 3 D structures of the plasmepsin II enzyme by using the SWISS-MODEL software. The optimized structures were subjected to virtual screening by Autodock Vina, an entity implicated in PyRx software. 21 were selected based on their binding affinity. The binding modes and interactions of the top-21 selected compounds were evaluated using AutoDock 4.2. Then, the pharmacokinetic proprieties and toxicity of these compounds were evaluated using ADMET analysis. Ten compounds were predicted to have ADMET characteristics with no side effects. Compounds M49 and M53 were found to be potential inhibitors. The stability of the selected two compounds was confirmed by MD simulation and MM/PBSA calculation during 200 ns. This study can be used to predict and to design new antimalarial drugs.Communicated by Ramaswamy H. Sarma.

Scalable Inhibitors of the Nsp3-Nsp4 Coupling in SARS-CoV-2

The human Betacoronavirus SARS-CoV-2 is a novel pathogen claiming millions of lives and causing a global pandemic that has disrupted international healthcare systems, economies, and communities. The virus is fast mutating and presenting more infectious but less lethal versions. Currently, some small-molecule therapeutics have received FDA emergency use authorization for the treatment of COVID-19, including Lagevrio (molnupiravir) and Paxlovid (nirmaltrevir/ritonavir), which target the RNA-dependent RNA polymerase and the 3CLpro main protease, respectively. Proteins downstream in the viral replication process, specifically the nonstructural proteins (Nsps1-16), are potential drug targets due to their crucial functions. Of these Nsps, Nsp4 is a particularly promising drug target due to its involvement in the SARS-CoV viral replication and double-membrane vesicle formation (mediated via interaction with Nsp3). Given the degree of sequence conservation of these two Nsps across the Betacoronavirus clade, their protein-protein interactions and functions are likely to be conserved as well in SARS-CoV-2. Through AlphaFold2 and its recent advancements, protein structures were generated of Nsp3 and 4 lumenal loops of interest. Then, using a combination of molecular docking suites and an existing library of lead-like compounds, we virtually screened 7 million ligands to identify five putative ligand inhibitors of Nsp4, which could present an alternative pharmaceutical approach against SARS-CoV-2. These ligands exhibit promising lead-like properties (ideal molecular weight and log P profiles), maintain fixed-Nsp4-ligand complexes in molecular dynamics (MD) simulations, and tightly associate with Nsp4 via hydrophobic interactions. Additionally, alternative peptide inhibitors based on Nsp3 were designed and shown in MD simulations to provide a highly stable binding to the Nsp4 protein. Finally, these therapeutics were attached to dendrimer structures to promote their multivalent binding with Nsp4, especially its large flexible luminal loop (Nsp4LLL). The therapeutics tested in this study represent many different approaches for targeting large flexible protein structures, especially those localized to the ER. This study is the first work targeting the membrane rearrangement system of viruses and will serve as a potential avenue for treating viruses with similar replicative function.

Homology Modeling of Transporter Proteins

Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.

Evaluating the accuracy of the AMBER protein force fields in modeling dihydrofolate reductase structures: misbalance in the conformational arrangements of the flexible loop domains

Protein flexible loop regions were once thought to be simple linkers between other more functional secondary structural elements. However, as it becomes clearer that these loop domains are critical players in a plethora of biological processes, accurate conformational sampling of 3D loop structures is vital to the advancement of drug design techniques and the overall growth of knowledge surrounding molecular systems. While experimental techniques provide a wealth of structural information, the resolution of flexible loop domains is sometimes low or entirely absent due to their complex and dynamic nature. This highlights an opportunity for de novo structure prediction using in silico methods with molecular dynamics (MDs). This study evaluates some of the AMBER protein force field's (ffs) ability to accurately model dihydrofolate reductase (DHFR) conformations, a protein complex characterized by specific arrangements and interactions of multiple flexible loops whose conformations are determined by the presence or absence of bound ligands and cofactors. Although the AMBER ffs, including ff19SB, studied well model most protein structures with rich secondary structure, results obtained here suggest the inability to significantly sample the expected DHFR loop-loop conformations - of the six distinct protein-ligand systems simulated, a majority lacked consistent stabilization of experimentally derived metrics definitive the three enzyme conformations. Although under-sampling and the chosen ff parameter combinations could be the cause, given past successes with these MD approaches for many protein systems, this suggests a potential misbalance in available ff parameters required to accurately predict the structure of multiple flexible loop regions present in proteins.Communicated by Ramaswamy H. Sarma.

Structure and dynamics of membrane protein in SARS-CoV-2

SARS-CoV-2 membrane (M) protein performs a variety of critical functions in virus infection cycle. However, the expression and purification of membrane protein structure is difficult despite tremendous progress. In this study, the 3 D structure is modeled followed by intensive validation and molecular dynamics simulation. The lack of suitable homologous templates (>30% sequence identities) leads us to construct the membrane protein models using template-free modeling (de novo or ab initio) approach with Robetta and trRosetta servers. Comparing with other model structures, it is evident that trRosetta (TM-score: 0.64; TM region RMSD: 2 Å) can provide the best model than Robetta (TM-score: 0.61; TM region RMSD: 3.3 Å) and I-TASSER (TM-score: 0.45; TM region RMSD: 6.5 Å). 100 ns molecular dynamics simulations are performed on the model structures by incorporating membrane environment. Moreover, secondary structure elements and principal component analysis (PCA) have also been performed on MD simulation data. Finally, trRosetta model is utilized for interpretation and visualization of interacting residues during protein-protein interactions. The common interacting residues including Phe103, Arg107, Met109, Trp110, Arg131, and Glu135 in the C-terminal domain of M protein are identified in membrane-spike and membrane-nucleocapsid protein complexes. The active site residues are also predicted for potential drug and peptide binding. Overall, this study might be helpful to design drugs and peptides against the modeled membrane protein of SARS-CoV-2 to accelerate further investigation. Communicated by Ramaswamy H. Sarma.

Rhodopsins: An Excitingly Versatile Protein Species for Research, Development and Creative Engineering

The first member and eponym of the rhodopsin family was identified in the 1930s as the visual pigment of the rod photoreceptor cell in the animal retina. It was found to be a membrane protein, owing its photosensitivity to the presence of a covalently bound chromophoric group. This group, derived from vitamin A, was appropriately dubbed retinal. In the 1970s a microbial counterpart of this species was discovered in an archaeon, being a membrane protein also harbouring retinal as a chromophore, and named bacteriorhodopsin. Since their discovery a photogenic panorama unfolded, where up to date new members and subspecies with a variety of light-driven functionality have been added to this family. The animal branch, meanwhile categorized as type-2 rhodopsins, turned out to form a large subclass in the superfamily of G protein-coupled receptors and are essential to multiple elements of light-dependent animal sensory physiology. The microbial branch, the type-1 rhodopsins, largely function as light-driven ion pumps or channels, but also contain sensory-active and enzyme-sustaining subspecies. In this review we will follow the development of this exciting membrane protein panorama in a representative number of highlights and will present a prospect of their extraordinary future potential.

Evolution of E. coli Phytase Toward Improved Hydrolysis of Inositol Tetraphosphate

Protein engineering campaigns are driven by the demand for superior enzyme performance under non-natural process conditions, such as elevated temperature or non-neutral pH, to achieve utmost efficiency and conserve limited resources. Phytases are industrial relevant feed enzymes that contribute to the overall phosphorus (P) management by catalyzing the stepwise phosphate hydrolysis from phytate, which is the main phosphorus storage in plants. Phosphorus is referred to as a critical disappearing nutrient, emphasizing the urgent need to implement strategies for a sustainable circular use and recovery of P from renewable resources. Engineered phytases already contribute today to an efficient phosphorus mobilization in the feeding industry and might pave the way to a circular P-bioeconomy. To date, a bottleneck in its application is the drastically reduced hydrolysis on lower phosphorylated reaction intermediates (lower inositol phosphates, ≤InsP4) and their subsequent accumulation. Here, we report the first KnowVolution campaign of the E. coli phytase toward improved hydrolysis on InsP4 and InsP3. As a prerequisite prior to evolution, a suitable screening setup was established and three isomers Ins(2,4,5)P3, Ins(2,3,4,5)P4 and Ins(1,2,5,6)P4 were generated through enzymatic hydrolysis of InsP6 and subsequent purification by HPLC. Screening of epPCR libraries identified clones with improved hydrolysis on Ins(1,2,5,6)P4 carrying substitutions involved in substrate binding and orientation. Saturation of seven positions and screening of, in total, 10,000 clones generated a dataset of 46 variants on their activity on all three isomers. This dataset was used for training, testing, and inferring models for machine learning guided recombination. The PyPEF method used allowed the prediction of recombinants from the identified substitutions, which were analyzed by reverse engineering to gain molecular understanding. Six variants with improved InsP4 hydrolysis of >2.5 were identified, of which variant T23L/K24S had a 3.7-fold improved relative activity on Ins(2,3,4,5)P4 and concomitantly shows a 2.7-fold improved hydrolysis of Ins(2,4,5)P3. Reported substitutions are the first published Ec phy variants with improved hydrolysis on InsP4 and InsP3.

The deleterious impact of a non-synonymous SNP on protein structure and function is apparent in hypertension

Essential hypertension (EH) is a significant health issue around the globe. The indifferent therapy regimen suggests varied physiological functions due to the lifestyle and genetic presentations of an individual. The endothelial nitric oxide synthase (NOS3) gene is a crucial vascular system marker in EH that contributes significantly to the phenotype. Hence, the present study aimed to employ the candidate gene approach and investigate the association between NOS3 single nucleotide polymorphism (SNP) E298D (G894T/rs1799983) by applying several in silico tools and validation through human samples screening. We corroborated computational findings through a case-control study comprising 294 controls and 299 patients; the 894T allele emerged significantly as the risk allele (odds ratio=2.07; P=6.38E-05). The in silico analyses highlighted the significance of E298D on the native structure and function of NOS3. The dynamics simulation study revealed that the variant type 298D caused structural destabilization of the protein to alter its function. Plasma nitrite levels were reduced in patients (P=0.0002), and the same correlated with the 894T allele. Furthermore, correlations were apparent between clinical, genotype, and routine biochemical parameters. To conclude, the study demonstrated a perceptible association between the SNP E298D and NOS3 protein structure stability that appears to have a bearing on the enzyme's function with a deleterious role in EH.

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

The efficacy of antibiotics to treat bacterial infections declines rapidly due to antibiotic resistance. This problem has stimulated the development of novel antibiotics, but most attempts have failed. Consequently, the idea of mining uncharacterized genes of pathogens to identify potential targets for entirely new classes of antibiotics was proposed. Without knowing the biochemical function of a protein, it is difficult to validate its potential for drug targeting; therefore, the functional characterization of bacterial proteins of unknown function must be accelerated. Here, we present a paradigm for comprehensively predicting the biochemical functions of a large set of proteins encoded by hypothetical genes in human pathogens to identify candidate drug targets. A high-throughput approach based on homology modelling with ten templates per target protein was applied to the set of 2103 P. aeruginosa proteins encoded by hypothetical genes. The >21000 homology modelling results obtained and available biological and biochemical information about several thousand templates were scrutinized to predict the function of reliably modelled proteins of unknown function. This approach resulted in assigning one or often multiple putative functions to hundreds of enzymes, ligand-binding proteins and transporters. New biochemical functions were predicted for 41 proteins whose essential or virulence-related roles in P. aeruginosa were already experimentally demonstrated. Eleven of them were shortlisted as promising drug targets that participate in essential pathways (maintaining genome and cell wall integrity), virulence-related processes (adhesion, cell motility, host recognition) or antibiotic resistance, which are general drug targets. These proteins are conserved in other WHO priority pathogens but not in humans; therefore, they represent high-potential targets for preclinical studies. These and many more biochemical functions assigned to uncharacterized proteins of P. aeruginosa, made available as PaPUF database, may guide the design of experimental screening of inhibitors, which is a crucial step towards the validation of the highest-potential targets for the development of novel drugs against P. aeruginosa and other high-priority pathogens.

Electronic Couplings and Electrostatic Interactions Behind the Light Absorption of Retinal Proteins

The photo-functional chromophore retinal exhibits a wide variety of optical absorption properties depending on its intermolecular interactions with surrounding proteins and other chromophores. By utilizing these properties, microbial and animal rhodopsins express biological functions such as ion-transport and signal transduction. In this review, we present the molecular mechanisms underlying light absorption in rhodopsins, as revealed by quantum chemical calculations. Here, symmetry-adapted cluster-configuration interaction (SAC-CI), combined quantum mechanical and molecular mechanical (QM/MM), and transition-density-fragment interaction (TDFI) methods are used to describe the electronic structure of the retinal, the surrounding protein environment, and the electronic coupling between chromophores, respectively. These computational approaches provide successful reproductions of experimentally observed absorption and circular dichroism (CD) spectra, as well as insights into the mechanisms of unique optical properties in terms of chromophore-protein electrostatic interactions and chromophore-chromophore electronic couplings. On the basis of the molecular mechanisms revealed in these studies, we also discuss strategies for artificial design of the optical absorption properties of rhodopsins.

Free Energy Computation for an Isomerizing Chromophore in a Molecular Cavity via the Average Solvent Electrostatic Configuration Model: Applications in Rhodopsin and Rhodopsin-Mimicking Systems

We present a novel technique for computing the free energy differences between two chromophore "isomers" hosted in a molecular environment (a generalized solvent). Such an environment may range from a relatively rigid protein cavity to a flexible solvent environment. The technique is characterized by the application of the previously reported "average electrostatic solvent configuration" method, and it is based on the idea of using the free energy perturbation theory along with a chromophore annihilation procedure in thermodynamic cycle calculations. The method is benchmarked by computing the ground-state room-temperature relative stabilities between (i) the cis and trans isomers of prototypal animal and microbial rhodopsins and (ii) the analogue isomers of a rhodopsin-like light-driven molecular switch in methanol. Furthermore, we show that the same technology can be used to estimate the activation free energy for the thermal isomerization of systems i-ii by replacing one isomer with a transition state. The results show that the computed relative stability and isomerization barrier magnitudes for the selected systems are in line with the available experimental observation in spite of their widely diverse complexity.

Antimicrobial Peptides Derived From Insects Offer a Novel Therapeutic Option to Combat Biofilm: A Review

Biofilms form a complex layer with defined structures, that attach on biotic or abiotic surfaces, are tough to eradicate and tend to cause some resistance against most antibiotics. Several studies confirmed that biofilm-producing bacteria exhibit higher resistance compared to the planktonic form of the same species. Antibiotic resistance factors are well understood in planktonic bacteria which is not so in case of biofilm producing forms. This may be due to the lack of available drugs with known resistance mechanisms for biofilms. Existing antibiotics cannot eradicate most biofilms, especially of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species). Insects produce complex and diverse set of chemicals for survival and defense. Antimicrobial peptides (AMPs), produced by most insects, generally have a broad spectrum of activity and the potential to bypass the resistance mechanisms of classical antibiotics. Besides, AMPs may well act synergistically with classical antibiotics for a double-pronged attack on infections. Thus, AMPs could be promising alternatives to overcome medically important biofilms, decrease the possibility of acquired resistance and treatment of multidrug-resistant pathogens including ESKAPE. The present review focuses on insect-derived AMPs with special reference to anti-biofilm-based strategies. It covers the AMP composition, pathways and mechanisms of action, the formation of biofilms, impact of biofilms on human diseases, current strategies as well as therapeutic options to combat biofilm with antimicrobial peptides from insects. In addition, the review also illustrates the importance of bioinformatics tools and molecular docking studies to boost the importance of select bioactive peptides those can be developed as drugs, as well as suggestions for further basic and clinical research.

Protein Structure Prediction: Conventional and Deep Learning Perspectives

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein-protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.

PCPD: Plant cytochrome P450 database and web-based tools for structural construction and ligand docking

Plant cytochrome P450s play key roles in the diversification and functional modification of plant natural products. Although over 200,000 plant P450 gene sequences have been recorded, only seven crystalized P450 genes severely hampered the functional characterization, gene mining and engineering of important P450s. Here, we combined Rosetta homologous modeling and MD-based refinement to construct a high-resolution P450 structure prediction process (PCPCM), which was applied to 181 plant P450s with identified functions. Furthermore, we constructed a ligand docking process (PCPLD) that can be applied for plant P450s virtual screening. 10 examples of virtual screening indicated the process can reduce about 80% screening space for next experimental verification. Finally, we constructed a plant P450 database (PCPD: http://p450.biodesign.ac.cn/), which includes the sequences, structures and functions of the 181 plant P450s, and a web service based on PCPCM and PCPLD. Our study not only developed methods for the P450-specific structure analysis, but also introduced a universal approach that can assist the mining and functional analysis of P450 enzymes.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

Study of Endogen Substrates, Drug Substrates and Inhibitors Binding Conformations on MRP4 and Its Variants by Molecular Docking and Molecular Dynamics

Multidrug resistance protein-4 (MRP4) belongs to the ABC transporter superfamily and promotes the transport of xenobiotics including drugs. A non-synonymous single nucleotide polymorphisms (nsSNPs) in the ABCC4 gene can promote changes in the structure and function of MRP4. In this work, the interaction of certain endogen substrates, drug substrates, and inhibitors with wild type-MRP4 (WT-MRP4) and its variants G187W and Y556C were studied to determine differences in the intermolecular interactions and affinity related to SNPs using protein threading modeling, molecular docking, all-atom, coarse grained, and umbrella sampling molecular dynamics simulations (AA-MDS and CG-MDS, respectively). The results showed that the three MRP4 structures had significantly different conformations at given sites, leading to differences in the docking scores (DS) and binding sites of three different groups of molecules. Folic acid (FA) had the highest variation in DS on G187W concerning WT-MRP4. WT-MRP4, G187W, Y556C, and FA had different conformations through 25 ns AA-MD. Umbrella sampling simulations indicated that the Y556C-FA complex was the most stable one with or without ATP. In Y556C, the cyclic adenosine monophosphate (cAMP) and ceefourin-1 binding sites are located out of the entrance of the inner cavity, which suggests that both cAMP and ceefourin-1 may not be transported. The binding site for cAMP and ceefourin-1 is quite similar and the affinity (binding energy) of ceefourin-1 to WT-MRP4, G187W, and Y556C is greater than the affinity of cAMP, which may suggest that ceefourin-1 works as a competitive inhibitor. In conclusion, the nsSNPs G187W and Y556C lead to changes in protein conformation, which modifies the ligand binding site, DS, and binding energy.

RubyACRs, nonalgal anion channelrhodopsins with highly red-shifted absorption

Channelrhodopsins are light-gated ion channels widely used to control neuronal firing with light (optogenetics). We report two previously unknown families of anion channelrhodopsins (ACRs), one from the heterotrophic protists labyrinthulea and the other from haptophyte algae. Four closely related labyrinthulea ACRs, named RubyACRs here, exhibit a unique retinal-binding pocket that creates spectral sensitivities with maxima at 590 to 610 nm, the most red-shifted channelrhodopsins known, long-sought for optogenetics, and more broadly the most red-shifted microbial rhodopsins thus far reported. We identified three spectral tuning residues critical for the red-shifted absorption. Photocurrents recorded from the RubyACR from Aurantiochytrium limacinum (designated AlACR1) under single-turnover excitation exhibited biphasic decay, the rate of which was only weakly voltage dependent, in contrast to that in previously characterized cryptophyte ACRs, indicating differences in channel gating mechanisms between the two ACR families. Moreover, in A. limacinum we identified three ACRs with absorption maxima at 485, 545, and 590 nm, indicating color-sensitive photosensing with blue, green, and red spectral variation of ACRs within individual species of the labyrinthulea family. We also report functional energy transfer from a cytoplasmic fluorescent protein domain to the retinal chromophore bound within RubyACRs.

An assessment of water placement algorithms in quantum mechanics/molecular mechanics modeling: the case of rhodopsins' first spectral absorption band maxima

Quantum mechanics/molecular mechanics (QM/MM) models are a widely used tool to obtain detailed insight into the properties and functioning of proteins. The outcome of QM/MM studies heavily depends on the quality of the applied QM/MM model. Prediction and right placement of internal water molecules in protein cavities is one of the critical parts of any QM/MM model construction. Herein, we performed a systematic study of four protein hydration algorithms. We tested these algorithms for their ability to predict X-ray-resolved water molecules for a set of membrane photosensitive rhodopsin proteins, as well as the influence of the applied water placement algorithms on the QM/MM calculated absorption maxima (λ_max) of these proteins. We used 49 rhodopsins and their intermediates with available X-ray structures as the test set. We found that a proper choice of hydration algorithms and setups is needed to predict functionally important water molecules in the chromophore-binding cavity of rhodopsins, such as the water cluster in the N-H region of bacteriorhodopsin or two water molecules in the binding pocket of bovine visual rhodopsin. The QM/MM calculated λ_max of rhodopsins is also quite sensitive to the applied protein hydration protocols. The best methodology allows obtaining an 18.0 nm average value for the absolute deviation of the calculated λ_max from the experimental λ_max. Although the major effect of water molecules on λ_max originates from the water molecules located in the binding pocket, the water molecules outside the binding pocket also affect the calculated λ_max mainly by causing a reorganization of the protein structure. The results reported in this study can be used for the evaluation and further development of hydration methodologies, in general, and rhodopsin QM/MM models, in particular.

Docking studies and molecular dynamics simulations of the binding characteristics of waldiomycin and its methyl ester analog to Staphylococcus aureus histidine kinase

Bacterial histidine kinases (HKs) are considered attractive drug targets because of their ability to govern adaptive responses coupled with their ubiquity. There are several classes of HK inhibitors; however, they suffer from drug resistance, poor bioavailability, and a lack of selectivity. The 3D structure of Staphylococcus aureus HK was not isolated in high-resolution coordinates, precluding further disclosure of structure-dependent binding to the specific antibiotics. To elucidate structure-dependent binding, the 3D structure of the catalytic domain WalK of S. aureus HK was constructed using homology modeling to investigate the WalK-ligand binding mechanisms through molecular docking studies and molecular dynamics simulations. The binding free energies of the waldiomycin and its methyl ester analog were calculated using molecular mechanics/generalized born surface area scoring. The key residues for protein-ligand binding were postulated. The structural divergence responsible for the 7.4-fold higher potency of waldiomycin than that of its ester analog was clearly observed. The optimized 3D macromolecule-ligand binding modes shed light on the S. aureus HK/WalK-ligand interactions that afford a means to assess binding affinity to design new HK/WalK inhibitors.

Systematic and Comparative Evaluation of Software Programs for Template-Based Modeling of Protein Structures

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template-based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile-based method, especially PSI-BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS-MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.

Structural and evolutionary analyses of the Plasmodium falciparum chloroquine resistance transporter

Mutations in the Plasmodium falciparum chloroquine resistance transporter (PfCRT) confer resistance to several antimalarial drugs such as chloroquine (CQ) or piperaquine (PPQ), a partner molecule in current artemisinin-based combination therapies. As a member of the Drug/Metabolite Transporter (DMT) superfamily, the vacuolar transporter PfCRT may translocate substrate molecule(s) across the membrane of the digestive vacuole (DV), a lysosome-like organelle. However, the physiological substrate(s), the transport mechanism and the functional regions of PfCRT remain to be fully characterized. Here, we hypothesized that identification of evolutionary conserved sites in a tertiary structural context could help locate putative functional regions of PfCRT. Hence, site-specific substitution rates were estimated over Plasmodium evolution at each amino acid sites, and the PfCRT tertiary structure was predicted in both inward-facing (open-to-vacuole) and occluded states through homology modeling using DMT template structures sharing <15% sequence identity with PfCRT. We found that the vacuolar-half and membrane-spanning domain (and especially the transmembrane helix 9) of PfCRT were more conserved, supporting that its physiological substrate is expelled out of the parasite DV. In the PfCRT occluded state, some evolutionary conserved sites, including positions related to drug resistance mutations, participate in a putative binding pocket located at the core of the PfCRT membrane-spanning domain. Through structural comparison with experimentally-characterized DMT transporters, we identified several conserved PfCRT amino acid sites located in this pocket as robust candidates for mediating substrate transport. Finally, in silico mutagenesis revealed that drug resistance mutations caused drastic changes in the electrostatic potential of the transporter vacuolar entry and pocket, facilitating the escape of protonated CQ and PPQ from the parasite DV.

An in silico structural approach to characterize human and rainbow trout estrogenicity of mycotoxins: Proof of concept study using zearalenone and alternariol

The mycotoxins zearalenone and alternariol may contaminate food and feed raising toxicological concerns due to their estrogenicity. Inter-species differences in their toxicokinetics and toxicodynamics may occur depending on evolution of taxa-specific traits. As a proof of principle, this manuscript investigates the comparative toxicodynamics of zearalenone, its metabolites (alpha-zearalenol and beta-zearalenol), and alternariol with regards to estrogenicity in humans and rainbow trout. An in silico structural approach based on docking simulations, pharmacophore modeling and molecular dynamics was applied and computational results were analyzed in comparison with available experimental data. The differences of estrogenicity among species of zearalenone and its metabolites have been structurally explained. Also, the low estrogenicity of alternariol in trout has been characterized here for the first time. This approach can provide a powerful tool for the characterization of interspecies differences in mycotoxin toxicity for a range of protein targets and relevant compounds for the food- and feed-safety area.

Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins

Computational chemistry provides versatile methods for studying the properties and functioning of biological systems at different levels of precision and at different time scales. The aim of this article is to review the computational methodologies that are applicable to rhodopsins as archetypes for photoactive membrane proteins that are of great importance both in nature and in modern technologies. For each class of computational techniques, from methods that use quantum mechanics for simulating rhodopsin photophysics to less-accurate coarse-grained methodologies used for long-scale protein dynamics, we consider possible applications and the main directions for improvement.

Binding site comparisons for target-centered drug discovery

INTRODUCTION

The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.