Long loop prediction using the protein local optimization program

Modeling receptor flexibility in the structure-based design of KRASG12C inhibitors

KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRAS^G12C has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRAS^G12C—sometimes referred to as the ‘switch-II pocket’—holds great potential in the design of direct inhibitors of KRAS^G12C. In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRAS^G12C, we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRAS^G12C inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets.

Binding and Activation of Serotonergic G-Protein Coupled Receptors by the Multimodal Antidepressant Vortioxetine

G-protein coupled receptors (GPCRs) are important pharmacological targets. Despite substantial progress, important questions still remain concerning the details of activation: how can a ligand act as an agonist in one receptor but as an antagonist in a homologous receptor, and how can agonists activate a receptor despite lacking polar functional groups able to interact with helix 5 as is the case for the related adrenergic receptors? Studying vortioxetine (VXT), an important multimodal antidepressant drug, may elucidate both questions. Herein, we present a thorough in silico analysis of VXT binding to 5-HT_1A, 5-HT_1B, and 5-HT₇ receptors and compare it with available experimental data. We are able to rationalize the differential mode of action of VXT at different receptors, but also, in the case of the 5-HT_1A receptor, we observe the initial steps of activation that inform about an activation mechanism that does not involve polar interaction with helix 5. The results extend our current understanding of agonist and antagonist action at aminergic GPCRs.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Exploration of small-molecule entry disruptors for chikungunya virus by targeting matrix remodelling associated protein

Background and purpose:

A genome-wide clustered regularly interspaced short palindromic repeats- associated protein 9-based screen has revealed that the cell adhesion molecule matrix remodelling associated protein 8 (Mxra8) acts as an entry mediator for many alphaviruses including chikungunya virus. The first X-ray crystal structure reported for Mxra8 a few months ago has a low-resolution of 3.49Å.

Experimental approach:

Homology modelling of Mxra8 protein was done employing the SWISS-MODEL and PRIME module of Maestro. To design novel Mxra8 inhibitors pharmacophore guided fragment-based drug design and structure-based virtual screening of Food and Drug Administration approved drug libraries were undertaken. Molecular docking and molecular dynamics (MD) simulations study were carried out to validate the findings.

Findings / Results:

The molecule H1a (dock score: -6.137, binding energy: -48.95 kcal/mol, and PHASE screen score: 1.528816) was identified as the best hit among the fragment-based designed ligands. Structure- based virtual screening suggested histamine, epinephrine, and capreomycin as potential hits which could be repurposed as Mxra8 inhibitor. MD simulations study suggested that only small molecules like histamine could be a potential inhibitor of Mxra8. H-bond interaction with Arg58 and Glu200 amino acid residues seems to be crucial for effective binding.

Conclusion and implications:

To the best of our knowledge, this is the first report on the design of novel inhibitors against Mxra8 protein to tackle the menace of alphaviruses infections. This design strategy could be used for structure-based drug design against other apo-proteins. This study also advances the application of in silico tools in the field of drug repurposing.

Loop Enhanced Conformational Resampling Method for Protein Structure Prediction

Protein structure prediction has been a long-standing problem for the past decades. In particular, the loop region structure remains an obstacle in forming an accurate protein tertiary structure because of its flexibility. In this study, Rama torsion angle and secondary structure feature-guided differential evolution named RSDE is proposed to predict three-dimensional structure with the exploitation on the loop region structure. In RSDE, the structure of the loop region is improved by the following: loop-based cross operator, which interchanges configuration of a randomly selected loop region between individuals, and loop-based mutate operator, which considers torsion angle feature into conformational sampling. A stochastic ranking selective strategy is designed to select conformations with low energy and near-native structure. Moreover, the conformational resampling method, which uses previously learned knowledge to guide subsequent sampling, is proposed to improve the sampling efficiency. Experiments on a total of 28 test proteins reveals that the proposed RSDE is effective and can obtain native-like models.

Fast design of arbitrary length loops in proteins using InteractiveRosetta

Background

With increasing interest in ab initio protein design, there is a desire to be able to fully explore the design space of insertions and deletions. Nature inserts and deletes residues to optimize energy and function, but allowing variable length indels in the context of an interactive protein design session presents challenges with regard to speed and accuracy.

Results

Here we present a new module (INDEL) for InteractiveRosetta which allows the user to specify a range of lengths for a desired indel, and which returns a set of low energy backbones in a matter of seconds. To make the loop search fast, loop anchor points are geometrically hashed using C α-C α and C β-C β distances, and the hash is mapped to start and end points in a pre-compiled random access file of non-redundant, protein backbone coordinates. Loops with superposable anchors are filtered for collisions and returned to InteractiveRosetta as poly-alanine for display and selective incorporation into the design template. Sidechains can then be added using RosettaDesign tools.

Conclusions

INDEL was able to find viable loops in 100% of 500 attempts for all lengths from 3 to 20 residues. INDEL has been applied to the task of designing a domain-swapping loop for T7-endonuclease I, changing its specificity from Holliday junctions to paranemic crossover (PX) DNA.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2345-5) contains supplementary material, which is available to authorized users.

The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation

Obtaining a detailed description of how active site flap motion affects substrate or ligand binding will advance structure-based drug design (SBDD) efforts on systems including the kinases, HSP90, HIV protease, ureases, etc. Through this understanding, we will be able to design better inhibitors and better proteins that have desired functions. Herein we address this issue by generating the relevant configurational states of a protein flap on the molecular energy landscape using an approach we call MT_Flex-b and then following this with a procedure to estimate the free energy associated with the motion of the flap region. To illustrate our overall workflow, we explored the free energy changes in the streptavidin/biotin system upon introducing conformational flexibility in loop_3-4 in the biotin unbound ( apo) and bound ( holo) state. The free energy surfaces were created using the Movable Type free energy method, and for further validation, we compared them to potential of mean force (PMF) generated free energy surfaces using MD simulations employing the FF99SBILDN and FF14SB force fields. We also estimated the free energy thermodynamic cycle using an ensemble of closed-like and open-like end states for the ligand unbound and bound states and estimated the binding free energy to be approximately -16.2 kcal/mol (experimental -18.3 kcal/mol). The good agreement between MT_Flex-b in combination with the MT method with experiment and MD simulations supports the effectiveness of our strategy in obtaining unique insights into the motions in proteins that can then be used in a range of biological and biomedical applications.

Fast de novo discovery of low-energy protein loop conformations

In the prediction of protein structure from amino acid sequence, loops are challenging regions for computational methods. Since loops are often located on the protein surface, they can have significant roles in determining protein functions and binding properties. Loop prediction without the aid of a structural template requires extensive conformational sampling and energy minimization, which are computationally difficult. In this article we present a new de novo loop sampling method, the Parallely filtered Energy Targeted All-atom Loop Sampler (PETALS) to rapidly locate low energy conformations. PETALS explores both backbone and side-chain positions of the loop region simultaneously according to the energy function selected by the user, and constructs a nonredundant ensemble of low energy loop conformations using filtering criteria. The method is illustrated with the DFIRE potential and DiSGro energy function for loops, and shown to be highly effective at discovering conformations with near-native (or better) energy. Using the same energy function as the DiSGro algorithm, PETALS samples conformations with both lower RMSDs and lower energies. PETALS is also useful for assessing the accuracy of different energy functions. PETALS runs rapidly, requiring an average time cost of 10 minutes for a length 12 loop on a single 3.2 GHz processor core, comparable to the fastest existing de novo methods for generating an ensemble of conformations. Proteins 2017; 85:1402-1412. © 2017 Wiley Periodicals, Inc.

Investigating Protein-Peptide Interactions Using the Schrödinger Computational Suite

The Schrödinger software suite contains a broad array of computational chemistry and molecular modeling tools that can be used to study the interaction of peptides with proteins. These include molecular docking using Glide and Piper, relative binding free energy predictions with FEP+, conformational searches using MacroModel and Desmond, and structural refinement using Prime and PrimeX. In this review we provide a comprehensive overview of these tools and describe their potential application in the identification and optimization of peptide ligands for proteins.

Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops

Antibodies recognize antigens through the complementary determining regions (CDR) formed by six-loop hypervariable regions crucial for the diversity of antigen specificities. Among the six CDR loops, the H3 loop is the most challenging to predict because of its much higher variation in sequence length and identity, resulting in much larger and complex structural space, compared to the other five loops. We developed a novel method based on a chain-growth sequential Monte Carlo method, called distance-guided sequential chain-growth Monte Carlo for H3 loops (DiSGro-H3). The new method samples protein chains in both forward and backward directions. It can efficiently generate low energy, near-native H3 loop structures using the conformation types predicted from the sequences of H3 loops. DiSGro-H3 performs significantly better than another ab initio method, RosettaAntibody, in both sampling and prediction, while taking less computational time. It performs comparably to template-based methods. As an ab initio method, DiSGro-H3 offers satisfactory accuracy while being able to predict any H3 loops without templates.

Elucidating a chemical defense mechanism of Antarctic sponges: A computational study

In 2000, a novel secondary metabolite (erebusinone, Ereb) was isolated from the Antarctic sea sponge, Isodictya erinacea. The bioactivity of Ereb was investigated, and it was found to inhibit molting when fed to the arthropod species Orchomene plebs. Xanthurenic acid (XA) is a known endogenous molt regulator present in arthropods. Experimental studies have confirmed that XA inhibits molting by binding to either (or both) of two P450 enzymes (CYP315a1 or CYP314a1) that are responsible for the final two hydroxylations in the production of the molt-inducing hormone, 20-hydroxyecdysone (20E). The lack of crystal structures and biochemical assays for CYP315a1 or CYP314a1, has prevented further experimental exploration of XA and Ereb's molt inhibition mechanisms. Herein, a wide array of computational techniques - homology modeling, molecular dynamics simulations, binding site bioinformatics, flexible receptor-flexible ligand docking, and molecular mechanics-generalized Born surface area calculations - have been employed to elucidate the structure-function relationships between the aforementioned P450s and the two described small molecule inhibitors (Ereb and XA). Results indicate that Ereb likely targets CYP315a1 by interacting with a network of aromatic residues in the binding site, while XA may inhibit both CYP315a1 and CYP314a1 because of its aromatic, as well as charged nature.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

Dihedral Angle-Based Sampling of Natural Product Polyketide Conformations: Application to Permeability Prediction

Macrocycles pose challenges for computer-aided drug design due to their conformational complexity. One fundamental challenge is identifying all low-energy conformations of the macrocyclic ring, which is important for modeling target binding, passive membrane permeation, and other conformation-dependent properties. Macrocyclic polyketides are medically and biologically important natural products characterized by structural and functional diversity. Advances in synthetic biology and semisynthetic methods may enable creation of an even more diverse set of non-natural product polyketides for drug discovery and other applications. However, the conformational sampling of these flexible compounds remains demanding. We developed and optimized a dihedral angle-based macrocycle conformational sampling method for macrocycles of arbitrary structure, and here we apply it to diverse polyketide natural products. First, we evaluated its performance using a data set of 37 polyketides with available crystal structures, with 9-22 rotatable bonds in the macrocyclic ring. Our optimized protocol was able to reproduce the crystal structure of polyketides' aglycone backbone within 0.50 Å RMSD for 31 out of 37 polyketides. Consistent with prior structural studies, our analysis suggests that polyketides tend to have multiple distinct low-energy structures, including the bioactive (target-bound) conformation as well as others of unknown significance. For this reason, we also introduce a strategy to improve both efficiency and accuracy of the conformational search by utilizing torsional restraints derived from NMR vicinal proton couplings to restrict the conformational search. Finally, as a first application of the method, we made blinded predictions of the passive membrane permeability of a diverse set of polyketides, based on their predicted structures in low- and high-dielectric media.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

Innovative computer-aided methods for the discovery of new kinase ligands

Recent evidence points to significant roles played by protein kinases in cell signaling and cellular proliferation. Faulty protein kinases are involved in cancer, diabetes and chronic inflammation. Efforts are continuously carried out to discover new inhibitors for selected protein kinases. In this review, we discuss two new computer-aided methodologies we developed to mine virtual databases for new bioactive compounds. One method is ligand-based exploration of the pharmacophoric space of inhibitors of any particular biotarget followed by quantitative structure-activity relationship-based selection of the best pharmacophore(s). The second approach is structure-based assuming that potent ligands come into contact with binding site spots distinct from those contacted by weakly potent ligands. Both approaches yield pharmacophores useful as 3D search queries for the discovery of new bioactive (kinase) inhibitors.

Evidence for the presence of key chlorophyll-biosynthesis-related proteins in the genus Rubrobacter (Phylum Actinobacteria) and its implications for the evolution and origin of photosynthesis

Homologs showing high degree of sequence similarity to the three subunits of the protochlorophyllide oxidoreductase enzyme complex (viz. BchL, BchN, and BchB), which carries out a central role in chlorophyll-bacteriochlorophyll (Bchl) biosynthesis, are uniquely found in photosynthetic organisms. The results of BLAST searches and homology modeling presented here show that proteins exhibiting a high degree of sequence and structural similarity to the BchB and BchN proteins are also present in organisms from the high G+C Gram-positive phylum of Actinobacteria, specifically in members of the genus Rubrobacter (R. x ylanophilus and R. r adiotolerans). The results presented exclude the possibility that the observed BLAST hits are for subunits of the nitrogenase complex or the chlorin reductase complex. The branching in phylogenetic trees and the sequence characteristics of the Rubrobacter BchB/BchN homologs indicate that these homologs are distinct from those found in other photosynthetic bacteria and that they may represent ancestral forms of the BchB/BchN proteins. Although a homolog showing high degree of sequence similarity to the BchL protein was not detected in Rubrobacter, another protein, belonging to the ParA/Soj/MinD family, present in these bacteria, exhibits high degree of structural similarity to the BchL. In addition to the BchB/BchN homologs, Rubrobacter species also contain homologs showing high degree of sequence similarity to different subunits of magnesium chelatase (BchD, BchH, and BchI) as well as proteins showing significant similarity to the BchP and BchG proteins. Interestingly, no homologs corresponding to the BchX, BchY, and BchZ proteins were detected in the Rubrobacter species. These results provide the first suggestive evidence that some form of photosynthesis either exists or was anciently present within the phylum Actinobacteria (high G+C Gram-positive) in members of the genus Rubrobacter. The significance of these results concerning the origin of the Bchl-based photosynthesis is also discussed.

A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design

The development and validation of computational macromolecular modeling and design methods depend on suitable benchmark datasets and informative metrics for comparing protocols. In addition, if a method is intended to be adopted broadly in diverse biological applications, there needs to be information on appropriate parameters for each protocol, as well as metrics describing the expected accuracy compared to experimental data. In certain disciplines, there exist established benchmarks and public resources where experts in a particular methodology are encouraged to supply their most efficient implementation of each particular benchmark. We aim to provide such a resource for protocols in macromolecular modeling and design. We present a freely accessible web resource (https://kortemmelab.ucsf.edu/benchmarks) to guide the development of protocols for protein modeling and design. The site provides benchmark datasets and metrics to compare the performance of a variety of modeling protocols using different computational sampling methods and energy functions, providing a "best practice" set of parameters for each method. Each benchmark has an associated downloadable benchmark capture archive containing the input files, analysis scripts, and tutorials for running the benchmark. The captures may be run with any suitable modeling method; we supply command lines for running the benchmarks using the Rosetta software suite. We have compiled initial benchmarks for the resource spanning three key areas: prediction of energetic effects of mutations, protein design, and protein structure prediction, each with associated state-of-the-art modeling protocols. With the help of the wider macromolecular modeling community, we hope to expand the variety of benchmarks included on the website and continue to evaluate new iterations of current methods as they become available.

Conformational sampling and structure prediction of multiple interacting loops in soluble and β-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method

MOTIVATION

Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task.

RESULTS

In this study, we report a new method called multi-loop Distance-guided Sequential chain-Growth Monte Carlo (M-DiSGro) for prediction of the conformations of multiple interacting loops in proteins. Our method achieves an average RMSD of 1.93 Å for lowest energy conformations of 36 pairs of interacting protein loops with the total length ranging from 12 to 24 residues. We further constructed a data set containing proteins with 2, 3 and 4 interacting loops. For the most challenging target proteins with four loops, the average RMSD of the lowest energy conformations is 2.35 Å. Our method is also tested for predicting multiple loops in β-barrel membrane proteins. For outer-membrane protein G, the lowest energy conformation has a RMSD of 2.62 Å for the three extracellular interacting loops with a total length of 34 residues (12, 12 and 10 residues in each loop).

AVAILABILITY AND IMPLEMENTATION

The software is freely available at: tanto.bioe.uic.edu/m-DiSGro.

CONTACT

jinfeng@stat.fsu.edu or jliang@uic.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

Comparative Protein Structure Modeling Using MODELLER

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

Antibody informatics for drug discovery

More and more antibody therapeutics are being approved every year, mainly due to their high efficacy and antigen selectivity. However, it is still difficult to identify the antigen, and thereby the function, of an antibody if no other information is available. There are obstacles inherent to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii) antibody numbering and IMGT. Here, we review "antibody informatics," which may integrate the above three fields so that bridging the gaps between industrial needs and academic solutions can be accelerated. This article is part of a Special Issue entitled: Recent advances in molecular engineering of antibody.

Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) - a promising target for breast cancer

Experimental evidences have observed enhanced expression of protease activated receptor 2 (PAR2) in breast cancer consistently. However, it is not yet recognized as an important therapeutic target for breast cancer as the primary molecular mechanisms of its activation are not yet well-defined. Nevertheless, recent reports on the mechanism of GPCR activation and signaling have given new insights to GPCR functioning. In the light of these details, we attempted to understand PAR2 structure & function using molecular modeling techniques. In this work, we generated averaged representative stable models of PAR2, using protease activated receptor 1 (PAR1) as a template and selected conformation based on their binding affinity with PAR2 specific agonist, GB110. Further, the selected model was used for studying the binding affinity of putative ligands. The selected ligands were based on a recent publication on phylogenetic analysis of Class A rhodopsin family of GPCRs. This study reports putative ligands, their interacting residues, binding affinity and molecular dynamics simulation studies on PAR2-ligand complexes. The results reported from this study would be useful for researchers and academicians to investigate PAR2 function as its physiological role is still hypothetical. Further, this information may provide a novel therapeutic scheme to manage breast cancer.

Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method

Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DISGRO). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is 1:53 A° , with a lowest energy RMSD of 2:99 A° , and an average ensembleRMSD of 5:23 A° . A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about 10 cpu minutes for 12-residue loops, compared to ca 180 cpu minutes using the FALCm method. Test results on benchmark datasets show that DISGRO performs comparably or better than previous successful methods, while requiring far less computing time. DISGRO is especially effective in modeling longer loops (10-17 residues).

Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction

We present the blinded prediction results in the Second Antibody Modeling Assessment (AMA-II) using a fully automatic antibody structure prediction method implemented in the programs BioLuminate and Prime. We have developed a novel knowledge based approach to model the CDR loops, using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physics-based energy function (VSGB2.0), which improves the model quality significantly. H3 loop modeling remains the most challenging task. Our ab initio loop prediction performs well for the H3 loop in the crystal structure context, and allows improved results when refining the H3 loops in the context of homology models. For the 10 human and mouse derived antibodies in this assessment, the average RMSDs for the homology model Fv and framework regions are 1.19 Å and 0.74 Å, respectively. The average RMSDs for five non-H3 CDR loops range from 0.61 Å to 1.05 Å, and the H3 loop average RMSD is 2.91 Å using our knowledge-based loop prediction approach. The ab initio H3 loop predictions yield an average RMSD of 1.28 Å when performed in the context of the crystal structure and 2.67 Å in the context of the homology modeled structure. Notably, our method for predicting the H3 loop in the crystal structure environment ranked first among the seven participating groups in AMA-II, and our method made the best prediction among all participants for seven of the ten targets.

LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains

Prediction of protein loop conformations without any prior knowledge (ab initio prediction) is an unsolved problem. Its solution will significantly impact protein homology and template-based modeling as well as ab initio protein-structure prediction. Here, we developed a coarse-grained, optimized scoring function for initial sampling and ranking of loop decoys. The resulting decoys are then further optimized in backbone and side-chain conformations and ranked by all-atom energy scoring functions. The final integrated technique called loop prediction by energy-assisted protocol achieved a median value of 2.1 Å root mean square deviation (RMSD) for 325 12-residue test loops and 2.0 Å RMSD for 45 12-residue loops from critical assessment of structure-prediction techniques (CASP) 10 target proteins with native core structures (backbone and side chains). If all side-chain conformations in protein cores were predicted in the absence of the target loop, loop-prediction accuracy only reduces slightly (0.2 Å difference in RMSD for 12-residue loops in the CASP target proteins). The accuracy obtained is about 1 Å RMSD or more improvement over other methods we tested. The executable file for a Linux system is freely available for academic users at http://sparks-lab.org.

LoopWeaver: loop modeling by the weighted scaling of verified proteins

Modeling loops is a necessary step in protein structure determination, even with experimental nuclear magnetic resonance (NMR) data, it is widely known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with subangstrom accuracy when compared with ab initio techniques, but the disadvantage of also producing a higher proportion of clashing or highly inaccurate predictions. We introduce LoopWeaver, a database method that uses multidimensional scaling to achieve better, clash-free placement of loops obtained from a database of protein structures. This allows us to maintain the above-mentioned advantage while avoiding the disadvantage. Test results show that we achieve significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper, before refinement. With refinement, our results (LoopWeaver and Loopy consensus) are better than ROSETTA, with 0.42 Å RMSD on average for 206 length 6 loops, 0.64 Å local RMSD for 168 length 7 loops, 0.81Å RMSD for 117 length 8 loops, and 0.98 Å RMSD for length 9 loops, while ROSETTA has 0.55, 0.79, 1.16, 1.42, respectively, at the same average time limit (3 hours). When we allow ROSETTA to run for over a week, it approaches, but does not surpass, our accuracy.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

Atomic-accuracy prediction of protein loop structures through an RNA-inspired Ansatz

Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased. Current approaches, such as the protein local optimization protocol or kinematic inversion closure (KIC) Monte Carlo, involve stages that coarse-grain proteins, simplifying modeling but precluding a systematic search of all-atom configurations. This article introduces an alternative modeling strategy based on a ‘stepwise ansatz’, recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA) protocol enables enumerative sampling of a 12 residue loop at a significant but achievable cost of thousands of CPU-hours. In a previously established benchmark, SWA recovers crystallographic conformations with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC modeling with a comparable expenditure of computational power. Furthermore, SWA gives high accuracy results on an additional set of 15 loops highlighted in the biological literature for their irregularity or unusual length. Successes include cis-Pro touch turns, loops that pass through tunnels of other side-chains, and loops of lengths up to 24 residues. Remaining problem cases are traced to inaccuracies in the Rosetta all-atom energy function. In five additional blind tests, SWA achieves sub-Angstrom accuracy models, including the first such success in a protein/RNA binding interface, the YbxF/kink-turn interaction in the fourth ‘RNA-puzzle’ competition. These results establish all-atom enumeration as an unusually systematic approach to ab initio protein structure modeling that can leverage high performance computing and physically realistic energy functions to more consistently achieve atomic accuracy.

Fragment-based modeling of membrane protein loops: successes, failures, and prospects for the future

Membrane proteins (MPs) have become a major focus in structure prediction, due to their medical importance. There is, however, a lack of fast and reliable methods that specialize in the modeling of MP loops. Often methods designed for soluble proteins (SPs) are applied directly to MPs. In this article, we investigate the validity of such an approach in the realm of fragment-based methods. We also examined the differences in membrane and soluble protein loops that might affect accuracy. We test our ability to predict soluble and MP loops with the previously published method FREAD. We show that it is possible to predict accurately the structure of MP loops using a database of MP fragments (0.5-1 Å median root-mean-square deviation). The presence of homologous proteins in the database helps prediction accuracy. However, even when homologues are removed better results are still achieved using fragments of MPs (0.8-1.6 Å) rather than SPs (1-4 Å) to model MP loops. We find that many fragments of SPs have shapes similar to their MP counterparts but have very different sequences; however, they do not appear to differ in their substitution patterns. Our findings may allow further improvements to fragment-based loop modeling algorithms for MPs. The current version of our proof-of-concept loop modeling protocol produces high-accuracy loop models for MPs and is available as a web server at http://medeller.info/fread.

Validating a Coarse-Grained Potential Energy Function through Protein Loop Modelling

Coarse-grained (CG) methods for sampling protein conformational space have the potential to increase computational efficiency by reducing the degrees of freedom. The gain in computational efficiency of CG methods often comes at the expense of non-protein like local conformational features. This could cause problems when transitioning to full atom models in a hierarchical framework. Here, a CG potential energy function was validated by applying it to the problem of loop prediction. A novel method to sample the conformational space of backbone atoms was benchmarked using a standard test set consisting of 351 distinct loops. This method used a sequence-independent CG potential energy function representing the protein using -carbon positions only and sampling conformations with a Monte Carlo simulated annealing based protocol. Backbone atoms were added using a method previously described and then gradient minimised in the Rosetta force field. Despite the CG potential energy function being sequence-independent, the method performed similarly to methods that explicitly use either fragments of known protein backbones with similar sequences or residue-specific /-maps to restrict the search space. The method was also able to predict with sub-Angstrom accuracy two out of seven loops from recently solved crystal structures of proteins with low sequence and structure similarity to previously deposited structures in the PDB. The ability to sample realistic loop conformations directly from a potential energy function enables the incorporation of additional geometric restraints and the use of more advanced sampling methods in a way that is not possible to do easily with fragment replacement methods and also enable multi-scale simulations for protein design and protein structure prediction. These restraints could be derived from experimental data or could be design restraints in the case of computational protein design. C++ source code is available for download from http://www.sbg.bio.ic.ac.uk/phyre2/PD2/.

Prediction of cytochrome P450 xenobiotic metabolism: tethered docking and reactivity derived from ligand molecular orbital analysis

Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.

patGPCR: a multitemplate approach for improving 3D structure prediction of transmembrane helices of G-protein-coupled receptors

The structures of the seven transmembrane helices of G-protein-coupled receptors are critically involved in many aspects of these receptors, such as receptor stability, ligand docking, and molecular function. Most of the previous multitemplate approaches have built a "super" template with very little merging of aligned fragments from different templates. Here, we present a parallelized multitemplate approach, patGPCR, to predict the 3D structures of transmembrane helices of G-protein-coupled receptors. patGPCR, which employs a bundle-packing related energy function that extends on the RosettaMem energy, parallelizes eight pipelines for transmembrane helix refinement and exchanges the optimized helix structures from multiple templates. We have investigated the performance of patGPCR on a test set containing eight determined G-protein-coupled receptors. The results indicate that patGPCR improves the TM RMSD of the predicted models by 33.64% on average against a single-template method. Compared with other homology approaches, the best models for five of the eight targets built by patGPCR had a lower TM RMSD than that obtained from SWISS-MODEL; patGPCR also showed lower average TM RMSD than single-template and multiple-template MODELLER.

Type II kinase inhibitors show an unexpected inhibition mode against Parkinson's disease-linked LRRK2 mutant G2019S

A number of well-known type II inhibitors (ATP-noncompetitive) that bind kinases in their DFG-out conformation were tested against wild-type LRRK2 and the most common Parkinson's disease-linked mutation, G2019S. We found that traditional type II inhibitors exhibit surprising variability in their inhibition mechanism between the wild type (WT) and the G2019S mutant of LRRK2. The type II kinase inhibitors were found to work in an ATP-competitive fashion against the G2019S mutant, whereas they appear to follow the expected noncompetitive mechanism against WT. Because the G2019S mutation lies in the DXG motif (DYG in LRRK2 but DFG in most other kinases) of the activation loop, we explored the structural consequence of the mutation on loop dynamics using an enhanced sampling method called metadynamics. The simulations suggest that the G2019S mutation stabilizes the DYG-in state of LRRK2 through a series of hydrogen bonds, leading to an increase in the conformational barrier between the active and inactive forms of the enzyme and a relative stabilization of the active form. The conformational bias toward the active form of LRRK2 mutants has two primary consequences. (1) The mutant enzyme becomes hyperactive, a known contributor to the Parkinsonian phenotype, as a consequence of being "locked" into the activated state, and (2) the mutation creates an unusual allosteric pocket that can bind type II inhibitors but in an ATP-competitive fashion. Our results suggest that developing type II inhibitors, which are generally considered superior to type I inhibitors because of desirable selectivity profiles, might be especially challenging for the G2019S LRRK2 mutant.

Conformational sampling in template-free protein loop structure modeling: an overview

Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a "mini protein folding problem" under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.

Prediction of Long Loops with Embedded Secondary Structure using the Protein Local Optimization Program

Robust homology modeling to atomic-level accuracy requires in the general case successful prediction of protein loops containing small segments of secondary structure. Further, as loop prediction advances to success with larger loops, the exclusion of loops containing secondary structure becomes awkward. Here, we extend the applicability of the Protein Local Optimization Program (PLOP) to loops up to 17 residues in length that contain either helical or hairpin segments. In general, PLOP hierarchically samples conformational space and ranks candidate loops with a high-quality molecular mechanics force field. For loops identified to possess α-helical segments, we employ an alternative dihedral library composed of (ϕ,ψ) angles commonly found in helices. The alternative library is searched over a user-specified range of residues that define the helical bounds. The source of these helical bounds can be from popular secondary structure prediction software or from analysis of past loop predictions where a propensity to form a helix is observed. Due to the maturity of our energy model, the lowest energy loop across all experiments can be selected with an accuracy of sub-Ångström RMSD in 80% of cases, 1.0 to 1.5 Å RMSD in 14% of cases, and poorer than 1.5 Å RMSD in 6% of cases. The effectiveness of our current methods in predicting hairpin-containing loops is explored with hairpins up to 13 residues in length and again reaching an accuracy of sub-Ångström RMSD in 83% of cases, 1.0 to 1.5 Å RMSD in 10% of cases, and poorer than 1.5 Å RMSD in 7% of cases. Finally, we explore the effect of an imprecise surrounding environment, in which side chains, but not the backbone, are initially in perturbed geometries. In these cases, loops perturbed to 3Å RMSD from the native environment were restored to their native conformation with sub-Ångström RMSD.

The protein local optimization program and G-protein-coupled receptors: loop restoration and applications to homology modeling

The protein local optimization program (PLOP) uses sophisticated sampling algorithms and a highly refined physics-based energy function to restore loops within a protein structure. In this chapter, we highlight some of the recent successes we have had with PLOP restoring long loops in their native environment as well as the intra- and extracellular loops of four G-protein-coupled receptors. This includes the very long second extracellular loops of bovine rhodopsin and the turkey β1- and human β2-adrenergic receptors. We then provide an extremely detailed description of PLOP's algorithms, as well as a sample file and explicit keywords so that a new user can successfully run PLOP.

Computer-aided antibody design

Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.