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Acquired immunodeficiency syndrome (AIDS) is caused by human immunodeficiency virus (HIV). HIV protease, reverse transcriptase, and integrase are targets of current drugs to treat the disease. However, anti-viral drug-resistant strains have emerged quickly due to the high mutation rate of the virus, leading to the demand for the development of new drugs. One attractive target is Gag-Pol polyprotein, which plays a key role in the life cycle of HIV. Recently, we found that a combination of M50I and V151I mutations in HIV-1 integrase can suppress virus release and inhibit the initiation of Gag-Pol autoprocessing and maturation without interfering with the dimerization of Gag-Pol. Additional mutations in integrase or RNase H domain in reverse transcriptase can compensate for the defect. However, the molecular mechanism is unknown. There is no tertiary structure of the full-length HIV-1 Pol protein available for further study. Therefore, we developed a workflow to predict the tertiary structure of HIV-1 NL4.3 Pol polyprotein. The modeled structure has comparable quality compared with the recently published partial HIV-1 Pol structure (PDB ID: 7SJX). Our HIV-1 NL4.3 Pol dimer model is the first full-length Pol tertiary structure. It can provide a structural platform for studying the autoprocessing mechanism of HIV-1 Pol and for developing new potent drugs. Moreover, the workflow can be used to predict other large protein structures that cannot be resolved via conventional experimental methods. [ABSTRACT FROM AUTHOR]

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field. [ABSTRACT FROM AUTHOR]

Acquired immunodeficiency syndrome (AIDS) is caused by human immunodeficiency virus (HIV). HIV protease, reverse transcriptase, and integrase are targets of current drugs to treat the disease. However, anti-viral drug-resistant strains have emerged quickly due to the high mutation rate of the virus, leading to the demand for the development of new drugs. One attractive target is Gag-Pol polyprotein, which plays a key role in the life cycle of HIV. Recently, we found that a combination of M50I and V151I mutations in HIV-1 integrase can suppress virus release and inhibit the initiation of Gag-Pol autoprocessing and maturation without interfering with the dimerization of Gag-Pol. Additional mutations in integrase or RNase H domain in reverse transcriptase can compensate for the defect. However, the molecular mechanism is unknown. There is no tertiary structure of the full-length HIV-1 Pol protein available for further study. Therefore, we developed a workflow to predict the tertiary structure of HIV-1 NL4.3 Pol polyprotein. The modeled structure has comparable quality compared with the recently published partial HIV-1 Pol structure (PDB ID: 7SJX). Our HIV-1 NL4.3 Pol dimer model is the first full-length Pol tertiary structure. It can provide a structural platform for studying the autoprocessing mechanism of HIV-1 Pol and for developing new potent drugs. Moreover, the workflow can be used to predict other large protein structures that cannot be resolved via conventional experimental methods.

The LLVM Clang Static Analyzer is a source code analysis tool which aims to find bugs in C, C++, and Objective-C programs using symbolic execution, i.e. it simulates the possible execution paths of the code. Currently the simulation of the loops is somewhat naive (but efficient), unrolling the loops a predefined constant number of times. However, this approach can result in a loss of coverage in various cases. This study aims to introduce two alternative approaches which can extend the current method and can be applied simultaneously: (1) determining loops worth to fully unroll with applied heuristics, and (2) using a widening mechanism to simulate an arbitrary number of iteration steps. These methods were evaluated on numerous open source projects, and proved to increase coverage in most of the cases. This work also laid the infrastructure for future loop modeling improvements. [ABSTRACT FROM AUTHOR]

The SARS‐CoV‐2 spike (S) protein facilitates viral infection, and has been the focus of many structure determination efforts. Its flexible loop regions are known to be involved in protein binding and may adopt multiple conformations. This article identifies the S protein loops and studies their conformational variability based on the available Protein Data Bank structures. While most loops had essentially one stable conformation, 17 of 44 loop regions were observed to be structurally variable with multiple substantively distinct conformations based on a cluster analysis. Loop modeling methods were then applied to the S protein loop targets, and the prediction accuracies discussed in relation to the characteristics of the conformational clusters identified. Loops with multiple conformations were found to be challenging to model based on a single structural template. [ABSTRACT FROM AUTHOR]

Loops are key components of protein structures, involved in many biological functions. Due to their conformational variability, the structural investigation of loops is a difficult topic, requiring a combination of experimental and computational methods. This paper provides a brief overview of current computational approaches to flexible loop modeling, and presents the main ingredients of the most standard protocols. Despite great progress in recent years, accurately modeling the conformational variability of long flexible loops remains a challenging problem. Future advances in this field will likely come from a tight coupling of experimental and computational techniques, which would enable a better understanding of the relationships between loop sequence, structural flexibility, and functional roles. In fine, accurate loop modeling will open the road to loop design problems of interest for applications in biomedicine and biotechnology.

Stimulator of interferon genes (STING) plays a significant role in a cell's intracellular defense against pathogens or self- DNA by inducing inflammation or apoptosis through a pathway known as cGAS-cGAMP-STING. STING uses one of its domains, the C-terminal tail (CTT) to recruit the members of the pathway. However, the structure of this domain has not been solved experimentally. STING conformation is open and more flexible when inactive. When STING gets activated by cGAMP, its conformation changes to a closed state covered by 4 beta-sheets over the binding site. This conformational change leads to its binding to Tank-binding kinase 1 (TBK1). TBK1 then phosphorylates STING aiding its entry to the cell's nucleus. In this study, we focused on the loop modeling of the CTT domain in both the active and inactive STING conformations. After the modeling step, the active and inactive STING structures were docked to one of the cGAS-cGAMP-STING pathway members, TBK1, to observe the differences of binding modes. CTT loop stayed higher in the active structure, while all the best-scored models, active or inactive, ended up around the same position with respect to TBK1. However, when the STING poses are compared with the cryo-EM image of the complex structure, the models in the active structure chain B displayed closer results to the complex structure. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCR) comprise the largest family of membrane proteins and are of considerable interest as targets for drug development. However, many GPCR structures remain unsolved. To address the structural ambiguity of these receptors, computational tools such as homology modeling and loop modeling are often employed to generate predictive receptor structures. Here we combined both methods to benchmark a protocol incorporating homology modeling based on a locally selected template and extracellular loop modeling that additionally evaluates the presence of template ligands during these modeling steps. Ligands were also docked using three docking methods and two pose selection methods to elucidate an optimal ligand pose selection method. Results suggest that local template-based homology models followed by loop modeling produce more accurate and predictive receptor models than models produced without loop modeling, with decreases in average receptor and ligand RMSD of 0.54 Å and 2.91 Å, respectively. Ligand docking results showcased the ability of MOE induced fit docking to produce ligand poses with atom root-mean-square deviation (RMSD) values at least 0.20 Å lower (on average) than the other two methods benchmarked in this study. In addition, pose selection methods (software-based scoring, ligand complementation) selected lower RMSD poses with MOE induced fit docking than either of the other methods (averaging at least 1.57 Å lower), indicating that MOE induced fit docking is most suited for docking into GPCR homology models in our hands. In addition, target receptor models produced with a template ligand present throughout the modeling process most often produced target ligand poses with RMSD values ≤ 4.5 Å and Tanimoto coefficients > 0.6 after selection based on ligand complementation than target receptor models produced in the absence of template ligands. Overall, the findings produced by this study support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions. [ABSTRACT FROM AUTHOR]

The problem of tripeptide loop closure is formulated in terms of the angles {Ti}(i)(3)(=1), describing the orientation of each peptide unit about the virtual axis joining the C alpha atoms. Imposing the constraint that at the junction of two such units the bond angle between the bonds C alpha-N and C alpha-C is fixed at some prescribed value 0 results in a system of three bivariate polynomials in u(i) : = tan tau(i)/2 of degree 2 in each variable. The system is analyzed for the existence of common solutions by making use of resultants, determinants of matrices composed of the coefficients of two (or more) polynomials, whose vanishing is a necessary and sufficient condition for the polynomials to have a common root. Two resultants are compared: the classical Sylvester resultant and the Dixon resultant. It is shown that when two of the variables are eliminated in favor of the third, a polynomial of degree 16 results. To each one of its real roots, there is a corresponding common zero of the system. To each such zero there corresponds a consistent conformation of the chain. The Sylvester method can find these zeros among the eigenvalues of a 24 X 24 matrix. For the Dixon approach, after removing extraneous factors, an optimally sized eigenvalue problem of size 16 X 16 results. Finally, the easy extension to the more general problem of triaxial loop closure is presented and an algorithm for implementing the method on arbitrary chains is given. (c) 2005 Wiley Periodicals, Inc.

G protein-coupled receptors (GPCR) are important drug discovery targets. Despite progress, many GPCR structures have not yet been solved. For these targets, comparative modeling is used in virtual ligand screening to prioritize experimental efforts. However, the structure of extracellular loop 2 (ECL2) is often poorly predicted. This is significant due to involvement of ECL2 in ligand binding for many Class A GPCR. Here we examine the performance of loop modeling protocols available in the Rosetta (cyclic coordinate descent [CCD], KIC with fragments [KICF] and next generation KIC [NGK]) and Molecular Operating Environment (MOE) software suites (de novo search). ECL2 from GPCR crystal structures served as the structure prediction targets and were divided into four sets depending on loop length. Results suggest that KICF and NGK sampled and scored more loop models with sub-angstrom and near-atomic accuracy than CCD or de novo search for loops of 24 or fewer residues. None of the methods were able to sample loop conformations with near-atomic accuracy for the longest targets ranging from 25 to 32 residues based on 1000 models generated. For these long loop targets, increased conformational sampling is necessary. The strongly conserved disulfide bond between Cys3.25 and Cys45.50 in ECL2 proved an effective filter. Setting an upper limit of 5.1 Å on the S–S distance improved the lowest RMSD model included in the top 10 scored structures in Groups 1–4 on average between 0.33 and 1.27 Å. Disulfide bond formation and geometry optimization of ECL2 provided an additional incremental benefit in structure quality. [ABSTRACT FROM AUTHOR]

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design. [ABSTRACT FROM AUTHOR]

Computational protein structure prediction is a long-standing challenge in bioinformatics. In the process of predicting protein 3D structures, it is common that parts of an experimental structure are missing or parts of a predicted structure need to be remodeled. The process of predicting local protein structures of particular regions is called loop modeling. In this paper, five new loop modeling methods based on machine learning techniques, called NearLooper, ConLooper, ResLooper, HyLooper1, and HyLooper2 are proposed. NearLooper is based on the nearest neighbor technique. ConLooper applies deep convolutional neural networks to predict ${\mathrm{C}}_{{{\alpha }}}$Cα atoms distance matrix as an orientation-independent representation of protein structure. ResLooper uses residual neural networks instead of deep convolutional neural networks. HyLooper1 combines the results of NearLooper and ConLooper while HyLooper2 combines NearLooper and ResLooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28 and 54 percent of average RMSD on two datasets while being comparable on the other one. [ABSTRACT FROM AUTHOR]

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field., (© The Author(s) 2024. Published by Oxford University Press.)

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. [ABSTRACT FROM AUTHOR]

A wide variety of protein and peptidomimetic design tasks require matching functional 3D motifs to potential oligomeric scaffolds. For example, during enzyme design, one aims to graft active-site patterns—typically consisting of 3–15 residues—onto new protein surfaces. Identifying protein scaffolds suitable for such active-site engraftment requires costly searches for protein folds that provide the correct side chain positioning to host the desired active site. Other examples of biodesign tasks that require similar fast exact geometric searches of potential side chain positioning include mimicking binding hotspots, design of metal binding clusters and the design of modular hydrogen binding networks for specificity. In these applications, the speed and scaling of geometric searches limits the scope of downstream design to small patterns. Here, we present an adaptive algorithm capable of searching for side chain take-off angles, which is compatible with an arbitrarily specified functional pattern and which enjoys substantive performance improvements over previous methods. We demonstrate this method in both genetically encoded (protein) and synthetic (peptidomimetic) design scenarios. Examples of using this method with the Rosetta framework for protein design are provided. Our implementation is compatible with multiple protein design frameworks and is freely available as a set of python scripts (https://github.com/JiangTian/adaptive-geometric-search-for-protein-design). [ABSTRACT FROM AUTHOR]

Abstract: Advances in protein model refinement techniques are required as diverse sources of protein structure information are available from low‐resolution experiments or informatics‐based computations such as cryo‐EM, NMR, homology models, or predicted residue contacts. Given semi‐reliable or incomplete structural information, structure quality of a protein model has to be improved by ab initio methods such as energy‐based simulation. In this study, we describe a new automatic refinement server method designed to improve locally inaccurate regions and overall structure simultaneously. Locally inaccurate regions may occur in protein structures due to non‐convergent or missing information in template structures used in homology modeling or due to intrinsic structural flexibilities not resolved by experimental techniques. However, such variable or dynamic regions often play important functional roles by participating in interactions with other biomolecules or in transitions between different functional states. The new refinement method introduced here utilizes diverse types of geometric operators which drive both local and global changes, and the effect of structure changes and relaxations are accumulated. This resulted in consistent refinement of both local and global structural features. Performance of this method in CASP12 is discussed. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification. [Display omitted] • Applications and recent successes of computational methods in G protein-coupled receptor ligand identification are reviewed. • Methods of G protein-coupled receptor structure prediction are discussed. • Ligand- and structure-based methods in computational G protein-coupled receptor ligand identification are reviewed. • Workflows addressing information deficits commonly faced in G protein-coupled receptor ligand identification are outlined. [ABSTRACT FROM AUTHOR]

The overexpression of ErbB4 is associated with aggressive disease biology and reduced the survival of breast cancer patients. We have used ErbB4 receptor as a novel drug target to spearhead the rational drug design. The present study is divided into two parts. In the first part, we have exploited the hidden information inside ErbB4 kinase receptor both at sequence and structural level. PSI-BLAST algorithm is used to search similar sequences against ErbB4 kinase sequence. Top 15 sequences with high identity were selected for finding conserved and variable regions among sequences using multiple sequence alignment. In the second part, available 3 D structure of ErbB4 kinase is curated using loop modeling, and anomalies in the modeled structure is improved by energy minimization. The resultant structure is validated by analyzing dihedral angles by Ramachandran plot analysis. Furthermore, the potential binding sites were detected by using DoGSite and CASTp server. The similarity-search criterion is used for the preparation of our in-house database of drugs from DrugBank database. In total, 409 drugs yet to be tested against ErbB4 kinase is used for screening purpose. Virtual screening results in identification of 11 compounds with better binding affinity than lapatinib and canertinib. Study of protein–ligand interactions reveals information about amino acid residues; Lys726, Thr771, Met774, Cys778, Arg822, Thr835, Asp836 and Phe837 at the binding pocket. The physicochemical properties and bioactivity score calculation of selected compounds suggest them as biological active. This study presents a rich array that assist in expediting new drug discovery for breast cancer. [ABSTRACT FROM PUBLISHER]

ABSTRACT The H3 loop in the Complementarity Determining Region of antibodies plays a key role in their ability to bind the diverse space of potential antigens. It is also exceptionally difficult to model computationally causing a significant hurdle for in silico development of antibody biotherapeutics. In this article, we show that most H3s have unique structural characteristics which may explain why they are so challenging to model. We found that over 75% of H3 loops do not have a sub-Angstrom structural neighbor in the non-antibody world. Also, in a comparison with a nonredundant set of all protein fragments over 30% of H3 loops have a unique structure, with the average for all of other loops being less than 3%. We further observed that this structural difference can be seen at the level of four residue fragments where H3 loops present numerous novel conformations, and also at the level of individual residues with Tyrosine and Glycine often found in energetically unfavorable conformations. Proteins 2017; 85:1311-1318. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Human organic cation transporters (hOCTs) belong to solute carriers (SLC) 22 family of membrane proteins that play a central role in transportation of chemotherapeutic drugs for several clinical and pathological conditions, including cancer and diabetes. These transporters mediate drug transport; however, the precise mechanism of drug-binding and transport by them is not fully uncovered yet, partly due to unavailability of any crystal structure record. In this work, we performed a multi-phasic approach to compute the 3D structural models of seven human organic cation transporters (hOCTs) starting from primary protein sequence. Our structure modeling approach included 1) I-TASSER based comparative sequence alignment, threading and ab-initio protein modeling; 2) models comparison with PSIPRED secondary structure prediction; 3) loop modeling for incongruent secondary structure in Chimera 1.10.1; 4) high resolution structure simulation, refinement, energy minimization using ModRefiner, and 5) validation of the structure models using PROCHECK at SAVEs. From structural point, the computed 3D structures of hOCTs consist of a typical major facilitator superfamily (MFS) fold of twelve α-transmembrane helix domains arranged in a manner rendering hOCTs a barrel shaped structure with a large cleft that opens in cytoplasm. The modeled 3D structure of all hOCTs closely resemble to human SLC2A3 (GLUT3) transporter (PDB ID: 5c65) and displayed an outward-open confirmation and putative cyclic C1 protein symmetry. In addition, hOCTs has a large (>100 amino acids) unique extracellular loop between TMH1 and TMH2 having potential glycosylation sites (Asn-Xaa-Ser/Thr) and cysteine residues, both features indicative of putative role in drug binding and uptake. There is an intracellular three/four-helix loop between TMH6 and TMH7 containing putative phosphorylation sites for precise regulation of hOCTs function as drug transporters. There are nine loops of 4 to 11 amino acids length that protrude from membrane, both intracellularly and extracellularly, and connect adjacent TMHs. The 2D structure prediction showed N in -C in topology of all hOCTs. In the unavailability of the crystal structures of hOCTs, the 3D structural models computed in-silico and presented herein can be used for studying the mechanism of drug binding and transport by hOCTs. [ABSTRACT FROM AUTHOR]

Unlike the secondary structure elements that connect in protein structures, loop fragments in protein chains are often highly mobile even in generally stable proteins. The structural variability of loops is often at the center of a protein’s stability, folding, and even biological function. Loops are found to mediate important biological processes, such as signaling, protein-ligand binding, and protein-protein interactions. Modeling conformations of a loop under physiological conditions remains an open problem in computational biology. This article reviews computational research in loop modeling, highlighting progress and challenges. Important insight is obtained on potential directions for future research.

Loops represent an important part of protein structures. The study of loop is critical for two main reasons: First, loops are often involved in protein function, stability and folding. Second, despite improvements in experimental and computational structure prediction methods, modeling the conformation of loops remains problematic. Here, we present a structural classification of loops, ArchDB, a mine of information with application in both mentioned fields: loop structure prediction and function prediction. ArchDB (http://sbi.imim.es/archdb) is a database of classified protein loop motifs. The current database provides four different classification sets tailored for different purposes. ArchDB-40, a loop classification derived from SCOP40, well suited for modeling common loop motifs. Since features relevant to loop structure or function can be more easily determined on well-populated clusters, we have developed ArchDB-95, a loop classification derived from SCOP95. This new classification set shows a ∼40% increase in the number of subclasses, and a large 7-fold increase in the number of putative structure/function-related subclasses. We also present ArchDB-EC, a classification of loop motifs from enzymes, and ArchDB-KI, a manually annotated classification of loop motifs from kinases. Information about ligand contacts and PDB sites has been included in all classification sets. Improvements in our classification scheme are described, as well as several new database features, such as the ability to query by conserved annotations, sequence similarity, or uploading 3D coordinates of a protein. The lengths of classified loops range between 0 and 36 residues long. ArchDB offers an exhaustive sampling of loop structures. Functional information about loops and links with related biological databases are also provided. All this information and the possibility to browse/query the database through a web-server outline an useful tool with application in the comparative study of loops, the analysis of loops involved in protein function and to obtain templates for loop modeling.

Protein structures predicted by state-of-the-art template-based methods may still have errors when the template proteins are not similar enough to the target protein. Overall target structure may deviate from the template structures owing to differences in sequences. Structural information for some local regions such as loops may not be available when there are sequence insertions or deletions. Those structural aspects that originate from deviations from templates can be dealt with by ab initio structure refinement methods to further improve model accuracy. In the CASP11 refinement experiment, we tested three different refinement methods that utilize overall structure relaxation, loop modeling, and quality assessment of multiple initial structures. From this experiment, we conclude that the overall relaxation method can consistently improve model quality. Loop modeling is the most useful when the initial model structure is high quality, with GDT-HA >60. The method that used multiple initial structures further refined the already refined models; the minor improvements with this method raise the issue of problem with the current energy function. Future research directions are also discussed. [ABSTRACT FROM AUTHOR]

The cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, β-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91 Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76 Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images. [ABSTRACT FROM PUBLISHER]

In both predicting protein loop structure and flexible areas in a docking interface, modeling local loop conformation plays a vital role. We address the challenge with a topological manipulation method based on the cyclic coordinate descent (CCD) algorithm. The protein loop conformation sampler (LMbCCD2) presented here allows us to move some key points of the loop toward some specific positions. We test the method's performance on sets of loops, and the results show that LMbCCD2 accurately controls the loop's topology. At last we use LMbCCD2 on flexible protein-peptide docking, and it shows that LMbCCD2 can improve the accuracy of flexible peptide folding and the full docking result. [ABSTRACT FROM AUTHOR]

Background: High precision protein loop modelling remains a challenge, both in template based and template independent approaches to protein structure prediction. Method: We introduce the concepts of protein loop clustering and percolation, to develop a quantitative approach to systematically classify the modular building blocks of loops in crystallographic folded proteins. These fragments are all different parameterisations of a unique kink solution to a generalised discrete nonlinear Schrödinger (DNLS) equation. Accordingly, the fragments are also local energy minima of the ensuing energy function. Results: We show how the loop fragments cover practically all ultrahigh resolution crystallographic protein structures in Protein Data Bank (PDB), with a 0.2 Ångström root-mean-square (RMS) precision. We find that no more than 12 different loop fragments are needed, to describe around 38 % of ultrahigh resolution loops in PDB. But there is also a large number of loop fragments that are either unique, or very rare, and examples of unique fragments are found even in the structure of a myoglobin. Conclusions: Protein loops are built in a modular fashion. The loops are composed of fragments that can be modelled by the kink of the DNLS equation. The majority of loop fragments are also common, which are shared by many proteins. These common fragments are probably important for supporting the overall protein conformation. But there are also several fragments that are either unique to a given protein, or very rare. Such fragments are probably related to the function of the protein. Furthermore, we have found that the amino acid sequence does not determine the structure in a unique fashion. There are many examples of loop fragments with an identical amino acid sequence, but with a very different structure. [ABSTRACT FROM AUTHOR]

ABSTRACT This study was a part of the second antibody modeling assessment. The assessment is a blind study of the performance of multiple software programs used for antibody homology modeling. In the study, research groups were given sequences for 11 antibodies and asked to predict their corresponding structures. The results were measured using root-mean-square deviation (rmsd) between the submitted models and X-ray crystal structures. In 10 of 11 cases, the results using SmrtMolAntibody show good agreement between the submitted models and X-ray crystal structures. In the first stage, the average rmsd was 1.4 Å. Average rmsd values for the framework was 1.2 Å and for the H3 loop was 3.0 Å. In stage two, there was a slight improvement with an rmsd for the H3 loop of 2.9 Å. Proteins 2014; 82:1636-1645. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]