Protein Structure Prediction Server Research Articles

Comparative Functional Genomics Studies for Understanding the Hypothetical Proteins in Mycobacterium Tuberculosis Variant Microti 12

Comparative Functional Genomics Studies for Understanding the Hypothetical Proteins in Mycobacterium Tuberculosis Variant Microti 12

AI protein structure prediction-based modeling and mutagenesis of a protostome receptor and peptide ligands reveal key residues for their interaction

The protostome leucokinin (LK) signaling system, including LK peptides and their G protein-coupled receptors, has been characterized in several species. Despite the progress, molecular mechanisms governing LK peptide–receptor interactions remain to be elucidated. Previously, we identified a precursor protein for Aplysia leucokinin-like peptides (ALKs) that contains the greatest number of amidated peptides among LK precursors in all species identified so far. Here, we identified the first ALK receptor from Aplysia, ALKR. We used cell-based IP1 activation assays to demonstrate that two ALK peptides with the most copies, ALK1 and ALK2, activated ALKR with high potencies. Other endogenous ALK-derived peptides bearing the FXXWX-amide motif also activated ALKR to various degrees. Our examination of cross-species activity of ALKs with the Anopheles LK receptor was consistent with a critical role for the FXXWX-amide motif in receptor activity. Furthermore, we showed, through alanine substitution of ALK1, the highly conserved phenylalanine (F), tryptophan (W), and C-terminal amidation were each essential for receptor activation. Finally, we used an artificial intelligence–based protein structure prediction server (Robetta) and Autodock Vina to predict the ligand-bound conformation of ALKR. Our model predicted several interactions (i.e., hydrophobic interactions, hydrogen bonds, and amide-pi stacking) between ALK peptides and ALKR, and several of our substitution and mutagenesis experiments were consistent with the predicted model. In conclusion, our results provide important information defining possible interactions between ALK peptides and their receptors. The workflow utilized here may be useful for studying other ligand–receptor interactions for a neuropeptide signaling system, particularly in protostomes.

Potential Mortalin-p53 complex abrogation of ent-kaurane diterpenoids from Croton tonkinensis revealed by homology modeling and docking simulation

Seven ent-kaurane diterpenoids from Croton tonkinensis were tested for cytotoxicity against human HCC HepG2 cell line. The abrogation of mortalin-p53 interaction represent an original anticancer therapeutic approach. Teritary structure of protein mortalin was constructed using Protein Structure Prediction Server and crystal structure of p53 was selected from Protein Data Bank involving mortalin-p53 binding domain. Molecular docking studies revealed that the interaction with protein mortalin is more prominent than p53 compound 5 and 1 as the most two potential mortalin-p53 binding inhibitors based on binding free energy and interacting residues analysis.

The Drosophila septate junctions beyond barrier function: Review of the literature, prediction of human orthologs of the SJ-related proteins and identification of protein domain families.

The involvement of Septate Junctions (SJs) in critical cellular functions that extend beyond their role as diffusion barriers in the epithelia and the nervous system has made the fruit fly an ideal model for the study of human diseases associated with impaired Tight Junction (TJ) function. In this study, we summarized current knowledge of the Drosophila melanogaster SJ-related proteins, focusing on their unconventional functions. Additionally, we sought to identify human orthologs of the corresponding genes as well as protein domain families. The systematic literature search was performed in PubMed and Scopus databases using relevant key terms. Orthologs were predicted using the DIOPT tool and aligned protein regions were determined from the Pfam database. 3-D models of the smooth SJ proteins were built on the Phyre2 and DMPFold protein structure prediction servers. A total of 30 proteins were identified as relatives to the SJ cellular structure. Key roles of these proteins, mainly in the regulation of morphogenetic events and cellular signalling, were highlighted. The investigation of protein domain families revealed that the SJ-related proteins contain conserved domains that are required not only for cell-cell interactions and cell polarity but also for cellular signalling and immunity. DIOPT analysis of orthologs identified novel human genes as putative functional homologs of the fruit fly SJ genes. A gap in our knowledge was identified regarding the domains that occur in the proteins encoded by eight SJ-associated genes. Future investigation of these domains is needed to provide functional information.

AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes.

The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.

The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction.

Prediction of the three-dimensional (3D) structure of a protein from its sequence is important for studying its biological function. With the advancement in deep learning contact distance prediction and residue-residue coevolutionary analysis, significant progress has been made in both template-based and template-free protein structure prediction in the last several years. Here, we provide a practical guide for our latest MULTICOM protein structure prediction system built on top of the latest advances, which wasrigorously tested in the 2018 CASP13 experiment. Its specific functionalities include: (1) prediction of 1D structural features (secondary structure, solvent accessibility, disordered regions) and 2D interresidue contacts; (2) domain boundary prediction; (3) template-based (or homology) 3D structure modeling; (4) contact distance-driven ab initio 3D structure modeling; and (5) large-scale protein quality assessment enhanced by deep learning and predicted contacts. The MULTICOM web server ( http://sysbio.rnet.missouri.edu/multicom_cluster/ ) presents all the 1D, 2D, and 3D prediction results and quality assessment to users via user-friendly web interfaces and e-mails. The source code of the MULTICOM package is also available at https://github.com/multicom-toolbox/multicom .

Music notes to amino acid sequence: A STEAM approach to study protein structure.

Recently, manuscripts that use bioinformatics tools to convert proteins and other biological materials into music have appeared in several journals. The authors claim that this type of studies adds joy to education of life sciences and attract public attention to biochemistry education along with other uses. We thought the reverse of the process might also be possible for the purpose. We converted notes into amino acids depending on the decreasing hydrophobicity and by using the famous nursery song "Twinkle, Twinkle, Little Star" notes, a hypothetical protein sequence was obtained. Three-dimensional structures were modeled by I-Tasser Protein Structure and Function Prediction server and the structure of the possible models were investigated. This Science, Technology, Engineering, Arts and Math (STEAM) approach may bring fun to protein structure education. © 2019 International Union of Biochemistry and Molecular Biology, 47(6):669-671, 2019.

I-TASSER gateway: A protein structure and function prediction server powered by XSEDE

There is an increasing gap between the number of known protein sequences and the number of proteins with experimentally characterized structure and function. To alleviate this issue, we have developed the I-TASSER gateway, an online server for automated and reliable protein structure and function prediction. For a given sequence, I-TASSER starts with template recognition from a known structure library, followed by full-length atomic model construction by iterative assembly simulations of the continuous structural fragments excised from the template alignments. Functional insights are then derived from comparative matching of the predicted model with a library of proteins with known function. The I-TASSER pipeline has been recently integrated with the XSEDE Gateway system to accommodate pressing demand from the user community and increasing computing costs. This report summarizes the configuration of the I-TASSER Gateway with the XSEDE-Comet supercomputer cluster, together with an overview of the I-TASSER method and milestones of its development.

Probing of Phytofungal Proteins for Fungicidal Activity by Molecular Docking

Background: Plant fungal diseases are the primary causes of foliage and crop loss eventually affecting the overall economic outcome and yield quality. Hence, various chemical compounds are employed to eradicate the fungi in agriculture. Methods: Virtual screening and molecular docking strategies provide themselves as great alternatives to find lead compounds. Lead compounds for each fungal infection was docked to target protein sequence and assessed for the strongest interaction. Findings: Various molecules were taken under the study, for being the target ligands to bring about a fungicidal reaction in the plant pathogen system. The screening of molecules was done thoroughly to produce the results. Ligands identified through this study allow us to make plant host fight against the fungal pathogen and prevent the occurrence of the disease. The interactions have been thoroughly studied with various softwares like SPDBV and PyMol and through various online databases like STRING, GenePept, PDB, UniProt, PatchDock, Protein structure prediction server -2 and others for the overall evaluation of the drug molecule designed and to study its overall effects for the overall higher efficacy and to prevent the occurrence of the fungal disease and management of the fungal pathogens in agriculture against various economically valuable plants. The lead compounds revealed several hydrophobic and polar contacts were demonstrated by comparing interactions. Applications: The molecular affinity of the fungicidal compound has been tested against the target pathogen as well as the host system components to understand the interaction and to draw out the functioning and the analysis. The compatibility between the molecule and the protein has been studied to decipher the effectiveness of the molecule and its effects in the system. The present results let us establish lead compounds that can be used for the development of antifungal drugs although structural activity relationship studies have to be undertaken.

Usability as the Key Factor to the Design of a Web Server for the CReF Protein Structure Predictor: The wCReF

Protein structure prediction servers use various computational methods to predict the three-dimensional structure of proteins from their amino acid sequence. Predicted models are used to infer protein function and guide experimental efforts. This can contribute to solving the problem of predicting tertiary protein structures, one of the main unsolved problems in bioinformatics. The challenge is to understand the relationship between the amino acid sequence of a protein and its three-dimensional structure, which is related to the function of these macromolecules. This article is an extended version of the article wCReF: The Web Server for the Central Residue Fragment-based Method (CReF) Protein Structure Predictor, published in the 14th International Conference on Information Technology: New Generations. In the first version, we presented the wCReF, a protein structure prediction server for the central residue fragment-based method. The wCReF interface was developed with a focus on usability and user interaction. With this tool, users can enter the amino acid sequence of their target protein and obtain its approximate 3D structure without the need to install all the multitude of necessary tools. In this extended version, we present the design process of the prediction server in detail, which includes: (A) identification of user needs: aiming at understanding the features of a protein structure prediction server, the end user profiles and the commonly-performed tasks; (B) server usability inspection: in order to define wCReF’s requirements and features, we have used heuristic evaluation guided by experts in both the human-computer interaction and bioinformatics domain areas, applied to the protein structure prediction servers I-TASSER, QUARK and Robetta; as a result, changes were found in all heuristics resulting in 89 usability problems; (C) software requirements document and prototype: assessment results guiding the key features that wCReF must have compiled in a software requirements document; from this step, prototyping was carried out; (D) wCReF usability analysis: a glimpse at the detection of new usability problems with end users by adapting the Ssemugabi satisfaction questionnaire; users’ evaluation had 80% positive feedback; (E) finally, some specific guidelines for interface design are presented, which may contribute to the design of interactive computational resources for the field of bioinformatics. In addition to the results of the original article, we present the methodology used in wCReF’s design and evaluation process (sample, procedures, evaluation tools) and the results obtained.

Comparative genomics for understanding the structure, function and sub-cellular localization of hypothetical proteins in Thermanerovibrio acidaminovorans DSM 6589 (tai)

The Thermanerovibrio acidaminovorans DSM 6589 (tai) is a unique bacterium isolated from anaerobic sludge bed reactor from sugar refinery in Netherland. The comparative genomic studies for understanding the hypothetical proteins in T. acidaminovorans DSM 6589 (tai) were carried out using different bioinformatic tools and web servers. In all 320 hypothetical proteins were screened from the total available genome. The Insilico function prediction for 320 hypothetical proteins was achieved by using different online servers like CDD-Blast, Interproscan and pfam whereas, the structure prediction for 202 hypothetical proteins were deciphered by using protein structure prediction server (PS2 server). The sub-cellular localization for the identified proteins was predicted by the use of cello v2.5 for 320. The study carried out has helped us to understand the structures and functions of unknown proteins available in T. acidaminovorans DSM 6589 (tai) through comparative genomic approach.

A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11.

BackgroundWith more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research. Traditional template-based protein structure prediction methods tend to focus on identifying the best templates, generating the best alignments, and applying the best energy function to rank models, which often cannot achieve the best performance because of the difficulty of obtaining best templates, alignments, and models.MethodsWe developed a large-scale conformation sampling and evaluation method and its servers to improve the reliability and robustness of protein structure prediction. In the first step, our method used a variety of alignment methods to sample relevant and complementary templates and to generate alternative and diverse target-template alignments, used a template and alignment combination protocol to combine alignments, and used template-based and template-free modeling methods to generate a pool of conformations for a target protein. In the second step, it used a large number of protein model quality assessment methods to evaluate and rank the models in the protein model pool, in conjunction with an exception handling strategy to deal with any additional failure in model ranking.ResultsThe method was implemented as two protein structure prediction servers: MULTICOM-CONSTRUCT and MULTICOM-CLUSTER that participated in the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014. The two servers were ranked among the best 10 server predictors.ConclusionsThe good performance of our servers in CASP11 demonstrates the effectiveness and robustness of the large-scale conformation sampling and evaluation. The MULTICOM server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0775-x) contains supplementary material, which is available to authorized users.

FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition.

The protein structure prediction approaches can be categorized into template-based modeling (including homology modeling and threading) and free modeling. However, the existing threading tools perform poorly on remote homologous proteins. Thus, improving fold recognition for remote homologous proteins remains a challenge. Besides, the proteome-wide structure prediction poses another challenge of increasing prediction throughput. In this study, we presented FALCON@home as a protein structure prediction server focusing on remote homologue identification. The design of FALCON@home is based on the observation that a structural template, especially for remote homologous proteins, consists of conserved regions interweaved with highly variable regions. The highly variable regions lead to vague alignments in threading approaches. Thus, FALCON@home first extracts conserved regions from each template and then aligns a query protein with conserved regions only rather than the full-length template directly. This helps avoid the vague alignments rooted in highly variable regions, improving remote homologue identification. We implemented FALCON@home using the Berkeley Open Infrastructure of Network Computing (BOINC) volunteer computing protocol. With computation power donated from over 20,000 volunteer CPUs, FALCON@home shows a throughput as high as processing of over 1000 proteins per day. In the Critical Assessment of protein Structure Prediction (CASP11), the FALCON@home-based prediction was ranked the 12th in the template-based modeling category. As an application, the structures of 880 mouse mitochondria proteins were predicted, which revealed the significant correlation between protein half-lives and protein structural factors. FALCON@home is freely available at http://protein.ict.ac.cn/FALCON/. shuaicli@cityu.edu.hk, dbu@ict.ac.cn Supplementary data are available at Bioinformatics online.

Selective refinement and selection of near-native models in protein structure prediction.

In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions.

Ab-InitioPrediction of Sequence and Structural Biology of Fish Muscle Proteins Using Homology Modeling, Phylogeny and Different Computational Approaches

Fish is a diverse group of organisms living in different aquatic environment and containing almost all essential amino acids. Fourteen muscle proteins including titin, dystrophin, filamin, myosin heavy chain, spectrin, M1/M2, nebulin, alpha- actinin, gelsolin, actin, tropomyosin, troponin, thymosin and plastin3 were chosen for in-silico characterization. Sequence analyses were performed using BindN, Conseq, DIANNA, PROFEAT and ProtFun for exploiting structural and functional importance. Homology modeling technique was applied for predicting 3D structure which will assist in future for searching catalytic role of proteins in metabolic pathway. 3D Structure of eight muscle proteins was predicted using Protein Structure Prediction Server (PS 2 ) based on MODELLER algorithm. Phylogenetic relationship was inferred by sequence alignment through CLUSTAL X and furthermore phylogenetic tree was constructed by using MEGA which was statistically evaluated by DIVIEN. From structural analyses, these muscle proteins were inferred to contain functional domains, number of motifs, beta turns with important secondary structural features. Furthermore sequence study suggested, these proteins have important biochemical features such as number of cysteines, disulphide bonds, DNA and RNA binding sites, functionally conserved amino acid residues and were characterized as non-allergen proteins which can be used for designing effective vaccines. Overall, evidence from computational study revealed that these muscle proteins have structural and functional significance, which can play important role in drug designing and in exploring gene diversity. This novel approach to study muscle proteins would be beneficial for human since both vertebrates and invertebrates have muscle proteins in common.

In silico sequence analysis, homology modeling and function annotation of leishmanolysin from Leishmania donovani.

Leishmaniases are a complex of diseases that range from the deadly visceral disease and some self-curing lesions to gross disfigurations. About 12 million peoples from 88 different countries get infected by this protozoan parasite through the sand flies. Visceral leishmaniasis is a potentially fatal disease endemic to large parts of Asia and Africa, primarily caused by the protozoan parasite Leishmania donovani. L. donovani is a species of Leishmania, a hemoflagellate parasite and causative agent of visceral leishmaniasis. Leishmanolysin is the major surface protein of the parasitic Leishmania. Leishmanolysin has been described as a parasite virulence factor and is involved in the direct interaction of promastigotes and host macrophage receptors and interaction with the complement cascade. In the current study we predicted the 3D structure of leishmanolysin using homology modeling as 3D structure prediction approach. Leishmanolysin is a stable extracellular stable protein of 561 amino acid residues. 3D structure of the leishmanolysin was determined using Protein Structure Prediction Server (PS2 Server) selecting MODELLER as 3D structure prediction method. Quality analysis of the model through its Ramchandran Plot and ERRAT value (94.25) indicated that it is a reliable model. Functional annotation showed that this protein is a member of the superfamily cl18220. The information thus discussed provides insight to the molecular understanding of structure and function of leishmanolysin from L. donovani. The predicted 3-D model may be further used in characterizing the protein in wet laboratory.

AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain-domain interaction prediction.

Most proteins consist of multiple domains, independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form new protein architectures. Template-based modeling methods can often detect homologous templates for individual domains, but templates that could be used to model the entire query protein are often not available. We have developed a fast docking algorithm ab initio domain assembly (AIDA) for assembling multi-domain protein structures, guided by the ab initio folding potential. This approach can be extended to discontinuous domains (i.e. domains with 'inserted' domains). When tested on experimentally solved structures of multi-domain proteins, the relative domain positions were accurately found among top 5000 models in 86% of cases. AIDA server can use domain assignments provided by the user or predict them from the provided sequence. The latter approach is particularly useful for automated protein structure prediction servers. The blind test consisting of 95 CASP10 targets shows that domain boundaries could be successfully determined for 97% of targets. The AIDA package as well as the benchmark sets used here are available for download at http://ffas.burnham.org/AIDA/. adam@sanfordburnham.org Supplementary data are available at Bioinformatics online.

Structure Prediction of Delta Aminolevulinic Acid Dehydratase (ALAD); An Enzyme that is Very Sensitive to the Toxic Effects of Lead

The ALAD (Aminolevulinic Acid Dehydratase) gene polymorphism is linked with the accumulation of lead in the bone, blood and the other internal organs and it may predispose for many critical symptoms in the lead exposed persons. The aim of this study is to determine the primary, secondary and tertiary structure of lead. This enzyme is susceptible towards the toxic effect of lead. Primary structure prediction was done by Protparam tool, Compute PI/MW tool, Proscale tool. Secondary structure prediction was done by Self –optimize prediction method (SOPMA) tool, Porter tool. Tertiary structure prediction was done by protein structure prediction server. Domain was determined by Simple Modular Architecture Research (SMART) tool.

Computational modeling suggests dimerization of equine infectious anemia virus Rev is required for RNA binding.

BackgroundThe lentiviral Rev protein mediates nuclear export of intron-containing viral RNAs that encode structural proteins or serve as the viral genome. Following translation, HIV-1 Rev localizes to the nucleus and binds its cognate sequence, termed the Rev-responsive element (RRE), in incompletely spliced viral RNA. Rev subsequently multimerizes along the viral RNA and associates with the cellular Crm1 export machinery to translocate the RNA-protein complex to the cytoplasm. Equine infectious anemia virus (EIAV) Rev is functionally homologous to HIV-1 Rev, but shares very little sequence similarity and differs in domain organization. EIAV Rev also contains a bipartite RNA binding domain comprising two short arginine-rich motifs (designated ARM-1 and ARM-2) spaced 79 residues apart in the amino acid sequence. To gain insight into the topology of the bipartite RNA binding domain, a computational approach was used to model the tertiary structure of EIAV Rev.ResultsThe tertiary structure of EIAV Rev was modeled using several protein structure prediction and model quality assessment servers. Two types of structures were predicted: an elongated structure with an extended central alpha helix, and a globular structure with a central bundle of helices. Assessment of models on the basis of biophysical properties indicated they were of average quality. In almost all models, ARM-1 and ARM-2 were spatially separated by >15 Å, suggesting that they do not form a single RNA binding interface on the monomer. A highly conserved canonical coiled-coil motif was identified in the central region of EIAV Rev, suggesting that an RNA binding interface could be formed through dimerization of Rev and juxtaposition of ARM-1 and ARM-2. In support of this, purified Rev protein migrated as a dimer in Blue native gels, and mutation of a residue predicted to form a key coiled-coil contact disrupted dimerization and abrogated RNA binding. In contrast, mutation of residues outside the predicted coiled-coil interface had no effect on dimerization or RNA binding.ConclusionsOur results suggest that EIAV Rev binding to the RRE requires dimerization via a coiled-coil motif to juxtapose two RNA binding motifs, ARM-1 and ARM-2.Electronic supplementary materialThe online version of this article (doi:10.1186/s12977-014-0115-7) contains supplementary material, which is available to authorized users.

Computer scientist in profile

"Yes, I did have a band in college, and I still love music," says Yang, who now has his own highly-successful computational biology laboratory at University of Michigan, Ann Arbor. While most of us are quick to associate his name with top protein structure prediction servers, such as I-TASSER and QUARK, not many of us know that Yang's first passion was music. "When I was young," he recalls, "my dream was to roam around the world with my guitar." His dream did not last long, as he made a conscious decision to pursue a graduate degree after completing his undergraduate studies in physics in China. His Ph.D. thesis was on the interaction of elementary particles, such as quarks (that explains a lot). He was awarded the prestigious Humboldt fellowship, which allowed him to study quarks at the Free University in Berlin for two years.