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Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2's predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments. [ABSTRACT FROM AUTHOR]

Glaciozyma antarctica PI12 is a psychrophilic yeast isolated from Antarctica. In this work, we describe the heterologous production, biochemical properties and in silico structure analysis of an arginase from this yeast (GaArg). GaArg is a metalloenzyme that catalyses the hydrolysis of l-arginine to l-ornithine and urea. The cDNA of GaArg was reversed transcribed, cloned, expressed and purified as a recombinant protein in Escherichia coli. The purified protein was active against l-arginine as its substrate in a reaction at 20 °C, pH 9. At 10–35 °C and pH 7–9, the catalytic activity of the protein was still present around 50%. Mn2+, Ni2+, Co2+ and K+ were able to enhance the enzyme activity more than two-fold, while GaArg is most sensitive to SDS, EDTA and DTT. The predicted structure model of GaArg showed a very similar overall fold with other known arginases. GaArg possesses predominantly smaller and uncharged amino acids, fewer salt bridges, hydrogen bonds and hydrophobic interactions compared to the other counterparts. GaArg is the first reported arginase that is cold-active, facilitated by unique structural characteristics for its adaptation of catalytic functions at low-temperature environments. The structure and function of cold-active GaArg provide insights into the potentiality of new applications in various biotechnology and pharmaceutical industries. [ABSTRACT FROM AUTHOR]

Clinical interpretation of genetic variants in the context of the patient's phenotype is a time‐consuming and costly process. In‐silico analysis using in‐silico prediction tools, and molecular modeling have been developed to predict the influence of genetic variants on the quality and/or quantity of the resulting translated protein, and in this way, to alert clinicians of disease likelihood in the absence of previous evidence. Our objectives were to evaluate the success rate of the in‐silico analysis in predicting the disease‐causing variants as pathogenic and the single‐nucleotide variants as neutral, and to establish the reliability of in‐silico analysis for determining pathogenicity or neutrality of von Willebrand factor gene‐associated genetic variants. Using in‐silico analysis, we studied pathogenicity in 31 disease‐causing variants, and neutrality in 61 single‐nucleotide variants from patients previously diagnosed as type 2 von Willebrand disease. Disease‐causing variants and non‐synonymous single‐nucleotide variants were explored by in‐silico tools that analyze the amino acidic sequence. Intronic and synonymous single‐nucleotide variants were analyzed by in‐silico methods that evaluate the nucleotidic sequence. We found a consistent agreement between predictions achieved by in‐silico prediction tools and molecular modeling, both for defining the pathogenicity of disease‐causing variants and the neutrality of single‐nucleotide variants. Based on our results, the in‐silico analysis would help to define the pathogenicity or neutrality in novel genetic variants observed in patients with clinical and laboratory phenotypes suggestive of von Willebrand disease. [ABSTRACT FROM AUTHOR]

Conformational space annealing (CSA), a global optimization method, has been applied to various protein structure modeling tasks. In this paper, we applied CSA to the cryo‐EM structure modeling task by combining the python subroutine of CSA (PyCSA) and the fast relax (FastRelax) protocol of PyRosetta. Refinement of initial structures generated from two methods, rigid fitting of predicted structures to the Cryo‐EM map and de novo protein modeling by tracing the Cryo‐EM map, was performed by CSA. In the refinement of the rigid‐fitted structures, the final models showed that CSA can generate reliable atomic structures of proteins, even when large movements of protein domains were required. In the de novo modeling case, although the overall structural qualities of the final models were rather dependent on the initial models, the final models generated by CSA showed improved MolProbity scores and cross‐correlation coefficients to the maps. These results suggest that CSA can accomplish flexible fitting and refinement together by sampling diverse conformations effectively and thus can be utilized for cryo‐EM structure modeling. [ABSTRACT FROM AUTHOR]

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2’s predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using C α atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO. [ABSTRACT FROM AUTHOR]

Circular permutation (CP) is a protein sequence rearrangement in which the amino- and carboxyl-termini of a protein can be created in different positions along the imaginary circularized sequence. Circularly permutated proteins usually exhibit conserved three-dimensional structures and functions. By comparing the structures of circular permutants (CPMs), protein research and bioengineering applications can be approached in ways that are difficult to achieve by traditional mutagenesis. Most current CP detection algorithms depend on structural information. Because there is a vast number of proteins with unknown structures, many CP pairs may remain unidentified. An efficient sequence-based CP detector will help identify more CP pairs and advance many protein studies. For instance, some hypothetical proteins may have CPMs with known functions and structures that are informative for functional annotation, but existing structure-based CP search methods cannot be applied when those hypothetical proteins lack structural information. Despite the considerable potential for applications, sequence-based CP search methods have not been well developed. We present a sequence-based method, SeqCP, which analyzes normal and duplicated sequence alignments to identify CPMs and determine candidate CP sites for proteins. SeqCP was trained by data obtained from the Circular Permutation Database and tested with nonredundant datasets from the Protein Data Bank. It shows high reliability in CP identification and achieves an AUC of 0.9. SeqCP has been implemented into a web server available at: http://pcnas.life.nthu.edu.tw/SeqCP/.

The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.

Gemin5 is a multifunctional RNA binding protein (RBP) organized in domains with a distinctive structural organization. The protein is a hub for several protein networks performing diverse RNA-dependent functions including regulation of translation, and recognition of small nuclear RNAs (snRNAs). Here we sought to identify the presence of phosphoresidues on the C-terminal half of Gemin5, a region of the protein that harbors a tetratricopeptide repeat (TPR)-like dimerization domain and a non-canonical RNA binding site (RBS1). We identified two phosphoresidues in the purified protein: P-T897 in the dimerization domain and P-T1355 in RBS1. Replacing T897 and T1355 with alanine led to decreased translation, and mass spectrometry analysis revealed that mutation T897A strongly abrogates the association with cellular proteins related to the regulation of translation. In contrast, the phosphomimetic substitutions to glutamate partially rescued the translation regulatory activity. The structural analysis of the TPR dimerization domain indicates that local rearrangements caused by phosphorylation of T897 affect the conformation of the flexible loop 2–3, and propagate across the dimerization interface, impacting the position of the C-terminal helices and the loop 12–13 shown to be mutated in patients with neurological disorders. Computational analysis of the potential relationship between post-translation modifications and currently known pathogenic variants indicates a lack of overlapping of the affected residues within the functional domains of the protein and provides molecular insights for the implication of the phosphorylated residues in translation regulation.

An evaluation system and a web infrastructure were developed for the second cryo-EM model challenge. The evaluation system includes tools to validate stereo-chemical plausibility of submitted models, check their fit to the corresponding density maps, estimate their overall and per-residue accuracy, and assess their similarity to reference cryo-EM or X-ray structures as well as other models submitted in this challenge. The web infrastructure provides a convenient interface for analyzing models at different levels of detail. It includes interactively sortable tables of evaluation scores for different subsets of models and different sublevels of structure organization, and a suite of visualization tools facilitating model analysis. The results are publicly accessible at http://model-compare.emdatabank.org.

The record high 42 model accuracy estimation methods were tested in CASP12. The paper presents results of the assessment of these methods in the whole-model and per-residue accuracy modes. Scores from four different model evaluation packages were used as the "ground truth" for assessing accuracy of methods' estimates. They include a rigid-body score-GDT_TS, and three local-structure based scores-LDDT, CAD and SphereGrinder. The ability of methods to identify best models from among several available, predict model's absolute accuracy score, distinguish between good and bad models, predict accuracy of the coordinate error self-estimates, and discriminate between reliable and unreliable regions in the models was assessed. Single-model methods advanced to the point where they are better than clustering methods in picking the best models from decoy sets. On the other hand, consensus methods, taking advantage of the availability of large number of models for the same target protein, are still better in distinguishing between good and bad models and predicting local accuracy of models. The best accuracy estimation methods were shown to perform better with respect to the frozen in time reference clustering method and the results of the best method in the corresponding class of methods from the previous CASP. Top performing single-model methods were shown to do better than all but three CASP12 tertiary structure predictors when evaluated as model selectors.

ATP-binding cassette (ABC) transporters mediate bacteria uptake or export of a variety of solutes across biological membranes. Bacterial uptake of the monosaccharides is important as a source of carbohydrate building blocks that contribute to the bacteria's major structure. Burkholderia pseudomallei is the etiological agent of melioidosis and within its genome, 33 genes related to monosaccharide ABC transporters have been predicted. The presence of these transporters is believed to assist in bacterial survival and adaptation in various environments. Despite a large number of genes in the genome, most of these systems have yet to be characterized, including the bpss0140-bpss0142 operon. Here, we predicted the 3D structure of each protein encoded by bpss0140-0142 and identified the specifically associated monosaccharides. In silico analyses of the structures demonstrated that BPSS0140 is a sugar-binding protein, BPSS0141 is a transmembrane permease and BPSS0142 is an ATPase. Through protein structure modeling and protein-ligand docking, several specific monosaccharide sugars were found to interact with the BPSS0140-BPSS0142 ABC transporter. To validate the in silico prediction, knock-out mutants for each of the genes were constructed. A growth profile between wild-type and mutants in an M9 medium supplemented with glucose, fructose, ribose, and galactose as predicted from the protein-ligand docking was then performed. The growth of mutants decreased significantly compared to the wild-type bacteria when grown in M9 supplemented with fructose as the sole carbon source indicating that this transporter is potentially the main fructose transporter in B. pseudomallei. [ABSTRACT FROM AUTHOR]

Abstract Background This work aims to help develop new protein engineering techniques based on a structural rearrangement phenomenon called circular permutation (CP), equivalent to connecting the native termini of a protein followed by creating new termini at another site. Although CP has been applied in many fields, its implementation is still costly because of inevitable trials and errors. Results Here we present CirPred, a structure modeling and termini linker design method for circularly permuted proteins. Compared with state-of-the-art protein structure modeling methods, CirPred is the only one fully capable of both circularly-permuted modeling and traditional co-linear modeling. CirPred performs well when the permutant shares low sequence identity with the native protein and even when the permutant adopts a different conformation from the native protein because of three-dimensional (3D) domain swapping. Linker redesign experiments demonstrated that the linker design algorithm of CirPred achieved subangstrom accuracy. Conclusions The CirPred system is capable of (1) predicting the structure of circular permutants, (2) designing termini linkers, (3) performing traditional co-linear protein structure modeling, and (4) identifying the CP-induced occurrence of 3D domain swapping. This method is supposed helpful for broadening the application of CP, and its web server is available at http://10.life.nctu.edu.tw/CirPred/ and http://lo.life.nctu.edu.tw/CirPred/ .

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models. [ABSTRACT FROM AUTHOR]

Background: Picrorhiza kurroa has been reported as an age-old ayurvedic hepato-protection to treat hepatic disorders due to the presence of iridoids such as picroside-II (P-II), picroside-I, and kutkoside. The acylation of catalpol and vanilloyl coenzyme A by acyltransferases (ATs) is critical step in P-II biosynthesis. Since accumulation of P-II occurs only in roots, rhizomes and stolons in comparison to leaves uprooting of this critically endangered herb has been the only source of this compound. Recently, we reported that P-II acylation likely happen in roots, while stolons serve as the vital P-II storage compartment. Therefore, developing an alternate engineered platform for P-II biosynthesis require identification of P-II specific AT/s. Methods and results: In that direction, egg-NOG function annotated 815 ATs from de novo RNA sequencing of tissue culture based 'shoots-only' system and nursery grown shoots, roots, and stolons varying in P-II content, were cross-compared in silico to arrive at ATs sequences unique and/or common to stolons and roots. Verification for organ and accession-wise upregulation in gene expression of these ATs by qRT-PCR has shortlisted six putative 'P-II-forming' ATs. Further, six-frame translation, ab initio protein structure modelling and protein-ligand molecular docking of these ATs signified one MBOAT domain containing AT with preferential binding to the vanillic acid CoA thiol ester as well as with P-II, implying that this could be potential AT decorating final structure of P-II. Conclusions: Organ-wise comparative transcriptome mining coupled with reverse transcription real time qRT-PCR and protein-ligand docking led to the identification of an acyltransferases, contributing to the final structure of P-II. [ABSTRACT FROM AUTHOR]

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.

Actinomycete is one of the beneficial bacteria groups inhabiting rhizosphere soil. They can promote plant growth through various mechanisms. In the previous study we have isolated rhizosphere actinomycetes from maize rhizosphere with direct plant growth promotion characters. The aims of the present study were to analyze the ability of maize rhizosphere actinomycetes to solubilize phosphate, determine alkaline phosphatase activity, and study their genetic diversity based on phoD gene. Thirteen rhizosphere actinomycete isolates were able to solubilize phosphate at concertation range 55.84±2.27 mg/L to 144.48±5.71 mg/L. The activity of extracellular alkaline phosphatase was exhibited by all maize rhizosphere actinomycetes isolates in various level ranging from 0.08 mU/mL to 0.51 mU/mL. The phoD gene, one of the three homologous genes which encode alkaline phosphatases, was successfully detected in all isolates and identified as alkaline phosphatase D of Streptomyces spp. The partial phoD sequences of the isolates were located within metallophosphatase domain of alkaline phosphatase D. Alignment analysis showed that the deduced amino acid sequences of PhoD were mostly conserved in the isolates and Streptomyces spp. Essential residues involved in the active core arrangement of PhoD which binds metal ion cofactors were conserved. Constructed phylogenetic tree showed that the isolates were divided into two groups within PhoD cluster. PhoD of the isolates and Streptomyces spp. had closer relationship to purple acid phosphatase compared to other homologous PhoA and PhoX which form separate cluster. Generated three-dimensional structure model of partial PhoD had high similarity to alkaline phosphatase D of Bacillus subtilis (2YEQ) and showed overlapping structure based on super-positioning analysis. [ABSTRACT FROM AUTHOR]

Modeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling. Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other areas. Proteins 2016; 84(Suppl 1):4-14. © 2016 Wiley Periodicals, Inc.

We outline the role of the Protein Structure Prediction Center (predictioncenter.org) in conducting the CASP11 and CASP ROLL experiments, discuss the experiment statistics, and provide an overview of the present CASP infrastructure. The biggest changes compared to the previous CASPs are the implementation of the evaluation system incorporating practically all evaluation measures, statistical tests, and visualization tools historically used by the CASP assessors, the expansion of the infrastructure to incorporate new categories of contact-assisted and multimeric predictions, and the redesign of the assessors' web-workspace enabling assessments based on multiple measures for different group categories and target sets. Proteins 2016; 84(Suppl 1):15-19. © 2016 Wiley Periodicals, Inc.

Background: This work aims to help develop new protein engineering techniques based on a structural rearrangement phenomenon called circular permutation (CP), equivalent to connecting the native termini of a protein followed by creating new termini at another site. Although CP has been applied in many fields, its implementation is still costly because of inevitable trials and errors. Results: Here we present CirPred, a structure modeling and termini linker design method for circularly permuted proteins. Compared with state-of-the-art protein structure modeling methods, CirPred is the only one fully capable of both circularly-permuted modeling and traditional co-linear modeling. CirPred performs well when the permutant shares low sequence identity with the native protein and even when the permutant adopts a different conformation from the native protein because of three-dimensional (3D) domain swapping. Linker redesign experiments demonstrated that the linker design algorithm of CirPred achieved subangstrom accuracy. Conclusions: The CirPred system is capable of (1) predicting the structure of circular permutants, (2) designing termini linkers, (3) performing traditional co-linear protein structure modeling, and (4) identifying the CP-induced occurrence of 3D domain swapping. This method is supposed helpful for broadening the application of CP, and its web server is available at http://10.life.nctu.edu.tw/CirPred/ and http://lo.life.nctu.edu.tw/CirPred/. [ABSTRACT FROM AUTHOR]

Abstract Background Podosphaera xanthii is the main causal agent of powdery mildew disease in cucurbits and is responsible for important yield losses in these crops worldwide. Powdery mildew fungi are obligate biotrophs. In these parasites, biotrophy is determined by the presence of haustoria, which are specialized structures of parasitism developed by these fungi for the acquisition of nutrients and the delivery of effectors. Detailed molecular studies of powdery mildew haustoria are scarce due mainly to difficulties in their isolation. Therefore, their analysis is considered an important challenge for powdery mildew research. The aim of this work was to gain insights into powdery mildew biology by analysing the haustorial transcriptome of P. xanthii. Results Prior to RNA isolation and massive-scale mRNA sequencing, a flow cytometric approach was developed to isolate P. xanthii haustoria free of visible contaminants. Next, several commercial kits were used to isolate total RNA and to construct the cDNA and Illumina libraries that were finally sequenced by the Illumina NextSeq system. Using this approach, the maximum amount of information from low-quality RNA that could be obtained was used to accomplish the de novo assembly of the P. xanthii haustorial transcriptome. The subsequent analysis of this transcriptome and comparison with the epiphytic transcriptome allowed us to identify the importance of several biological processes for haustorial cells such as protection against reactive oxygen species, the acquisition of different nutrients and genetic regulation mediated by non-coding RNAs. In addition, we could also identify several secreted proteins expressed exclusively in haustoria such as cell adhesion proteins that have not been related to powdery mildew biology to date. Conclusions This work provides a novel approach to study the molecular aspects of powdery mildew haustoria. In addition, the results of this study have also allowed us to identify certain previously unknown processes and proteins involved in the biology of powdery mildews that could be essential for their biotrophy and pathogenesis.

Expansion of glutamine residue track (polyQ) within soluble protein is responsible for eight autosomal-dominant genetic neurodegenerative disorders. These disorders affect cerebellum, striatum, basal ganglia and other brain regions. Each disease develops when polyQ expansion exceeds a pathogenic threshold (Q th). A pathogenic threshold is unique for each disease but the reasons for variability in Q th within this family of proteins are poorly understood. In the previous publication we proposed that polarity of the regions flanking polyQ track in each protein plays a key role in defining Q th value [1]. To explain the correlation between the polarity of the flanking sequences and Q th we performed quantitative analysis of interactions between polyQ-expanded proteins and proteasome. Based on structural and theoretical modeling, we predict that Q th value is determined by the energy of polar interaction of the flanking regions with the polyQ and proteasome. More polar flanking regions facilitate unfolding of α-helical polyQ conformation adopted inside the proteasome and as a result, increase Qth. Predictions of our model are consistent with Q th values observed in clinic for each of the eight polyQ-expansion disorders. Our results suggest that the agents that can destabilize polyQ α-helical structure may have a beneficial therapeutic effect for treatment of polyQ-expansion disorders. • A potential explanation for a variability of pathogenic polyglutamine (polyQ) expansion threshold is proposed. • The pathogenic threshold is determined by the energy of polar interactions between polyQ flanking regions and proteasome. • Predictions of the model are consistent with pathogenic polyglutamine expansion threshold values observed in the clinic. [ABSTRACT FROM AUTHOR]

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Finger millet (Eleusine coracana) is one of important crops, and its grains contain an exceptionally high content of calcium. In order to investigate the molecular mechanism by which it orchestrate the accumulation of Ca2+ during grain filling, some candidate genes encoding calcium transporters [calcium exchangers (CAX1, CAX3)] and sensors [calcineurin-B like (CBL4 and 10)], a CBL-interacting protein kinase (CIPK24), and calmodulin (CaM) were identified using transcriptomics and differential expression analysis in two genotypes of finger millet differing in grain calcium content. These transporters and sensors are highly expressed in leaves and developing spikes of the genotype with a high grain Ca2+ indicating their potential role in Ca2+ accumulation. Calcium transporters, mainly CAXs, pump Ca2+ inside the cell through plasmalemma and tonoplast, and their activities are regulated by CaM dependent and independent Ca2+ sensor proteins of CaM and CBL-CIPK networks. Abundance of CaM in a high grain Ca2+ genotype is suggestive that CaM might also contribute for grain calcium accumulation by interaction with Ca2+ATPase. The upregulation of CAX1 in vegetative tissues and developing spikes and CAX3 only in developing spikes provides the most plausible clue for calcium transport and accumulation regulated by tripartite interaction in finger millet.

Collagen triple helix repeat containing protein 1 (Cthrc1) is a secreted glycoprotein reported to regulate collagen deposition and to be linked to the Transforming growth factor β /Bone morphogenetic protein and the Wnt/planar cell polarity pathways. It was first identified as being induced upon injury to rat arteries and was found to be highly expressed in multiple human cancer types. Here, we explore the phylogenetic and evolutionary trends of this metazoan gene family, previously studied only in vertebrates. We identify Cthrc1 orthologs in two distant cnidarian species, the sea anemone Nematostella vectensis and the hydrozoan Clytia hemisphaerica , both of which harbor multiple copies of this gene. We find that Cthrc1 clade-specific diversification occurred multiple times in cnidarians as well as in most metazoan clades where we detected this gene. Many other groups, such as arthropods and nematodes, have entirely lost this gene family. Most vertebrates display a single highly conserved gene, and we show that the sequence evolutionary rate of Cthrc1 drastically decreased within the gnathostome lineage. Interestingly, this reduction coincided with the origin of its conserved upstream neighboring gene, Frizzled 6 (FZD6), which in mice has been shown to functionally interact with Cthrc1. Structural modeling methods further reveal that the yet uncharacterized C-terminal domain of Cthrc1 is similar in structure to the globular C1q superfamily domain, also found in the C-termini of collagens VIII and X. Thus, our studies show that the Cthrc1 genes are a collagen-like family with a variable short collagen triple helix domain and a highly conserved C-terminal domain structure resembling the C1q family. [ABSTRACT FROM AUTHOR]

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre‐release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4‐day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross‐validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO—either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo‐oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods. [ABSTRACT FROM AUTHOR]

Background and Aims: Salmonella enterica serovar Typhimurium is one of the common causes of food poisoning in human. Since the selection of appropriate markers is one of the main challenges for the detection of this pathogen, in the current study, genetic markers of this serovar were screened using bioinformatical tools. In the second phase, structure and function of proteins encoded by these markers, were determined. Materials and Methods: This study was conducted between 2016 and 2017. In order to find the genetic markers of Salmonella enterica serovar Typhimurium, 45 complete genomes belonging to Salmonella enterica serovar Typhimurium and the other genera of Enterobacteriacea family were compared using Mauve software. To determine the structure and function of proteins encoded by these sequences, I-TASSER and Phyre2 software beside CDD, Inter Pro Scan, DALI, and Pro Func databases were used for structural and functional modeling, respectively. Results: Special regions of STM4491-STM4496 genes were determined as specific markers for Salmonella enterica serovar Typhimurium. The function of proteins encoded by these markers were proposed to be classified in five groups, including Lon protease, nucleotide binding proteins, nucleotide three phosphatases (NTP), proteins involved in the DNA repair , and DNA methylase. Conclusions: Specific regions of STM4491-STM4496 genes can be used as effective diagnostic targets for the detection of pathogenic Salmonella enterica serovar Typhimurium. Moreover, proteins encoded by these genes can be suggested as suitable targets for the design of new therapeutic agents to prevent and treat the infections caused by this pathogen.

We introduce a side‐chain‐inclusive scoring function, named OPUS‐SSF, for ranking protein structural models. The method builds a scoring function based on the native distributions of the coordinate components of certain anchoring points in a local molecular system for peptide segments of 5, 7, 9, and 11 residues in length. Differing from our previous OPUS‐CSF [Xu et al., Protein Sci. 2018; 27: 286–292], which exclusively uses main chain information, OPUS‐SSF employs anchoring points on side chains so that the effect of side chains is taken into account. The performance of OPUS‐SSF was tested on 15 decoy sets containing totally 603 proteins, and 571 of them had their native structures recognized from their decoys. Similar to OPUS‐CSF, OPUS‐SSF does not employ the Boltzmann formula in constructing scoring functions. The results indicate that OPUS‐SSF has achieved a significant improvement on decoy recognition and it should be a very useful tool for protein structural prediction and modeling. [ABSTRACT FROM AUTHOR]

Abstract Protein tertiary structure modeling is a critical step for the interpretation of three dimensional (3D) election microscopy density. Our group participated the 2015/2016 EM Model Challenge using the MAINMAST software for a de novo main chain modeling. The software generates local dense points using the mean shifting algorithm, and connects them into Cα models by calculating the minimum spanning tree and the longest path. Subsequently, full atom structure models are generated, which are subject to structural refinement. Here, we summarize the qualities of our submitted models and examine successful and unsuccessful models, including 3D models we did not submit to the Challenge. Our protocol using the MAINMAST software was sometimes able to build correct conformations with 3.4–5.1 Å RMSD. Unsuccessful models had failure of chain traces, however, their Cα positions and some local structures were quite correctly built. For evaluate the quality of the models, the MAINMAST software provides a confidence score for each Cα position from the consensus of top 100 scoring models. [ABSTRACT FROM AUTHOR]

Finger millet (Eleusine coracana) is one of important crops, and its grains contain an exceptionally high content of calcium. In order to investigate the molecular mechanism by which it orchestrate the accumulation of Ca2+ during grain filling, some candidate genes encoding calcium transporters [calcium exchangers (CAX1, CAX3)] and sensors [calcineurin-B like (CBL4 and 10)], a CBL-interacting protein kinase (CIPK24), and calmodulin (CaM) were identified using transcriptomics and differential expression analysis in two genotypes of finger millet differing in grain calcium content. These transporters and sensors are highly expressed in leaves and developing spikes of the genotype with a high grain Ca2+ indicating their potential role in Ca2+ accumulation. Calcium transporters, mainly CAXs, pump Ca2+ inside the cell through plasmalemma and tonoplast, and their activities are regulated by CaM dependent and independent Ca2+ sensor proteins of CaM and CBL-CIPK networks. Abundance of CaM in a high grain Ca2+ genotype is suggestive that CaM might also contribute for grain calcium accumulation by interaction with Ca2+ATPase. The upregulation of CAX1 in vegetative tissues and developing spikes and CAX3 only in developing spikes provides the most plausible clue for calcium transport and accumulation regulated by tripartite interaction in finger millet. [ABSTRACT FROM AUTHOR]

Stochastic search is often the only viable option to address complex optimization problems. Recently, evolutionary algorithms have been shown to handle challenging continuous optimization problems related to protein structure modeling. Building on recent work in our laboratories, we propose an evolutionary algorithm for efficiently mapping the multi-basin energy landscapes of dynamic proteins that switch between thermodynamically stable or semi-stable structural states to regulate their biological activity in the cell. The proposed algorithm balances computational resources between exploration and exploitation of the nonlinear, multimodal landscapes that characterize multi-state proteins via a novel combination of global and local search to generate a dynamically-updated, information-rich map of a protein's energy landscape. This new mapping-oriented EA is applied to several dynamic proteins and their disease-implicated variants to illustrate its ability to map complex energy landscapes in a computationally feasible manner. We further show that, given the availability of such maps, comparison between the maps of wildtype and variants of a protein allows for the formulation of a structural and thermodynamic basis for the impact of sequence mutations on dysfunction that may prove useful in guiding further wet-laboratory investigations of dysfunction and molecular interventions. [ABSTRACT FROM AUTHOR]

Abstract: For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence‐structure alignment, three‐dimensional (3D) chain building, and side‐chain re‐modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue‐residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template‐based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded. [ABSTRACT FROM AUTHOR]

Abstract: In CASP12, 2 types of data‐assisted protein structure modeling were experimented. Either SAXS experimental data or cross‐linking experimental data was provided for a selected number of CASP12 targets that the CASP12 predictor could utilize for better protein structure modeling. We devised 2 separate energy terms for SAXS data and cross‐linking data to drive the model structures into more native‐like structures that satisfied the given experimental data as much as possible. In CASP11, we successfully performed protein structure modeling using simulated sparse and ambiguously assigned NOE data and/or correct residue‐residue contact information, where the only energy term that folded the protein into its native structure was the term which was originated from the given experimental data. However, the 2 types of experimental data provided in CASP12 were far from being sufficient enough to fold the target protein into its native structure because SAXS data provides only the overall shape of the molecule and the cross‐linking contact information provides only very low‐resolution distance information. For this reason, we combined the SAXS or cross‐linking energy term with our regular modeling energy function that includes both the template energy term and the de novo energy terms. By optimizing the newly formulated energy function, we obtained protein models that fit better with provided SAXS data than the X‐ray structure of the target. However, the improvement of the model relative to the 1 modeled without the SAXS data, was not significant. Consistent structural improvement was achieved by incorporating cross‐linking data into the protein structure modeling. [ABSTRACT FROM AUTHOR]

Abstract: Every second year, the community experiment “Critical Assessment of Techniques for Structure Prediction” (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The “Continuous Automated Model EvaluatiOn (CAMEO)” platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre‐release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4‐day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross‐validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites. [ABSTRACT FROM AUTHOR]

We report a C-atom-based scoring function, named OPUS-CSF, for ranking protein structural models. Rather than using traditional Boltzmann formula, we built a scoring function (CSF score) based on the native distributions (derived from the entire PDB) of coordinate components of mainchain C (carbonyl) atoms on selected residues of peptide segments of 5, 7, 9, and 11 residues in length. In testing OPUS-CSF on decoy recognition, it maximally recognized 257 native structures out of 278 targets in 11 commonly used decoy sets, significantly outperforming other popular all-atom empirical potentials. The average correlation coefficient with TM-score was also comparable with those of other potentials. OPUS-CSF is a highly coarse-grained scoring function, which only requires input of partial mainchain information, and very fast. Thus, it is suitable for applications at early stage of structural building. [ABSTRACT FROM AUTHOR]

Darobactins represent a class of ribosomally synthesized and post-translationally modified peptide (RiPP) antibiotics featuring a rare bicyclic structure. They target the Bam-complex of Gram-negative bacteria and exhibit in vivo activity against drug-resistant pathogens. First isolated from Photorhabdus species, the corresponding biosynthetic gene clusters (BGCs) are widespread among γ-proteobacteria, including the genera Vibrio , Yersinia , and Pseudoalteromonas (P.). While the organization of the BGC core is highly conserved, a small subset of Pseudoalteromonas carries an extended BGC with additional genes. Here, we report the identification of brominated and dehydrated darobactin derivatives from P. luteoviolacea strains. The marine derivatives are active against multidrug-resistant (MDR) Gram-negative bacteria and showed solubility and plasma protein binding ability different from darobactin A, rendering it more active than darobactin A. The halogenation reaction is catalyzed by DarH, a new class of flavin-dependent halogenases with a novel fold. [Display omitted] • Brominated and dehydrated darobactin derivates from P. luteoviolacea are identified • Derivatives are active against multidrug-resistant Gram-negative bacteria • They show altered physico-chemical properties (compared to source molecules) • A new class of FAD-dependent halogenases harbors a novel fold and catalyzes bromination Böhringer et al. identified brominated darobactin derivatives from Pseudoalteromonas luteoviolacea strains. The halogenase involved in the process was of a new type. The bromine substituent leads to higher activity against Gram-negative bacteria and altered physico-chemical properties. It paves the way for further modification. [ABSTRACT FROM AUTHOR]