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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]

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.

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]

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.

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]

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]

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]

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]

CASP (Critical Assessment of protein Structure Prediction) is a community-wide experiment for protein structure prediction taking place every two years since 1994. In CASP11 held in 2014, according to the official CASP11 assessment, our method named `nns' was ranked as the second best server method based on models ranked as first out of 81 targets. In `nns', we applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For the fold recognition, a new alignment method called CRFalign was used. The good performance of the nns server method is attributed to the successful fold recognition carried out by combined methods including CRFalign, and the current modeling formulation incorporating accurate structural aspects collected from multiple templates. In this article, we provide a successful example of `nns' predictions for T0776, for which all details of intermediate modeling data are provided.

The zea1 mutant of marine microalga Dunaliella tertiolecta accumulates zeaxanthin under normal growth conditions, and its phenotype has been speculated to be related to zeaxanthin epoxidase (ZEP). In this study, we isolated the ZEP gene from both wild-type D. tertiolecta and the mutant. We found that the zea1 mutant has a point mutation of the 1337th nucleotide of the ZEP sequence (a change from guanine to adenine), resulting in a change of glycine to aspartate in a highly conserved region in the catalytic domain. Similar expression levels of ZEP mRNA and protein in both wild-type and zea1 were confirmed by using qRT-PCR and western blot analysis, respectively. Additionally, the enzyme activity analysis of ZEPs in the presence of cofactors showed that the inactivation of ZEP in zea1 was not caused by deficiency in the levels of cofactors. From the predicted three-dimensional ZEP structure of zea1, we observed a conformational change on the substrate-binding site in the ZEP. A comparative analysis of the ZEP structures suggested that the conformational change induced by a single amino acid mutation might impact the interaction between the substrate and substrate-binding site, resulting in loss of zeaxanthin epoxidase function.

Background: Subtilisin-like serine proteases or Subtilases in fungi are important for penetration and colonization of host. In Hypocreales, these proteins share several properties with other fungal, bacterial, plant and mammalian homologs. However, adoption of specific roles in entomopathogenesis may be governed by attainment of unique biochemical and structural features during the evolutionary course. Due to such functional shifts Subtilases coded by different family members of Hypocreales acquire distinct features according to respective hosts and lifestyle. We conducted phylogenetic and DIVERGE analyses and identified important protein residues that putatively assign functional specificity to Subtilases in fungal families/species under the order Hypocreales. Results: A total of 161 Subtilases coded by 10 species from five different families under the fungal order Hypocreales was included in the analysis. Based on the presence of conserved domains, the Subtilase genes were divided into three subfamilies, Subtilisin (S08.005), Proteinase K (S08.054) and Serine-carboxyl peptidases (S53.001). These subfamilies were investigated for phylogenetic associations, protein residues under positive selection and functional divergence among paralogous clades. The observations were co-related with the life-styles of the fungal families/species. Phylogenetic and Divergence analyses of Subtilisin (S08.005) and Proteinase K (S08.054) families of proteins revealed that the paralogous clades were clear-cut representation of familial origin of the protein sequences. We observed divergence between the paralogous clades of plant-pathogenic fungi (Nectriaceae), insect-pathogenic fungi (Cordycipitaceae/Clavicipitaceae) and nematophagous fungi (Ophiocordycipitaceae). In addition, Subtilase genes from the nematode-parasitic fungus Purpureocillium lilacinum made a unique cluster which putatively indicated that the fungus might have developed distinctive mechanisms for nematode-pathogenesis. Our evolutionary genetics analysis revealed evidence of positive selection on the Subtilisin (S08.005) and Proteinase K (S08.054) protein sequences of the entomopathogenic and nematophagous species belonging to Cordycipitaceae, Clavicipitaceae and Ophiocordycipitaceae families of Hypocreales. Conclusions: Our study provided new insights into the evolution of Subtilisin like serine proteases in Hypocreales, a fungal order largely consisting of biological control species. Subtilisin (S08.005) and Proteinase K (S08.054) proteins seemed to play important roles during life style modifications among different families and species of Hypocreales. Protein residues found significant in functional divergence analysis in the present study may provide support for protein engineering in future. [ABSTRACT FROM AUTHOR]

The structure of a protein can be very informative of its function. However, determining protein structures experimentally can often be very challenging. Computational methods have been used successfully in modeling structures with sufficient accuracy. Here we have used computational tools to predict the structure of an evolutionarily conserved and functionally significant domain of Integrase interactor (INI)1/hSNF5 protein. INI1 is a component of the chromatin remodeling SWI/SNF complex, a tumor suppressor and is involved in many protein-protein interactions. It belongs to SNF5 family of proteins that contain two conserved repeat (Rpt) domains. Rpt1 domain of INI1 binds to HIV-1 Integrase, and acts as a dominant negative mutant to inhibit viral replication. Rpt1 domain also interacts with oncogene c-MYC and modulates its transcriptional activity. We carried out an ab initio modeling of a segment of INI1 protein containing the Rpt1 domain. The structural model suggested the presence of a compact and well defined ββαα topology as core structure in the Rpt1 domain of INI1. This topology in Rpt1 was similar to PFU domain of Phospholipase A2 Activating Protein, PLAA. Interestingly, PFU domain shares similarity with Ubiquitin and has ubiquitin binding activity. Because of the structural similarity between Rpt1 domain of INI1 and PFU domain of PLAA, we propose that Rpt1 domain of INI1 may participate in ubiquitin recognition or binding with ubiquitin or ubiquitin related proteins. This modeling study may shed light on the mode of interactions of Rpt1 domain of INI1 and is likely to facilitate future functional studies of INI1. [ABSTRACT FROM AUTHOR]

Computational methods for protein structure prediction allow us to determine a three-dimensional structure of a protein based on its pure amino acid sequence. These methods are a very important alternative to costly and slow experimental methods, like X-ray crystallography or Nuclear Magnetic Resonance. However, conventional calculations of protein structure are time-consuming and require ample computational resources, especially when carried out with the use of ab initio methods that rely on physical forces and interactions between atoms in a protein. Fortunately, at the present stage of the development of computer science, such huge computational resources are available from public cloud providers on a pay-as-you-go basis. We have designed and developed a scalable and extensible system, called Cloud4PSP, which enables predictions of 3D protein structures in the Microsoft Azure commercial cloud. The system makes use of the Warecki-Znamirowski method as a sample procedure for protein structure prediction, and this prediction method was used to test the scalability of the system. The results of the efficiency tests performed proved good acceleration of predictions when scaling the system vertically and horizontally. In the paper, we show the system architecture that allowed us to achieve such good results, the Cloud4PSP processing model, and the results of the scalability tests. At the end of the paper, we try to answer which of the scaling techniques, scaling out or scaling up, is better for solving such computational problems with the use of Cloud computing. [ABSTRACT FROM AUTHOR]

Evidence is emerging that many proteins involved in proteinopathies are dynamic molecules switching between stable and semistable structures to modulate their function. A detailed understanding of the relationship between structure and function in such molecules demands a comprehensive characterization of their conformation space. Currently, only stochastic optimization methods are capable of exploring conformation spaces to obtain sample-based representations of associated energy surfaces. These methods have to address the fundamental but challenging issue of balancing computational resources between exploration (obtaining a broad view of the space) and exploitation (going deep in the energy surface). We propose a novel algorithm that strikes an effective balance by employing concepts from evolutionary computation. The algorithm leverages deposited crystal structures of wildtype and variant sequences of a protein to define a reduced, low-dimensional search space from where to rapidly draw samples. A multiscale technique maps samples to local minima of the all-atom energy surface of a protein under investigation. Several novel algorithmic strategies are employed to avoid premature convergence to particular minima and obtain a broad view of a possibly multibasin energy surface. Analysis of applications on different proteins demonstrates the broad utility of the algorithm to map multibasin energy landscapes and advance modeling of multibasin proteins. In particular, applications on wildtype and variant sequences of proteins involved in proteinopathies demonstrate that the algorithm makes an important first step toward understanding the impact of sequence mutations on misfunction by providing the energy landscape as the intermediate explanatory link between protein sequence and function. [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/., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)