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Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community

BackgroundDespite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today’s top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions.ResultsHere, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94±8% of the beta barrel TMPs (53 of 57) and 98±1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060).ConclusionsBased on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2.AvailabilityOur code, method, and data sets are freely available in the GitHub repository, https://github.com/BernhoferM/TMbed.

Motivation CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues missed by state-of-the-art hidden Markov model (HMM)-based approaches. The method developed (CATHe) combines a neural network with sequence representations obtained from protein language models. It was assessed using a dataset of remote homologues having less than 20% sequence identity to any domain in the training set. Results The CATHe models trained on 1773 largest and 50 largest CATH superfamilies had an accuracy of 85.6 ± 0.4% and 98.2 ± 0.3%, respectively. As a further test of the power of CATHe to detect more remote homologues missed by HMMs derived from CATH domains, we used a dataset consisting of protein domains that had annotations in Pfam, but not in CATH. By using highly reliable CATHe predictions (expected error rate Availability and implementation The code for the developed models is available on https://github.com/vam-sin/CATHe, and the datasets developed in this study can be accessed on https://zenodo.org/record/6327572. Supplementary information Supplementary data are available at Bioinformatics online.

The availability of accurate and fast Artificial Intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserverLambdaPPaspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input,LambdaPPprovides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided byLambdaPP- leveragingColabFold and computed in minutes- is based onMMseqs2multiple sequence alignments. All other feature prediction methods are based on the pLMProtT5. Queried by a protein sequence,LambdaPPcomputes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function.Accessibility StatementLambdaPP is freely available for everyone to use underembed.predictprotein.org, the interactive results for the case study can be found underhttps://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image atghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.Impact StatementWe introduce LambdaPP, a webserver integrating fast and accurate sequence-only protein feature predictions based on embeddings from protein Language Models (pLMs) available in seconds along with high-quality protein structure predictions. The intuitive interface invites experts and novices to benefit from the latest machine learning tools. LambdaPP’s unique combination of predicted features may help in formulating hypotheses for experiments and as input to bioinformatics pipelines.

Motivation Classifying proteins into functional families can improve our understanding of protein function and can allow transferring annotations within one family. For this, functional families need to be ‘pure’, i.e., contain only proteins with identical function. Functional Families (FunFams) cluster proteins within CATH superfamilies into such groups of proteins sharing function. 11% of all FunFams (22 830 of 203 639) contain EC annotations and of those, 7% (1526 of 22 830) have inconsistent functional annotations. Results We propose an approach to further cluster FunFams into functionally more consistent sub-families by encoding their sequences through embeddings. These embeddings originate from language models transferring knowledge gained from predicting missing amino acids in a sequence (ProtBERT) and have been further optimized to distinguish between proteins belonging to the same or a different CATH superfamily (PB-Tucker). Using distances between embeddings and DBSCAN to cluster FunFams and identify outliers, doubled the number of pure clusters per FunFam compared to random clustering. Our approach was not limited to FunFams but also succeeded on families created using sequence similarity alone. Complementing EC annotations, we observed similar results for binding annotations. Thus, we expect an increased purity also for other aspects of function. Our results can help generating FunFams; the resulting clusters with improved functional consistency allow more reliable inference of annotations. We expect this approach to succeed equally for any other grouping of proteins by their phenotypes. Availability and implementation Code and embeddings are available via GitHub: https://github.com/Rostlab/FunFamsClustering. Supplementary information Supplementary data are available at Bioinformatics online.

Visualizing protein data remains a challenging and stimulating task. Useful and intuitive visualization tools may help advance biomolecular and medical research; unintuitive tools may bar important breakthroughs. This protocol describes two use cases for the CellMap (http://cellmap.protein.properties) web tool. The tool allows researchers to visualize human protein-protein interaction data constrained by protein subcellular localizations. In the simplest form, proteins are visualized on cell images that also show protein-protein interactions (PPIs) through lines (edges) connecting the proteins across the compartments. At a glance, this simultaneously highlights spatial constraints that proteins are subject to in their physical environment and visualizes PPIs against these localizations. Visualizing two realities helps in decluttering the protein interaction visualization from "hairball" phenomena that arise when single proteins or groups thereof interact with hundreds of partners. © 2019 The Authors. Basic Protocol 1: Visualizing proteins and their interactions on cell images Basic Protocol 2: Displaying all interaction partners for a protein.

Binding small ligands such as ions or macromolecules such as DNA, RNA, and other proteins is one important aspect of the molecular function of proteins. Many binding sites remain without experimental annotations. Predicting binding sites on a per-residue level is challenging, but if 3D structures are known, information about coevolving residue pairs (evolutionary couplings) can predict catalytic residues through mutual information. Here, we predicted protein binding sites from evolutionary couplings derived from a global statistical model using maximum entropy. Additionally, we included information from sequence variation. A simple method using a weighted sum over eight scores substantially outperformed random (F1 = 19.3% ± 0.7% vs F1 = 2% for random). Training a neural network on these eight scores (along with predicted solvent accessibility and conservation in protein families) improved substantially (F1 = 26.2% ±0.8%). Although the machine learning was limited by the small data set and possibly wrong annotations of binding sites, the predicted binding sites formed spatial clusters in the protein. The source code of the binding site predictions is available through GitHub: https://github.com/Rostlab/bindPredict.

Motivation The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods. Results Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The extraction of sequence variants from the literature remains an important task. Existing methods primarily target standard (ST) mutation mentions (e.g. ‘E6V’), leaving relevant mentions natural language (NL) largely untapped (e.g. ‘glutamic acid was substituted by valine at residue 6’). Results We introduced three new corpora suggesting named-entity recognition (NER) to be more challenging than anticipated: 28–77% of all articles contained mentions only available in NL. Our new method nala captured NL and ST by combining conditional random fields with word embedding features learned unsupervised from the entire PubMed. In our hands, nala substantially outperformed the state-of-the-art. For instance, we compared all unique mentions in new discoveries correctly detected by any of three methods (SETH, tmVar, or nala). Neither SETH nor tmVar discovered anything missed by nala, while nala uniquely tagged 33% mentions. For NL mentions the corresponding value shot up to 100% nala-only. Availability and Implementation Source code, API and corpora freely available at: http://tagtog.net/-corpora/IDP4+. Supplementary information Supplementary data are available at Bioinformatics online.

Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bridged the gap by predicting transmembrane helices (TMHs), but room for improvement remains. Here, we present TMSEG, a novel method identifying TMPs and accurately predicting their TMHs and their topology. The method combines machine learning with empirical filters. Testing it on a non-redundant dataset of 41 TMPs and 285 soluble proteins, and applying strict performance measures, TMSEG outperformed the state-of-the-art in our hands. TMSEG correctly distinguished helical TMPs from other proteins with a sensitivity of 98 ± 2% and a false positive rate as low as 3 ± 1%. Individual TMHs were predicted with a precision of 87 ± 3% and recall of 84 ± 3%. Furthermore, in 63 ± 6% of helical TMPs the placement of all TMHs and their inside/outside topology was correctly predicted. There are two main features that distinguish TMSEG from other methods. First, the errors in finding all helical TMPs in an organism are significantly reduced. For example, in human this leads to 200 and 1600 fewer misclassifications compared to the second and third best method available, and 4400 fewer mistakes than by a simple hydrophobicity-based method. Second, TMSEG provides an add-on improvement for any existing method to benefit from. Proteins 2016; 84:1706-1716. © 2016 Wiley Periodicals, Inc.

A bacterial defense mechanism Polymyxins are antibiotics that disrupt the bacterial cell membrane and are used to treat multidrug-resistant infections. A bacterial enzyme called ArnT can mediate resistance to polymyxins by transferring a sugar group from a lipid carrier to lipid A, a component of the bacterial outer membrane. Petrou et al. described structures of ArnT alone and in complex with a lipid carrier and identified a cavity where lipid A probably binds. Insights into the enzyme mechanism could be exploited to design drugs that combat polymyxin resistance. Science , this issue p. 608

Despite substantial and successful projects for structural genomics, many proteins remain for which neither experimental structures nor homology-based models are known for any part of the amino acid sequence. These have been called "dark proteins," in contrast to non-dark proteins, in which at least part of the sequence has a known or inferred structure. It has been hypothesized that non-dark proteins may be more abundantly expressed than dark proteins, which are known to have much fewer sequence relatives. Surprisingly, the opposite has been observed: human dark and non-dark proteins had quite similar levels of expression, in terms of both mRNA and protein abundance. Such high levels of expression strongly indicate that dark proteins-as a group-are important for cellular function. This is remarkable, given how carefully structural biologists have focused on proteins crucial for function, and highlights the important challenge posed by dark proteins in future research.

MotivationProtein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods.ResultsWe extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.

Motivation Many DNA-binding proteins recognize their target sequences indirectly, by sensing DNA’s response to mechanical distortion. ThreaDNA estimates this response based on high-resolution structures of the protein–DNA complex of interest. Implementing an efficient nanoscale modeling of DNA deformations involving essentially no adjustable parameters, it returns the profile of deformation energy along whole genomes, at base-pair resolution, within minutes on usual laptop/desktop computers. Our predictions can also be easily combined with estimations of direct selectivity through a generalized form of position-weight-matrices. The formalism of ThreaDNA is accessible to a wide audience. Results We demonstrate the importance of indirect readout for the nucleosome as well as the bacterial regulators Fis and CRP. Combined with the direct contribution provided by usual sequence motifs, it significantly improves the prediction of sequence selectivity, and allows quantifying the two distinct physical mechanisms underlying it. Availability and implementation Python software available at bioinfo.insa-lyon.fr, natively executable on Linux/MacOS systems with a user-friendly graphical interface. Galaxy webserver version available. Supplementary information Supplementary data are available at Bioinformatics online.

Following publication of the original article [1], the author reported that an incorrect figure has been published as Figure 2. The correct Figure 2 is shown below.

We surveyed the "dark" proteome-that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44-54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology.

Motivation: Many methods predict the physical interaction between two proteins (protein-protein interactions; PPIs) from sequence alone. Their performance drops substantially for proteins not used for training. Results: Here, we introduce a new approach to predict PPIs from sequence alone which is based on evolutionary profiles and profile-kernel support vector machines. It improved over the state-of-the-art, in particular for proteins that are sequence-dissimilar to proteins with known interaction partners. Filtering by gene expression data increased accuracy further for the few, most reliably predicted interactions (low recall). The overall improvement was so substantial that we compiled a list of the most reliably predicted PPIs in human. Our method makes a significant difference for biology because it improves most for the majority of proteins without experimental annotations. Availability and implementation: Implementation and most reliably predicted human PPIs available at https://rostlab.org/owiki/index.php/Profppikernel. Contact: rost@in.tum.de Supplementary information: Supplementary data are available at Bioinformatics online.

Structural clues to protein function Translocator protein (TSPO) is a mitochondrial membrane protein thought to transport cholesterol and porphyrins. Its detailed function remains unclear, but interest in it is high because TSPO is involved in a variety of human diseases. Two papers now present crystal structures of bacterial TSPOs. Li et al. show that a mutant that mimics a human single polymorphism associated with psychiatric disorders has structural changes in a region implicated in cholesterol binding. Guo et al. suggest that TSPO may be more than a transporter. They show how it catalyzes the degradation of porphyrins, a function that could be important in protection against oxidative stress. Science , this issue p. 555 , p. 551

Experimental structure determination continues to be challenging for membrane proteins. Computational prediction methods are therefore needed and widely used to supplement experimental data. Here, we re-examined the state of the art in transmembrane helix prediction based on a nonredundant dataset with 190 high-resolution structures. Analyzing 12 widely-used and well-known methods using a stringent performance measure, we largely confirmed the expected high level of performance. On the other hand, all methods performed worse for proteins that could not have been used for development. A few results stood out: First, all methods predicted proteins in eukaryotes better than those in bacteria. Second, methods worked less well for proteins with many transmembrane helices. Third, most methods correctly discriminated between soluble and transmembrane proteins. However, several older methods often mistook signal peptides for transmembrane helices. Some newer methods have overcome this shortcoming. In our hands, PolyPhobius and MEMSAT-SVM outperformed other methods.

Sulfur, most abundantly found in the environment as sulfate (SO42-), is an essential element in metabolites required by all living cells, including amino acids, co-factors and vitamins. However, current understanding of the cellular delivery of SO42- at the molecular level is limited. CysZ has been described as a SO42- permease, but its sequence family is without known structural precedent. Based on crystallographic structure information, SO42- binding and flux experiments, we provide insight into the molecular mechanism of CysZ-mediated translocation of SO42- across membranes. CysZ structures from three different bacterial species display a hitherto unknown fold and have subunits organized with inverted transmembrane topology. CysZ from Pseudomonas denitrificans assembles as a trimer of antiparallel dimers and the CysZ structures from two other species recapitulate dimers from this assembly. Mutational studies highlight the functional relevance of conserved CysZ residues.

Motivation Many applications monitor predictions of a whole range of features for biological datasets, e.g. the fraction of secreted human proteins in the human proteome. Results and error estimates are typically derived from publications. Results Here, we present a simple, alternative approximation that uses performance estimates of methods to error-correct the predicted distributions. This approximation uses the confusion matrix (TP true positives, TN true negatives, FP false positives and FN false negatives) describing the performance of the prediction tool for correction. As proof-of-principle, the correction was applied to a two-class (membrane/not) and to a seven-class (localization) prediction. Availability and implementation Datasets and a simple JavaScript tool available freely for all users at http://www.rostlab.org/services/distributions. Supplementary information Supplementary data are available at Bioinformatics online.

Author(s): Yachdav, Guy; Wilzbach, Sebastian; Rauscher, Benedikt; Sheridan, Robert; Sillitoe, Ian; Procter, James; Lewis, Suzanna E; Rost, Burkhard; Goldberg, Tatyana | Abstract: The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is 'web ready': written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components.Availability and implementationThe MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/Supplementary information: Supplementary data are available at Bioinformatics online.Contactmsa@bio.sh.

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.

The attachment of a sugar to a hydrophobic polyisoprenyl carrier is the first step for all extracellular glycosylation processes. The enzymes that perform these reactions, polyisoprenyl-glycosyltransferases (PI-GTs) include dolichol phosphate mannose synthase (DPMS), which generates the mannose donor for glycosylation in the endoplasmic reticulum. Here we report the 3.0Å resolution crystal structure of GtrB, a glucose-specific PI-GT from Synechocystis, showing a tetramer in which each protomer contributes two helices to a membrane-spanning bundle. The active site is 15 Å from the membrane, raising the question of how water-soluble and membrane-embedded substrates are brought into apposition for catalysis. A conserved juxtamembrane domain harbours disease mutations, which compromised activity in GtrB in vitro and in human DPM1 tested in zebrafish. We hypothesize a role of this domain in shielding the polyisoprenyl-phosphate for transport to the active site. Our results reveal the basis of PI-GT function, and provide a potential molecular explanation for DPM1-related disease., Polyisoprenyl-glycosyltransferases (PI-GTs) catalyse the addition of sugar to lipid carriers, which is the first step in the production of sugar donors for glycosylation. Here Ardiccioni et al. present the structure of a bacterial PI-GT and propose a mechanistic basis for sugar transfer.

Motivation: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled. Results: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data. Availability: Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2. Contact: localization@rostlab.org Supplementary Information: Supplementary data are available at Bioinformatics online.

The ribosome consists of small and large subunits each composed of dozens of proteins and RNA molecules. However, the functions of many of the individual protomers within the ribosome are still unknown. In this article, we describe the solution NMR structure of the ribosomal protein RP-L35Ae from the archaeon Pyrococcus furiosus. RP-L35Ae is buried within the large subunit of the ribosome and belongs to Pfam protein domain family PF01247, which is highly conserved in eukaryotes, present in a few archaeal genomes, but absent in bacteria. The protein adopts a six-stranded anti-parallel β-barrel analogous to the “tRNA binding motif” fold. The structure of the P. furiosus RP-L35Ae presented in this article constitutes the first structural representative from this protein domain family. Proteins 2012. © 2012 Wiley Periodicals, Inc.

Saccharides have a central role in the nutrition of all living organisms. Whereas several saccharide uptake systems are shared between the different phylogenetic kingdoms, the phosphoenolpyruvate-dependent phosphotransferase system exists almost exclusively in bacteria. This multi-component system includes an integral membrane protein EIIC that transports saccharides and assists in their phosphorylation. Here we present the crystal structure of an EIIC from Bacillus cereus that transports diacetylchitobiose. The EIIC is a homodimer, with an expansive interface formed between the amino-terminal halves of the two protomers. The carboxy-terminal half of each protomer has a large binding pocket that contains a diacetylchitobiose, which is occluded from both sides of the membrane with its site of phosphorylation near the conserved His250 and Glu334 residues. The structure shows the architecture of this important class of transporters, identifies the determinants of substrate binding and phosphorylation, and provides a framework for understanding the mechanism of sugar translocation.

The TrkH/TrkG/KtrB proteins mediate K+ uptake in bacteria and probably evolved from simple K+ channels by multiple gene duplications or fusions. Here we present the crystal structure of a TrkH from Vibrio parahaemolyticus. TrkH is a homodimer, and each protomer contains an ion permeation pathway. A selectivity filter, similar in architecture to those of K+ channels but significantly shorter, is lined by backbone and side-chain oxygen atoms. Functional studies showed that TrkH is selective for permeation of K+ and Rb+ over smaller ions such as Na+ or Li+. Immediately intracellular to the selectivity filter are an intramembrane loop and an arginine residue, both highly conserved, which constrict the permeation pathway. Substituting the arginine with an alanine significantly increases the rate of K+ flux. These results reveal the molecular basis of K+ selectivity and suggest a novel gating mechanism for this large and important family of membrane transport proteins. All living cells accumulate potassium ions, and a high intracellular potassium concentration is essential for many physiological processes. In bacteria, yeast and plants, potassium ion uptake is achieved by a superfamily of transporters, the SKT proteins. These are thought to have evolved from simple potassium channels, but the mechanism of potassium ion selectivity and transport is unknown. The crystal structure of a bacterial SKT protein, the TrkH potassium transporter from Vibrio parahaemolyticus, has now been determined. Its selectivity filter is similar to that of potassium ion channels, but it is shorter. Biochemical studies suggest that K+ selectivity depends on a novel gating mechanism that excludes smaller ions such as Na+ and Li+ in favour of the larger K+ and Rb+. Here, the X-ray crystal structure of TrkH is solved, a protein that mediates potassium uptake in Vibrio parahaemolyticus, a bacterium. The selectivity filter of the ion transporter is very similar to what is seen in (much simpler) potassium ion channels, but it is significantly shorter. Biochemical studies were performed to explore the molecular basis of K+ selectivity, and it is believed that a novel gating mechanism is involved.

Oxygenic photosynthesis is initiated by photosystem II (PSII) in the thylakoid membranes of plants, algae and cyanobacteria. PSII is a multi-subunit pigment-protein complex responsible for splitting water into oxygen gas, hydrogen ions and electrons transferred to electron acceptors during photosynthesis.1 Two homologous membrane-spanning proteins D1 (PsbA) and D2 (PsbD) form the PSII complex core.1 Peripherally, two chlorophyll (Chl)-binding inner antenna proteins CP47 (PsbB) and CP43 (PsbC) are bound to the D1-D2 PSII complex core.1 These four large proteins are surrounded by a large number of smaller membrane proteins.2 Most of these small proteins have been observed in the crystal structures of the PSII complex from cyanobacteria.3,4 However, one small protein, Psb28, previously detected as a nonstoichiometric component of PSII,5 was not observed in the crystal structures indicating that Psb28 might not be a true PSII subunit. Recent studies revealed that Psb28 was preferentially bound to PSII core complex lacking CP43 (RC47) and involved in the biogenesis of CP47.6 Understanding the association of Psb28 with the PSII core complex should provide additional insight into its role in PSII-mediated function. However, the structure of Psb28 has remained unknown up until now. In this note, we report the solution NMR structure of Psb28 protein encoded by gene sll1398 [gi|952386] of Synechocystis sp. strain PCC 6803 (SWISS-PROT ID: PSB28_SYNY3, NESG target ID: SgR171).7 This protein, also named Psb13 or ycf79, belongs to the Psb28 protein family (Pfam ID: PF03912), which is currently made up of ~48 protein sequences (E score less than 0.001 using PSI-BLAST, Table S1). Both PSI-BLAST sequence similarity and Dali8 structure similarity searches indicate that this is the first atomic resolution structure available for the Psb28 family. ConSurf9 was used to identify conserved surface residues potentially involved in binding to the PSII core complex.10

The plant SLAC1 anion channel controls turgor pressure in the aperture-defining guard cells of plant stomata, thereby regulating the exchange of water vapour and photosynthetic gases in response to environmental signals such as drought or high levels of carbon dioxide. Here we determine the crystal structure of a bacterial homologue (Haemophilus influenzae) of SLAC1 at 1.20 Å resolution, and use structure-inspired mutagenesis to analyse the conductance properties of SLAC1 channels. SLAC1 is a symmetrical trimer composed from quasi-symmetrical subunits, each having ten transmembrane helices arranged from helical hairpin pairs to form a central five-helix transmembrane pore that is gated by an extremely conserved phenylalanine residue. Conformational features indicate a mechanism for control of gating by kinase activation, and electrostatic features of the pore coupled with electrophysiological characteristics indicate that selectivity among different anions is largely a function of the energetic cost of ion dehydration.

The New York Consortium on Membrane Protein Structure (NYCOMPS) was formed to accelerate the acquisition of structural information on membrane proteins by applying a structural genomics approach. NY-COMPS comprises a bioinformatics group, a centralized facility operating a high-throughput cloning and screening pipeline, a set of associated wet labs that perform high-level protein production and structure determination by x-ray crystallography and NMR, and a set of investigators focused on methods development. In the first three years of operation, the NYCOMPS pipeline has so far produced and screened 7,250 expression constructs for 8,045 target proteins. Approximately 600 of these verified targets were scaled up to levels required for structural studies, so far yielding 24 membrane protein crystals. Here we describe the overall structure of NYCOMPS and provide details on the high-throughput pipeline.

Lin0431 protein from Listeria innocua (UniProtKB/TrEMBL ID {"type":"entrez-protein","attrs":{"text":"Q92EM7","term_id":"81527521","term_text":"Q92EM7"}}Q92EM7/Q92EM7_LISIN) was selected as a target of the Northeast Structural Genomics Consortium (target ID: LkR112). Here, we present the high-quality NMR solution structure of this protein which is the first representative for a member of DUF1312 domain family. Lin0431 protein exhibits a β-sandwich topology. Four anti-parallel β-strands form one face of the sandwich and the other three anti-parallel β-strands together with a short α-helix form the other face of the sandwich. Structure alignment by Dali reveals an unexpected structural similarity with domain II of NusG from Aquifex aeolicus. Analyses of the electrostatic protein surface potential and searches for protein surface cavities reveal the conserved basic charged surface cavities of both the Lin0431 and domain II of AaeNusG, suggesting they may bind the negatively charged nucleic acids and/or and other binding partners. The high structural similarity and similar surface features, despite the lack of recognizable sequence similarity, between Lin0431 and AaeNusG domain II suggest that the domain II of NusG and DUF1312 domains have a homologous relationship and may share similar biochemical functions.

Alkyltransferase-like proteins (ATLs) are a novel class of DNA repair proteins related to O(6)-alkylguanine-DNA alkyltransferases (AGTs) that tightly bind alkylated DNA and shunt the damaged DNA into the nucleotide excision repair pathway. Here, we present the first structure of a bacterial ATL, from Vibrio parahaemolyticus (vpAtl). We demonstrate that vpAtl adopts an AGT-like fold and that the protein is capable of tightly binding to O(6)-methylguanine-containing DNA and disrupting its repair by human AGT, a hallmark of ATLs. Mutation of highly conserved residues Tyr(23) and Arg(37) demonstrate their critical roles in a conserved mechanism of ATL binding to alkylated DNA. NMR relaxation data reveal a role for conformational plasticity in the guanine-lesion recognition cavity. Our results provide further evidence for the conserved role of ATLs in this primordial mechanism of DNA repair.

Motivation: The mutation of amino acids often impacts protein function and structure. Mutations without negative effect sustain evolutionary pressure. We study a particular aspect of structural robustness with respect to mutations: regular protein secondary structure and natively unstructured (intrinsically disordered) regions. Is the formation of regular secondary structure an intrinsic feature of amino acid sequences, or is it a feature that is lost upon mutation and is maintained by evolution against the odds? Similarly, is disorder an intrinsic sequence feature or is it difficult to maintain? To tackle these questions, we in silico mutated native protein sequences into random sequence-like ensembles and monitored the change in predicted secondary structure and disorder. Results: We established that by our coarse-grained measures for change, predictions and observations were similar, suggesting that our results were not biased by prediction mistakes. Changes in secondary structure and disorder predictions were linearly proportional to the change in sequence. Surprisingly, neither the content nor the length distribution for the predicted secondary structure changed substantially. Regions with long disorder behaved differently in that significantly fewer such regions were predicted after a few mutation steps. Our findings suggest that the formation of regular secondary structure is an intrinsic feature of random amino acid sequences, while the formation of long-disordered regions is not an intrinsic feature of proteins with disordered regions. Put differently, helices and strands appear to be maintained easily by evolution, whereas maintaining disordered regions appears difficult. Neutral mutations with respect to disorder are therefore very unlikely. Contact: schaefer@rostlab.org Supplementary Information: Supplementary data are available at Bioinformatics online.

Protein with 83 residues, VPA0419 (residues 17–99, numbered 1–83) (gi|81726230, SwissProt/TrEMBL ID Q87J34_VIBPA, accession number {"type":"entrez-protein","attrs":{"text":"Q87J34","term_id":"81726230","term_text":"Q87J34"}}Q87J34)1 from Vibrio parahaemolyticus and 99-residue protein yiiS (gi|81722782, SwissProt/TrEMBL ID Q83IT9_SHIFL, accession number {"type":"entrez-protein","attrs":{"text":"Q83IT9","term_id":"81722782","term_text":"Q83IT9"}}Q83IT9)2, 3 from Shigella flexneri were selected as targets for the Protein Structure Initiative-2 and assigned to the Northeast Structural Genomics Consortium (NESG) for structure determination (NESG target ID VpR68 for VPA0419 and SfR90 for yiiS). VPA0419 and yiiS share 36% sequence identity, but show no significant sequence identity with any protein with known three-dimensional structure in the Protein Data Bank (PDB4). The two proteins belong to Pfam5 domain family PF04175 which currently contains 123 members with unknown three-dimensional structures and functional annotation, all of which appear to be found in gamma proteobacteria (for a sequence alignment, see Fig. S1 in Supporting Information). The NMR structures of VPA0419 and yiiS were solved using a protocol for high-throughput protein structure determination6 and represent the first ones for protein family PF04175. As these structures are the first for PF04175, “high leverage”7 of the experimental structures can be expected for calculating homology models.8, 9

The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space. Electronic supplementary material The online version of this article (doi:10.1007/s10969-009-9071-1) contains supplementary material, which is available to authorized users.

The melanocortin 4 receptor (MC4R) is a G-protein-coupled receptor (GPCR) and a key molecule in the regulation of energy homeostasis. At least 159 substitutions in the coding region of human MC4R (hMC4R) have been described experimentally; over 80 of those occur naturally, and many have been implicated in obesity. However, assessment of the presumably functionally essential residues remains incomplete. Here we have performed a complete in silico mutagenesis analysis to assess the functional essentiality of all possible nonnative point mutants in the entire hMC4R protein (332 residues). We applied SNAP, which is a method for quantifying functional consequences of single amino acid (AA) substitutions, to calculate the effects of all possible substitutions at each position in the hMC4R AA sequence. We compiled a mutability score that reflects the degree to which a particular residue is likely to be functionally important. We performed the same experiment for a paralogue human melanocortin receptor (hMC1R) and a mouse orthologue (mMC4R) in order to compare computational evaluations of highly related sequences. Three results are most salient: 1) our predictions largely agree with the available experimental annotations; 2) this analysis identified several AAs that are likely to be functionally critical, but have not yet been studied experimentally; and 3) the differential analysis of the receptors implicates a number of residues as specifically important to MC4Rs vs. other GPCRs, such as hMC1R.—Bromberg, Y., Overton, J., Vaisse, C., Leibel, R. L., Rost, B. In silico mutagenesis: a case study of the melanocortin 4 receptor.

The more proteins diverged in sequence, the more difficult it becomes for bioinformatics to infer similarities of protein function and structure from sequence. The precise thresholds used in automated genome annotations depend on the particular aspect of protein function transferred by homology. Here, we presented the first large-scale analysis of the relation between sequence similarity and identity in subcellular localization. Three results stood out: (1) The subcellular compartment is generally more conserved than what might have been expected given that short sequence motifs like nuclear localization signals can alter the native compartment; (2) the sequence conservation of localization is similar between different compartments; and (3) it is similar to the conservation of structure and enzymatic activity. In particular, we found the transition between the regions of conserved and nonconserved localization to be very sharp, although the thresholds for conservation were less well defined than for structure and enzymatic activity. We found that a simple measure for sequence similarity accounting for pairwise sequence identity and alignment length, the HSSP distance, distinguished accurately between protein pairs of identical and different localizations. In fact, BLAST expectation values outperformed the HSSP distance only for alignments in the subtwilight zone. We succeeded in slightly improving the accuracy of inferring localization through homology by fine tuning the thresholds. Finally, we applied our results to the entire SWISS-PROT database and five entirely sequenced eukaryotes.

The strategy for evaluating template-based models submitted to CASP has continuously evolved from CASP1 to CASP5, leading to a standard procedure that has been used in all subsequent editions. The established approach includes methods for calculating the quality of each individual model, for assigning scores based on the distribution of the results for each target and for computing the statistical significance of the differences in scores between prediction methods. These data are made available to the assessor of the template-based modeling category, who uses them as a starting point for further evaluations and analyses. This article describes the detailed workflow of the procedure, provides justifications for a number of choices that are customarily made for CASP data evaluation, and reports the results of the analysis of template-based predictions at CASP8. Proteins 2009. © 2009 Wiley-Liss, Inc.

For cell regulation, E2-like ubiquitin-fold modifier conjugating enzyme 1 (Ufc1) is involved in the transfer of ubiquitin-fold modifier 1 (Ufm1), a ubiquitin like protein which is activated by E1-like enzyme Uba5, to various target proteins. Thereby, Ufc1 participates in the very recently discovered Ufm1-Uba5-Ufc1 ubiquination pathway which is found in metazoan organisms. The structure of human Ufc1 was solved by using both NMR spectroscopy and X-ray crystallography. The complementary insights obtained with the two techniques provided a unique basis for understanding the function of Ufc1 at atomic resolution. The Ufc1 structure consists of the catalytic core domain conserved in all E2-like enzymes and an additional N-terminal helix. The active site Cys(116), which forms a thio-ester bond with Ufm1, is located in a flexible loop that is highly solvent accessible. Based on the Ufc1 and Ufm1 NMR structures, a model could be derived for the Ufc1-Ufm1 complex in which the C-terminal Gly(83) of Ufm1 may well form the expected thio-ester with Cys(116), suggesting that Ufm1-Ufc1 functions as described for other E1-E2-E3 machineries. alpha-helix 1 of Ufc1 adopts different conformations in the crystal and in solution, suggesting that this helix plays a key role to mediate specificity.

Motivation: Microarray expression data reveal functionally associated proteins. However, most proteins that are associated are not actually in direct physical contact. Predicting physical interactions directly from microarrays is both a challenging and important task that we addressed by developing a novel machine learning method optimized for this task. Results: We validated our support vector machine-based method on several independent datasets. At the same levels of accuracy, our method recovered more experimentally observed physical interactions than a conventional correlation-based approach. Pairs predicted by our method to very likely interact were close in the overall network of interaction, suggesting our method as an aid for functional annotation. We applied the method to predict interactions in yeast (Saccharomyces cerevisiae). A Gene Ontology function annotation analysis and literature search revealed several probable and novel predictions worthy of future experimental validation. We therefore hope our new method will improve the annotation of interactions as one component of multi-source integrated systems. Contact: ts2186@columbia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

We report on several proteins recently solved by structural genomics consortia, in particular by the Northeast Structural Genomics consortium (NESG). The proteins considered in this study differ substantially in their sequences but they share a similar structural core, characterized by a pseudobarrel five-stranded beta sheet. This core corresponds to the PUA domain-like architecture in the SCOP database. By connecting sequence information with structural knowledge, we characterize a new subgroup of these proteins that we propose to be distinctly different from previously described PUA domain-like domains such as PUA proper or ASCH. We refer to these newly defined domains as EVE. Although EVE may have retained the ability of PUA domains to bind RNA, the available experimental and computational data suggests that both the details of its molecular function and its cellular function differ from those of other PUA domain-like domains. This study of EVE and its relatives illustrates how the combination of structure and genomics creates new insights by connecting a cornucopia of structures that map to the same evolutionary potential. Primary sequence information alone would have not been sufficient to reveal these evolutionary links.

Many important characteristics of proteins such as biochemical activity and subcellular localization present a challenge to machine-learning methods: it is often difficult to encode the appropriate input features at the residue level for the purpose of making a prediction for the entire protein. The problem is usually that the biophysics of the connection between a machine-learning method's input (sequence feature) and its output (observed phenomenon to be predicted) remains unknown; in other words, we may only know that a certain protein is an enzyme (output) without knowing which region may contain the active site residues (input). The goal then becomes to dissect a protein into a vast set of sequence-derived features and to correlate those features with the desired output. We introduce a framework that begins with a set of global sequence features and then vastly expands the feature space by generically encoding the coexistence of residue-based features. It is this combination of individual features, that is the step from the fractions of serine and buried (input space 20 + 2) to the fraction of buried serine (input space 20 ☆ 2) that implicitly shifts the search space from global feature inputs to features that can capture very local evidence such as a the individual residues of a catalytic triad. The vast feature space created is explored by a genetic algorithm (GA) paired with neural networks and support vector machines. We find that the GA is critical for selecting combinations of features that are neither too general resulting in poor performance, nor too specific, leading to overtraining. The final framework manages to effectively sample a feature space that is far too large for exhaustive enumeration. We demonstrate the power of the concept by applying it to prediction of protein enzymatic activity. Proteins 2009. © 2008 Wiley-Liss, Inc.

Motivation: Mutating residues into alanine (alanine scanning) is one of the fastest experimental means of probing hypotheses about protein function. Alanine scans can reveal functional hot spots, i.e. residues that alter function upon mutation. In vitro mutagenesis is cumbersome and costly: probing all residues in a protein is typically as impossible as substituting by all non-native amino acids. In contrast, such exhaustive mutagenesis is feasible in silico. Results: Previously, we developed SNAP to predict functional changes due to non-synonymous single nucleotide polymorphisms. Here, we applied SNAP to all experimental mutations in the ASEdb database of alanine scans; we identified 70% of the hot spots (≥1 kCal/mol change in binding energy); more severe changes were predicted more accurately. Encouraged, we carried out a complete all-against-all in silico mutagenesis for human glucokinase. Many of the residues predicted as functionally important have indeed been confirmed in the literature, others await experimental verification, and our method is ready to aid in the design of in vitro mutagenesis. Availability: ASEdb and glucokinase scores are available at http://www.rostlab.org/services/SNAP. For submissions of large/whole proteins for processing please contact the author. Contact: yb2009@columbia.edu

Solution NMR structure of the SOS response protein YnzC from Bacillus subtilis James M. Aramini,* Seema Sharma, Yuanpeng J. Huang, G. V. T. Swapna, Chi Kent Ho, Karishma Shetty, Kellie Cunningham, Li-Chung Ma, Li Zhao, Leah A. Owens, Mei Jiang, Rong Xiao, Jinfeng Liu, Michael C. Baran, Thomas B. Acton, Burkhard Rost, and Gaetano T. Montelione* 1Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, Rutgers

NMR structure of the peptidyl-tRNA hydrolase domain from Pseudomonas syringae expands the structural coverage of the hydrolysis domains of class 1 peptide chain release factors Kiran Kumar Singarapu, Rong Xiao, Thomas Acton, Burkhard Rost, Gaetano T. Montelione, and Thomas Szyperski* 1Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000 2Northeast Structural Genomics Consortium 3Department of Molecular Biology and Biochemistry, Center of Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey 08854-5638 4Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032

Motivation: Natively unstructured (also dubbed intrinsically disordered) regions in proteins lack a defined 3D structure under physiological conditions and often adopt regular structures under particular conditions. Proteins with such regions are overly abundant in eukaryotes, they may increase functional complexity of organisms and they usually evade structure determination in the unbound form. Low propensity for the formation of internal residue contacts has been previously used to predict natively unstructured regions.Results: We combined PROFcon predictions for protein-specific contacts with a generic pairwise potential to predict unstructured regions. This novel method, Ucon, outperformed the best available methods in predicting proteins with long unstructured regions. Furthermore, Ucon correctly identified cases missed by other methods. By computing the difference between predictions based on specific contacts (approach introduced here) and those based on generic potentials (realized in other methods), we might identify unstructured regions that are involved in protein–protein binding. We discussed one example to illustrate this ambitious aim. Overall, Ucon added quality and an orthogonal aspect that may help in the experimental study of unstructured regions in network hubs.Availability: http://www.predictprotein.org/submit_ucon.htmlContact: as2067@columbia.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available. Results: It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these characteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein–DNA complexes available today, we found that 89% of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information. Availability: http://cubic.bioc.columbia.edu/services/disis Contact: yo135@columbia.edu

Solution NMR structure of Escherichia coli ytfP expands the structural coverage of the UPF0131 protein domain family James M. Aramini,* Yuanpeng J. Huang, G.V.T. Swapna, John R. Cort, P.K. Rajan, Rong Xiao, Ritu Shastry, Thomas B. Acton, Jinfeng Liu, Burkhard Rost, Michael A. Kennedy, and Gaetano T. Montelione* 1 Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, New Jersey 08854 2Northeast Structural Genomics Consortium, Rutgers University, Piscataway, New Jersey 08854 3 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 4Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032 5Department of Biochemistry, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey 08854