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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.

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.

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.

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.

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.

Machine-learning techniques can classify functionally related proteins where homology-transfer as well as sequence and structure motifs fail. Here, we present a method that aimed at complementing homology-transfer in the identification of cell cycle control kinases from sequence alone. First, we identified functionally significant residues in cell cycle proteins through their high sequence conservation and biophysical properties. We then incorporated these residues and their features into support vector machines (SVM) to identify new kinases and more specifically to differentiate cell cycle kinases from other kinases and other proteins. As expected, the most informative residues tend to be highly conserved and tend to localize in the ATP binding regions of the kinases. Another observation confirmed that ATP binding regions are typically not found on the surface but in partially buried sites, and that this fact is correctly captured by accessibility predictions. Using these highly conserved, semi-buried residues and their biophysical properties, we could distinguish cell cycle S/T kinases from other kinase families at levels around 70–80% accuracy and 62–81% coverage. An application to the entire human proteome predicted at least 97 human proteins with limited previous annotations to be candidates for cell cycle kinases. Proteins 2009. © 2008 Wiley-Liss, Inc.

We developed a method CHOP dissecting proteins into domain-like fragments. The basic idea was to cut proteins beginning from very reliable experimental information (PDB), pro- ceeding to expert annotations of domain-like re- gions (Pfam-A), and completing through cuts based on termini of known proteins. In this way, CHOP dissected more than two thirds of all proteins from 62 proteomes. Analysis of our structural domain-like fragments revealed four surprising results. First, >70% of all dissected proteins con- tained more than one fragment. Second, most domains spanned on average over 100 residues. This average was similar for eukaryotic and pro- karyotic proteins, and it is also valid—although previously not described—for all proteins in the PDB. Third, single-domain proteins were signifi- cant longer than most domains in multidomain proteins. Fourth, three fourths of all domains appeared shorter than 210 residues. We believe that our CHOP fragments constituted an impor- tant resource for functional and structural genom- ics. Nevertheless, our main motivation to develop CHOP was that the single-linkage clustering method failed to adequately group full-length pro- teins. In contrast, CLUP—the simple clustering scheme CLUP introduced here—succeeded largely to group the CHOP fragments from 62 proteomes such that all members of one cluster shared a basic structural core. CLUP found >63,000 multi- and >118,000 single-member clusters. Although most fragments were restricted to a particular cluster, 24% of the fragments were duplicated in at least two clusters. Our thresholds for grouping two fragments into the same cluster were rather conservative. Nevertheless, our results suggested that structural genomics initiatives have to target >30,000 fragments to at least cover the multi- member clusters in 62 proteomes. Proteins 2004

The native sub-cellular compartment of a protein is one aspect of its function. Thus, predicting localization is an important step toward predicting function. Short zip code-like sequence fragments regulate some of the shuttling between compartments. Cataloguing and predicting such motifs is the most accurate means of determining localization in silico. However, only few motifs are currently known, and not all the trafficking appears regulated in this way. The amino acid composition of a protein correlates with its localization. All general prediction methods employed this observation. Here, we explored the evolutionary information contained in multiple alignments and aspects of protein structure to predict localization in absence of homology and targeting motifs. Our final system combined statistical rules and a variety of neural networks to achieve an overall four-state accuracy above 65%, a significant improvement over systems using only composition. The system was at its best for extra-cellular and nuclear proteins; it was significantly less accurate than TargetP for mitochondrial proteins. Interestingly, all methods that were developed on SWISS-PROT sequences failed grossly when fed with sequences from proteins of known structures taken from PDB. We therefore developed two separate systems: one for proteins of known structure and one for proteins of unknown structure. Finally, we applied the PDB-based system along with homology-based inferences and automatic text analysis to annotate all eukaryotic proteins in the PDB (http://cubic.bioc.columbia.edu/db/LOC3D). We imagine that this pilot method-certainly in combination with similar tools-may be valuable target selection in structural genomics.

EVA is a web-based server that evaluates automatic structure prediction servers continuously and objectively. Since June 2000, EVA collected more than 20,000 secondary structure predictions. The EVA sets sufficed to conclude that the field of secondary structure prediction has advanced again. Accuracy increased substantially in the 1990s through using evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than 4% to its current height around 76% of all residues predicted correctly in one of the three states: helix, strand, or other. The best current methods solved most of the problems raised at earlier CASP meetings: All good methods now get segments right and perform well on strands. Is the recent increase in accuracy significant enough to make predictions even more useful? We believe the answer is affirmative. What is the limit of prediction accuracy? We shall see. All data are available through the EVA web site at [cubic.bioc.columbia.edu/eva/]. The raw data for the results presented are available at [eva]/sec/bup_common/2001_02_22/.

The results of the second Critical Assessment of Fully Automated Structure Prediction (CAFASP2) are presented. The goals of CAFASP are to (i) assess the performance of fully automatic web servers for structure prediction, by using the same blind prediction targets as those used at CASP4, (ii) inform the community of users about the capabilities of the servers, (iii) allow human groups participating in CASP to use and analyze the results of the servers while preparing their nonautomated predictions for CASP, and (iv) compare the performance of the automated servers to that of the human-expert groups of CASP. More than 30 servers from around the world participated in CAFASP2, covering all categories of structure prediction. The category with the largest participation was fold recognition, where 24 CAFASP servers filed predictions along with 103 other CASP human groups. The CAFASP evaluation indicated that it is difficult to establish an exact ranking of the servers because the number of prediction targets was relatively small and the differences among many servers were also small. However, roughly a group of five "best" fold recognition servers could be identified. The CASP evaluation identified the same group of top servers albeit with a slightly different relative order. Both evaluations ranked a semiautomated method named CAFASP-CONSENSUS, that filed predictions using the CAFASP results of the servers, above any of the individual servers. Although the predictions of the CAFASP servers were available to human CASP predictors before the CASP submission deadline, the CASP assessment identified only 11 human groups that performed better than the best server. Furthermore, about one fourth of the top 30 performing groups corresponded to automated servers. At least half of the top 11 groups corresponded to human groups that also had a server in CAFASP or to human groups that used the CAFASP results to prepare their predictions. In particular, the CAFASP-CONSENSUS group was ranked 7. This shows that the automated predictions of the servers can be very helpful to human predictors. We conclude that as servers continue to improve, they will become increasingly important in any prediction process, especially when dealing with genome-scale prediction tasks. We expect that in the near future, the performance difference between humans and machines will continue to narrow and that fully automated structure prediction will become an effective companion and complement to experimental structural genomics.

Using information from sequence alignments significantly improves protein secondary structure prediction. Typically, more divergent profiles yield better predictions. Recently, various groups have shown that accuracy can be improved significantly by using PSI-BLAST profiles to develop new prediction methods. Here, we focused on the influences of various alignment strategies on two 8-year-old PHD methods. The following results stood out. (i) PHD using pairwise alignments predicts about 72% of all residues correctly in one of the three states: helix, strand, and other. Using larger databases and PSI-BLAST raised accuracy to 75%. (ii) More than 60% of the improvement originated from the growth of current sequence databases; about 20% resulted from detailed changes in the alignment procedure (substitution matrix, thresholds, and gap penalties). Another 20% of the improvement resulted from carefully using iterated PSI-BLAST searches. (iii) It is of interest that we failed to improve prediction accuracy further when attempting to refine the alignment by dynamic programming (MaxHom and ClustalW). (iv) Improvement through family growth appears to saturate at some point. However, most families have not reached this saturation. Hence, we anticipate that prediction accuracy will continue to rise with database growth.

Accuracy of predicting protein secondary structure and solvent accessibility from sequence information has been improved significantly by using information contained in multiple sequence alignments as input to a neural 'network system. For the Asilomar meeting, predictions for 13 proteins were generated automatically using the publicly available prediction method PHD. The results confirm the estimate of 72% three-state prediction accuracy. The fairly accurate predictions of secondary structure segments made the tool useful as a starting point for modeling of higher dimensional aspects of protein structure. © 1995 Wiley-Liss, Inc.

Using evolutionary information contained in multiple sequence alignments as input to neural networks, secondary structure can be predicted at significantly increased accuracy. Here, we extend our previous three-level system of neural networks by using additional input information derived from multiple alignments. Using a position-specific conservation weight as part of the input increases performance. Using the number of insertions and deletions reduces the tendency for overprediction and increases overall accuracy. Addition of the global amino acid content yields a further improvement, mainly in predicting structural class. The final network system has sustained overall accuracy of 71.6% in a multiple cross-validation test on 126 unique protein chains. A test on a new set of 124 recently solved protein structures that have no significant sequence similarity to the learning set confirms the high level of accuracy. The average cross-validated accuracy for all 250 sequence-unique chains is above 72%. Using various data sets, the method is compared to alternative prediction methods, some of which also use multiple alignments: the performance advantage of the network system is at least 6 percentage points in three-state accuracy. In addition, the network estimates secondary structure content from multiple sequence alignments about as well as circular dichroism spectroscopy on a single protein and classifies 75% of the 250 proteins correctly into one of four protein structural classes. Of particular practical importance is the definition of a position-specific reliability index. For 40% of all residues the method has a sustained three-state accuracy of 88%, as high as the overall average for homology modelling. A further strength of the method is greatly increased accuracy in predicting the placement of secondary structure segments.