A hidden Markov model for predicting protein interfaces

Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.     

Reducing the false positive rate in the non-parametric analysis of molecular coevolution

Background  

The strength of selective constraints operating on amino acid sites of proteins has a multifactorial nature. In fact, amino acid sites within proteins coevolve due to their functional and/or structural relationships. Different methods have been developed that attempt to account for the evolutionary dependencies between amino acid sites. Researchers have invested a significant effort to increase the sensitivity of such methods. However, the difficulty in disentangling functional co-dependencies from historical covariation has fuelled the scepticism over their power to detect biologically meaningful results. In addition, the biological parameters connecting linear sequence evolution to structure evolution remain elusive. For these reasons, most of the evolutionary studies aimed at identifying functional dependencies among protein domains have focused on the structural properties of proteins rather than on the information extracted from linear multiple sequence alignments (MSA). Non-parametric methods to detect coevolution have been reported to be especially susceptible to produce false positive results based on the properties of MSAs. However, no formal statistical analysis has been performed to definitively test the differential effects of these properties on the sensitivity of such methods.     

Exploiting publicly available biological and biochemical information for the discovery of novel short linear motifs

The function of proteins is often mediated by short linear segments of their amino acid sequence, called Short Linear Motifs or SLiMs, the identification of which can provide important information about a protein function. However, the short length of the motifs and their variable degree of conservation makes their identification hard since it is difficult to correctly estimate the statistical significance of their occurrence. Consequently, only a small fraction of them have been discovered so far. We describe here an approach for the discovery of SLiMs based on their occurrence in evolutionarily unrelated proteins belonging to the same biological, signalling or metabolic pathway and give specific examples of its effectiveness in both rediscovering known motifs and in discovering novel ones. An automatic implementation of the procedure, available for download, allows significant motifs to be identified, automatically annotated with functional, evolutionary and structural information and organized in a database that can be inspected and queried. An instance of the database populated with pre-computed data on seven organisms is accessible through a publicly available server and we believe it constitutes by itself a useful resource for the life sciences (http://www.biocomputing.it/modipath).     

A novel way to express proline-selectively labeled proteins with a wheat germ cell-free protein synthesis system

For high-throughput protein structural analyses, it is essential to develop a reliable protein overexpression system. Although many protein overexpression systems, such as ones involving Escherichia coli cells, have been developed, the number of overexpressed proteins exhibiting the same biological activities as those of the native ones is limited. A novel wheat germ cell-free protein synthesis system was developed recently, and most of the synthesized proteins that should function in solution were found to be in soluble forms. This suggests the applicability of this protein synthesis method to determination of the functional structures of soluble proteins. In our previous work, we developed a selective labeling technique for amino acids having amide functional groups (other than proline residues) involving the use of several inhibitors for transaminases. This paper in turn describes a proline-selective labeling technique. Based on our results, we have succeeded in constructing a complete amino acid selective labeling technique for the wheat germ cell-free protein synthesis system.     

Searching for interpretable rules for disease mutations: a simulated annealing bump hunting strategy

Background  

Understanding how amino acid substitutions affect protein functions is critical for the study of proteins and their implications in diseases. Although methods have been developed for predicting potential effects of amino acid substitutions using sequence, three-dimensional structural, and evolutionary properties of proteins, the applications are limited by the complication of the features and the availability of protein structural information. Another limitation is that the prediction results are hard to be interpreted with physicochemical principles and biological knowledge.     

无序蛋白质的判定及其结构、功能和进化特征

固有无序蛋白质(intrinsically disordered proteins,IDPs)是天然条件下自身不能折叠为明确唯一的空间结构,却具有生物学功能的一类新发现的蛋白质.这类蛋白质的发现是对传统的"结构-功能"关系认识模式的挑战.本文首先总结了无序蛋白质的实验鉴定手段、预测方法、数据库;并介绍了无序蛋白质结构(包括一级结构、二级结构、结构域无序性及变构效应)和功能特征;然后重点总结了无序蛋白质在进化角度研究的进展,包括无序区域产生的进化机制、进化速率,蛋白无序性的进化在蛋白质功能进化及生物学复杂性增加等方面的重要作用;最后展望了无序蛋白质在医药方面的应用前景.本文对于深入认识无序蛋白质的形成机制、结构和功能特征及其潜在的临床应用前景具有重要意义.     

Functional diversity of cysteine residues in proteins and unique features of catalytic redox-active cysteines in thiol oxidoreductases

Thiol-dependent redox systems are involved in regulation of diverse biological processes, such as response to stress, signal transduction, and protein folding. The thiol-based redox control is provided by mechanistically similar, but structurally distinct families of enzymes known as thiol oxidoreductases. Many such enzymes have been characterized, but identities and functions of the entire sets of thiol oxidoreductases in organisms are not known. Extreme sequence and structural divergence makes identification of these proteins difficult. Thiol oxidoreductases contain a redox-active cysteine residue, or its functional analog selenocysteine, in their active sites. Here, we describe computational methods for in silico prediction of thiol oxidoreductases in nucleotide and protein sequence databases and identification of their redox-active cysteines. We discuss different functional categories of cysteine residues, describe methods for discrimination between catalytic and noncatalytic and between redox and non-redox cysteine residues and highlight unique properties of the redox-active cysteines based on evolutionary conservation, secondary and three-dimensional structures, and sporadic replacement of cysteines with catalytically superior selenocysteine residues.     

Distinguishing structural and functional restraints in evolution in order to identify interaction sites

Structural genomics projects are producing many three-dimensional structures of proteins that have been identified only from their gene sequences. It is therefore important to develop computational methods that will predict sites involved in productive intermolecular interactions that might give clues about functions. Techniques based on evolutionary conservation of amino acids have the advantage over physiochemical methods in that they are more general. However, the majority of techniques neither use all available structural and sequence information, nor are able to distinguish between evolutionary restraints that arise from the need to maintain structure and those that arise from function. Three methods to identify evolutionary restraints on protein sequence and structure are described here. The first identifies those residues that have a higher degree of conservation than expected: this is achieved by comparing for each amino acid position the sequence conservation observed in the homologous family of proteins with the degree of conservation predicted on the basis of amino acid type and local environment. The second uses information theory to identify those positions where environment-specific substitution tables make poor predictions of the overall amino acid substitution pattern. The third method identifies those residues that have highly conserved positions when three-dimensional structures of proteins in a homologous family are superposed. The scores derived from these methods are mapped onto the protein three-dimensional structures and contoured, allowing identification clusters of residues with strong evolutionary restraints that are sites of interaction in proteins involved in a variety of functions. Our method differs from other published techniques by making use of structural information to identify restraints that arise from the structure of the protein and differentiating these restraints from others that derive from intermolecular interactions that mediate functions in the whole organism.     

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.     

Molecular principles of the interactions of disordered proteins

Thorough knowledge of the molecular principles of protein-protein recognition is essential to our understanding of protein function at the cellular level. Whereas interactions of ordered proteins have been analyzed in great detail, complexes of intrinsically unstructured/disordered proteins (IUPs) have hardly been addressed so far. Here, we have collected a database of 39 complexes of experimentally verified IUPs, and compared their interfaces with those of 72 complexes of ordered, globular proteins. The characteristic differences found between the two types of complexes suggest that IUPs represent a distinct molecular implementation of the principles of protein-protein recognition. The interfaces do not differ in size, but those of IUPs cover a much larger part of the surface of the protein than for their ordered counterparts. Moreover, IUP interfaces are significantly more hydrophobic relative to their overall amino acid composition, but also in absolute terms. They rely more on hydrophobic-hydrophobic than on polar-polar interactions. Their amino acids in the interface realize more intermolecular contacts, which suggests a better fit with the partner due to induced folding upon binding that results in a better adaptation to the partner. The two modes of interaction also differ in that IUPs usually use only a single continuous segment for partner binding, whereas the binding sites of ordered proteins are more segmented. Probably, all these features contribute to the increased evolutionary conservation of IUP interface residues. These noted molecular differences are also manifested in the interaction energies of IUPs. Our approximation of these by low-resolution force-fields shows that IUPs gain much more stabilization energy from intermolecular contacts, than from folding, i.e. they use their binding energy for folding. Overall, our findings provide a structural rationale to the prior suggestions that many IUPs are specialized for functions realized by protein-protein interactions.     

Predicting Protein Function and Binding Profile via Matching of Local Evolutionary and Geometric Surface Patterns

Inferring protein functions from structures is a challenging task, as a large number of orphan protein structures from structural genomics project are now solved without their biochemical functions characterized. For proteins binding to similar substrates or ligands and carrying out similar functions, their binding surfaces are under similar physicochemical constraints, and hence the sets of allowed and forbidden residue substitutions are similar. However, it is difficult to isolate such selection pressure due to protein function from selection pressure due to protein folding, and evolutionary relationship reflected by global sequence and structure similarities between proteins is often unreliable for inferring protein function. We have developed a method, called pevoSOAR (pocket-based evolutionary search of amino acid residues), for predicting protein functions by solving the problem of uncovering amino acids residue substitution pattern due to protein function and separating it from amino acids substitution pattern due to protein folding. We incorporate evolutionary information specific to an individual binding region and match local surfaces on a large scale with millions of precomputed protein surfaces to identify those with similar functions. Our pevoSOAR method also generates a probablistic model called the computed binding a profile that characterizes protein-binding activities that may involve multiple substrates or ligands. We show that our method can be used to predict enzyme functions with accuracy. Our method can also assess enzyme binding specificity and promiscuity. In an objective large-scale test of 100 enzyme families with thousands of structures, our predictions are found to be sensitive and specific: At the stringent specificity level of 99.98%, we can correctly predict enzyme functions for 80.55% of the proteins. The overall area under the receiver operating characteristic curve measuring the performance of our prediction is 0.955, close to the perfect value of 1.00. The best Matthews coefficient is 86.6%. Our method also works well in predicting the biochemical functions of orphan proteins from structural genomics projects.     

PIER: protein interface recognition for structural proteomics

Recent advances in structural proteomics call for development of fast and reliable automatic methods for prediction of functional surfaces of proteins with known three-dimensional structure, including binding sites for known and unknown protein partners as well as oligomerization interfaces. Despite significant progress the problem is still far from being solved. Most existing methods rely, at least partially, on evolutionary information from multiple sequence alignments projected on protein surface. The common drawback of such methods is their limited applicability to the proteins with a sparse set of sequential homologs, as well as inability to detect interfaces in evolutionary variable regions. In this study, the authors developed an improved method for predicting interfaces from a single protein structure, which is based on local statistical properties of the protein surface derived at the level of atomic groups. The proposed Protein IntErface Recognition (PIER) method achieved the overall precision of 60% at the recall threshold of 50% at the residue level on a diverse benchmark of 490 homodimeric, 62 heterodimeric, and 196 transient interfaces (compared with 25% precision at 50% recall expected from random residue function assignment). For 70% of proteins in the benchmark, the binding patch residues were successfully detected with precision exceeding 50% at 50% recall. The calculation only took seconds for an average 300-residue protein. The authors demonstrated that adding the evolutionary conservation signal only marginally influenced the overall prediction performance on the benchmark; moreover, for certain classes of proteins, using this signal actually resulted in a deteriorated prediction. Thorough benchmarking using other datasets from literature showed that PIER yielded improved performance as compared with several alignment-free or alignment-dependent predictions. The accuracy, efficiency, and dependence on structure alone make PIER a suitable tool for automated high-throughput annotation of protein structures emerging from structural proteomics projects.     

Predicting nucleic acid binding interfaces from structural models of proteins

The function of DNA‐ and RNA‐binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure‐based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high‐resolution three‐dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I‐TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high‐resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I‐TASSER produces high‐quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low‐resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Deep learning methods for 3D structural proteome and interactome modeling

Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in tasks ranging from protein contact map prediction to protein folding, prediction of protein–protein interaction interfaces, and characterization of protein–drug binding pockets. In particular, emergence of ab initio protein structure prediction methods including AlphaFold2 has revolutionized protein structural modeling. From a protein function perspective, numerous deep learning methods have facilitated deconvolution of the exact amino acid residues and protein surface regions responsible for binding other proteins or small molecule drugs. In this review, we provide a comprehensive overview of recent deep learning methods applied in structural proteomics.     

A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of proteins

MOTIVATION: Various multiple sequence alignment-based methods have been proposed to detect functional surfaces in proteins, such as active sites or protein interfaces. The effect that the choice of sequences has on the conclusions of such analysis has seldom been discussed. In particular, no method has been discussed in terms of its ability to optimize the sequence selection for the reliable detection of functional surfaces. RESULTS: Here we propose, for the case of proteins with known structure, a heuristic Metropolis Monte Carlo strategy to select sequences from a large set of homologues, in order to improve detection of functional surfaces. The quantity guiding the optimization is the clustering of residues which are under increased evolutionary pressure, according to the sample of sequences under consideration. We show that we can either improve the overlap of our prediction with known functional surfaces in comparison with the sequence similarity criteria of selection or match the quality of prediction obtained through more elaborate non-structure based-methods of sequence selection. For the purpose of demonstration we use a set of 50 homodimerizing enzymes which were co-crystallized with their substrates and cofactors.     

A simple physical model for the prediction and design of protein-DNA interactions

Protein-DNA interactions are crucial for many biological processes. Attempts to model these interactions have generally taken the form of amino acid-base recognition codes or purely sequence-based profile methods, which depend on the availability of extensive sequence and structural information for specific structural families, neglect side-chain conformational variability, and lack generality beyond the structural family used to train the model. Here, we take advantage of recent advances in rotamer-based protein design and the large number of structurally characterized protein-DNA complexes to develop and parameterize a simple physical model for protein-DNA interactions. The model shows considerable promise for redesigning amino acids at protein-DNA interfaces, as design calculations recover the amino acid residue identities and conformations at these interfaces with accuracies comparable to sequence recovery in globular proteins. The model shows promise also for predicting DNA-binding specificity for fixed protein sequences: native DNA sequences are selected correctly from pools of competing DNA substrates; however, incorporation of backbone movement will likely be required to improve performance in homology modeling applications. Interestingly, optimization of zinc finger protein amino acid sequences for high-affinity binding to specific DNA sequences results in proteins with little or no predicted specificity, suggesting that naturally occurring DNA-binding proteins are optimized for specificity rather than affinity. When combined with algorithms that optimize specificity directly, the simple computational model developed here should be useful for the engineering of proteins with novel DNA-binding specificities.     

Euclidian space and grouping of biological objects

MOTIVATION: Biological objects tend to cluster into discrete groups. Objects within a group typically possess similar properties. It is important to have fast and efficient tools for grouping objects that result in biologically meaningful clusters. Protein sequences reflect biological diversity and offer an extraordinary variety of objects for polishing clustering strategies. Grouping of sequences should reflect their evolutionary history and their functional properties. Visualization of relationships between sequences is of no less importance. Tree-building methods are typically used for such visualization. An alternative concept to visualization is a multidimensional sequence space. In this space, proteins are defined as points and distances between the points reflect the relationships between the proteins. Such a space can also be a basis for model-based clustering strategies that typically produce results correlating better with biological properties of proteins. RESULTS: We developed an approach to classification of biological objects that combines evolutionary measures of their similarity with a model-based clustering procedure. We apply the methodology to amino acid sequences. On the first step, given a multiple sequence alignment, we estimate evolutionary distances between proteins measured in expected numbers of amino acid substitutions per site. These distances are additive and are suitable for evolutionary tree reconstruction. On the second step, we find the best fit approximation of the evolutionary distances by Euclidian distances and thus represent each protein by a point in a multidimensional space. The Euclidian space may be projected in two or three dimensions and the projections can be used to visualize relationships between proteins. On the third step, we find a non-parametric estimate of the probability density of the points and cluster the points that belong to the same local maximum of this density in a group. The number of groups is controlled by a sigma-parameter that determines the shape of the density estimate and the number of maxima in it. The grouping procedure outperforms commonly used methods such as UPGMA and single linkage clustering.