A simplified approach to disulfide connectivity prediction from protein sequences

<p>Abstract</p> <p>Background</p> <p>Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on different machine learning algorithms have been applied to solve this problem and public domain prediction services exist. These methods are however still potentially subject to significant improvements both in terms of prediction accuracy and overall architectural complexity.</p> <p>Results</p> <p>We introduce new methods for predicting disulfide bridges from protein sequences. The methods take advantage of two new decomposition kernels for measuring the similarity between protein sequences according to the amino acid environments around cysteines. Disulfide connectivity is predicted in two passes. First, a binary classifier is trained to predict whether a given protein chain has at least one intra-chain disulfide bridge. Second, a multiclass classifier (plemented by 1-nearest neighbor) is trained to predict connectivity patterns. The two passes can be easily cascaded to obtain connectivity prediction from sequence alone. We report an extensive experimental comparison on several data sets that have been previously employed in the literature to assess the accuracy of cysteine bonding state and disulfide connectivity predictors.</p> <p>Conclusion</p> <p>We reach state-of-the-art results on bonding state prediction with a simple method that classifies chains rather than individual residues. The prediction accuracy reached by our connectivity prediction method compares favorably with respect to all but the most complex other approaches. On the other hand, our method does not need any model selection or hyperparameter tuning, a property that makes it less prone to overfitting and prediction accuracy overestimation.</p

Prediction of Oxidation States of Cysteines and Disulphide Connectivity

Knowledge on cysteine oxidation state and disulfide bond connectivity is of great importance to protein chemistry and 3-D structures. This research is aimed at finding the most relevant features in prediction of cysteines oxidation states and the disulfide bonds connectivity of proteins. Models predicting the oxidation states of cysteines are developed with machine learning techniques such as Support Vector Machines (SVMs) and Associative Neural Networks (ASNNs). A record high prediction accuracy of oxidation state, 95%, is achieved by incorporating the oxidation states of N-terminus cysteines, flanking sequences of cysteines and global information on the protein chain (number of cysteines, length of the chain and amino acids composition of the chain etc.) into the SVM encoding. This is 5% higher than the current methods. This indicates to us that the oxidation states of amino terminal cysteines infer the oxidation states of other cysteines in the same protein chain. Satisfactory prediction results are also obtained with the newer and more inclusive SPX dataset, especially for chains with higher number of cysteines. Compared to literature methods, our approach is a one-step prediction system, which is easier to implement and use. A side by side comparison of SVM and ASNN is conducted. Results indicated that SVM outperform ASNN on this particular problem. For the prediction of correct pairings of cysteines to form disulfide bonds, we first study disulfide connectivity by calculating the local interaction potentials between the flanking sequences of the cysteine pairs. The obtained interaction potential is further adjusted by the coefficients related to the binding motif of enzymes during disulfide formation and also by the linear distance between the cysteine pairs. Finally, maximized weight matching algorithm is applied and performance of the interaction potentials evaluated. Overall prediction accuracy is unsatisfactory compared with the literature. SVM is used to predict the disulfide connectivity with the assumption that oxidation states of cysteines on the protein are known. Information on binding region during disulfide formation, distance between cysteine pairs, global information of the protein chain and the flanking sequences around the cysteine pairs are included in the SVM encoding. Prediction results illustrate the advantage of using possible anchor region information

Improving Structural Features Prediction in Protein Structure Modeling

Proteins play a vital role in the biological activities of all living species. In nature, a protein folds into a specific and energetically favorable three-dimensional structure which is critical to its biological function. Hence, there has been a great effort by researchers in both experimentally determining and computationally predicting the structures of proteins. The current experimental methods of protein structure determination are complicated, time-consuming, and expensive. On the other hand, the sequencing of proteins is fast, simple, and relatively less expensive. Thus, the gap between the number of known sequences and the determined structures is growing, and is expected to keep expanding. In contrast, computational approaches that can generate three-dimensional protein models with high resolution are attractive, due to their broad economic and scientific impacts. Accurately predicting protein structural features, such as secondary structures, disulfide bonds, and solvent accessibility is a critical intermediate step stone to obtain correct three-dimensional models ultimately. In this dissertation, we report a set of approaches for improving the accuracy of structural features prediction in protein structure modeling. First of all, we derive a statistical model to generate context-based scores characterizing the favorability of segments of residues in adopting certain structural features. Then, together with other information such as evolutionary and sequence information, we incorporate the context-based scores in machine learning approaches to predict secondary structures, disulfide bonds, and solvent accessibility. Furthermore, we take advantage of the emerging high performance computing architectures in GPU to accelerate the calculation of pairwise and high-order interactions in context-based scores. Finally, we make these prediction methods available to the public via web services and software packages

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLAS

Analysis on conservation of disulphide bonds and their structural features in homologous protein domain families

International audienceBackground: Disulphide bridges are well known to play key roles in stability, folding and functions of proteins. Introduction or deletion of disulphides by site-directed mutagenesis have produced varying effects on stability and folding depending upon the protein and location of disulphide in the 3-D structure. Given the lack of complete understanding it is worthwhile to learn from an analysis of extent of conservation of disulphides in homologous proteins. We have also addressed the question of what structural interactions replaces a disulphide in a homologue in another homologue.Results: Using a dataset involving 34,752 pairwise comparisons of homologous protein domains corresponding to 300 protein domain families of known 3-D structures, we provide a comprehensive analysis of extent of conservation of disulphide bridges and their structural features. We report that only 54% of all the disulphide bonds compared between the homologous pairs are conserved, even if, a small fraction of the non-conserved disulphides do include cytoplasmic proteins. Also, only about one fourth of the distinct disulphides are conserved in all the members in protein families. We note that while conservation of disulphide is common in many families, disulphide bond mutations are quite prevalent. Interestingly, we note that there is no clear relationship between sequence identity between two homologous proteins and disulphide bond conservation. Our analysis on structural features at the sites where cysteines forming disulphide in one homologue are replaced by non-Cys residues show that the elimination of a disulphide in a homologue need not always result in stabilizing interactions between equivalent residues.Conclusion: We observe that in the homologous proteins, disulphide bonds are conserved only to a modest extent. Very interestingly, we note that extent of conservation of disulphide in homologous proteins is unrelated to the overall sequence identity between homologues. The non-conserved disulphides are often associated with variable structural features that were recruited to be associated with differentiation or specialisation of protein function

Multidimensional Feature Engineering for Post-Translational Modification Prediction Problems

Protein sequence data has been produced at an astounding speed. This creates an opportunity to characterize these proteins for the treatment of illness. A crucial characterization of proteins is their post translational modifications (PTM). There are 20 amino acids coded by DNA after coding (translation) nearly every protein is modified at an amino acid level. We focus on three specific PTMs. First is the bonding formed between two cysteine amino acids, thus introducing a loop to the straight chain of a protein. Second, we predict which cysteines can generally be modified (oxidized). Finally, we predict which lysine amino acids are modified by the active form of Vitamin B6 (PLP/pyridoxal-5-phosphate.) Our work aims to predict the PTM\u27s from protein sequencing data. When available, we integrate other data sources to improve prediction. Data mining finds patterns in data and uses these patterns to give a confidence score to unknown PTMs. There are many steps to data mining; however, our focus is on the feature engineering step i.e. the transforming of raw data into an intelligible form for a prediction algorithm. Our primary innovation is as follows: First, we created the Local Similarity Matrix (LSM), a description of the evolutionarily relatedness of a cysteine and its neighboring amino acids. This feature is taken two at a time and template matched to other cysteine pairs. If they are similar, then we give a high probability of it sharing the same bonding state. LSM is a three step algorithm, 1) a matrix of amino acid probabilities is created for each cysteine and its neighbors from an alignment. 2) We multiply the iv square of the BLOSUM62 matrix diagonal to each of the corresponding amino acids. 3) We z-score normalize the matrix by row. Next, we innovated the Residue Adjacency Matrix (RAM) for sequential and 3-D space (integration of protein coordinate data). This matrix describes cysteine\u27s neighbors but at much greater distances than most algorithms. It is particularly effective at finding conserved residues that are further away while still remaining a compact description. More data than necessary incurs the curse of dimensionality. RAM runs in O(n) time, making it very useful for large datasets. Finally, we produced the Windowed Alignment Scoring algorithm (WAS). This is a vector of protein window alignment bit scores. The alignments are one to all. Then we apply dimensionality reduction for gains in speed and performance. WAS uses the BLAST algorithm to align sequences within a window surrounding potential PTMs, in this case PLP attached to Lysine. In the case of WAS, we tried many alignment algorithms and used the approximation that BLAST provides to reduce computational time from months to days. The performances of different alignment algorithms did not vary significantly. The applications of this work are many. It has been shown that cysteine bonding configurations play a critical role in the folding of proteins. Solving the protein folding problem will help us to find the solution to Alzheimer\u27s disease that is due to a misfolding of the amyloid-beta protein. Cysteine oxidation has been shown to play a role in oxidative stress, a situation when free radicals become too abundant in the body. Oxidative stress leads to chronic illness such as diabetes, cancer, heart disease and Parkinson\u27s. Lysine in concert with PLP catalyzes the aminotransferase reaction. Research suggests that anti-cancer drugs will potentially selectively inhibit this reaction. Others have targeted this reaction for the treatment of epilepsy and addictions

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

Introduction to protein folding for physicists

The prediction of the three-dimensional native structure of proteins from the knowledge of their amino acid sequence, known as the protein folding problem, is one of the most important yet unsolved issues of modern science. Since the conformational behaviour of flexible molecules is nothing more than a complex physical problem, increasingly more physicists are moving into the study of protein systems, bringing with them powerful mathematical and computational tools, as well as the sharp intuition and deep images inherent to the physics discipline. This work attempts to facilitate the first steps of such a transition. In order to achieve this goal, we provide an exhaustive account of the reasons underlying the protein folding problem enormous relevance and summarize the present-day status of the methods aimed to solving it. We also provide an introduction to the particular structure of these biological heteropolymers, and we physically define the problem stating the assumptions behind this (commonly implicit) definition. Finally, we review the 'special flavor' of statistical mechanics that is typically used to study the astronomically large phase spaces of macromolecules. Throughout the whole work, much material that is found scattered in the literature has been put together here to improve comprehension and to serve as a handy reference.Comment: 53 pages, 18 figures, the figures are at a low resolution due to arXiv restrictions, for high-res figures, go to http://www.pabloechenique.co

Machine learning for predicting lifespan-extending chemical compounds

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age‐related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans’ lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound’s chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans’ lifespan in the DGIdb database, where the effect of the compounds on an organism’s lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti‐inflammatories, and compounds for gonadotropin‐ releasing hormone therapies

Patterns in the sequence context of protein disulfide bonds

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Biology, February 2002.Includes bibliographical references (leaves 60-62).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Disulfide bonds play an important role in the structural stability of the proteins that contain them. Yet, little is known about the specificity with which they are formed. To address this, a representative set of disulfide bonds from nonhomologous eukaryotic polypeptides was created. The amino acid sequences flanking these disulfide bonds were searched for conserved patterns that may reflect recognition sites by the disulfide bond forming enzyme protein disulfide isomerase (PDI). Several methods of classifying disulfide bonds were explored, and each class was analyzed for conserved sequence patterns. To maximize the chances of finding a conserved recognition site, a simulated annealing algorithm was implemented to divide a set of disulfide-bonded cysteines into two sets of cysteines with an average sequence environment that is as far from randomly-distributed as possible. No significant conserved patterns were found in the set of disulfide bonds or within any of the classification schemes introduced. Additionally, several methods for predicting disulfide bond connectivity were explored. The most successful methods predicted connectivity based on the sequential distance between cysteines.by Aron Charles Eklund.S.M