Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance. In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods. The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.

/pdf/prediction-of-protein-binding-sites-in-protein-structures-s25wpus3fe.pdf

Prediction of protein binding sites in protein structures using hidden Markov support vector machine.

Motivation: Disulfide bonds stabilize protein structures and play relevant roles in their functions. Their formation requires an oxidizing environment and their stability is consequently depending on the redox ambient potential, which may differ according to the subcellular compartment. Several methods are available to predict cysteine-bonding state and connectivity patterns. However, none of them takes into consideration the relevance of protein subcellular localization.

Results: Here we develop DISLOCATE, a two-step method based on machine learning models for predicting both the bonding state and the connectivity patterns of cysteine residues in a protein chain. We find that the inclusion of protein subcellular localization improves the performance of these predictive steps by 3 and 2 percentage points, respectively. When compared with previously developed methods for predicting disulfide bonds from sequence, DISLOCATE improves the overall performance by more than 10 percentage points.

Availability: The method and the dataset are available at the Web page http://www.biocomp.unibo.it/savojard/Dislocate.html. GRHCRF code is available at http://www.biocomp.unibo.it/savojard/biocrf.html.

Contact: piero.fariselli@unibo.it

Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization

The native structures of proteins and peptides are stabilized by a number of interactions that dictate directly or indirectly the folding, conformation, andflexibility of themolecule.Most of these interactions, such as hydrogen bonds andhydrophobic interactions, are noncovalent and relatively weak. Covalent interactions, on the other hand, are generally stronger, and are thought to exert a greater influence on stability and conformation, particularly in peptides, which tend to have fewer and weaker hydrophobic interactions because of their smaller size. The most common example of a covalent interaction is the disulfide bond, formedbetween the sulfur atomsof two cysteine residues [1]. Disulfide bonds are typically present in extracellular proteins and peptides, such as growth factors, hormones, enzymes, and toxins, and have also been found in several thermostable intracellular proteins of archaeal microbes [2]. They can play a role in covalently linking subunits in protein complexes (e.g., the heavy and light chains of antibodies or the two peptide chains of insulin) [3]. Structurally, the strong covalent links formed by disulfide bonds are thought to confer additional conformational stability on proteins such as keratin that have a high disulfide content [4]. Disulfides also play a catalytic role in enzymes such as thioredoxin, which acts as a cellular redox sensor via the oxidation status of its thiol groups [5–7]. Many disulfides in proteins appear to have no direct functional role, rather their main purpose is to maintain the conformation of the protein. In certain cases, conformational changes associated with the reduction and oxidation of these bonds may allow a protein to switch between different functions [8, 9]. The relationship between disulfide bonds and the conformation of peptides and proteins is thus an intriguing one. This chapter provides a broad overview of the roles of disulfide bonds in peptides, focusing on their roles in the folding and stability of proteins, as well as how different disulfide bonding patterns and connectivity give rise to different protein topologies and conformations, and ultimately a diverse range of protein functions. An understanding of these roles also has important applications in the field of protein engineering and drug design.

Role of Disulfide Bonds in Peptide and Protein Conformation

Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins. In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction. The 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%. Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named "Dinosolve" available at: http://hpcr.cs.odu.edu/dinosolve
 .

/pdf/dinosolve-a-protein-disulfide-bonding-prediction-server-1mkb7r865z.pdf

Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy

Background
Recently, information derived by correlated mutations in proteins has regained relevance for predicting protein contacts. This is due to new forms of mutual information analysis that have been proven to be more suitable to highlight direct coupling between pairs of residues in protein structures and to the large number of protein chains that are currently available for statistical validation. It was previously discussed that disulfide bond topology in proteins is also constrained by correlated mutations.

/pdf/prediction-of-disulfide-connectivity-in-proteins-with-127vbrfjm9.pdf

Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations

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. 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. 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.

/pdf/a-simplified-approach-to-disulfide-connectivity-prediction-1ndz9pr9yf.pdf

A simplified approach to disulfide connectivity prediction from protein sequences.

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A simplified approach to disulfide connectivity prediction from protein sequences.