Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction

Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class

Optimizing Data Selection for Contact Prediction in Proteins

Proteins are essential to life across all organisms. They act as enzymes, antibodies, transporters of molecules, structural elements, among other important roles. Their ability to interact with specific molecules in a selective manner, is what makes them important. Being able to understand their interaction can provide many advantages in fields such as drug design and metabolic engineering. Current methods of predicting protein interaction attempt to geometrically fit the structures of two proteins together by generating a large amount of potential configurations and then discriminating the correct pose from the remaining ones. Given the large search space, approaches to reduce the complexity are often employed. Identifying a contact point between the pairing proteins is a good constraining factor. If at least one contact can be predicted among a small set of possibilities (e.g. 100), the search space will be significantly reduced. Using structural and evolutionary information of the interacting proteins, a machine learning predictor can be developed for this task. Such evolutionary measures are computed over a substantial amount of homologous sequences, which can be filtered and ordered in many different ways. As a result, a machine learning solution was developed that focused in measuring the effects that differing homolog arrangements can have over the final prediction

Structure-based prediction of protein-protein interaction sites

Protein-protein interactions play a central role in the formation of protein complexes and the biological pathways that orchestrate virtually all cellular processes. Reliable identification of the specific amino acid residues that form the interface of a protein with one or more other proteins is critical to understanding the structural and physico-chemical basis of protein interactions and their role in key cellular processes, predicting protein complexes, validating protein interactions predicted by high throughput methods, and identifying and prioritizing drug targets in computational drug design. Because of the difficulty and the high cost of experimental characterization of interface residues, there is an urgent need for computational methods for reliable predicting protein-protein interface residues from the sequence, and when available, the structure of a query protein, and when known, its putative interacting partner. Against this background, this thesis develops improved methods for predicting protein-protein interface residues and protein-protein interfaces from the three dimensional structure of an unbound query protein without considering information of its binding protein partner. Towards this end, we develop (i) ProtInDb (http://protindb.cs.iastate.edu), a database of protein-protein interface residues to facilitate (a) the generation of datasets of protein-protein interface residues that can be used to perform analysis of interaction sites and to train and evaluate predictors of interface residues, and (b) the visualization of interaction sites between proteins in both the amino acid sequences and the 3D protein structures, among other applications; (ii) PoInterS (http://pointers.cs.iastate.edu/), a method for predicting protein-protein interaction sites formed by spatially contiguous clusters of interface residues based on the predictions generated by a protein interface residue predictor. PoInterS divides a protein surface into a series of patches composed of several surface residues, and uses the outputs of the interface residue predictors to rank and select a small set of patches that are the most likely to constitute the interaction sites; and (iii) PrISE (http://prise.cs.iastate.edu/), a method for predicting protein-protein interface residues based on the similarity of the structural element formed by the query residue and its neighboring residues and the structural elements extracted from the interface and non-interface regions of proteins that are members of experimentally determined protein complexes. A structural element captures the atomic composition and solvent accessibility of a central residue and its closest neighbors in the protein structure. PrISE decomposes a query protein into a set of structural elements and searches for similar elements in a large set of proteins that belong to one or more experimentally determined complexes. The structural elements that are most similar to each structural element extracted from the query protein are then used to infer whether its central residue is or is not an interface residue. The results of our experiments using a variety of benchmark datasets show that PoInterS and PrISE generally outperform the state-of-the-art structure-based methods for predicting interaction patches and interface residues, respectively

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

BACKGROUND: Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. RESULTS: The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions. CONCLUSION: Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general

Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that is otherwise obscured. The objective of this dissertation is to develop machine learning based effective tools to predict disordered protein, its properties and dynamics, and interaction paradigm by systematically mining and analyzing large-scale biological data. In this dissertation, we propose a robust framework to predict disordered proteins given only sequence information, using an optimized SVM with RBF kernel. Through appropriate reasoning, we highlight the structure-like behavior of IDPs in disease-associated complexes. Further, we develop a fast and effective predictor of Accessible Surface Area (ASA) of protein residues, a useful structural property that defines protein’s exposure to partners, using regularized regression with 3rd-degree polynomial kernel function and genetic algorithm. As a key outcome of this research, we then introduce a novel method to extract position specific energy (PSEE) of protein residues by modeling the pairwise thermodynamic interactions and hydrophobic effect. PSEE is found to be an effective feature in identifying the enthalpy-gain of the folded state of a protein and otherwise the neutral state of the unstructured proteins. Moreover, we study the peptide-protein transient interactions that involve the induced folding of short peptides through disorder-to-order conformational changes to bind to an appropriate partner. A suite of predictors is developed to identify the residue-patterns of Peptide-Recognition Domains from protein sequence that can recognize and bind to the peptide-motifs and phospho-peptides with post-translational-modifications (PTMs) of amino acid, responsible for critical human diseases, using the stacked generalization ensemble technique. The involved biologically relevant case-studies demonstrate possibilities of discovering new knowledge using the developed tools

PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method

Gram-negative bacteria use various secretion systems to deliver their secreted effectors. Among them, type IV secretion system exists widely in a variety of bacterial species, and secretes type IV secreted effectors (T4SEs), which play vital roles in host-pathogen interactions. However, experimental approaches to identify T4SEs are time- and resource-consuming. In the present study, we aim to develop an in silico stacked ensemble method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. The protein sequences were encoded by the feature of position specific scoring matrix (PSSM)-composition by summing rows that correspond to the same amino acid residues in PSSM profiles. Based on the PSSM-composition features, we develop a stacked ensemble model PredT4SE-Stack to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system. Our results demonstrated that the framework of PredT4SE-Stack was a feasible and effective way to accurately identify T4SEs based on protein sequence information. The datasets and source code of PredT4SE-Stack are freely available at http://xbioinfo.sjtu.edu.cn/PredT4SE_Stack/index.php

Predicting the Most Tractable Protein Surfaces in the Human Proteome for Developing New Therapeutics

A critical step in the target identification phase of drug discovery is evaluating druggability, i.e., whether a protein can be targeted with high affinity using drug-like ligands. The overarching goal of my PhD thesis is to build a machine learning model that predicts the binding affinity that can be attained when addressing a given protein surface. I begin by examining the lead optimization phase of drug development, where I find that in a test set of 297 examples, 41 of these (14%) change binding mode when a ligand is elaborated. My analysis shows that while certain ligand physiochemical properties predispose changes in binding mode, particularly those properties that define fragments, simple structure-based modeling proves far more effective for identifying substitutions that alter the binding mode. My proposed measure of RMAC (rmsd after minimization of the aligned complex) can help determine whether a given ligand can be reliably elaborated without changing binding mode, thus enabling straightforward interpretation of the resulting structure-activity relationships. Moving forward, I next noted that a very popular machine learning algorithm for regression tasks, random forest, has a systematic bias in the predictions it generates; this bias is present in both real-world datasets and synthetic datasets. To address this, I define a numerical transformation that can be applied to the output of random forest models. This transformation fully removes the bias in the resulting predictions, and yields improved predictions across all datasets. Finally, taking advantage of this improved machine learning approach, I describe a model that predicts the “attainable binding affinity” for a given binding pocket on a protein surface. This model uses 13 physiochemical and structural features calculated from the protein structure, without any information about the ligand. While details of the ligand must (of course) contribute somewhat to the binding affinity, I find that this model still recapitulates the binding affinity for 848 different protein-ligand complexes (across 230 different proteins) with correlation coefficient 0.57. I further find that this model is not limited to “traditional” drug targets, but rather that it works just as well for emerging “non-traditional” drug targets such as inhibitors of protein-protein interactions. Collectively, I anticipate that the tools and insights generated in the course of my PhD research will play an important role in facilitating the key target selection phase of drug discovery projects

Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.Comment: 46 pages, 10 figure