ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space

Studying the function of proteins is important for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Still, the determination of the function of a protein structure remains a difficult, costly, and time consuming task. The difficulties are often due to the essential role of spatial and topological structures in the determination of protein functions in living cells. In this paper, we propose ProtNN, a novel approach for protein function prediction. Given an unannotated protein structure and a set of annotated proteins, ProtNN finds the nearest neighbor annotated structures based on protein-graph pairwise similarities. Given a query protein, ProtNN finds the nearest neighbor reference proteins based on a graph representation model and a pairwise similarity between vector embedding of both query and reference protein-graphs in structural and topological spaces. ProtNN assigns to the query protein the function with the highest number of votes across the set of k nearest neighbor reference proteins, where k is a user-defined parameter. Experimental evaluation demonstrates that ProtNN is able to accurately classify several datasets in an extremely fast runtime compared to state-of-the-art approaches. We further show that ProtNN is able to scale up to a whole PDB dataset in a single-process mode with no parallelization, with a gain of thousands order of magnitude of runtime compared to state-of-the-art approaches

Automated Protein Structure Classification: A Survey

Classification of proteins based on their structure provides a valuable resource for studying protein structure, function and evolutionary relationships. With the rapidly increasing number of known protein structures, manual and semi-automatic classification is becoming ever more difficult and prohibitively slow. Therefore, there is a growing need for automated, accurate and efficient classification methods to generate classification databases or increase the speed and accuracy of semi-automatic techniques. Recognizing this need, several automated classification methods have been developed. In this survey, we overview recent developments in this area. We classify different methods based on their characteristics and compare their methodology, accuracy and efficiency. We then present a few open problems and explain future directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront

Similarity-based virtual screening using 2D fingerprints

This paper summarises recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprint-based similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available

A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.Comment: 8 pages, 5 figures, 4 table

Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSIC

Secondary structure spatial conformation footprint: a novel method for fast protein structure comparison and classification

BACKGROUND: Recently a new class of methods for fast protein structure comparison has emerged. We call the methods in this class projection methods as they rely on a mapping of protein structure into a high-dimensional vector space. Once the mapping is done, the structure comparison is reduced to distance computation between corresponding vectors. As structural similarity is approximated by distance between projections, the success of any projection method depends on how well its mapping function is able to capture the salient features of protein structure. There is no agreement on what constitutes a good projection technique and the three currently known projection methods utilize very different approaches to the mapping construction, both in terms of what structural elements are included and how this information is integrated to produce a vector representation. RESULTS: In this paper we propose a novel projection method that uses secondary structure information to produce the mapping. First, a diverse set of spatial arrangements of triplets of secondary structure elements, a set of structural models, is automatically selected. Then, each protein structure is mapped into a high-dimensional vector of "counts" or footprint, where each count corresponds to the number of times a given structural model is observed in the structure, weighted by the precision with which the model is reproduced. We perform the first comprehensive evaluation of our method together with all other currently known projection methods. CONCLUSION: The results of our evaluation suggest that the type of structural information used by a projection method affects the ability of the method to detect structural similarity. In particular, our method that uses the spatial conformations of triplets of secondary structure elements outperforms other methods in most of the tests

A Protein Classification Benchmark collection for machine learning

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection () was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It is primarily meant for method developers and users interested in comparing methods under standardized conditions. The collection contains datasets of sequences and structures, and each set is subdivided into positive/negative, training/test sets in several ways. There is a total of 6405 classification tasks, 3297 on protein sequences, 3095 on protein structures and 10 on protein coding regions in DNA. Typical tasks include the classification of structural domains in the SCOP and CATH databases based on their sequences or structures, as well as various functional and taxonomic classification problems. In the case of hierarchical classification schemes, the classification tasks can be defined at various levels of the hierarchy (such as classes, folds, superfamilies, etc.). For each dataset there are distance matrices available that contain all vs. all comparison of the data, based on various sequence or structure comparison methods, as well as a set of classification performance measures computed with various classifier algorithms

Graph-based methods for large-scale protein classification and orthology inference

The quest for understanding how proteins evolve and function has been a prominent and costly human endeavor. With advances in genomics and use of bioinformatics tools, the diversity of proteins in present day genomes can now be studied more efficiently than ever before. This thesis describes computational methods suitable for large-scale protein classification of many proteomes of diverse species. Specifically, we focus on methods that combine unsupervised learning (clustering) techniques with the knowledge of molecular phylogenetics, particularly that of orthology. In chapter 1 we introduce the biological context of protein structure, function and evolution, review the state-of-the-art sequence-based protein classification methods, and then describe methods used to validate the predictions. Finally, we present the outline and objectives of this thesis. Evolutionary (phylogenetic) concepts are instrumental in studying subjects as diverse as the diversity of genomes, cellular networks, protein structures and functions, and functional genome annotation. In particular, the detection of orthologous proteins (genes) across genomes provides reliable means to infer biological functions and processes from one organism to another. Chapter 2 evaluates the available computational tools, such as algorithms and databases, used to infer orthologous relationships between genes from fully sequenced genomes. We discuss the main caveats of large-scale orthology detection in general as well as the merits and pitfalls of each method in particular. We argue that establishing true orthologous relationships requires a phylogenetic approach which combines both trees and graphs (networks), reliable species phylogeny, genomic data for more than two species, and an insight into the processes of molecular evolution. Also proposed is a set of guidelines to aid researchers in selecting the correct tool. Moreover, this review motivates further research in developing reliable and scalable methods for functional and phylogenetic classification of large protein collections. Chapter 3 proposes a framework in which various protein knowledge-bases are combined into unique network of mappings (links), and hence allows comparisons to be made between expert curated and fully-automated protein classifications from a single entry point. We developed an integrated annotation resource for protein orthology, ProGMap (Protein Group Mappings, http://www.bioinformatics.nl/progmap), to help researchers and database annotators who often need to assess the coherence of proposed annotations and/or group assignments, as well as users of high throughput methodologies (e.g., microarrays or proteomics) who deal with partially annotated genomic data. ProGMap is based on a non-redundant dataset of over 6.6 million protein sequences which is mapped to 240,000 protein group descriptions collected from UniProt, RefSeq, Ensembl, COG, KOG, OrthoMCL-DB, HomoloGene, TRIBES and PIRSF using a fast and fully automated sequence-based mapping approach. The ProGMap database is equipped with a web interface that enables queries to be made using synonymous sequence identifiers, gene symbols, protein functions, and amino acid or nucleotide sequences. It incorporates also services, namely BLAST similarity search and QuickMatch identity search, for finding sequences similar (or identical) to a query sequence, and tools for presenting the results in graphic form. Graphs (networks) have gained an increasing attention in contemporary biology because they have enabled complex biological systems and processes to be modeled and better understood. For example, protein similarity networks constructed of all-versus-all sequence comparisons are frequently used to delineate similarity groups, such as protein families or orthologous groups in comparative genomics studies. Chapter 4.1 presents a benchmark study of freely available graph software used for this purpose. Specifically, the computational complexity of the programs is investigated using both simulated and biological networks. We show that most available software is not suitable for large networks, such as those encountered in large-scale proteome analyzes, because of the high demands on computational resources. To address this, we developed a fast and memory-efficient graph software, netclust (http://www.bioinformatics.nl/netclust/), which can scale to large protein networks, such as those constructed of millions of proteins and sequence similarities, on a standard computer. An extended version of this program called Multi-netclust is presented in chapter 4.2. This tool that can find connected clusters of data presented by different network data sets. It uses user-defined threshold values to combine the data sets in such a way that clusters connected in all or in either of the networks can be retrieved efficiently. Automated protein sequence clustering is an important task in genome annotation projects and phylogenomic studies. During the past years, several protein clustering programs have been developed for delineating protein families or orthologous groups from large sequence collections. However, most of these programs have not been benchmarked systematically, in particular with respect to the trade-off between computational complexity and biological soundness. In chapter 5 we evaluate three best known algorithms on different protein similarity networks and validation (or 'gold' standard) data sets to find out which one can scale to hundreds of proteomes and still delineate high quality similarity groups at the minimum computational cost. For this, a reliable partition-based approach was used to assess the biological soundness of predicted groups using known protein functions, manually curated protein/domain families and orthologous groups available in expert-curated databases. Our benchmark results support the view that a simple and computationally cheap method such as netclust can perform similar to and in cases even better than more sophisticated, yet much more costly methods. Moreover, we introduce an efficient graph-based method that can delineate protein orthologs of hundreds of proteomes into hierarchical similarity groups de novo. The validity of this method is demonstrated on data obtained from 347 prokaryotic proteomes. The resulting hierarchical protein classification is not only in agreement with manually curated classifications but also provides an enriched framework in which the functional and evolutionary relationships between proteins can be studied at various levels of specificity. Finally, in chapter 6 we summarize the main findings and discuss the merits and shortcomings of the methods developed herein. We also propose directions for future research. The ever increasing flood of new sequence data makes it clear that we need improved tools to be able to handle and extract relevant (orthological) information from these protein data. This thesis summarizes these needs and how they can be addressed by the available tools, or be improved by the new tools that were developed in the course of this research. <br/