Probabilistic ensembles for improved inference in protein-structure determination

The New York Consortium on Membrane Protein Structure (NYCOMPS) was formed to accelerate the acquisition of structural information on membrane proteins by applying a structural genomics approach. NYCOMPS comprises a bioinformatics group, a centralized facility operating a high-throughput cloning and screening pipeline, a set of associated wet labs that perform high-level protein production and structure determination by x-ray crystallography and NMR, and a set of investigators focused on methods development. In the first three years of operation, the NYCOMPS pipeline has so far produced and screened 7,250 expression constructs for 8,045 target proteins. Approximately 600 of these verified targets were scaled up to levels required for structural studies, so far yielding 24 membrane protein crystals. Here we describe the overall structure of NYCOMPS and provide details on the high-throughput pipeline.

Protein engineering, the process by which novel proteins with desired properties are developed, has grown by leaps and bounds since the first examples of protein mutagenesis were described over three decades ago. In the last 30 years, protein engineers have successfully created a wide range of proteins tailored to specific industrial, medical, and research applications. Yet challenges remain that prevent the engineering of complex protein functions on demand. With the aim of creating a forum for the protein engineering community, the inaugural Protein Engineering Canada Conference was held on June 20–22, 2014, in Ottawa, Canada. Over the course of the 2-day conference, 115 protein scientists from over 30 institutions representing 5 countries shared ideas, networked with colleagues, and learned about exciting new research covering a broad array of topics such as enzyme engineering, computational protein design, X-ray crystallography, protein NMR, molecular modeling, and protein evolution. This special issue highlights the breadth of research that was covered during the Protein Engineering Canada Conference by including previously unpublished research articles submitted by speakers as well as some selections that were submitted directly to Protein Science.

In applied Bayesian inference scenarios, users may have access to a large number of pre-existing model evaluations, for example from maximum-a-posteriori (MAP) optimization runs. However, traditional approximate inference techniques make little to no use of this available information. We propose the framework of post-process Bayesian inference as a means to obtain a quick posterior approximation from existing target density evaluations, with no further model calls. Within this framework, we introduce Variational Sparse Bayesian Quadrature (vsbq), a method for post-process approximate inference for models with black-box and potentially noisy likelihoods. vsbq reuses existing target density evaluations to build a sparse Gaussian process (GP) surrogate model of the log posterior density function. Subsequently, we leverage sparse-GP Bayesian quadrature combined with variational inference to achieve fast approximate posterior inference over the surrogate. We validate our method on challenging synthetic scenarios and real-world applications from computational neuroscience. The experiments show that vsbq builds high-quality posterior approximations by post-processing existing optimization traces, with no further model evaluations.

The “21st century of biology” is built on advances in protein structure determination developed over the last five decades. Exponential growth in the number of determined protein structures has largely been driven by new technologies in protein X-ray crystallography, including synchrotron X-ray sources, X-ray detectors, computational tools, and methods of handling protein crystals. In consequence, the time to acquire a routine, complete crystallographic dataset has gone from years to minutes. However, many important macromolecular structures are still undetermined and many challenges remain, especially in cases where crystals are either hard to obtain, particularly sensitive to X-ray damage, or exhibit transient states that are difficult to capture by structural analysis. The latest tool in the crystallographic arsenal, the X-ray free electron laser (XFEL) (1), promises to help surmount these challenges (2), provided efficient ways are found to handle crystals in the demanding XFEL environment. In PNAS, Cohen et al. (3) show that crystal handling technology developed primarily for use at synchrotron storage ring X-ray sources can also be effectively adapted for use at XFELs.

Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. The proposed method, called Generalized Expectation Consistency (GEC), can be applied to both maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimation. Here we characterize its fixed points, convergence, and performance relative to the replica prediction of optimality.

Chapter 13 - Bayesian Learning: Approximate Inference and Nonparametric Models

The determination of a novel protein structure by X-ray diffraction is seldom straightforward. Three hurdles present themselves (i) the protein must be purified in sufficient quantity to allow crystallization trials, (ii) crystals must be grown to adequate size and must diffract to a resolution that will allow atomic detail to be revealed, and (iii) phases must be determined for the diffracted X-ray beams in order that an initial electron-density map may be calculated.

Proteins are essential parts of our life and participate in virtually every process within a cell. The understanding of protein structures is vital to determine the function of a protein. Protein structure prediction (PSP) from amino acid sequence is one of the high focus problems in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its three dimensional structure. Thus, protein structure prediction is a fundamental area of computational biology. Its importance is intensed by large amounts of sequence data coming from PDB (Protein Data Bank) and the fact that experimentally methods such as X-ray crystallography or Nuclear Magnetic Resonance (NMR)which are used to determining protein structures remains very expensive and time consuming. For minimizing the time, computational methods are used for protein folding and structure prediction problem. In this paper results of protein p53 are discussed.

Owing to a large and significant genome data outcome from sequencing projects in the last three decades from many organisms with 100 million of nucleotide sequence has been deposited in major DNA sequence databases. Numerous millions of nucleotide bases have been translated into amino acid sequences and deposited in UniprotKB and UniprotTrEMBL databases. However, the biological functions of protein are inherently dependent on its three-dimensional structure, the deposited and novel protein sequences are insufficient to determine the most appropriate functions of them. In contrast, the X-ray crystallography and NMR techniques thrive to be the most accurate structural characterization of biomolecules. Due to the technical complications and labor intensiveness of these methods, various protein structures unraveled by experimental methods (NMR and X-ray) lag far behind the 274accumulation of protein sequences. Technical advancement in the last few decades, with major efforts in protein structure determination, the concept of protein structure determination and homology modeling has speeded up in protein three-dimensional structural modeling. The significant goal of homology modeling technique is to predict a structure from its sequence by comparing to its most optimum aligned protein structural template. This technique allows users to speedily generate in silico protein models in all the contexts, where experimental methods like X-ray and NMR have failed to date.

This chapter discusses protein structure determinations, which involve experimental and computational methods, and combined applications of both. For many years, X-ray crystallography and fibre diffraction were the only methods for determining the positions of individual atoms in macromolecular structures. A companion appeared in the 1980s, when K. Wüthrich, R.R. Ernst, and their coworkers developed methods for solving protein structures by nuclear magnetic resonance (NMR) spectroscopy. With a third technique, cryo-electron microscopy (cryo-EM), it has been possible to determine structures of large aggregates, including viral capsids, large protein complexes, and intact ribosomes. Cryo-EM of large multiprotein complexes plus X-ray crystal structures of individual components has proved a powerful combination. The chapter also considers protein structure prediction and modelling.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Proteins, considered fundamental building blocks of life, play an important role across various scientific domains, including novel protein design, protein structure prediction, and new drug discovery. Each of these fields necessitates the detailed analysis of a large number of protein structures. Traditionally, the determination of protein structures through biological and physical methods has been prohibitively expensive. Consequently, computational methods for predicting protein structures have gained significant traction. Such methods often generate multiple structural approximations, or decoys, which are then evaluated using protein quality assessment (QA) techniques to identify the most promising candidates for further research. This evaluation process, also known as estimation of model accuracy (EMA), is crucial for advancing our understanding of protein. The task of protein structure QA can be viewed as mapping a protein's structure to its evaluative score, a process increasingly addressed through deep learning technologies. These technologies, recognized for their powerful pattern learning capabilities, are at the forefront of tackling significant research challenges within this domain. This dissertation describes five major contributions to the field. Firstly, DeepRank2, a multimodel method, utilizes a 1D multi-layer perceptron to predict the GDT-TS score of tertiary protein structures. Secondly, the performance of DeepRank2 and its variants was assessed in the CASP14 category for model accuracy estimation. Thirdly, DISTEMA employs an attentive 2D convolutional neural network (CNN) that relies solely on distance-based features to predict the GDT-TS score of tertiary structures. Fourthly, DProQA introduces an innovative gated-graph transformer approach for evaluating protein quaternary structures. Lastly, this work includes a comprehensive survey on deep learning-based methods for quaternary protein structure QA, covering relevant datasets, evaluation metrics, protein representations, and advancements in deep learning techniques over the past five years. Both DISTEMA and DProQA have been released as open-source software and are available to the scientific community.

Databases are the computerized platform where information is stored and can be retrieved easily by public users. Biological databases are the repositories of biological data. These biological data libraries contain facts and figures related to various disciplines of research including genomics, proteomics, microarray technology, metabolomics and phylogenetics. By using biological databases, a broad collection of essential biological information can be exploited ranging from function, structure and localization of gene, clinical consequences of mutation to similarity index among biological sequences and structures. Nowadays, different kinds of biological databases are available on the web. The present write up focuses on biological databases and bioinformatics tools for protein structure analysis. This review also aims to elaborate the searching schemes, available in different structural databases. The wide variety of different levels and types of information content related to 3D protein structures are available on web-based databases. Regarding the biological functions and 3D structures of various proteins, these databases provide a huge range of useful links, schematic diagrams as well as strategies for detailed analysis of proteins and other macromolecules structures. 3D structural illustration of proteins stored in structural databases is determined and visualized by X-ray crystallography, electron microscopy and NMR spectroscopy. On regular basis, a large number of protein structures are submitted by structural biologists, updated and curated by subject experts. Most familiar biological databases that store 3D protein and other macromolecules structures include, PDB, 3D Genomics, CATH, &amp; SCOP. These databases contain valuable information of overall protein structures, domains and motif structures, protein-protein complex systems and complex of protein with other biomolecules.

Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm

This chapter provides a perspective on 3 important collaborative areas in systems biology research. These areas represent biological problems of clinical significance. The first area deals with macromolecular crystallization, which is a crucial step in protein structure determination. The second area deals with proteomic biomarker discovery from high-throughput mass spectral technologies; while the third area is protein structure prediction and complex fold recognition from sequence and prior knowledge of structure properties. For each area, successful case studies are revisited from the perspective of computer- aided knowledge discovery using machine learning and statistical methods. Information about protein sequence, structure, and function is slowly accumulating in standardized forms within databases. Methods are needed to maximize the use of this prior information for prediction and analysis purposes. This chapter provides insights into such methods by which available information in existing databases can be processed and combined with systems biology expertise to expedite biomedical discoveries.