Institute of Structural and Molecular Biology

An efficient means for generating mutation data matrices from large numbers of protein sequences is presented here. By means of an approximate peptide-based sequence comparison algorithm, the set sequences are clustered at the 85% identity level. The closest relating pairs of sequences are aligned, and observed amino acid exchanges tallied in a matrix. The raw mutation frequency matrix is processed in a similar way to that described by Dayhoff et al. (1978), and so the resulting matrices may be easily used in current sequence analysis applications, in place of the standard mutation data matrices, which have not been updated for 13 years. The method is fast enough to process the entire SWISS-PROT databank in 20 h on a Sun SPARCstation 1, and is fast enough to generate a matrix from a specific family or class of proteins in minutes. Differences observed between our 250 PAM mutation data matrix and the matrix calculated by Dayhoff et al. are briefly discussed.

The LIGPLOT program automatically generates schematic 2-D representations of protein-ligand complexes from standard Protein Data Bank file input. The output is a colour, or black-and-white, PostScript file giving a simple and informative representation of the intermolecular interactions and their strengths, including hydrogen bonds, hydrophobic interactions and atom accessibilities. The program is completely general for any ligand and can also be used to show other types of interaction in proteins and nucleic acids. It was designed to facilitate the rapid inspection of many enzyme complexes, but has found many other applications.

This review examines protein complexes in the Brookhaven Protein Databank to gain a better understanding of the principles governing the interactions involved in protein-protein recognition. The factors that influence the formation of protein-protein complexes are explored in four different types of protein-protein complexes--homodimeric proteins, heterodimeric proteins, enzyme-inhibitor complexes, and antibody-protein complexes. The comparison between the complexes highlights differences that reflect their biological roles.

The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. Here, we report recent developments with InterPro (version 90.0) and its associated software, including updates to data content and to the website. These developments extend and enrich the information provided by InterPro, and provide a more user friendly access to the data. Additionally, we have worked on adding Pfam website features to the InterPro website, as the Pfam website will be retired in late 2022. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB. Moreover, we report the development of a card game as a method of engaging the non-scientific community. Finally, we discuss the benefits and challenges brought by the use of artificial intelligence for protein structure prediction.

The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. InterProScan is the underlying software that allows protein and nucleic acid sequences to be searched against InterPro's signatures. Signatures are predictive models which describe protein families, domains or sites, and are provided by multiple databases. InterPro combines signatures representing equivalent families, domains or sites, and provides additional information such as descriptions, literature references and Gene Ontology (GO) terms, to produce a comprehensive resource for protein classification. Founded in 1999, InterPro has become one of the most widely used resources for protein family annotation. Here, we report the status of InterPro (version 81.0) in its 20th year of operation, and its associated software, including updates to database content, the release of a new website and REST API, and performance improvements in InterProScan.

This article reviews the involvement of the mitochondrial permeability transition pore in necrotic and apoptotic cell death. The pore is formed from a complex of the voltage-dependent anion channel (VDAC), the adenine nucleotide translocase and cyclophilin-D (CyP-D) at contact sites between the mitochondrial outer and inner membranes. In vitro, under pseudopathological conditions of oxidative stress, relatively high Ca2+ and low ATP, the complex flickers into an open-pore state allowing free diffusion of low-Mr solutes across the inner membrane. These conditions correspond to those that unfold during tissue ischaemia and reperfusion, suggesting that pore opening may be an important factor in the pathogenesis of necrotic cell death following ischaemia/reperfusion. Evidence that the pore does open during ischaemia/reperfusion is discussed. There are also strong indications that the VDAC-adenine nucleotide translocase-CyP-D complex can recruit a number of other proteins, including Bax, and that the complex is utilized in some capacity during apoptosis. The apoptotic pathway is amplified by the release of apoptogenic proteins from the mitochondrial intermembrane space, including cytochrome c, apoptosis-inducing factor and some procaspases. Current evidence that the pore complex is involved in outer-membrane rupture and release of these proteins during programmed cell death is reviewed, along with indications that transient pore opening may provoke 'accidental' apoptosis.

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) and its precursor IUPHAR-DB, have captured expert-curated interactions between targets and ligands from selected papers in pharmacology and drug discovery since 2003. This resource continues to be developed in conjunction with the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS). As previously described, our unique model of content selection and quality control is based on 96 target-class subcommittees comprising 512 scientists collaborating with in-house curators. This update describes content expansion, new features and interoperability improvements introduced in the 10 releases since August 2015. Our relationship matrix now describes ∼9000 ligands, ∼15 000 binding constants, ∼6000 papers and ∼1700 human proteins. As an important addition, we also introduce our newly funded project for the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb, www.guidetoimmunopharmacology.org). This has been 'forked' from the well-established GtoPdb data model and expanded into new types of data related to the immune system and inflammatory processes. This includes new ligands, targets, pathways, cell types and diseases for which we are recruiting new IUPHAR expert committees. Designed as an immunopharmacological gateway, it also has an emphasis on potential therapeutic interventions.

InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.

Methods have been developed to assess the stereochemical quality of any protein structure both globally and locally using various criteria. Several parameters can be derived from the coordinates of a given structure. Global parameters include the distribution of phi, psi and chi 1 torsion angles, and hydrogen bond energies. There are clear correlations between these parameters and resolution; as the resolution improves, the distribution of the parameters becomes more clustered. These features show a broad distribution about ideal values derived from high-resolution structures. Some structures have tightly clustered distributions even at relatively low resolutions, while others show abnormal scatter though the data go to high resolution. Additional indicators of local irregularity include proline phi angles, peptide bond planarities, disulfide bond lengths, and their chi 3 torsion angles. These stereochemical parameters have been used to generate measures of stereochemical quality which provide a simple guide as to the reliability of a structure, in addition to the most important measures, resolution and R-factor. The parameters used in this evaluation are not novel, and are easily calculated from structure coordinates. A program suite is currently being developed which will quickly check a given structure, highlighting unusual stereochemistry and possible errors.

The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36,766 member database signatures integrated into 26,238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

In nanopore analytics, individual molecules pass through a single nanopore giving rise to detectable temporary blockades in ionic pore current. Reflecting its simplicity, nanopore analytics has gained popularity and can be conducted with natural protein as well as man-made polymeric and inorganic pores. The spectrum of detectable analytes ranges from nucleic acids, peptides, proteins, and biomolecular complexes to organic polymers and small molecules. Apart from being an analytical tool, nanopores have developed into a general platform technology to investigate the biophysics, physicochemistry, and chemistry of individual molecules (critical review, 310 references).

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

The cellular activity of several regulatory and signal transduction proteins, which depend on the Hsp90 molecular chaperone for folding, is markedly decreased by geldanamycin and by radicicol (monorden). We now show that these unrelated compounds both bind to the N-terminal ATP/ADP-binding domain of Hsp90, with radicicol displaying nanomolar affinity, and both inhibit the inherent ATPase activity of Hsp90 which is essential for its function in vivo. Crystal structure determinations of Hsp90 N-terminal domain complexes with geldanamycin and radicicol identify key aspects of their nucleotide mimicry and suggest a rational basis for the design of novel antichaperone drugs.

Insulin plays a key role in regulating a wide range of cellular processes. However, until recently little was known about the signalling pathways that are involved in linking the insulin receptor with downstream responses. It is now apparent that the activation of class 1a phosphoinositide 3-kinase (PI 3-kinase) is necessary and in some cases sufficient to elicit many of insulin's effects on glucose and lipid metabolism. The lipid products of PI 3-kinase act as both membrane anchors and allosteric regulators, serving to localize and activate downstream enzymes and their protein substrates. One of the major ways these lipid products of PI 3-kinase act in insulin signalling is by binding to pleckstrin homology (PH) domains of phosphoinositide-dependent protein kinase (PDK) and protein kinase B (PKB) and in the process regulating the phosphorylation of PKB by PDK. Using mechanisms such as this, PI 3-kinase is able to act as a molecular switch to regulate the activity of serine/threonine-specific kinase cascades important in mediating insulin's effects on endpoint responses.

Growth factor induced activation of phosphoinositide 3-kinase and protein kinase B (PKB) leads to increased activity of the mammalian target of rapamycin (mTOR). This subsequently leads to increased phosphorylation of eIF4E binding protein-1 (4EBP1) and activation of p70 ribosomal S6 protein kinase (p70(S6K)), both of which are important steps in the stimulation of protein translation. The stimulation of translation is attenuated in cells deprived of amino acids and this is associated with the attenuation of 4EBP1 phosphorylation and p70(S6K) activation. It has been suggested that PKB regulates mTOR function by phosphorylation although direct phosphorylation of mTOR by PKB has not been demonstrated previously. In the present work, we have found that PKB directly phosphorylates mTOR and, using phosphospecific antibodies, we have shown this phosphorylation occurs at Ser(2448). Insulin also induces phosphorylation on Ser(2448) and this effect is blocked by wortmannin but not rapamycin, consistent with the effect being mediated by PKB. Amino-acid starvation rapidly attenuated the reactivity of the Ser(2448) phosphospecific antibody with mTOR and this could not be restored by either insulin stimulation of cells or incubation with PKB in vitro. Our findings demonstrate that mTOR is a direct target for PKB and support the conclusion that regulation of phosphorylation of Ser(2448) is a point of convergence for the counteracting regulatory effects of growth factors and amino acid levels.

Nanoparticles present enormous surface areas and are found to enhance the rate of protein fibrillation by decreasing the lag time for nucleation. Protein fibrillation is involved in many human diseases, including Alzheimer's, Creutzfeld-Jacob disease, and dialysis-related amyloidosis. Fibril formation occurs by nucleation-dependent kinetics, wherein formation of a critical nucleus is the key rate-determining step, after which fibrillation proceeds rapidly. We show that nanoparticles (copolymer particles, cerium oxide particles, quantum dots, and carbon nanotubes) enhance the probability of appearance of a critical nucleus for nucleation of protein fibrils from human beta(2)-microglobulin. The observed shorter lag (nucleation) phase depends on the amount and nature of particle surface. There is an exchange of protein between solution and nanoparticle surface, and beta(2)-microglobulin forms multiple layers on the particle surface, providing a locally increased protein concentration promoting oligomer formation. This and the shortened lag phase suggest a mechanism involving surface-assisted nucleation that may increase the risk for toxic cluster and amyloid formation. It also opens the door to new routes for the controlled self-assembly of proteins and peptides into novel nanomaterials.

InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences into families. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and predict the presence of domains and significant sites. The InterPro database provides annotations for over 200 million sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mappings to several other major resources, such as Gene Ontology (GO), Protein Data Bank in Europe (PDBe) and the AlphaFold Protein Structure Database. In this publication, we report on the status of InterPro (version 101.0), detailing new developments in the database, associated web interface and software. Notable updates include the increased integration of structures predicted by AlphaFold and the enhanced description of protein families using artificial intelligence. Over the past two years, more than 5000 new InterPro entries have been created. The InterPro website now offers access to 85 000 protein families and domains from its member databases and serves as a long-term archive for retired databases. InterPro data, software and tools are freely available.

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.