CMWeb: an interactive on-line tool for analysing residue-residue contacts and contact prediction methods

A cellular selection identifies elongated flavodoxins that support electron transfer to sulfite reductase

Flavodoxins (Flds) mediate the flux of electrons between oxidoreductases in diverse metabolic pathways. To investigate whether Flds can support electron transfer to a sulfite reductase (SIR) that evolved to couple with a ferredoxin, we evaluated the ability of Flds to transfer electrons from a ferredoxin-NADP reductase (FNR) to a ferredoxin-dependent SIR using growth complementation of an Escherichia coli strain with a sulfur metabolism defect. We show that Flds from cyanobacteria complement this growth defect when coexpressed with an FNR and an SIR that evolved to couple with a plant ferredoxin. When we evaluated the effect of peptide insertion on Fld-mediated electron transfer, we observed a sensitivity to insertions within regions predicted to be proximal to the cofactor and partner binding sites, while a high insertion tolerance was detected within loops distal from the cofactor and within regions of helices and sheets that are proximal to those loops. Bioinformatic analysis showed that natural Fld sequence variability predicts a large fraction of the motifs that tolerate insertion of the octapeptide SGRPGSLS. These results represent the first evidence that Flds can support electron transfer to assimilatory SIRs, and they suggest that the pattern of insertion tolerance is influenced by interactions with oxidoreductase partners.

GPCRana: A web server for quantitative analysis of GPCR structures

G protein-coupled receptors (GPCRs) attract tremendous attention from both industrial and academic researchers with currently over 900 released structures. Structural analysis is widely used to understand receptor functionality and pharmacology, but more user-friendly tools are needed. Residue-residue contact score (RRCS) is an atomic distance-based method that allows a quantitative description of GPCR structures. Here, we present GPCRana, a web server that provides a user-friendly interface to analyze GPCR structures. After uploading selected structures, GPCRana immediately generates a comprehensive report covering four aspects: (i) RRCS for all residue pairs incorporated with real-time 3D visualization; (ii) ligand-receptor interactions; (iii) activation pathway analysis; and (iv) RRCS_TMs that indicates the global movements of transmembrane helices. Moreover, conformational changes between two structures can be analyzed. Applying GPCRana on AlphaFold2-predicted models reveals differentiated inter-helical packing forms in a receptor-dependent manner. Our web server offers a fast and precise way to study GPCR structures and is freely available at http://gpcranalysis.com/#/.

Urtica dioica Agglutinin: A plant protein candidate for inhibition of SARS-COV-2 receptor-binding domain for control of Covid19 Infection

Despite using effective drugs and vaccines for Covid 19, due to some limitations of current strategies and the high rate of coronavirus mutation, the development of medicines with effective inhibitory activity against this infection is essential. The SARS-CoV-2 enters the cell by attaching its receptor-binding domain (RBD) of Spike to angiotensin-converting enzyme-2 (ACE2). According to previous studies, the natural peptide Urtica dioica agglutinin (UDA) exhibited an antiviral effect on SARS-CoV, but its mechanism has not precisely been elucidated. Here, we studied the interaction between UDA and RBD of Spike protein of SARS-CoV-2. So, protein-protein docking of RBD-UDA was performed using Cluspro 2.0. To further confirm the stability of the complex, the RBD-UDA docked complex with higher binding affinity was studied using Molecular Dynamic simulation (via Gromacs 2020.2), and MM-PBSA calculated the binding free energy of the system. In addition, ELISA assay was used to examine the binding of UDA with RBD protein. Results were compared to ELISA of RBD-bound samples of convalescent serum IgG (from donors who recovered from Covid 19). Finally, the toxicity of UDA is assessed by using MTT assay. The docking results show UDA binds to the RBD binding site. MD simulation illustrates the UDA-RBD complex is stable during 100 ns of simulation, and the average binding energy was calculated to be -47.505 kJ/mol. ELISA and, MTT results show that UDA binds to RBD like IgG-RBD binding and may be safe in human cells. Data presented here indicate UDA interaction with S-protein inhibits the binding sites of RBD, it can prevent the virus from attaching to ACE2 and entering the host cell.

Determinants of Multiheme Cytochrome Extracellular Electron Transfer Uncovered by Systematic Peptide Insertion

The multiheme cytochrome MtrA enables microbial respiration by transferring electrons across the outer membrane to extracellular electron acceptors. While structural studies have identified residues that mediate the binding of MtrA to hemes and to other cytochromes that facilitate extracellular electron transfer (EET), the relative importance of these interactions for EET is not known. To better understand EET, we evaluated how insertion of an octapeptide across all MtrA backbone locations affects Shewanella oneidensis MR-1 respiration on Fe(III). The EET efficiency was found to be inversely correlated with the proximity of the insertion to the heme prosthetic groups. Mutants with decreased EET efficiencies also arose from insertions in a subset of the regions that make residue-residue contacts with the porin MtrB, while all sites contacting the extracellular cytochrome MtrC presented high peptide insertion tolerance. MtrA variants having peptide insertions within the CXXCH motifs that coordinate heme cofactors retained some ability to support respiration on Fe(III), although these variants presented significantly decreased EET efficiencies. Furthermore, the fitness of cells expressing different MtrA variants under Fe(III) respiration conditions correlated with anode reduction. The peptide insertion profile, which represents the first comprehensive sequence-structure-function map for a multiheme cytochrome, implicates MtrA as a strategic protein engineering target for the regulation of EET.

OUP accepted manuscript

Correct identification and effective visualization of interactions in biomolecular structures facilitate understanding of their functions and molecular design. In response to the practical needs of structure-based analysis, we have created a Mapiya web server. The Mapiya integrates four main functionalities: (i) generation of contact maps - intramolecular and intermolecular-for proteins, nucleic acids, and their complexes; (ii) characterization of the interactions physicochemical nature, (iii) interactive visualization of biomolecular conformations with automatic zoom on selected contacts using Molstar and (iv) additional sequence- and structure-based analyses performed with third-party software and in-house algorithms combined into an easy-to-use interface. Thus, Mapiya offers a highly customized analysis of the molecular interactions' in various biological systems. The web server is available at: http://mapiya.lcbio.pl/.

Allosteric pockets and dynamic residue network hubs of falcipain 2 in mutations including those linked to artemisinin resistance

Continually emerging resistant strains of malarial parasites to current drugs present challenges. Understanding the underlying resistance mechanisms, especially those linked to allostery is, thus, highly crucial for drug design. This forms the main concern of the paper through a case study of falcipain 2 (FP-2) and its mutations, some of which are linked to artemisinin (ART) drug resistance. Here, we applied a variety of in silico approaches and tools that we developed recently, together with existing computational tools. This included novel essential dynamics and dynamic residue network (DRN) analysis algorithms. We identified six pockets demonstrating dynamic differences in the presence of some mutations. We observed striking allosteric effects in two mutant proteins. In the presence of M245I, a cryptic pocket was detected via a unique mechanism in which Pocket 2 fused with Pocket 6. In the presence of the A353T mutation, which is located at Pocket 2, the pocket became the most rigid among all protein systems analyzed. Pocket 6 was also highly stable in all cases, except in the presence of M245I mutation. The effect of ART linked mutations was more subtle, and the changes were at residue level. Importantly, we identified an allosteric communication path formed by four unique averaged BC hubs going from the mutated residue to the catalytic site and passing through the interface of three identified pockets. Collectively, we established and demonstrated that we have robust tools and a pipeline that can be applicable to the analysis of mutations.

Combining the Fecal Immunochemical Test with a Logistic Regression Model for Screening Colorectal Neoplasia

Background: The fecal immunochemical test (FIT) is a widely used strategy for colorectal cancer (CRC) screening with moderate sensitivity. To further increase the sensitivity of FIT in identifying colorectal neoplasia, in this study, we established a classifier model by combining FIT result and other demographic and clinical features.

Methods: A total of 4,477 participants were examined with FIT and those who tested positive (over 100 ng/ml) were followed up by a colonoscopy examination. Demographic and clinical information of participants including four domains (basic information, clinical history, diet habits and life styles) that consist of 15 features were retrieved from questionnaire surveys. A mean decrease accuracy (MDA) score was used to select features that are mostly related to CRC. Five different algorithms including logistic regression (LR), classification and regression tree (CART), support vector machine (SVM), artificial neural network (ANN) and random forest (RF) were used to generate a classifier model, through a 10X cross validation process. Area under curve (AUC) and normalized mean squared error (NMSE) were used in the evaluation of the performance of the model.

Results: The top six features that are mostly related to CRC include age, gender, history of intestinal adenoma or polyposis, smoking history, gastrointestinal discomfort symptom and fruit eating habit were selected. LR algorithm was used in the generation of the model. An AUC score of 0.92 and an NMSE score of 0.076 were obtained by the final classifier model in separating normal individuals from participants with colorectal neoplasia.

Conclusion: Our results provide a new “Funnel” strategy in colorectal neoplasia screening via adding a classifier model filtering step between FIT and colonoscopy examination. This strategy minimizes the need of colonoscopy examination while increases the sensitivity of FIT-based CRC screening.

ConPlot: Web-based application for the visualisation of protein contact maps integrated with other data

Summary

Covariance-based predictions of residue contacts and inter-residue distances are an increasingly popular data type in protein bioinformatics. Here we present ConPlot, a web-based application for convenient display and analysis of contact maps and distograms. Integration of predicted contact data with other predictions is often required to facilitate inference of structural features. ConPlot can therefore use the empty space near the contact map diagonal to display multiple coloured tracks representing other sequence-based predictions. Popular file formats are natively read and bespoke data can also be flexibly displayed. This novel visualization will enable easier interpretation of predicted contact maps.

Availability and implementation

available online at www.conplot.org, along with documentation and examples. Alternatively, ConPlot can be installed and used locally using the docker image from the project’s Docker Hub repository. ConPlot is licensed under the BSD 3-Clause.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structural and evolutionary analyses of the Plasmodium falciparum chloroquine resistance transporter

Mutations in the Plasmodium falciparum chloroquine resistance transporter (PfCRT) confer resistance to several antimalarial drugs such as chloroquine (CQ) or piperaquine (PPQ), a partner molecule in current artemisinin-based combination therapies. As a member of the Drug/Metabolite Transporter (DMT) superfamily, the vacuolar transporter PfCRT may translocate substrate molecule(s) across the membrane of the digestive vacuole (DV), a lysosome-like organelle. However, the physiological substrate(s), the transport mechanism and the functional regions of PfCRT remain to be fully characterized. Here, we hypothesized that identification of evolutionary conserved sites in a tertiary structural context could help locate putative functional regions of PfCRT. Hence, site-specific substitution rates were estimated over Plasmodium evolution at each amino acid sites, and the PfCRT tertiary structure was predicted in both inward-facing (open-to-vacuole) and occluded states through homology modeling using DMT template structures sharing <15% sequence identity with PfCRT. We found that the vacuolar-half and membrane-spanning domain (and especially the transmembrane helix 9) of PfCRT were more conserved, supporting that its physiological substrate is expelled out of the parasite DV. In the PfCRT occluded state, some evolutionary conserved sites, including positions related to drug resistance mutations, participate in a putative binding pocket located at the core of the PfCRT membrane-spanning domain. Through structural comparison with experimentally-characterized DMT transporters, we identified several conserved PfCRT amino acid sites located in this pocket as robust candidates for mediating substrate transport. Finally, in silico mutagenesis revealed that drug resistance mutations caused drastic changes in the electrostatic potential of the transporter vacuolar entry and pocket, facilitating the escape of protonated CQ and PPQ from the parasite DV.

Atomic structure of PI3-kinase SH3 amyloid fibrils by cryo-electron microscopy

High resolution structural information on amyloid fibrils is crucial for the understanding of their formation mechanisms and for the rational design of amyloid inhibitors in the context of protein misfolding diseases. The Src-homology 3 domain of phosphatidyl-inositol-3-kinase (PI3K-SH3) is a model amyloid system that plays a pivotal role in our basic understanding of protein misfolding and aggregation. Here, we present the atomic model of the PI3K-SH3 amyloid fibril with a resolution determined to 3.4 Å by cryo-electron microscopy (cryo-EM). The fibril is composed of two intertwined protofilaments that create an interface spanning 13 residues from each monomer. The model comprises residues 1-77 out of 86 amino acids in total, with the missing residues located in the highly flexible C-terminus. The fibril structure allows us to rationalise the effects of chemically conservative point mutations as well as of the previously reported sequence perturbations on PI3K-SH3 fibril formation and growth.

CONAN: A Tool to Decode Dynamical Information from Molecular Interaction Maps

The analysis of contacts is a powerful tool to understand biomolecular function in a series of contexts, from the investigation of dynamical behavior at equilibrium to the study of nonequilibrium dynamics in which the system moves between multiple states. We thus propose a tool called CONtact ANalysis (CONAN) that, from molecular dynamics (MD) trajectories, analyzes interresidue contacts, creates videos of time-resolved contact maps, and performs correlation, principal component, and cluster analysis, revealing how specific contacts relate to functionally relevant states sampled by MD. We present how CONAN can identify features describing the dynamics of ubiquitin both at equilibrium and during mechanical unfolding. Additionally, we show the analysis of MD trajectories of an α-synuclein mutant peptide that undergoes an α-β conformational transition that can be easily monitored using CONAN, which identifies the multiple states that the peptide explores along its conformational dynamics. The high versatility and ease of use of the software make CONAN a tool that can significantly facilitate the understanding of the complex dynamical behavior of proteins or other biomolecules. CONAN and its documentation are freely available for download on GitHub.

PDBparam: Online Resource for Computing Structural Parameters of Proteins

Understanding the structure-function relationship in proteins is a longstanding goal in molecular and computational biology. The development of structure-based parameters has helped to relate the structure with the function of a protein. Although several structural features have been reported in the literature, no single server can calculate a wide-ranging set of structure-based features from protein three-dimensional structures. In this work, we have developed a web-based tool, PDBparam, for computing more than 50 structure-based features for any given protein structure. These features are classified into four major categories: (i) interresidue interactions, which include short-, medium-, and long-range interactions, contact order, long-range order, total contact distance, contact number, and multiple contact index, (ii) secondary structure propensities such as α-helical propensity, β-sheet propensity, and propensity of amino acids to exist at various positions of α-helix and amino acid compositions in high B-value regions, (iii) physicochemical properties containing ionic interactions, hydrogen bond interactions, hydrophobic interactions, disulfide interactions, aromatic interactions, surrounding hydrophobicity, and buriedness, and (iv) identification of binding site residues in protein-protein, protein-nucleic acid, and protein-ligand complexes. The server can be freely accessed at http://www.iitm.ac.in/bioinfo/pdbparam/. We suggest the use of PDBparam as an effective tool for analyzing protein structures.

NAPS: Network Analysis of Protein Structures

Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue-residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein-protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

The human transmembrane proteome

Background

Transmembrane proteins have important roles in cells, as they are involved in energy production, signal transduction, cell-cell interaction, cell-cell communication and more. In human cells, they are frequently targets for pharmaceuticals; therefore, knowledge about their properties and structure is crucial. Topology of transmembrane proteins provide a low resolution structural information, which can be a starting point for either laboratory experiments or modelling their 3D structures.

Results

Here, we present a database of the human α-helical transmembrane proteome, including the predicted and/or experimentally established topology of each transmembrane protein, together with the reliability of the prediction. In order to distinguish transmembrane proteins in the proteome as well as for topology prediction, we used a newly developed consensus method (CCTOP) that incorporates recent state of the art methods, with tested accuracies on a novel human benchmark protein set. CCTOP utilizes all available structure and topology data as well as bioinformatical evidences for topology prediction in a probabilistic framework provided by the hidden Markov model. This method shows the highest accuracy (98.5 % for discrinimating between transmembrane and non-transmembrane proteins and 84 % for per protein topology prediction) among the dozen tested topology prediction methods. Analysis of the human proteome with the CCTOP indicates that it contains 4998 (26 %) transmembrane proteins. Besides predicting topology, reliability of the predictions is estimated as well, and it is demonstrated that the per protein prediction accuracies of more than 60 % of the predictions are over 98 % on the benchmark sets and most probably on the predicted human transmembrane proteome too.

Conclusions

Here, we present the most accurate prediction of the human transmembrane proteome together with the experimental topology data. These data, as well as various statistics about the human transmembrane proteins and their topologies can be downloaded from and can be visualized at the website of the human transmembrane proteome (http://htp.enzim.hu).

Reviewers

This article was reviewed by Dr. Sandor Pongor, Dr. Michael Galperin and Dr. Pascale Gaudet (nominated by Dr Michael Galperin).

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0061-x) contains supplementary material, which is available to authorized users.

CCTOP: a Consensus Constrained TOPology prediction web server

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided.

Expediting topology data gathering for the TOPDB database

The Topology Data Bank of Transmembrane Proteins (TOPDB, http://topdb.enzim.ttk.mta.hu) contains experimentally determined topology data of transmembrane proteins. Recently, we have updated TOPDB from several sources and utilized a newly developed topology prediction algorithm to determine the most reliable topology using the results of experiments as constraints. In addition to collecting the experimentally determined topology data published in the last couple of years, we gathered topographies defined by the TMDET algorithm using 3D structures from the PDBTM. Results of global topology analysis of various organisms as well as topology data generated by high throughput techniques, like the sequential positions of N- or O-glycosylations were incorporated into the TOPDB database. Moreover, a new algorithm was developed to integrate scattered topology data from various publicly available databases and a new method was introduced to measure the reliability of predicted topologies. We show that reliability values highly correlate with the per protein topology accuracy of the utilized prediction method. Altogether, more than 52 000 new topology data and more than 2600 new transmembrane proteins have been collected since the last public release of the TOPDB database.

Recent advances in functional region prediction by using structural and evolutionary information - Remaining problems and future extensions

Structural genomics projects have solved many new structures with unknown functions. One strategy to investigate the function of a structure is to computationally find the functionally important residues or regions on it. Therefore, the development of functional region prediction methods has become an important research subject. An effective approach is to use a method employing structural and evolutionary information, such as the evolutionary trace (ET) method. ET ranks the residues of a protein structure by calculating the scores for relative evolutionary importance, and locates functionally important sites by identifying spatial clusters of highly ranked residues. After ET was developed, numerous ET-like methods were subsequently reported, and many of them are in practical use, although they require certain conditions. In this mini review, we first introduce the remaining problems and the recent improvements in the methods using structural and evolutionary information. We then summarize the recent developments of the methods. Finally, we conclude by describing possible extensions of the evolution- and structure-based methods.

MISTIC: Mutual information server to infer coevolution

MISTIC (mutual information server to infer coevolution) is a web server for graphical representation of the information contained within a MSA (multiple sequence alignment) and a complete analysis tool for Mutual Information networks in protein families. The server outputs a graphical visualization of several information-related quantities using a circos representation. This provides an integrated view of the MSA in terms of (i) the mutual information (MI) between residue pairs, (ii) sequence conservation and (iii) the residue cumulative and proximity MI scores. Further, an interactive interface to explore and characterize the MI network is provided. Several tools are offered for selecting subsets of nodes from the network for visualization. Node coloring can be set to match different attributes, such as conservation, cumulative MI, proximity MI and secondary structure. Finally, a zip file containing all results can be downloaded. The server is available at http://mistic.leloir.org.ar. In summary, MISTIC allows for a comprehensive, compact, visually rich view of the information contained within an MSA in a manner unique to any other publicly available web server. In particular, the use of circos representation of MI networks and the visualization of the cumulative MI and proximity MI concepts is novel.

CNNcon: improved protein contact maps prediction using cascaded neural networks

BACKGROUNDS

Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence) alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly.

METHODS

CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction.

RESULTS

The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective prediction of long length proteins could be possible by the CNNcon.

PDBTM: Protein Data Bank of transmembrane proteins after 8 years

The PDBTM database (available at http://pdbtm.enzim.hu), the first comprehensive and up-to-date transmembrane protein selection of the Protein Data Bank, was launched in 2004. The database was created and has been continuously updated by the TMDET algorithm that is able to distinguish between transmembrane and non-transmembrane proteins using their 3D atomic coordinates only. The TMDET algorithm can locate the spatial positions of transmembrane proteins in lipid bilayer as well. During the last 8 years not only the size of the PDBTM database has been steadily growing from ∼400 to 1700 entries but also new structural elements have been identified, in addition to the well-known α-helical bundle and β-barrel structures. Numerous ‘exotic’ transmembrane protein structures have been solved since the first release, which has made it necessary to define these new structural elements, such as membrane loops or interfacial helices in the database. This article reports the new features of the PDBTM database that have been added since its first release, and our current efforts to keep the database up-to-date and easy to use so that it may continue to serve as a fundamental resource for the scientific community.