A PDB-wide, evolution-based assessment of protein-protein interfaces

Rational design of monomeric IL37 variants guided by stability and dynamical analyses of IL37 dimers

IL37 plays important roles in the regulation of innate immunity and its oligomeric status is critical to these roles. In its monomeric state, IL37 can effectively inhibit the inflammatory response of IL18 by binding to IL18Rα, a capacity lost in its dimeric form, underlining the pivotal role of the oligomeric status of IL37 in its anti-inflammatory action. Until now, two IL37 dimer structures have been deposited in PDB, reflecting a substantial difference in their dimer interfaces. Given this discrepancy, we analyzed the PDB structures of the IL37 dimer (PDB IDs: 6ncu, 5hn1) along with a AF2-multimer prediction by molecular dynamics (MD) simulations. Results showed that the 5hn1 and AF2-predicted dimers have the same interface and stably maintained their conformations throughout simulations, while the recent IL37 dimer (PDB ID: 6ncu) with a different interface did not, proposing a possible issue with the recent IL37 dimer structure (6ncu). Next, focusing on the stable dimer structures, we have identified five critical positions of V71/Y85/I86/E89/S114, three new positions compared to the literature, that would reduce dimer stability without affecting the monomer structure. Two quintuple mutants were tested by MD simulations and showed partial or complete dissociation of the dimer. Overall, the insights gained from this study reinforce the validity of the 5hn1 and AF2 multimer structures, while also advancing our understanding of the IL37 dimer interface through the generation of monomer-locked IL37 variants.

Establishing comprehensive quaternary structural proteomes from genome sequence

A critical body of knowledge has developed through advances in protein microscopy, protein-fold modeling, structural biology software, availability of sequenced bacterial genomes, large-scale mutation databases, and genome-scale models. Based on these recent advances, we develop a computational framework that; i) identifies the oligomeric structural proteome encoded by an organism's genome from available structural resources; ii) maps multi-strain alleleomic variation, resulting in the structural proteome for a species; and iii) calculates the 3D orientation of proteins across subcellular compartments with residue-level precision. Using the platform, we; iv) compute the quaternary E. coli K-12 MG1655 structural proteome; v) use a dataset of 12,000 mutations to build Random Forest classifiers that can predict the severity of mutations; and, in combination with a genome-scale model that computes proteome allocation, vi) obtain the spatial allocation of the E. coli proteome. Thus, in conjunction with relevant datasets and increasingly accurate computational models, we can now annotate quaternary structural proteomes, at genome-scale, to obtain a molecular-level understanding of whole-cell functions.

Significance

Advancements in experimental and computational methods have revealed the shapes of multi-subunit proteins. The absence of a unified platform that maps actionable datatypes onto these increasingly accurate structures creates a barrier to structural analyses, especially at the genome-scale. Here, we describe QSPACE, a computational annotation platform that evaluates existing resources to identify the best-available structure for each protein in a user's query, maps the 3D location of actionable datatypes ( e.g. , active sites, published mutations) onto the selected structures, and uses third-party APIs to determine the subcellular compartment of all amino acids of a protein. As proof-of-concept, we deployed QSPACE to generate the quaternary structural proteome of E. coli MG1655 and demonstrate two use-cases involving large-scale mutant analysis and genome-scale modelling.

EGGNet, a Generalizable Geometric Deep Learning Framework for Protein Complex Pose Scoring

Computational prediction of molecule-protein interactions has been key for developing new molecules to interact with a target protein for therapeutics development. Previous work includes two independent streams of approaches: (1) predicting protein-protein interactions (PPIs) between naturally occurring proteins and (2) predicting binding affinities between proteins and small-molecule ligands [also known as drug-target interaction (DTI)]. Studying the two problems in isolation has limited the ability of these computational models to generalize across the PPI and DTI tasks, both of which ultimately involve noncovalent interactions with a protein target. In this work, we developed Equivariant Graph of Graphs neural Network (EGGNet), a geometric deep learning (GDL) framework, for molecule-protein binding predictions that can handle three types of molecules for interacting with a target protein: (1) small molecules, (2) synthetic peptides, and (3) natural proteins. EGGNet leverages a graph of graphs (GoG) representation constructed from the molecular structures at atomic resolution and utilizes a multiresolution equivariant graph neural network to learn from such representations. In addition, EGGNet leverages the underlying biophysics and makes use of both atom- and residue-level interactions, which improve EGGNet's ability to rank candidate poses from blind docking. EGGNet achieves competitive performance on both a public protein-small-molecule binding affinity prediction task (80.2% top 1 success rate on CASF-2016) and a synthetic protein interface prediction task (88.4% area under the precision-recall curve). We envision that the proposed GDL framework can generalize to many other protein interaction prediction problems, such as binding site prediction and molecular docking, helping accelerate protein engineering and structure-based drug development.

DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model

Motivation

Protein-Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the 3D structures of the correspond protein complexes valuable insights can be obtained, providing, e.g. starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we have previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSMs), which are computationally expensive to generate, significantly limits the algorithm's usability.

Results

We introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the ESM-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes).

Availability and implementation

DeepRank-GNN-esm is freely available from https://github.com/DeepRank/DeepRank-GNN-esm.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Structural and Kinetic Characterization of the SpeG Spermidine/Spermine N-acetyltransferase from Methicillin-Resistant Staphylococcus aureus USA300

Polyamines are simple yet critical molecules with diverse roles in numerous pathogenic and non-pathogenic organisms. Regulating polyamine concentrations affects the transcription and translation of genes and proteins important for cell growth, stress, and toxicity. One way polyamine concentrations are maintained within the cell is via spermidine/spermine N-acetyltransferases (SSATs) that acetylate intracellular polyamines so they can be exported. The bacterial SpeG enzyme is an SSAT that exhibits a unique dodecameric structure and allosteric site compared to other SSATs that have been previously characterized. While its overall 3D structure is conserved, its presence and role in different bacterial pathogens are inconsistent. For example, not all bacteria have speG encoded in their genomes; in some bacteria, the speG gene is present but has become silenced, and in other bacteria, it has been acquired on mobile genetic elements. The latter is the case for methicillin-resistant Staphylococcus aureus (MRSA) USA300, where it appears to aid pathogenesis. To gain a greater understanding of the structure/function relationship of SpeG from the MRSA USA300 strain (SaSpeG), we determined its X-ray crystal structure in the presence and absence of spermine. Additionally, we showed the oligomeric state of SaSpeG is dynamic, and its homogeneity is affected by polyamines and AcCoA. Enzyme kinetic assays showed that pre-incubation with polyamines significantly affected the positive cooperativity toward spermine and spermidine and the catalytic efficiency of the enzyme. Furthermore, we showed bacterial SpeG enzymes do not have equivalent capabilities to acetylate aminopropyl versus aminbutyl ends of spermidine. Overall, this study provides new insight that will assist in understanding the SpeG enzyme and its role in pathogenic and non-pathogenic bacteria at a molecular level.

GDockScore: a graph-based protein-protein docking scoring function

Summary

Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions.

Availability and implementation

The model implementation is available at https://gitlab.com/mcfeemat/gdockscore.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces

MOTIVATION

Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations.

RESULTS

We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank.

AVAILABILITY AND IMPLEMENTATION

DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The protein common assembly database (ProtCAD)-a comprehensive structural resource of protein complexes

Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

A novel splice site variant in the POPDC3 causes autosomal recessive limb-girdle muscular dystrophy type 26

Limb-Girdle muscular dystrophy (LGMD) is a group of muscle disorders with highly heterogeneous genetic patterns and clinical phenotypes, and this group includes multiple subtypes. Different LGMD subtypes have similar phenotypes and clinical overlaps，these subtypes are difficult to distinguish by clinical symptoms alone and can only be accurately diagnosed by analysis in combination with definitive genetic test results. Here, we report a female presenting features of LGMD. After analysis of whole-exome sequencing data, a novel homozygous POPDC3 variant c.486-1G>A (rs113419658) located in the acceptor splice site of intron 2 was identified in the proband. The variant effect on splicing were analyzed by genetic analysis based on cDNA synthesised by the patient's RNA. cDNA analysis indicated that the novel homozygous POPDC3 splice variant disrupted original acceptor splice site, which can cause a frameshift in the mRNA of the POPDC3 gene, thereby producing a truncated POPDC3 protein and ultimately affecting its normal function. POPDC3 variant was recently associated with recessive limb-girdle muscular dystrophy type 26 (LGMDR26). Based on the above results, we hypothesize that this variant is probably a pathogenic variant, and expand the gene variant spectrum of POPDC3. This article is protected by copyright. All rights reserved.

Structure of the poxvirus decapping enzyme D9 reveals its mechanism of cap recognition and catalysis

Poxviruses encode decapping enzymes that remove the protective 5' cap from both host and viral mRNAs to commit transcripts for decay by the cellular exonuclease Xrn1. Decapping by these enzymes is critical for poxvirus pathogenicity by means of simultaneously suppressing host protein synthesis and limiting the accumulation of viral double-stranded RNA (dsRNA), a trigger for antiviral responses. Here we present a high-resolution structural view of the vaccinia virus decapping enzyme D9. This Nudix enzyme contains a domain organization different from other decapping enzymes in which a three-helix bundle is inserted into the catalytic Nudix domain. The 5' mRNA cap is positioned in a bipartite active site at the interface of the two domains. Specificity for the methylated guanosine cap is achieved by stacking between conserved aromatic residues in a manner similar to that observed in canonical cap-binding proteins VP39, eIF4E, and CBP20, and distinct from eukaryotic decapping enzyme Dcp2.

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

The identification of physiologically relevant quaternary structures (QSs) in crystal lattices is challenging. To predict the physiological relevance of a particular QS, QSalign searches for homologous structures in which subunits interact in the same geometry. This approach proved accurate but was limited to structures already present in the Protein Data Bank (PDB). Here, we introduce a webserver (www.QSalign.org) allowing users to submit homo-oligomeric structures of their choice to the QSalign pipeline. Given a user-uploaded structure, the sequence is extracted and used to search homologs based on sequence similarity and PFAM domain architecture. If structural conservation is detected between a homolog and the user-uploaded QS, physiological relevance is inferred. The web server also generates alternative QSs with PISA and processes them the same way as the query submitted to widen the predictions. The result page also shows representative QSs in the protein family of the query, which is informative if no QS conservation was detected or if the protein appears monomeric. These representative QSs can also serve as a starting point for homology modeling.

Structural dynamics in the evolution of a bilobed protein scaffold

Proteins conduct numerous complex biological functions by use of tailored structural dynamics. The molecular details of how these emerged from ancestral peptides remains mysterious. How does nature utilize the same repertoire of folds to diversify function? To shed light on this, we analyzed bilobed proteins with a common structural core, which is spread throughout the tree of life and is involved in diverse biological functions such as transcription, enzymatic catalysis, membrane transport, and signaling. We show here that the structural dynamics of the structural core differentiate predominantly via terminal additions during a long-period evolution. This diversifies substrate specificity and, ultimately, biological function.

Novel biophysical tools allow the structural dynamics of proteins and the regulation of such dynamics by binding partners to be explored in unprecedented detail. Although this has provided critical insights into protein function, the means by which structural dynamics direct protein evolution remain poorly understood. Here, we investigated how proteins with a bilobed structure, composed of two related domains from the periplasmic-binding protein–like II domain family, have undergone divergent evolution, leading to adaptation of their structural dynamics. We performed a structural analysis on ∼600 bilobed proteins with a common primordial structural core, which we complemented with biophysical studies to explore the structural dynamics of selected examples by single-molecule Förster resonance energy transfer and Hydrogen–Deuterium exchange mass spectrometry. We show that evolutionary modifications of the structural core, largely at its termini, enable distinct structural dynamics, allowing the diversification of these proteins into transcription factors, enzymes, and extracytoplasmic transport-related proteins. Structural embellishments of the core created interdomain interactions that stabilized structural states, reshaping the active site geometry, and ultimately altered substrate specificity. Our findings reveal an as-yet-unrecognized mechanism for the emergence of functional promiscuity during long periods of evolution and are applicable to a large number of domain architectures.

DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

Crystal Structure and Functional Characterization of the Bifunctional N-(5'-Phosphoribosyl)anthranilate Isomerase-indole-3-glycerol-phosphate Synthase from Corynebacterium glutamicum

L-Tryptophan is known as an aromatic amino acid and one of the essential amino acids that must be ingested through various additives or food. TrpCF is a bifunctional enzyme that has indole-glycerol-phosphate synthase (IGPS) and phosphoribosylanthranilate isomerase (PRAI) activity. In this report, we identified the crystal structure of TrpCF from Corynebacterium glutamicum (CgTrpCF) and successfully elucidated the active site by attaching rCdRP similar to the substrate and product of the TrpCF reaction. Also, we revealed that CgTrpCF shows a conformational change at the loops upon substrate binding. We analyzed amino acid sequences of the homologues of CgTrpCF, and the residues of the substrate-binding site in TrpCF were highly conserved except for some residues. These less conserved residues were replaced by site-directed mutagenesis experiments. Consequently, we obtained the CgTrpCF^P294K (PRAI^CD/P294K) variant that has enhanced activity.

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches

Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.

Synthetic Peptides That Antagonize the Angiotensin-Converting Enzyme-2 (ACE-2) Interaction with SARS-CoV-2 Receptor Binding Spike Protein

The SARS-CoV-2 viral spike protein S receptor-binding domain (S-RBD) binds ACE2 on host cells to initiate molecular events, resulting in intracellular release of the viral genome. Therefore, antagonists of this interaction could allow a modality for therapeutic intervention. Peptides can inhibit the S-RBD:ACE2 interaction by interacting with the protein–protein interface. In this study, protein contact atlas data and molecular dynamics simulations were used to locate interaction hotspots on the secondary structure elements α1, α2, α3, β3, and β4 of ACE2. We designed a library of discontinuous peptides based upon a combination of the hotspot interactions, which were synthesized and screened in a bioluminescence-based assay. The peptides demonstrated high efficacy in antagonizing the SARS-CoV-2 S-RBD:ACE2 interaction and were validated by microscale thermophoresis which demonstrated strong binding affinity (∼10 nM) of these peptides to S-RBD. We anticipate that such discontinuous peptides may hold the potential for an efficient therapeutic treatment for COVID-19.

Differential seminal plasma proteome signatures of boars with high and low resistance to hypothermic semen preservation at 5°C

BACKGROUND

Hypothermic storage at 5°C has been investigated as an alternative to promote the prudent use of antibiotics for boar artificial insemination doses. However, this temperature is challenging for some ejaculates or boars.

OBJECTIVE

The present study aimed to identify putative biomarkers for semen resistance to hypothermic storage at 5°C by comparing the seminal plasma proteomes of boars with high and low seminal resistance to preservation at 5°C.

MATERIALS AND METHODS

From an initial group of 34 boars, 15 were selected based on the following criteria: ejaculate with ≤20% abnormal spermatozoa and at least 70% progressive motility at 120 hours of storage at 17°C. Then, based on the response to semen hypothermic storage at 5°C, boars were classified into two categories: high resistance-progressive motility of >75% in the three collections (n = 3); and low resistance-progressive motility of <75% in the three collections (n = 3). Seminal plasma proteins were analyzed in pools, and differential proteomics was performed using Multidimensional Protein Identification Technology.

RESULTS

Progressive motility was lower at 120 hours of storage in low resistance, compared to high resistance boars (P < .05). Acrosome and plasma membrane integrity were not affected by the boar category, storage time, or their interaction (P ≥ .104). Sixty-five proteins were considered for differential proteomics. Among the differentially expressed and exclusive proteins, the identification of proteins such cathepsin B, legumain, and cystatin B suggests significant changes in key enzymes (eg, metalloproteinases) involved in spermatogenesis, sperm integrity, and fertilizing potential.

DISCUSSION AND CONCLUSION

Differences in the seminal plasma suggest that proteins involved in the proteolytic activation of metalloproteinases and proteins related to immune response modulation could disrupt key cellular pathways during spermatogenesis and epididymal maturation, resulting in altered resistance to chilling injury. Further in vivo studies focusing on the immunological crosstalk between epithelial cells and gametes might explain how the immune regulators influence sperm resistance to hipothermic storage.

The Sensitivity of the Pair-Angle Distribution Function to Protein Structure

The continued development of X-ray free-electron lasers and serial crystallography techniques has opened up new experimental frontiers. Nanoscale dynamical processes such as crystal growth can now be probed at unprecedented time and spatial resolutions. Pair-angle distribution function (PADF) analysis is a correlation-based technique that has the potential to extend the limits of current serial crystallography experiments, by relaxing the requirements for crystal order, size and number density per exposure. However, unlike traditional crystallographic methods, the PADF technique does not recover the electron density directly. Instead it encodes substantial information about local three-dimensional structure in the form of three- and four-body correlations. It is not yet known how protein structure maps into the many-body PADF correlations. In this paper, we explore the relationship between the PADF and protein conformation. We calculate correlations in reciprocal and real space for model systems exhibiting increasing degrees of order and secondary structural complexity, from disordered polypeptides, single alpha helices, helix bundles and finally a folded 100 kilodalton protein. These models systems inform us about the distinctive angular correlations generated by bonding, polypeptide chains, secondary structure and tertiary structure. They further indicate the potential to use angular correlations as a sensitive measure of conformation change that is complementary to existing structural analysis techniques.

PRODIGY-crystal: a web-tool for classification of biological interfaces in protein complexes

Summary

Distinguishing biologically relevant interfaces from crystallographic ones in biological complexes is fundamental in order to associate cellular functions to the correct macromolecular assemblies. Recently, we described a detailed study reporting the differences in the type of intermolecular residue–residue contacts between biological and crystallographic interfaces. Our findings allowed us to develop a fast predictor of biological interfaces reaching an accuracy of 0.92 and competitive to the current state of the art. Here we present its web-server implementation, PRODIGY-CRYSTAL, aimed at the classification of biological and crystallographic interfaces. PRODIGY-CRYSTAL has the advantage of being fast, accurate and simple. This, together with its user-friendly interface and user support forum, ensures its broad accessibility.

Availability and implementation

PRODIGY-CRYSTAL is freely available without registration requirements at https://haddock.science.uu.nl/services/PRODIGY-CRYSTAL.

Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification

Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method.

ProtCID: a data resource for structural information on protein interactions

Structural information on the interactions of proteins with other molecules is plentiful, and for some proteins and protein families, there may be 100s of available structures. It can be very difficult for a scientist who is not trained in structural bioinformatics to access this information comprehensively. Previously, we developed the Protein Common Interface Database (ProtCID), which provided clusters of the interfaces of full-length protein chains as a means of identifying biological assemblies. Because proteins consist of domains that act as modular functional units, we have extended the analysis in ProtCID to the individual domain level. This has greatly increased the number of large protein-protein clusters in ProtCID, enabling the generation of hypotheses on the structures of biological assemblies of many systems. The analysis of domain families allows us to extend ProtCID to the interactions of domains with peptides, nucleic acids, and ligands. ProtCID provides complete annotations and coordinate sets for every cluster.

The authors previously developed the Protein Common Interface Database (ProtCID), which compares and clusters the interfaces of pairs of full-length protein chains with defined Pfam domain architectures in different PDB entries to identify biological assemblies. Here the authors extend ProtCID to the clustering of domain-domain interactions that also allows analyzing domain interactions with peptides, nucleic acids, and ligands.

Accurate Classification of Biological and non-Biological Interfaces in Protein Crystal Structures using Subtle Covariation Signals

Proteins often work as oligomers or multimers in vivo. Therefore, elucidating their oligomeric or multimeric form (quaternary structure) is crucially important to ascertain their function. X-ray crystal structures of numerous proteins have been accumulated, providing information related to their biological units. Extracting information of biological units from protein crystal structures represents a meaningful task for modern biology. Nevertheless, although many methods have been proposed for identifying biological units appearing in protein crystal structures, it is difficult to distinguish biological protein-protein interfaces from crystallographic ones. Therefore, our simple but highly accurate classifier was developed to infer biological units in protein crystal structures using large amounts of protein sequence information and a modern contact prediction method to exploit covariation signals (CSs) in proteins. We demonstrate that our proposed method is promising even for weak signals of biological interfaces. We also discuss the relation between classification accuracy and conservation of biological units, and illustrate how the selection of sequences included in multiple sequence alignments as sources for obtaining CSs affects the results. With increased amounts of sequence data, the proposed method is expected to become increasingly useful.

Principles and characteristics of biological assemblies in experimentally determined protein structures

More than half of all structures in the PDB are assemblies of two or more proteins, including both homooligomers and heterooligomers. Structural information on these assemblies comes from X-ray crystallography, NMR, and cryo-EM spectroscopy. The correct assembly in an X-ray structure is often ambiguous, and computational methods have been developed to identify the most likely biologically relevant assembly based on physical properties of assemblies and sequence conservation in interfaces. Taking advantage of the large number of structures now available, some of the most recent methods have relied on similarity of interfaces and assemblies across structures of homologous proteins.

Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification

BACKGROUND

Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems.

RESULTS

In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on "pair-properties" of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA).

CONCLUSION

In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier .

Engagement Rules That Underpin DBL-DARC Interactions for Ingress of Plasmodium knowlesi and Plasmodium vivax Into Human Erythrocytes

Malaria parasite erythrocytic stages comprise of repeated bursts of parasites via cyclical invasion of host erythrocytes using dedicated receptor-ligand interactions. A family of erythrocyte-binding proteins from Plasmodium knowlesi (Pk) and Plasmodium vivax (Pv) attach to human Duffy antigen receptor for chemokines (DARC) via their Duffy binding-like domains (DBLs) for invasion. Here we provide a novel, testable and overarching interaction model that rationalizes even contradictory pieces of evidence that have so far existed in the literature on Pk/Pv-DBL/DARC binding determinants. We further address the conundrum of how parasite-encoded Pk/Pv-DBLs recognize human DARC and collate evidence for two distinct DARC integration sites on Pk/Pv-DBLs.

The structure of the C-terminal domain of the nucleoprotein from the Bundibugyo strain of the Ebola virus in complex with a pan-specific synthetic Fab

The vast majority of platforms for the detection of viral or bacterial antigens rely on immunoassays, typically ELISA or sandwich ELISA, that are contingent on the availability of suitable monoclonal antibodies (mAbs). This is a major bottleneck, since the generation and production of mAbs is time-consuming and expensive. Synthetic antibody fragments (sFabs) generated by phage-display selection offer an alternative with many advantages over Fabs obtained from natural antibodies using hybridoma technology. Unlike mAbs, sFabs are generated using phage display, allowing selection for binding to specific strains or for pan-specificity, for identification of structural epitopes or unique protein conformations and even for complexes. Further, they can easily be produced in Escherichia coli in large quantities and engineered for purposes of detection technologies and other applications. Here, the use of phage-display selection to generate a pan-specific Fab (MJ20), based on a Herceptin Fab scaffold, with the ability to bind selectively and with high affinity to the C-terminal domains of the nucleoproteins (NPs) from all five known strains of the Ebola virus is reported. The high-resolution crystal structure of the complex of MJ20 with the antigen from the Bundibugyo strain of the Ebola virus reveals the basis for pan-specificity and illustrates how the phage-display technology can be used to manufacture suitable Fabs for use in diagnostic or therapeutic applications.

Investigation of protein quaternary structure via stoichiometry and symmetry information

The Protein Data Bank (PDB) is the single worldwide archive of experimentally-determined three-dimensional (3D) structures of proteins and nucleic acids. As of January 2017, the PDB housed more than 125,000 structures and was growing by more than 11,000 structures annually. Since the 3D structure of a protein is vital to understand the mechanisms of biological processes, diseases, and drug design, correct oligomeric assembly information is of critical importance. Unfortunately, the biologically relevant oligomeric form of a 3D structure is not directly obtainable by X-ray crystallography, whilst in solution methods (NMR or single particle EM) it is known from the experiment. Instead, this information may be provided by the PDB Depositor as metadata coming from additional experiments, be inferred by sequence-sequence comparisons with similar proteins of known oligomeric state, or predicted using software, such as PISA (Proteins, Interfaces, Structures and Assemblies) or EPPIC (Evolutionary Protein Protein Interface Classifier). Despite significant efforts by professional PDB Biocurators during data deposition, there remain a number of structures in the archive with incorrect quaternary structure descriptions (or annotations). Further investigation is, therefore, needed to evaluate the correctness of quaternary structure annotations. In this study, we aim to identify the most probable oligomeric states for proteins represented in the PDB. Our approach evaluated the performance of four independent prediction methods, including text mining of primary publications, inference from homologous protein structures, and two computational methods (PISA and EPPIC). Aggregating predictions to give consensus results outperformed all four of the independent prediction methods, yielding 83% correct, 9% wrong, and 8% inconclusive predictions, when tested with a well-curated benchmark dataset. We have developed a freely-available web-based tool to make this approach accessible to researchers and PDB Biocurators (http://quatstruct.rcsb.org/).

Automated evaluation of quaternary structures from protein crystals

A correct assessment of the quaternary structure of proteins is a fundamental prerequisite to understanding their function, physico-chemical properties and mode of interaction with other proteins. Currently about 90% of structures in the Protein Data Bank are crystal structures, in which the correct quaternary structure is embedded in the crystal lattice among a number of crystal contacts. Computational methods are required to 1) classify all protein-protein contacts in crystal lattices as biologically relevant or crystal contacts and 2) provide an assessment of how the biologically relevant interfaces combine into a biological assembly. In our previous work we addressed the first problem with our EPPIC (Evolutionary Protein Protein Interface Classifier) method. Here, we present our solution to the second problem with a new method that combines the interface classification results with symmetry and topology considerations. The new algorithm enumerates all possible valid assemblies within the crystal using a graph representation of the lattice and predicts the most probable biological unit based on the pairwise interface scoring. Our method achieves 85% precision (ranging from 76% to 90% for different oligomeric types) on a new dataset of 1,481 biological assemblies with consensus of PDB annotations. Although almost the same precision is achieved by PISA, currently the most popular quaternary structure assignment method, we show that, due to the fundamentally different approach to the problem, the two methods are complementary and could be combined to improve biological assembly assignments. The software for the automatic assessment of protein assemblies (EPPIC version 3) has been made available through a web server at http://www.eppic-web.org.

X-ray diffraction experiments are the main experimental technique to reveal the detailed atomic 3-dimensional structure of proteins. In these experiments, proteins are packed into crystals, an environment that is far away from their native solution environment. Determining which parts of the structure reflect the protein’s state in the cell rather than being artifacts of the crystal environment can be a difficult task. How the different protein subunits assemble together in solution is known as the quaternary structure. Finding the correct quaternary structure is important both to understand protein oligomerization and for the understanding of protein-protein interactions at large. Here we present a new method to automatically determine the quaternary structure of proteins given their crystal structure. We provide a theoretical basis for properties that correct protein assemblies should possess, and provide a systematic evaluation of all possible assemblies according to these properties. The method provides a guidance to the experimental structural biologist as well as to structural bioinformaticians analyzing protein structures in bulk. Assemblies are provided for all proteins in the Protein Data Bank through a public website and database that is updated weekly as new structures are released.

Inferring and Using Protein Quaternary Structure Information from Crystallographic Data

A precise knowledge of the quaternary structure of proteins is essential to illuminate both their function and their evolution. The major part of our knowledge on quaternary structure is inferred from X-ray crystallography data, but this inference process is hard and error-prone. The difficulty lies in discriminating fortuitous protein contacts, which make up the lattice of protein crystals, from biological protein contacts that exist in the native cellular environment. Here, we review methods devised to discriminate between both types of contacts and describe resources for downloading protein quaternary structure information and identifying high-confidence quaternary structures. The use of high-confidence datasets of quaternary structures will be critical for the analysis of structural, functional, and evolutionary properties of proteins.

The challenge of modeling protein assemblies: the CASP12-CAPRI experiment

We present the quality assessment of 5613 models submitted by predictor groups from both CAPRI and CASP for the total of 15 most tractable targets from the second joint CASP-CAPRI protein assembly prediction experiment. These targets comprised 12 homo-oligomers and 3 hetero-complexes. The bulk of the analysis focuses on 10 targets (of CAPRI Round 37), which included all 3 hetero-complexes, and whose protein chains or the full assembly could be readily modeled from structural templates in the PDB. On average, 28 CAPRI groups and 10 CASP groups (including automatic servers), submitted models for each of these 10 targets. Additionally, about 16 groups participated in the CAPRI scoring experiments. A range of acceptable to high quality models were obtained for 6 of the 10 Round 37 targets, for which templates were available for the full assembly. Poorer results were achieved for the remaining targets due to the lower quality of the templates available for the full complex or the individual protein chains, highlighting the unmet challenge of modeling the structural adjustments of the protein components that occur upon binding or which must be accounted for in template-based modeling. On the other hand, our analysis indicated that residues in binding interfaces were correctly predicted in a sizable fraction of otherwise poorly modeled assemblies and this with higher accuracy than published methods that do not use information on the binding partner. Lastly, the strengths and weaknesses of the assessment methods are evaluated and improvements suggested.

Assessment of protein assembly prediction in CASP12

We present the results of the first independent assessment of protein assemblies in CASP. A total of 1624 oligomeric models were submitted by 108 predictor groups for the 30 oligomeric targets in the CASP12 edition. We evaluated the accuracy of oligomeric predictions by comparison to their reference structures at the interface patch and residue contact levels. We find that interface patches are more reliably predicted than the specific residue contacts. Whereas none of the 15 hard oligomeric targets have successful predictions for the residue contacts at the interface, six have models with resemblance in the interface patch. Successful predictions of interface patch and contacts exist for all targets suitable for homology modeling, with at least one group improving over the best available template for each target. However, the participation in protein assembly prediction is low and uneven. Three human groups are closely ranked at the top by overall performance, but a server outperforms all other predictors for targets suitable for homology modeling. The state of the art of protein assembly prediction methods is in development and has apparent room for improvement, especially for assemblies without templates.

What macromolecular crystallogenesis tells us - what is needed in the future

Crystallogenesis is a longstanding topic that has transformed into a discipline that is mainly focused on the preparation of crystals for practising crystallo-graphers. Although the idiosyncratic features of proteins have to be taken into account, the crystallization of proteins is governed by the same physics as the crystallization of inorganic materials. At present, a diversified panel of crystallization methods adapted to proteins has been validated, and although only a few methods are in current practice, the success rate of crystallization has increased constantly, leading to the determination of ∼105 X-ray structures. These structures reveal a huge repertoire of protein folds, but they only cover a restricted part of macromolecular diversity across the tree of life. In the future, crystals representative of missing structures or that will better document the structural dynamics and functional steps underlying biological processes need to be grown. For the pertinent choice of biologically relevant targets, computer-guided analysis of structural databases is needed. From another perspective, crystallization is a self-assembly process that can occur in the bulk of crowded fluids, with crystals being supramolecular assemblies. Life also uses self-assembly and supramolecular processes leading to transient, or less often stable, complexes. An integrated view of supramolecularity implies that proteins crystallizing either in vitro or in vivo or participating in cellular processes share common attributes, notably determinants and antideterminants that favour or disfavour their correct or incorrect associations. As a result, under in vivo conditions proteins show a balance between features that favour or disfavour association. If this balance is broken, disorders/diseases occur. Understanding crystallization under in vivo conditions is a challenge for the future. In this quest, the analysis of packing contacts and contacts within oligomers will be crucial in order to decipher the rules governing protein self-assembly and will guide the engineering of novel biomaterials. In a wider perspective, understanding such contacts will open the route towards supramolecular biology and generalized crystallogenesis.

Mutations in SULT2B1 Cause Autosomal-Recessive Congenital Ichthyosis in Humans

Ichthyoses are a clinically and genetically heterogeneous group of genodermatoses associated with abnormal scaling of the skin over the whole body. Mutations in nine genes are known to cause non-syndromic forms of autosomal-recessive congenital ichthyosis (ARCI). However, not all genetic causes for ARCI have been discovered to date. Using whole-exome sequencing (WES) and multigene panel screening, we identified 6 ARCI-affected individuals from three unrelated families with mutations in Sulfotransferase family 2B member 1 (SULT2B1), showing their causative association with ARCI. Cytosolic sulfotransferases form a large family of enzymes that are involved in the synthesis and metabolism of several steroids in humans. We identified four distinct mutations including missense, nonsense, and splice site mutations. We demonstrated the loss of SULT2B1 expression at RNA and protein levels in keratinocytes from individuals with ARCI by functional analyses. Furthermore, we succeeded in reconstructing the morphologic skin alterations in a 3D organotypic tissue culture model with SULT2B1-deficient keratinocytes and fibroblasts. By thin layer chromatography (TLC) of extracts from these organotypic cultures, we could show the absence of cholesterol sulfate, the metabolite of SULT2B1, and an increased level of cholesterol, indicating a disturbed cholesterol metabolism of the skin upon loss-of-function mutation in SULT2B1. In conclusion, our study reveals an essential role for SULT2B1 in the proper development of healthy human skin. Mutation in SULT2B1 leads to an ARCI phenotype via increased proliferation of human keratinocytes, thickening of epithelial layers, and altered epidermal cholesterol metabolism.

Systematic substitutions at BLIP position 50 result in changes in binding specificity for class A β-lactamases

Background

The production of β-lactamases by bacteria is the most common mechanism of resistance to the widely prescribed β-lactam antibiotics. β-lactamase inhibitory protein (BLIP) competitively inhibits class A β-lactamases via two binding loops that occlude the active site. It has been shown that BLIP Tyr50 is a specificity determinant in that substitutions at this position result in large differential changes in the relative affinity of BLIP for class A β-lactamases.

Results

In this study, the effect of systematic substitutions at BLIP position 50 on binding to class A β-lactamases was examined to further explore the role of BLIP Tyr50 in modulating specificity. The results indicate the sequence requirements at position 50 are widely different depending on the target β-lactamase. Stringent sequence requirements were observed at Tyr50 for binding Bacillus anthracis Bla1 while moderate requirements for binding TEM-1 and relaxed requirements for binding KPC-2 β-lactamase were seen. These findings cannot be easily rationalized based on the β-lactamase residues in direct contact with BLIP Tyr50 since they are identical for Bla1 and KPC-2 suggesting that differences in the BLIP-β-lactamase interface outside the local environment of Tyr50 influence the effect of substitutions.

Conclusions

Results from this study and previous studies suggest that substitutions at BLIP Tyr50 may induce changes at the interface outside its local environment and point to the complexity of predicting the impact of substitutions at a protein-protein interaction interface.

BLIP-II Employs Differential Hotspot Residues To Bind Structurally Similar Staphylococcus aureus PBP2a and Class A β-Lactamases

The interaction of β-lactamase inhibitory protein II (BLIP-II) with β-lactamases serves as a model system to investigate the principles underlying protein-protein interactions. Previous studies have focused on identifying the determinants of binding affinity and specificity between BLIP-II and class A β-lactamases. However, interactions between BLIP-II and other bacterial proteins have yet to be explored. Here, we provide evidence that BLIP-II binds penicillin binding protein 2a (PBP2a) from methicillin-resistant Staphylococcus aureus (MRSA) with a K_D in the low micromolar range. In comparison to the binding constants for the potent interaction between BLIP-II and TEM-1 β-lactamase (K_D = 0.5 pM), the on-rate for BLIP-II binding PBP2a is 44 000 times slower and the off-rate is 170 times faster. Therefore, a slow association rate is a limiting factor for the potency of the interaction between BLIP-II and PBP2a. Results from alanine scanning mutagenesis of the predicted interface residues of BLIP-II indicate that charged residues on the periphery of the BLIP-II interface play a critical role for binding PBP2a, in contrast to previous findings that aromatic residues at the center of the BLIP-II interface are critical for the interaction with β-lactamases. Interestingly, many of the alanine mutants at the BLIP-II interface increase k_on for binding PBP2a, consistent with the association rate being a limiting factor for affinity. In summary, the results of the study reveal that BLIP-II binds PBP2a, although weakly compared to binding of β-lactamases, and provides insights into the different binding strategies used for these targets.

Biosynthesis of Violacein, Structure and Function of l-Tryptophan Oxidase VioA from Chromobacterium violaceum

Violacein is a natural purple pigment of Chromobacterium violaceum with potential medical applications as antimicrobial, antiviral, and anticancer drugs. The initial step of violacein biosynthesis is the oxidative conversion of l-tryptophan into the corresponding α-imine catalyzed by the flavoenzyme l-tryptophan oxidase (VioA). A substrate-related (3-(1H-indol-3-yl)-2-methylpropanoic acid) and a product-related (2-(1H-indol-3-ylmethyl)prop-2-enoic acid) competitive VioA inhibitor was synthesized for subsequent kinetic and x-ray crystallographic investigations. Structures of the binary VioA·FADH2 and of the ternary VioA·FADH2·2-(1H-indol-3-ylmethyl)prop-2-enoic acid complex were resolved. VioA forms a "loosely associated" homodimer as indicated by small-angle x-ray scattering experiments. VioA belongs to the glutathione reductase family 2 of FAD-dependent oxidoreductases according to the structurally conserved cofactor binding domain. The substrate-binding domain of VioA is mainly responsible for the specific recognition of l-tryptophan. Other canonical amino acids were efficiently discriminated with a minor conversion of l-phenylalanine. Furthermore, 7-aza-tryptophan, 1-methyl-tryptophan, 5-methyl-tryptophan, and 5-fluoro-tryptophan were efficient substrates of VioA. The ternary product-related VioA structure indicated involvement of protein domain movement during enzyme catalysis. Extensive structure-based mutagenesis in combination with enzyme kinetics (using l-tryptophan and substrate analogs) identified Arg(64), Lys(269), and Tyr(309) as key catalytic residues of VioA. An increased enzyme activity of protein variant H163A in the presence of l-phenylalanine indicated a functional role of His(163) in substrate binding. The combined structural and mutational analyses lead to the detailed understanding of VioA substrate recognition. Related strategies for the in vivo synthesis of novel violacein derivatives are discussed.

Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts

Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein–protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein–protein docking.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact:guido.capitani@psi.ch

A structural dissection of large protein-protein crystal packing contacts

With the rapid increase in crystal structures of protein-protein complexes deposited in the Protein Data Bank (PDB), more and more crystal contacts have been shown to have similar or even larger interface areas than biological interfaces. However, little attention has been paid to these large crystal packing contacts and their structural principles remain unknown. To address this issue, we used a comparative feature analysis to analyze the geometric and physicochemical properties of large crystal packing contacts by comparing two types of specific protein-protein interactions (PPIs), weak transient complexes and permanent homodimers. Our results show that although large crystal packing contacts have a similar interface area and contact size as permanent homodimers, they tend to be more planar, loosely packed and less hydrophobic than permanent homodimers and cannot form a central core region that is fully buried during interaction. However, the properties of large crystal packing contacts, except for the interface area and contact size, more closely resemble those of weak transient complexes. The large overlap between biological and large crystal packing contacts indicates that interface properties are not efficient indicators for classification of biological interfaces from large crystal packing contacts and finding other specific features urgently needed.