ENCoM is a coarse-grained normal mode analysis method recently introduced that unlike previous such methods is unique in that it accounts for the nature of amino acids. The inclusion of this layer of information was shown to improve conformational space sampling and apply for the first time a coarse-grained normal mode analysis method to predict the effect of single point mutations on protein dynamics and thermostability resulting from vibrational entropy changes. Here we present a web server that allows non-technical users to have access to ENCoM calculations to predict the effect of mutations on thermostability and dynamics as well as to generate geometrically realistic conformational ensembles. The server is accessible at: http://bcb.med.usherbrooke.ca/encom.

Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with Cα−only representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.

Three billion years of evolution have produced a tremendous diversity of protein molecules, and yet the full potential of this molecular class is likely far greater. Accessing this potential has been challenging for computation and experiments because the space of possible protein molecules is much larger than the space of those likely to host function. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems based on random graph neural networks that enables long-range reasoning with sub-quadratic scaling, equivariant layers for efficiently synthesizing 3D structures of proteins from predicted inter-residue geometries, and a general low-temperature sampling algorithm for diffusion models. We suggest that Chroma can effectively realize protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics, and even natural language prompts. With this unified approach, we hope to accelerate the prospect of programming protein matter for human health, materials science, and synthetic biology.

PixelDB, the Peptide Exosite Location Database, compiles 1966 non-redundant, high-resolution structures of protein-peptide complexes filtered to minimize the impact of crystal packing on peptide conformation. The database is organized to facilitate study of structurally conserved versus non-conserved elements of protein-peptide engagement. PixelDB clusters complexes based on the structural similarity of the peptide-binding protein, and by comparing complexes within a cluster highlights examples of domains that engage peptides using more than one binding mode. PixelDB also identifies conserved peptide core structural motifs characteristic of each binding mode. Peptide regions that flank core motifs often make non-structurally conserved interactions with the protein surface in regions we call exosites. Many examples establish that exosite contacts can be important for enhancing protein binding and interaction specificity. PixelDB provides a resource for computational and structural biologists to study, model, and predict core-motif and exosite-contacting peptide interactions. PixelDB is available to the community without restriction in a convenient flat-file format with accompanying visualization tools.Keywords: protein-peptide interactions; exosites; structural biology database; peptide binding motifs; peptide docking Abbreviations: CSV, comma separated values; ECR, exosite contacting region; ELM, eukaryotic linear motif; NISR, non-interacting surface residues; PDB, protein data bank; TM, template modeling Additional Supporting Information may be found in the online version of this article.Short Statement: The protein data bank (PDB) contains >100,000 solved structures and is a rich source of information about how proteins execute their functions. To provide a resource for scientists studying how proteins bind to peptides, the database PixelDB compiles 1966 high-quality structures of protein-peptide complexes and organizes them into related clusters. The database annotates structurally conserved and non-conserved elements in interaction interfaces and can be used to study determinants of peptide binding affinity and specificity.

Understanding the relationship between protein sequence and structure well enough to rationally design novel proteins or protein complexes is a longstanding goal in protein science. The Protein Data Bank (PDB) is a key resource for defining sequence-structure relationships that has supported the development of critical resources such as rotamer libraries and backbone torsional statistics that quantify the probabilities of protein sequences adopting different structures. Here, we show that well-defined, noncontiguous structural motifs (TERMs) in the PDB can also provide rich information useful for proteinpeptide interaction prediction and design. Specifically, we show that it is possible to rapidly predict the binding energies of peptides to Bcl-2 family proteins as accurately as can be done with widely used structure-based tools, without explicit atomistic modeling. One benefit of a TERM-based approach is that prediction performance is less sensitive to the details of the input structure than are methods that evaluate energies using precise atomic coordinates. We show that protein design using TERM energies (dTERMen) can generate highly novel and diverse peptides to target anti-apoptotic proteins Bfl-1 and Mcl-1. 15 of 17 peptides designed using dTERMen bound tightly to their intended targets, and these peptides have just 15 -38% sequence identity to any known native Bcl-2 family protein ligand. Highresolution structures of four designed peptides bound to their targets provided opportunities to analyze strengths and limitations of this approach. Dramatic success designing peptides using dTERMen, which comprised going from input structure to experimental validation of high-affinity binders in approximately one month, provides strong motivation for further developing TERM-based approaches to design.

Graphical AbstractHighlights d Information in tertiary structural motifs is used to score protein complexes d Bcl-2 protein interactions are predicted and designed without atomistic modeling d Motif-based design generates high-affinity peptides quickly and reliably d High-resolution structures show how designs engage Bfl-1 and Mcl-1 SUMMARYUnderstanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. Highresolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.

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Tuberculosis remains as one of the main cause of mortality worldwide due to a single infectious agent, Mycobacterium tuberculosis. The aroK-encoded M. tuberculosis Shikimate Kinase (MtSK), shown to be essential for survival of bacilli, catalyzes the phosphoryl transfer from ATP to the carbon-3 hydroxyl group of shikimate (SKH), yielding shikimate-3-phosphate and ADP. Here we present purification to homogeneity, and oligomeric state determination of recombinant MtSK. Biochemical and biophysical data suggest that the chemical reaction catalyzed by monomeric MtSK follows a rapid-equilibrium random order of substrate binding, and ordered product release. Isothermal titration calorimetry (ITC) for binding of ligands to MtSK provided thermodynamic signatures of non-covalent interactions to each process. A comparison of steady-state kinetics parameters and equilibrium dissociation constant value determined by ITC showed that ATP binding does not increase the affinity of MtSK for SKH. We suggest that MtSK would more appropriately be described as an aroL-encoded type II shikimate kinase. Our manuscript also gives thermodynamic description of SKH binding to MtSK and data for the number of protons exchanged during this bimolecular interaction. The negative value for the change in constant pressure heat capacity (ΔCp) and molecular homology model building suggest a pronounced contribution of desolvation of non-polar groups upon binary complex formation. Thermodynamic parameters were deconvoluted into hydrophobic and vibrational contributions upon MtSK:SKH binary complex formation. Data for the number of protons exchanged during this bimolecular interaction are interpreted in light of a structural model to try to propose the likely amino acid side chains that are the proton donors to bulk solvent following MtSK:SKH complex formation.