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Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins., Author Summary Proteins, the ubiquitous worker molecules of the cell, are a diverse class of molecules that perform very specific tasks. Understanding how proteins achieve specificity is a critical step towards understanding biological systems and a key prerequisite for rationally engineering new proteins. To examine electrostatic influences on specificity in proteins, this paper presents VASP-E, a software tool that generates solid representations of the electrostatic potential fields that surround proteins. VASP-E compares solids with constructive solid geometry, a class of techniques developed first for modeling complex machine parts. We observed that solid representations could quantify the degree of charge complementarity in protein-protein interactions and identify key residues that strengthen or weaken them. VASP-E correctly identified amino acids with established experimental influences on protein-protein binding specificity. We also observed that solid representations of electrostatic fields could identify electrostatic conservations and variations that relate to similarities and differences in binding specificity between proteins and small molecules.

Antigen recognition by T-cells is guided by the T-cell receptor (TCR) heterodimer formed by α and β chains. A huge diversity of TCR sequences should be maintained by the immune system in order to be able to mount an effective response towards foreign pathogens, so, due to cooperative binding of α and β chains to the pathogen, any constraints on chain pairing can have a profound effect on immune repertoire structure, diversity and antigen specificity. By integrating available structural data and paired chain sequencing results we were able to show that there are almost no constraints on pairing in TCRαβ complexes, allowing naive T-cell repertoire to reach the highest possible diversity. Additional analysis reveals that the specific choice of contacting amino acids can still have a profound effect on complex conformation. Moreover, antigen-driven selection can distort the uniform landscape of chain pairing, while small, yet significant, differences in the pairing can be attributed to various specialized T-cell subsets such as MAIT and iNKT T-cells, as well as other TCR sets specific to certain antigens., Author summary In the present paper we study chain pairing preferences in the T-cell receptor (TCR) heterodimer complex. The TCR molecule is formed by α and β chains and binding of both of these chains to an antigen presented by the major histocompatibility complex (MHC) molecule is required in order to trigger an immune response against foreign pathogens and neoantigens. We show that chain pairing in the TCR complex is nearly random ensuring a highly diverse set of TCRs required for recognition of a vast set of antigens. Our results also show that chain pairing preferences can nevertheless influence TCR complex geometry and biases in TCR chain pairing can be used to identify antigen-driven selection or selection towards specialized subsets of T-cells such as mucosal-associated and natural killer invariant T-cells.

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, learning from these data to predict how a cell will respond to genetic changes, a key need for engineering, remains challenging. A promising technique for leveraging omics measurements in metabolic modeling involves the construction of kinetic descriptions of the enzymatic reactions that occur within a cell. Parameterizing these models from biological data can be computationally difficult, since methods must also quantify the uncertainty in model parameters resulting from the observed data. While the field of Bayesian inference offers a wide range of methods for efficiently estimating distributions in parameter uncertainty, such techniques are poorly suited to traditional kinetic models due to their complex rate laws and resulting nonlinear dynamics. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and inference methods. We demonstrate that detailed information on the posterior distribution of parameters can be obtained efficiently at a variety of problem scales, including nearly genome-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and in developing new, efficient strain designs., Author Summary Genetic engineering of microbes is a promising strategy to enable more efficient and environmentally friendly production routes for chemicals and materials traditionally produced from petroleum. While the tools to both edit microbial genomes and measure the resulting changes to cellular physiology are growing increasingly efficient, our ability to predict which genetic interventions will have the greatest likelihood of achieving the desired phenotype is still lacking. In particular, computational methods are needed that are able to efficiently ingest the increasing amount of data generated from large biological datasets to guide subsequent rounds of experiments. This study presents an efficient algorithm coupled to modern computational tools that permits a closer integration of modeling in the design, build, test, and learn cycle of genetic engineering.

The enzymes of the GCN5-related N-acetyltransferase (GNAT) superfamily count more than 870 000 members through all kingdoms of life and share the same structural fold. GNAT enzymes transfer an acyl moiety from acyl coenzyme A to a wide range of substrates including aminoglycosides, serotonin, glucosamine-6-phosphate, protein N-termini and lysine residues of histones and other proteins. The GNAT subtype of protein N-terminal acetyltransferases (NATs) alone targets a majority of all eukaryotic proteins stressing the omnipresence of the GNAT enzymes. Despite the highly conserved GNAT fold, sequence similarity is quite low between members of this superfamily even when substrates are similar. Furthermore, this superfamily is phylogenetically not well characterized. Thus functional annotation based on sequence similarity is unreliable and strongly hampered for thousands of GNAT members that remain biochemically uncharacterized. Here we used sequence similarity networks to map the sequence space and propose a new classification for eukaryotic GNAT acetyltransferases. Using the new classification, we built a phylogenetic tree, representing the entire GNAT acetyltransferase superfamily. Our results show that protein NATs have evolved more than once on the GNAT acetylation scaffold. We use our classification to predict the function of uncharacterized sequences and verify by in vitro protein assays that two fungal genes encode NAT enzymes targeting specific protein N-terminal sequences, showing that even slight changes on the GNAT fold can lead to change in substrate specificity. In addition to providing a new map of the relationship between eukaryotic acetyltransferases the classification proposed constitutes a tool to improve functional annotation of GNAT acetyltransferases., Author summary Enzymes of the GCN5-related N-acetyltransferase (GNAT) superfamily transfer an acetyl group from one molecule to another. This reaction is called acetylation and is one of the most common reactions inside the cell. The GNAT superfamily counts more than 870 000 members through all kingdoms of life. Despite sharing the same fold the GNAT superfamily is very diverse in terms of amino acid sequence and substrates. The eight N-terminal acetyltransferases (NatA, NatB, etc.. to NatH) are a GNAT subtype which acetylates the free amine group of polypeptide chains. This modification is called N-terminal acetylation and is one of the most abundant protein modifications in eukaryotic cells. This subtype is also characterized by a high sequence diversity even though they share the same substrate. In addition, the phylogeny of the superfamily is not characterized. This hampers functional annotation based on sequence similarity, and discovery of novel NATs. In this work we set out to solve the problem of the classification of eukaryotic GCN5-related acetyltransferases and report the first classification framework of the superfamily. This framework can be used as a tool for annotation of all GCN5-related acetyltransferases. As an example of what can be achieved we report in this paper the computational prediction and in vitro verification of the function of two previously uncharacterized N-terminal acetyltransferases. We also report the first acetyltransferase phylogenetic tree of the GCN5 superfamily. It indicates that N-terminal acetyltransferases do not constitute one homogeneous protein family, but that the ability to bind and acetylate protein N-termini had evolved more than once on the same acetylation scaffold. We also show that even small changes in key positions can lead to altered enzyme specificity.

Highly coordinated water molecules are frequently an integral part of protein-protein and protein-ligand interfaces. We introduce an updated energy model that efficiently captures the energetic effects of these ordered water molecules on the surfaces of proteins. A two-stage method is developed in which polar groups arranged in geometries suitable for water placement are first identified, then a modified Monte Carlo simulation allows highly coordinated waters to be placed on the surface of a protein while simultaneously sampling amino acid side chain orientations. This “semi-explicit” water model is implemented in Rosetta and is suitable for both structure prediction and protein design. We show that our new approach and energy model yield significant improvements in native structure recovery of protein-protein and protein-ligand docking discrimination tests., Author summary Well-coordinated water molecules—those forming multiple hydrogen bonds with nearby polar groups—play an important role in the structure of biomolecular systems, yet the effect of these waters is often not considered in molecular energy computations. In this paper, we describe a method to efficiently consider these water molecules both implicitly and explicitly at the interfaces formed by two polar molecules. In computations related to determining how a protein interacts with binding partners, we show that the use of this new method significantly improves results. Future application of this approach may improve the design of new protein and small molecule drugs.