Supplemental methods Homology ModellingThe homology model of the NatA WT complex was built with Modeller version 9.8 (1) using the NatA complex of S. pombe as a template. The sequence identity between the target and the template is around 67% and 37% for the subunits Naa10 and Naa15, respectively (Figure S1). The sequence alignments were obtained using ClustalW. Thirty models were generated and evaluated using the Discrete Optimized Protein Energy potential (DOPE score). The models with the lowest overall DOPE score were selected. The S37P NatA complex was designed from the human WT NatA complex using SCWRL instead of Modeller, in order to have starting structures as similar as possible as to avoid bias in the comparison between the wild type and mutant NatA complex. System PreparationPROPKA (2) was used to determine the protonation state of histidines. All other titratable groups were modelled in their standard protonation states. Hydrogen atoms were constructed using the HBUILD module of the CHARMM program (3). The complexes were solvated in cubic boxes of TIP3P water (4) with 120 Å-long edges. Water molecules overlapping the proteins, determined by a cut-off of 2.8Å, were removed. Molecular dynamicsMolecular dynamics (MD) simulations were used to explore the conformational space. As the aim was to uncover differences between the two systems, we ran long simulations (100 ns) in order to allow the systems to rearrange. These simulations were performed at a temperature of 300K using the NAMD program (5) and the CHARMM27 force field (4). The SHAKE algorithm was used to constrain all bonds between hydrogen and heavy atoms. Non-bonded interactions were truncated at a cut-off of 14Å, using a switch function for both the van der Waals, and electrostatic interactions (6). The particle-mesh Ewald algorithm (7) was used to evaluate the long range electrostatic interactions. The system was subjected to an energy minimization of 1000 steps using the conjugated gradient algorithm, followed by a gradual heating consisting of four successive simulations at temperatures of 10K, 100K, 200K and 300K. This was followed by a 1 ns equilibration phase during which velocities were reassigned every picosecond. The production phase consisted of a 100 ns simulation in the NPT ensemble, with a time step of 1fs. Two simulations (replicas) using a different set of

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

A set of software tools for building and distributing models of macromolecular assemblies uses an integrative structure modeling approach, which casts the building of models as a computational optimization problem where information is encoded into a scoring function used to evaluate candidate models.