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.

The following resources for comparative protein structure modeling and analysis are described (http://salilab.org): MODELLER, a program for comparative modeling by satisfaction of spatial restraints; MODWEB, a web server for automated comparative modeling that relies on PSI-BLAST, IMPALA and MODELLER; MODLOOP, a web server for automated loop modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparative models based on distant known structures; MODBASE, a comprehensive database of annotated comparative models for all sequences detectably related to a known structure; MODVIEW, a Netscape plugin for Linux that integrates viewing of multiple sequences and structures; and SNPWEB, a web server for structure-based prediction of the functional impact of a single amino acid substitution.

The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.

Residue depth accurately measures burial and parameterizes local protein environment. Depth is the distance of any atom/residue to the closest bulk water. We consider the non-bulk waters to occupy cavities, whose volumes are determined using a Voronoi procedure. Our estimation of cavity sizes is statistically superior to estimates made by CASTp and VOIDOO, and on par with McVol over a data set of 40 cavities. Our calculated cavity volumes correlated best with the experimentally determined destabilization of 34 mutants from five proteins. Some of the cavities identified are capable of binding small molecule ligands. In this study, we have enhanced our depth-based predictions of binding sites by including evolutionary information. We have demonstrated that on a database (LigASite) of ∼200 proteins, we perform on par with ConCavity and better than MetaPocket 2.0. Our predictions, while less sensitive, are more specific and precise. Finally, we use depth (and other features) to predict pKas of GLU, ASP, LYS and HIS residues. Our results produce an average error of just <1 pH unit over 60 predictions. Our simple empirical method is statistically on par with two and superior to three other methods while inferior to only one. The DEPTH server (http://mspc.bii.a-star.edu.sg/depth/) is an ideal tool for rapid yet accurate structural analyses of protein structures.