Number of estimated complex folds for a range of numbers of complex families.

<p>Number of estimated complex folds for a range of numbers of complex families.</p

The estimated number of quaternary folds versus the number of quaternary families in nature.

<p>The solid curve is the fitting from Eq. 13 and dotted line indicates the number of quaternary families following Orengo <i>et al</i>. estimation.</p

Histogram of complex structural clusters versus size of the clusters.

<p>The solid curve is the fitting result from Eq. 12. Inset: the same data drawn in logarithm scale.</p

The number of new complex structure entries deposited per year in the PDB.

<p>Data are presented in terms of unique structures (sequence identity <90%), families (mapped with unique Pfam families), and folds (rTM-score <0.5).</p

Evaluation of designed sequences.

<p>Data is averaged over 87 test proteins. The details on each protein can be found at <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003298#pcbi.1003298.s007" target="_blank">Table S2</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003298#pcbi.1003298.s008" target="_blank">S3</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003298#pcbi.1003298.s009" target="_blank">S4</a>.</p>a<p>TM-score between the first I-TASSER model and the target scaffold.</p>b<p>RMSD between the first I-TASSER model and the target scaffold.</p>c<p>SS: Secondary structure.</p>d<p>SA: Solvent accessibility.</p>e<p>PBM: Physics-based method using FoldX.</p>f<p>EvBM: Evolution-based method using only evolutionary terms in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003298#pcbi.1003298.e004" target="_blank">Eq. (1)</a>.</p>g<p>EBM: Evolutionary based method using both evolutionary and physics-based terms in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003298#pcbi.1003298.e006" target="_blank">Eq. (3)</a>.</p

Summary of experimental validation results for the five designed sequences^a.

a<p>The sign of “+” and “−” indicates positive and negative experimental results respectively.</p>b<p>RMSD between the first I-TASSER model and the target scaffold.</p>c<p>Protein expression and solubility determined by visual identification via comassie stain gels.</p>d<p>Presence of secondary structural elements defined by circular dichroism.</p>e<p>Possession of a stable tertiary fold determined by the presence of secondary structural elements (CD) and NMR spectroscopy.</p>f<p>Percentage of α-helix residues decided by the CD spectra (the values in parentheses are the number in the scaffold structure.</p

Free energy of folding was determined by circular dichroism.

<p>(A) thioredoxin; (B) CISK-PX domains. The figure plots free energy (kJ/mol) versus the concentration [M] of chemical denaturant urea. The unfolding assay was conducted in 25 mM NaPO₄, 150 mM NaF, pH 7.5 with 2–3 uM protein concentration and 0–9.5 M urea concentrations at 298 K. The free energies of folding are equal to the intercept through linear regression <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003298#pcbi.1003298-Greenfield1" target="_blank">[76]</a>.</p

Average RMSD between the scaffold structures and I-TASSER models on the proteins designed by PBM and EBM.

<p>The dataset is divided by the TM-score cutoff of the template proteins that were used for constructing the sequence profiles.</p

Average fractional difference in amino acid composition between the target and designed sequences.

<p>(A) EBM; (B) PBM.</p

An Evolution-Based Approach to <i>De Novo</i> Protein Design and Case Study on <i>Mycobacterium tuberculosis</i>

<div><p>Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria <i>Mycobacterium tuberculosis</i>, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.</p></div