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Calculating protein stability and predicting stabilizing mutations remain exceedingly difficult tasks, largely due to the inadequacy of potential functions, the difficulty of modeling entropy and the unfolded state, and challenges of sampling, particularly of backbone conformations. Yet, computational design has produced some remarkably stable proteins in recent years, apparently owing to near ideality in structure and sequence features. With caveats, computational prediction of stability can be used to guide mutation, and mutations derived from consensus sequence analysis, especially improved by recent co-variation filters, are very likely to stabilize without sacrificing function. The combination of computational and statistical approaches with library approaches, including new technologies such as deep sequencing and high throughput stability measurements, point to a very exciting near term future for stability engineering, even with difficult computational issues remaining., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Most proteins are only barely stable, which impedes research, complicates therapeutic applications, and makes proteins susceptible to pathologically destabilizing mutations. Our ability to predict the thermodynamic consequences of even single point mutations is still surprisingly limited, and established methods of measuring stability are slow. Recent advances are bringing protein stability studies into the high-throughput realm. Some methods are based on inferential read-outs such as activity, proteolytic resistance or split-protein fragment reassembly. Other methods use miniaturization of direct measurements, such as intrinsic fluorescence, H/D exchange, cysteine reactivity, aggregation and hydrophobic dye binding (DSF). Protein engineering based on statistical analysis (consensus and correlated occurrences of amino acids) is promising, but much work remains to understand and implement these methods., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The low stability of natural proteins often limits their use in therapeutic, industrial, and research applications. The scale and throughput of methods such as circular dichroism, fluorescence spectroscopy, and calorimetry severely limit the number of variants that can be examined. Here we demonstrate a high-throughput thermal scanning (HTTS) method for determining the approximate stabilities of protein variants at high throughput and low cost. The method is based on binding to a hydrophobic dye akin to ANS, which fluoresces upon binding to molten globules and thermal denaturation intermediates. No inherent properties of the protein, such as enzymatic activity or presence of an intrinsic fluorophore, are required. Very small sample sizes are analyzed using a real-time PCR machine, enabling the use of high-throughput purification. We show that the apparent T(M) values obtained from HTTS are approximately linearly related to those from CD thermal denaturation for a series of four-helix bundle hydrophobic core variants. We demonstrate similar results for a small set of TIM barrel variants. This inexpensive, general, and scaleable approach enables the search for conservative, stable mutants of biotechnologically important proteins and provides a method for statistical correlation of sequence-stability relationships.

The concept of consensus in multiple sequence alignments (MSAs) has been used to design and engineer proteins previously with some success. However, consensus design implicitly assumes that all amino acid positions function independently, whereas in reality, the amino acids in a protein interact with each other and work cooperatively to produce the optimum structure required for its function. Correlation analysis is a tool that can capture the effect of such interactions. In a previously published study, we made consensus variants of the triosephosphate isomerase (TIM) protein using MSAs that included sequences form both prokaryotic and eukaryotic organisms. These variants were not completely native‐like and were also surprisingly different from each other in terms of oligomeric state, structural dynamics, and activity. Extensive correlation analysis of the TIM database has revealed some clues about factors leading to the unusual behavior of the previously constructed consensus proteins. Among other things, we have found that the more ill‐behaved consensus mutant had more broken correlations than the better‐behaved consensus variant. Moreover, we report three correlation and phylogeny‐based consensus variants of TIM. These variants were more native‐like than the previous consensus mutants and considerably more stable than a wild‐type TIM from a mesophilic organism. This study highlights the importance of choosing the appropriate diversity of MSA for consensus analysis and provides information that can be used to engineer stable enzymes. [ABSTRACT FROM AUTHOR]

Abstract: Non‐natural protein sequences with native‐like structures and functions can be constructed successfully using consensus design. This design strategy is relatively well understood in repeat proteins with simple binding function, however detailed studies are lacking in globular enzymes. The SOD1 family is a good model for such studies due to the availability of large amount of sequence and structure data motivated by involvement of human SOD1 in the fatal motor neuron disease amyotrophic lateral sclerosis (ALS). We constructed two consensus SOD1 enzymes from multiple sequence alignments from all organisms and eukaryotic organisms. A significant difference in their catalytic activities shows that the phylogenetic spread of the sequences used affects the fitness of the construct obtained. A mutation in an electrostatic loop and overall design incompatibilities between bacterial and eukaryotic sequences were implicated in this disparity. Based on this analysis, a bioinformatics approach was used to classify mutations thought to cause familial ALS providing a unique high level view of the physical basis of disease‐causing aggregation of human SOD1. [ABSTRACT FROM AUTHOR]

The sequence of a protein specifies its three-dimensional structure, but different sequences can specify the same fold, and the stabilities of these variants can differ vastly. Nearly every biological process, from metabolism to signal transduction, to the structural integrity of the cell, involves proteins. Yet the understanding of the fundamental forces that stabilize structurally diverse proteins still amounts mainly to rules-of-thumb, derived from a relatively small number of mutants of a relatively small number of well-studied proteins. Many common diseases are the result of protein mutations that lead to instability; many therapeutically or industrially interesting proteins are insufficiently stable for administration or process conditions; the identification of ever more sequences from genomic efforts makes the prediction of structure from sequence increasingly important; and, despite a few recent successes, protein design is still in its infancy, and even fairly successful computational approaches generally neglect protein backbone motion and employ simple potential functions of which the validity is difficult to assess. In fact, there is no generally valid way to calculate the stability of a protein, or even to quantitatively predict the effects of point mutation.

Abstract: Understanding the determinants of protein stability remains one of protein science''s greatest challenges. There are still no computational solutions that calculate the stability effects of even point mutations with sufficient reliability for practical use. Amino acid substitutions rarely increase the stability of native proteins; hence, large libraries and high-throughput screens or selections are needed to stabilize proteins using directed evolution. Consensus mutations have proven effective for increasing stability, but these mutations are successful only about half the time. We set out to understand why some consensus mutations fail to stabilize, and what criteria might be useful to predict stabilization more accurately. Overall, consensus mutations at more conserved positions were more likely to be stabilizing in our model, triosephosphate isomerase (TIM) from Saccharomyces cerevisiae. However, positions coupled to other sites were more likely not to stabilize upon mutation. Destabilizing mutations could be removed both by removing sites with high statistical correlations to other positions and by removing nearly invariant positions at which “hidden correlations” can occur. Application of these rules resulted in identification of stabilizing mutations in 9 out of 10 positions, and amalgamation of all predicted stabilizing positions resulted in the most stable yeast TIM variant we produced (+8 °C). In contrast, a multimutant with 14 mutations each found to stabilize TIM independently was destabilized by 2 °C. Our results are a practical extension to the consensus concept of protein stabilization, and they further suggest the importance of positional independence in the mechanism of consensus stabilization. [Copyright &y& Elsevier]