Sequence periodicity and secondary structure propensity in model proteins

Human antibody polyreactivity is governed primarily by the heavy-chain complementarity-determining regions

Although antibody variable regions mediate antigen-specific binding, they can also mediate non-specific interactions with non-cognate antigens, impacting diverse immunological processes and the efficacy, safety, and half-life of antibody therapeutics. To understand the molecular basis of antibody non-specificity, we sorted two dissimilar human naïve antibody libraries against multiple reagents to enrich for variants with different levels of polyreactivity. Sequence analysis of >300,000 paired antibody variable regions revealed that the heavy chain primarily mediates human antibody polyreactivity, and this is due to the high positive charge, high hydrophobicity, and combinations thereof in the corresponding complementarity-determining regions, which can be predicted using a machine learning model developed in this work. Notably, a subset of the most important features governing antibody non-specific interactions, namely those that contain tyrosine, also govern specific antigen recognition. Our findings are broadly relevant for understanding fundamental aspects of antibody molecular recognition and the applied aspects of antibody-drug design.

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning

Regulatory proteins have evolved diverse repressor domains (RDs) to enable precise context-specific repression of transcription. However, our understanding of how sequence variation impacts the functional activity of RDs is limited. To address this gap, we generated a high-throughput mutational scanning dataset measuring the repressor activity of 115,000 variant sequences spanning more than 50 RDs in human cells. We identified thousands of clinical variants with loss or gain of repressor function, including TWIST1 HLH variants associated with Saethre-Chotzen syndrome and MECP2 domain variants associated with Rett syndrome. We also leveraged these data to annotate short linear interacting motifs (SLiMs) that are critical for repression in disordered RDs. Then, we designed a deep learning model called TENet ( T ranscriptional E ffector Net work) that integrates sequence, structure and biochemical representations of sequence variants to accurately predict repressor activity. We systematically tested generalization within and across domains with varying homology using the mutational scanning dataset. Finally, we employed TENet within a directed evolution sequence editing framework to tune the activity of both structured and disordered RDs and experimentally test thousands of designs. Our work highlights critical considerations for future dataset design and model training strategies to improve functional variant prioritization and precision design of synthetic regulatory proteins.

Simultaneous codon usage, the origin of the proteome, and the emergence of de-novo proteins

Genetic coding generally uses only one of a gene's two strands; its complement serving as template for replication. Aminoacyl-tRNA synthetases, aaRS, apparently first emerged as pairs on bidirectional genes, in which anticodons in the template strand served as codons for an entirely different protein. Interpreting both strands in frame constrained such genes sufficiently that it was rapidly superseded, leaving only traces in the elevated pairing between codon middle bases in antiparallel alignments. Codon assignments actually promote using information from both strands in multiple reading frames. Related phenomena, known as overprinting, are widely associated with viruses. In-frame bidirectional coding and overprinting nevertheless imply different structural and functional relationships, and different roles in generating folded proteins throughout the evolution of the proteome.

Intelligent Design of 14-3-3 Docking Proteins Utilizing Synthetic Evolution Artificial Intelligence (SYN-AI)

The ability to write DNA code from scratch will allow for the discovery of new and interesting chemistries as well as allowing the rewiring of cell signal pathways. Herein, we have utilized synthetic evolution artificial intelligence (SYN-AI) to intelligently design a set of 14-3-3 docking genes. SYN-AI engineers synthetic genes utilizing a parental gene as an evolution template. Wherein, evolution is fast-forwarded by transforming template gene sequences to DNA secondary and tertiary codes based upon gene hierarchical structural levels. The DNA secondary code allows identification of genomic building blocks across an orthologous sequence space comprising multiple genomes. Where, the DNA tertiary code allows engineering of supersecondary structures. SYN-AI constructed a library of 10 million genes that was reduced to three structurally functional 14-3-3 docking genes by applying natural selection protocols. Synthetic protein identity was verified utilizing Clustal Omega sequence alignments and Phylogeny.fr phylogenetic analysis. Wherein, we were able to confirm the three-dimensional structure utilizing I-TASSER and protein-ligand interactions utilizing COACH and Cofactor. The conservation of allosteric communications was confirmed utilizing elastic and anisotropic network models. Whereby, we utilized elNemo and ANM2.1 to confirm conservation of the 14-3-3 ζ amphipathic groove. Notably, to the best of our knowledge, we report the first 14-3-3 docking genes to be written from scratch.

A topology-based investigation of protein interaction sites using Hydrophobic Cluster Analysis

Hydrophobic clusters, as defined by Hydrophobic Cluster Analysis (HCA), are conditioned binary patterns, made of hydrophobic and non-hydrophobic positions, whose limits fit well those of regular secondary structures. They were proved to be useful for predicting secondary structures in proteins from the only information of a single amino acid sequence and have permitted to assess, in a comprehensive way, the leading role of binary patterns in secondary structure preference towards a particular state. Here, we considered the available experimental 3D structures of protein globular domains to enlarge our previously reported hydrophobic cluster database (HCDB), almost doubling the number of hydrophobic cluster species (each species being defined by a unique binary pattern) that represent the most frequent structural bricks encountered within protein globular domains. We then used this updated HCDB to show that the hydrophobic amino acids of discordant clusters, i.e. those less abundant clusters for which the observed secondary structure is in disagreement with the binary pattern preference of the species to which they belong, are more exposed to solvent and are more involved in protein interfaces than the hydrophobic amino acids of concordant clusters. As amino acid composition differs between concordant/discordant clusters, considering binary patterns may be used to gain novel insights into key features of protein globular domain cores and surfaces. It can also provide useful information on possible conformational plasticity, including disorder to order transitions.

Dihedral angle preferences of amino acid residues forming various non-local interactions in proteins

In theory, a polypeptide chain can adopt a vast number of conformations, each corresponding to a set of backbone rotation angles. Many of these conformations are excluded due to steric overlaps. Ramachandran and coworkers were the first to look into this problem by plotting backbone dihedral angles in a two-dimensional plot. The conformational space in the Ramachandran map is further refined by considering the energetic contributions of various non-bonded interactions. Alternatively, the conformation adopted by a polypeptide chain may also be examined by investigating interactions between the residues. Since the Ramachandran map essentially focuses on local interactions (residues closer in sequence), out of interest, we have analyzed the dihedral angle preferences of residues that make non-local interactions (residues far away in sequence and closer in space) in the folded structures of proteins. The non-local interactions have been grouped into different types such as hydrogen bond, van der Waals interactions between hydrophobic groups, ion pairs (salt bridges), and ππ-stacking interactions. The results show the propensity of amino acid residues in proteins forming local and non-local interactions. Our results point to the vital role of different types of non-local interactions and their effect on dihedral angles in forming secondary and tertiary structural elements to adopt their native fold.

Melody discrimination and protein fold classification

One of the greatest challenges in theoretical biophysics and bioinformatics is the identification of protein folds from sequence data. This can be regarded as a pattern recognition problem. In this paper we report the use of a melody generation software where the inputs are derived from calculations of evolutionary information, secondary structure, flexibility, hydropathy and solvent accessibility from multiple sequence alignment data. The melodies so generated are derived from the sequence, and by inference, of the fold, in ways that give each fold a sound representation that may facilitate analysis, recognition, or comparison with other sequences.

Using the folding landscapes of proteins to understand protein function

Proteins fold on a biologically-relevant timescale because of a funnel-shaped energy landscape. This landscape is sculpted through evolution by selecting amino-acid sequences that stabilize native interactions while suppressing stable non-native interactions that occur during folding. However, there is strong evolutionary selection for functional residues and these cannot be chosen to optimize folding. Their presence impacts the folding energy landscape in a variety of ways. Here, we survey the effects of functional residues on folding by providing several examples. We then review how such effects can be detected computationally and be used as assays for protein function. Overall, an understanding of how functional residues modulate folding should provide insights into the design of natural proteins and their homeostasis.

Effect of lipid head group interactions on membrane properties and membrane-induced cationic β-hairpin folding

Stages in membrane induced SVS-1 folding.

Functionally Relevant Specific Packing Can Determine Protein Folding Routes

Functional residues can modulate the folding mechanisms of proteins. In some proteins, mutations to such residues can radically change the primary folding route. Is it possible then to learn more about the functional regions of a protein by investigating just its choice of folding route? The folding and the function of the protein Escherichia coli ribonuclease H (ecoRNase-H) have been extensively studied and its folding route is known to near-residue resolution. Here, we computationally study the folding of ecoRNase-H using molecular dynamics simulations of structure-based models of increasing complexity. The differences between a model that correctly predicts the experimentally determined folding route and a simpler model that does not can be attributed to a set of six aromatic residues clustered together in a region of the protein called CORE. This clustering, which we term "specific" packing, drives CORE to fold early and determines the folding route. Both the residues involved in specific packing and their packing are largely conserved across E. coli-like RNase-Hs from diverse species. Residue conservation is usually implicated in function. Here, the identified residues either are known to bind substrate in ecoRNase-H or pack against the substrate in the homologous human RNase-H where a substrate-bound crystal structure exists. Thus, the folding mechanism of ecoRNase-H is a byproduct of functional demands upon its sequence. Using our observations on specific packing, we suggest mutations to an engineered HIV RNase-H to make its function better. Our results show that understanding folding route choice in proteins can provide unexpected insights into their function.

The dipeptide conformations of all twenty amino acid types in the context of biosynthesis

There have been many studies of dipeptide structure at a high level of accuracy using quantum chemical methods. Such calculations are resource-consuming (in terms of memory, CPU and other computational imperatives) which is the reason why most previous studies were restricted to the two simplest amino-acid residue types, glycine and alanine. We improve on this by extending the scope of residue types to include all 20 naturally occurring residue types. Our results reveal differences in secondary structure preferences for the all residue types. There are in most cases very deep energy troughs corresponding either to the polyproline II (collagen) helix and the α-helix or both. The β-strand was not strongly favoured energetically although the extent of this depression in the energy surface is, while not “deeper” (energetically), has a wider extent than the other two types of secondary structure. There is currently great interest in the question of cotranslational folding, the extent to which the nascent polypeptide begins to fold prior to emerging from the ribosome exit tunnel. Accordingly, while most previous quantum studies of dipeptides were carried out in the (simulated) gas or aqueous phase, we wished to consider the first step in polypeptide biosynthesis on the ribosome where neither gas nor aqueous conditions apply. We used a dielectric constant that would be compatible with the water-poor macromolecular (ribosome) environment.

Evolution and design of protein structure by folding nucleus symmetric expansion

Models of symmetric protein evolution typically invoke gene duplication and fusion events, in which repetition of a structural motif generates foldable, stable symmetric protein architecture. Success of such evolutionary processes suggests that the duplicated structural motif must be capable of nucleating protein folding. If correct, symmetric expansion of a folding nucleus sequence derived from an extant symmetric fold may be an elegant and computationally tractable solution to de novo protein design. We report the efficient de novo design of a β-trefoil protein by symmetric expansion of a β-trefoil folding nucleus, previously identified by ɸ-value analysis. The resulting protein, having exact sequence symmetry, exhibits superior folding properties compared to its naturally evolved progenitor-with the potential for redundant folding nuclei. In principle, folding nucleus symmetric expansion can be applied to any given symmetric protein fold (that is, nearly one-third of the known proteome) provided information of the folding nucleus is available.

The Purine Bias of Coding Sequences is Determined by Physicochemical Constraints on Proteins

For this report, we analyzed protein secondary structures in relation to the statistics of three nucleotide codon positions. The purpose of this investigation was to find which properties of the ribosome, tRNA or protein level, could explain the purine bias (Rrr) as it is observed in coding DNA. We found that the Rrr pattern is the consequence of a regularity (the codon structure) resulting from physicochemical constraints on proteins and thermodynamic constraints on ribosomal machinery. The physicochemical constraints on proteins mainly come from the hydropathy and molecular weight (MW) of secondary structures as well as the energy cost of amino acid synthesis. These constraints appear through a network of statistical correlations, such as (i) the cost of amino acid synthesis, which is in favor of a higher level of guanine in the first codon position, (ii) the constructive contribution of hydropathy alternation in proteins, (iii) the spatial organization of secondary structure in proteins according to solvent accessibility, (iv) the spatial organization of secondary structure according to amino acid hydropathy, (v) the statistical correlation of MW with protein secondary structures and their overall hydropathy, (vi) the statistical correlation of thymine in the second codon position with hydropathy and the energy cost of amino acid synthesis, and (vii) the statistical correlation of adenine in the second codon position with amino acid complexity and the MW of secondary protein structures. Amino acid physicochemical properties and functional constraints on proteins constitute a code that is translated into a purine bias within the coding DNA via tRNAs. In that sense, the Rrr pattern within coding DNA is the effect of information transfer on nucleotide composition from protein to DNA by selection according to the codon positions. Thus, coding DNA structure and ribosomal machinery co-evolved to minimize the energy cost of protein coding given the functional constraints on proteins.

Role of Backbone Dipole Interactions in the Formation of Secondary and Supersecondary Structures of Proteins

We present a generic solvated coarse-grained protein model that can be used to characterize the driving forces behind protein folding. Each amino acid is coarse-grained with two beads, a backbone, and a side chain. Although the backbone beads are modeled as polar entities, side chains are hydrophobic, polar, or charged, thus allowing the exploration of how sequence patterning determines a protein fold. The change in orientation of the atoms of the coarse-grained unit is captured by the addition of two oppositely charged dummy particles inside the backbone coarse-grained bead. These two dummy charges represent a dipole that can fluctuate, thus introducing structural polarization into the coarse-grained model. Realistic α/β content is achieved de novo without any biases in the force field toward a particular secondary structure. The dipoles created by the dummy particles interact with each other and drive the protein models to fold into unique structures depending on the amino acid patterning and presence of capping residues. We have also characterized the role of dipole–dipole and dipole–charge interactions in shaping the secondary and supersecondary structure of proteins. Formation of helix bundles and β-strands are also discussed.

Balancing bond, nonbond, and gō-like terms in coarse grain simulations of conformational dynamics

Characterization of the protein conformational landscape remains a challenging problem, whether it concerns elucidating folding mechanisms, predicting native structures or modeling functional transitions. Coarse-grained molecular dynamics simulation methods enable exhaustive sampling of the energetic landscape at resolutions of biological interest. The general utility of structure-based models is reviewed along with their differing levels of approximation. Simple Gō models incorporate attractive native interactions and repulsive nonnative contacts, resulting in an ideal smooth landscape. Non-Gō coarse-grained models reduce the parameter set as needed but do not include bias to any desired native structure. While non-Gō models have achieved limited success in protein coarse-graining, they can be combined with native structured-based potentials to create a balanced and powerful force field. Recent applications of such Gō-like models have yielded insight into complex folding mechanisms and conformational transitions in large macromolecules. The accuracy and usefulness of reduced representations are also revealed to be a function of the mathematical treatment of the intrinsic bonded topology.

A coarse-grained protein model in a water-like solvent

Simulations employing an explicit atom description of proteins in solvent can be computationally expensive. On the other hand, coarse-grained protein models in implicit solvent miss essential features of the hydrophobic effect, especially its temperature dependence, and have limited ability to capture the kinetics of protein folding. We propose a free space two-letter protein (“H-P”) model in a simple, but qualitatively accurate description for water, the Jagla model, which coarse-grains water into an isotropically interacting sphere. Using Monte Carlo simulations, we design protein-like sequences that can undergo a collapse, exposing the “Jagla-philic” monomers to the solvent, while maintaining a “hydrophobic” core. This protein-like model manifests heat and cold denaturation in a manner that is reminiscent of proteins. While this protein-like model lacks the details that would introduce secondary structure formation, we believe that these ideas represent a first step in developing a useful, but computationally expedient, means of modeling proteins.

Effects of sequence and solvation on the temperature-pressure conformational landscape of proteinlike heteropolymers

We study the role of sequence and solvation in shaping the temperature-pressure (T, P) conformational landscape of model heteropolymers with a coarse-grained model. We design foldable primarily hydrophobic sequences with fixed polar content in water at physiological conditions, which demonstrate (T, P) dependence of conformational stability similar to biological proteins. Inherent helicity emerges as a result of local polar-polar interactions in the sequences that mimic biological α helices. The helical propensity is reduced upon solvation and remains unaltered at cold T and high P, which is driven by the T-P induced changes of the hydration shell. Consequently, at nonphysiological conditions the weakening of hydrophobic interactions facilitates population of non-native, helical, compact conformations stabilized through direct nonlocal interactions between polar residues.

Factors that drive peptide assembly and fibril formation: experimental and theoretical analysis of Sup35 NNQQNY mutants

Residue mutations have substantial effects on aggregation kinetics and propensities of amyloid peptides and their aggregate morphologies. Such effects are attributed to conformational transitions accessed by various types of oligomers such as steric zipper or single β-sheet. We have studied the aggregation propensities of six NNQQNY mutants: NVVVVY, NNVVNV, NNVVNY, VIQVVY, NVVQIY, and NVQVVY in water using a combination of ion-mobility mass spectrometry, transmission electron microscopy, atomic force microscopy, and all-atom molecular dynamics simulations. Our data show a strong correlation between the tendency to form early β-sheet oligomers and the subsequent aggregation propensity. Our molecular dynamics simulations indicate that the stability of a steric zipper structure can enhance the propensity for fibril formation. Such stability can be attained by either hydrophobic interactions in the mutant peptide or polar side-chain interdigitations in the wild-type peptide. The overall results display only modest agreement with the aggregation propensity prediction methods such as PASTA, Zyggregator, and RosettaProfile, suggesting the need for better parametrization and model peptides for these algorithms.

Simplified protein design biased for prebiotic amino acids yields a foldable, halophilic protein

A compendium of different types of abiotic chemical syntheses identifies a consensus set of 10 "prebiotic" α-amino acids. Before the emergence of biosynthetic pathways, this set is the most plausible resource for protein formation (i.e., proteogenesis) within the overall process of abiogenesis. An essential unsolved question regarding this prebiotic set is whether it defines a "foldable set"--that is, does it contain sufficient chemical information to permit cooperatively folding polypeptides? If so, what (if any) characteristic properties might such polypeptides exhibit? To investigate these questions, two "primitive" versions of an extant protein fold (the β-trefoil) were produced by top-down symmetric deconstruction, resulting in a reduced alphabet size of 12 or 13 amino acids and a percentage of prebiotic amino acids approaching 80%. These proteins show a substantial acidification of pI and require high salt concentrations for cooperative folding. The results suggest that the prebiotic amino acids do comprise a foldable set within the halophile environment.

Coarse-grained models for protein folding and aggregation

Coarse-grained models for protein folding and aggregation are used to explore large dimension scales and timescales that are inaccessible to all-atom models in explicit aqueous solution. Combined with enhanced configuration search methods, these simplified models with various levels of granularity offer the possibility to determine equilibrium structures, compare folding kinetics and thermodynamics with experiments for single proteins and understand the dynamic assembly of amyloid proteins leading to neurodegenerative diseases. I shall describe recent progress in developing such models, and discuss their potentials and limitations in probing the folding and misfolding of proteins with computer simulations.

Inversion of the balance between hydrophobic and hydrogen bonding interactions in protein folding and aggregation

Identifying the forces that drive proteins to misfold and aggregate, rather than to fold into their functional states, is fundamental to our understanding of living systems and to our ability to combat protein deposition disorders such as Alzheimer's disease and the spongiform encephalopathies. We report here the finding that the balance between hydrophobic and hydrogen bonding interactions is different for proteins in the processes of folding to their native states and misfolding to the alternative amyloid structures. We find that the minima of the protein free energy landscape for folding and misfolding tend to be respectively dominated by hydrophobic and by hydrogen bonding interactions. These results characterise the nature of the interactions that determine the competition between folding and misfolding of proteins by revealing that the stability of native proteins is primarily determined by hydrophobic interactions between side-chains, while the stability of amyloid fibrils depends more on backbone intermolecular hydrogen bonding interactions.

In order to carry out their biological functions, most proteins fold into well-defined conformations known as native states. Failure to fold, or to remain folded correctly, may result in misfolding and aggregation, which are processes associated with a wide range of highly debilitating, and so far incurable, human conditions that include Alzheimer's and Parkinson's diseases and type II diabetes. In our work we investigate the nature of the fundamental interactions that are responsible for the folding and misfolding behaviour of proteins, finding that interactions between protein side-chains play a major role in stabilising native states, whilst backbone hydrogen bonding interactions are key in determining the stability of amyloid fibrils.

Hydrophobic interactions in the formation of secondary structures in small peptides

Effects of the attractive and repulsive parts of hydrophobic interactions on α helices and β sheets in small peptides are investigated using a simple atomic potential. Typically, a physical spatial range of attraction tends to favor β sheets, but α helices would be favored if the attractive range were more extended. We also found that desolvation barriers favor β sheets in collapsed conformations of polyalanine, polyvaline, polyleucine, and three fragments of amyloid peptides tested in this study. Our results provide insight into the multifaceted role of hydrophobicity in secondary structure formation, including the α to β transitions in certain amyloid peptides.

Assisted peptide folding by surface pattern recognition

Natively disordered proteins belong to a unique class of biomolecules whose function is related to their flexibility and their ability to adopt desired conformations upon binding to substrates. In some cases these proteins can bind multiple partners, which can lead to distinct structures and promiscuity in functions. In other words, the capacity to recognize molecular patterns on the substrate is often essential for the folding and function of intrinsically disordered proteins. Biomolecular pattern recognition is extremely relevant both in vivo (e.g., for oligomerization, immune response, induced folding, substrate binding, and molecular switches) and in vitro (e.g., for biosensing, catalysis, chromatography, and implantation). Here, we use a minimalist computational model system to investigate how polar/nonpolar patterns on a surface can induce the folding of an otherwise unstructured peptide. We show that a model peptide that exists in the bulk as a molten globular state consisting of many interconverting structures can fold into either a helix-coil-helix or an extended helix structure in the presence of a complementary designed patterned surface at low hydrophobicity (3.7%) or a uniform surface at high hydrophobicity (50%). However, we find that a carefully chosen surface pattern can bind to and catalyze the folding of a natively unfolded protein much more readily or effectively than a surface with a noncomplementary or uniform distribution of hydrophobic residues.

Relative stability of de novo four-helix bundle proteins: insights from coarse grained molecular simulations

We use a recently developed coarse-grained computational model to investigate the relative stability of two different sets of de novo designed four-helix bundle proteins. Our simulations suggest a possible explanation for the experimentally observed increase in stability of the four-helix bundles with increasing sequence length. In details, we show that both short subsequences composed only by polar residues and additional nonpolar residues inserted, via different point mutations in ad hoc positions, seem to play a significant role in stabilizing the four-helix bundle conformation in the longer sequences. Finally, we propose an additional mutation that rescues a short amino acid sequence that would otherwise adopt a compact misfolded state. Our work suggests that simple computational models can be used as a complementary tool in the design process of de novo proteins.

Global characteristics of protein sequences and their implications

Computational studies of the relationships between protein sequence, structure, and folding have traditionally relied on purely local sequence representations. Here we show that global representations, on the basis of parameters that encode information about complete sequences, contain otherwise inaccessible information about the organization of sequences. By studying the spectral properties of these parameters, we demonstrate that amino acid physical properties fall into two distinct classes. One class is comprised of properties that favor sequentially localized interaction clusters. The other class is comprised of properties that favor globally distributed interactions. This observation provides a bridge between two classic models of protein folding-the collapse model and the nucleation model-and provides a basis for understanding how any degree of intermediacy between these two extremes can occur.