Identification of potential allosteric communication pathways between functional sites of the bacterial ribosome by graph and elastic network models

The role of ribosomal protein networks in ribosome dynamics

Accurate protein synthesis requires ribosomes to integrate signals from distant functional sites and execute complex dynamics. Despite advances in understanding ribosome structure and function, two key questions remain: how information is transmitted between these distant sites, and how ribosomal movements are synchronized? We recently highlighted the existence of ribosomal protein networks, likely evolved to participate in ribosome signaling. Here, we investigate the relationship between ribosomal protein networks and ribosome dynamics. Our findings show that major motion centers in the bacterial ribosome interact specifically with r-proteins, and that ribosomal RNA exhibits high mobility around each r-protein. This suggests that periodic electrostatic changes in the context of negatively charged residues (Glu and Asp) induce RNA-protein 'distance-approach' cycles, controlling key ribosomal movements during translocation. These charged residues play a critical role in modulating electrostatic repulsion between RNA and proteins, thus coordinating ribosomal dynamics. We propose that r-protein networks synchronize ribosomal dynamics through an 'electrostatic domino' effect, extending the concept of allostery to the regulation of movements within supramolecular assemblies.

Illuminating Protein Allostery by Chemically Accurate Contact Response Analysis (ChACRA)

Decoding allostery at the atomic level is essential for understanding the relationship between a protein's sequence, structure, and dynamics. Recently, we have shown that decomposing temperature responses of inter-residue contacts can reveal allosteric couplings and provide useful insight into the functional dynamics of proteins. The details of this Chemically Accurate Contact Response Analysis (ChACRA) are presented here along with its application to two well-known allosteric proteins. The first protein, IGPS, is a model of ensemble allostery that lacks clear structural differences between the active and inactive states. We show that the application of ChACRA reveals the experimentally identified allosteric coupling between effector and active sites of IGPS. The second protein, ATCase, is a classic example of allostery with distinct active and inactive structural states. Using ChACRA, we directly identify the most significant residue level interactions underlying the enzyme's cooperative behavior. Both test cases demonstrate the utility of ChACRA's unsupervised machine learning approach for dissecting allostery at the residue level.

Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.

Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation

The chemokine receptor CXCR4 is a critical target for the treatment of several cancer types and HIV-1 infections. While orthosteric and allosteric modulators have been developed targeting its extracellular or transmembrane regions, the intramembrane region of CXCR4 may also include allosteric binding sites suitable for the development of allosteric drugs. To investigate this, we apply the Gaussian Network Model (GNM) to the monomeric and dimeric forms of CXCR4 to identify residues essential for its local and global motions located in the hinge regions of the protein. Residue interaction network (RIN) analysis suggests hub residues that participate in allosteric communication throughout the receptor. Mutual residues from the network models reside in regions with a high capacity to alter receptor dynamics upon ligand binding. We then investigate the druggability of these potential allosteric regions using the site identification by ligand competitive saturation (SILCS) approach, revealing two putative allosteric sites on the monomer and three on the homodimer. Two screening campaigns with Glide and SILCS-Monte Carlo docking using FDA-approved drugs suggest 20 putative hit compounds including antifungal drugs, anticancer agents, HIV protease inhibitors, and antimalarial drugs. In vitro assays considering mAB 12G5 and CXCL12 demonstrate both positive and negative allosteric activities of these compounds, supporting our computational approach. However, in vivo functional assays based on the recruitment of β-arrestin to CXCR4 do not show significant agonism and antagonism at a single compound concentration. The present computational pipeline brings a new perspective to computer-aided drug design by combining conformational dynamics based on network analysis and cosolvent analysis based on the SILCS technology to identify putative allosteric binding sites using CXCR4 as a showcase.

Tunnel-like region observed as a potential allosteric site in Staphylococcus aureus Glyceraldehyde-3-phosphate dehydrogenase

Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) catalyzing the sixth step of glycolysis has been investigated for allosteric features that might be used as potential target for specific inhibition of Staphylococcus aureus (S.aureus). X-ray structure of bacterial enzyme for which a tunnel-like opening passing through the center previously proposed as an allosteric site has been subjected to six independent 500 ns long Molecular Dynamics simulations. Harmonic bond restraints were employed at key residues to underline the allosteric feature of this region. A noticeable reduction was observed in the mobility of NAD+ binding domains when restrictions were applied. Also, a substantial decrease in cross-correlations between distant Cα fluctuations was detected throughout the structure. Mutual information (MI) analysis revealed a similar decrease in the degree of correspondence in positional fluctuations in all directions everywhere in the receptor. MI between backbone and side-chain torsional variations changed its distribution profile and decreased considerably around the catalytic sites when restraints were employed. Principal component analysis clearly showed that the restrained state sampled a narrower range of conformations than apo state, especially in the first principal mode due to restriction in the conformational flexibility of NAD+ binding domain. Clustering the trajectory based on catalytic site residues displayed a smaller repertoire of conformations for restrained state compared to apo. Representative snapshots subjected to k-shortest pathway analysis revealed the impact of bond restraints on the allosteric communication which displayed distinct optimal and suboptimal pathways for two states, where observed frequencies of critical residues Gln51 and Val283 at the proposed site changed considerably.

Machine learning and protein allostery

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

The "violin model": Looking at community networks for dynamic allostery

Allosteric regulation of proteins continues to be an engaging research topic for the scientific community. Models describing allosteric communication have evolved from focusing on conformation-based descriptors of protein structural changes to appreciating the role of internal protein dynamics as a mediator of allostery. Here, we explain a "violin model" for allostery as a contemporary method for approaching the Cooper-Dryden model based on redistribution of protein thermal fluctuations. Based on graph theory, the violin model makes use of community network analysis to functionally cluster correlated protein motions obtained from molecular dynamics simulations. This Review provides the theory and workflow of the methodology and explains the application of violin model to unravel the workings of protein kinase A.

Allostery Modulates Interactions between Proteasome Core Particles and Regulatory Particles

Allostery-regulation at distant sites is a key concept in biology. The proteasome exhibits multiple forms of allosteric regulation. This regulatory communication can span a distance exceeding 100 Ångstroms and can modulate interactions between the two major proteasome modules: its core particle and regulatory complexes. Allostery can further influence the assembly of the core particle with regulatory particles. In this focused review, known and postulated interactions between these proteasome modules are described. Allostery may explain how cells build and maintain diverse populations of proteasome assemblies and can provide opportunities for therapeutic interventions.

Exploring species-specific inhibitors with multiple target sites on S. aureus pyruvate kinase using a computational workflow

Experimental evidence indicated that bacterial pyruvate kinase of glycolysis can be evaluated as an alternative target to eliminate infections, while antibiotic resistance poses a global threat. Here, we use a computational workflow to reveal and investigate the potential allosteric sites of methicillin-resistant S. aureus PK, which can help in designing species-specific drugs to inhibit activity of this organism. Residue interaction networks point to a known allosteric site at the small C-C interface, a potential allosteric site near the small interface (site #1), and a second potential allosteric site at the large interface (site #2). 2 µs-long molecular dynamics (MD) simulations with AMBER16 generate different conformations of one narrow target site. Known and potential allosteric sites on the selected conformers are investigated using ensemble docking with AutoDock Vina and a library of 2447 FDA-approved drugs. We determine 18 hits, comprising ergot-alkaloids, anti-cancer-agents, antivirals, analgesics, cardiac glycosides, all with a high docking z-score for three sites. 5 selected compounds with high, average and low z-scores are subjected to 50 ns-long MD simulations for MM-GBSA calculations. ΔG_bind values up to -49.3 kcal/mol at the C-C interface, up to -32.7 kcal/mol at site #1, and up to -53.3 kcal/mol at site #2 support the docking calculations. We investigate mitapivat and TT-232 as reference compounds under clinical trial, targeting human PK isomers. We suggest 18 FDA-approved hits from the docking calculations and TT-232 as potential inhibitors with multiple target sites on S. aureus PK. This study also proposes pharmacophores models for de novo drug design.Communicated by Ramaswamy H. Sarma.

Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has serious negative effects on health, social life, and economics. Recently, vaccines from various companies have been urgently approved to control SARS-CoV-2 infections. However, any specific antiviral drug has not been confirmed so far for regular treatment. An important target is the main protease (Mpro ), which plays a major role in replication of the virus. In this study, Gaussian and residue network models are employed to reveal two distinct potential allosteric sites on Mpro that can be evaluated as drug targets besides the active site. Then, Food and Drug Administration (FDA)-approved drugs are docked to three distinct sites with flexible docking using AutoDock Vina to identify potential drug candidates. Fourteen best molecule hits for the active site of Mpro are determined. Six of these also exhibit high docking scores for the potential allosteric regions. Full-atom molecular dynamics simulations with MM-GBSA method indicate that compounds docked to active and potential allosteric sites form stable interactions with high binding free energy (∆Gbind ) values. ∆Gbind values reach -52.06 kcal/mol for the active site, -51.08 kcal/mol for the potential allosteric site 1, and - 42.93 kcal/mol for the potential allosteric site 2. Energy decomposition calculations per residue elucidate key binding residues stabilizing the ligands that can further serve to design pharmacophores. This systematic and efficient computational analysis successfully determines ivermectine, diosmin, and selinexor currently subjected to clinical trials, and further proposes bromocriptine, elbasvir as Mpro inhibitor candidates to be evaluated against SARS-CoV-2 infections.

Comparative Analysis of Structural and Dynamical Features of Ribosome Upon Association With mRNA Reveals Potential Role of Ribosomal Proteins

Ribosomes play a critical role in maintaining cellular proteostasis. The binding of messenger RNA (mRNA) to the ribosome regulates kinetics of protein synthesis. To generate an understanding of the structural, mechanistic, and dynamical features of mRNA recognition in the ribosome, we have analysed mRNA-protein interactions through a structural comparison of the ribosomal complex in the presence and absence of mRNA. To do so, we compared the 3-Dimensional (3D) structures of components of the two assembly structures and analysed their structural differences because of mRNA binding, using elastic network models and structural network-based analysis. We observe that the head region of 30S ribosomal subunit undergoes structural displacement and subunit rearrangement to accommodate incoming mRNA. We find that these changes are observed in proteins that lie far from the mRNA-protein interface, implying allostery. Further, through perturbation response scanning, we show that the proteins S13, S19, and S20 act as universal sensors that are sensitive to changes in the inter protein network, upon binding of 30S complex with mRNA and other initiation factors. Our study highlights the significance of mRNA binding in the ribosome complex and identifies putative allosteric sites corresponding to alterations in structure and/or dynamics, in regions away from mRNA binding sites in the complex. Overall, our work provides fresh insights into mRNA association with the ribosome, highlighting changes in the interactions and dynamics of the ribosome assembly because of the binding.

Identification of Allosteric Effects in Proteins by Elastic Network Models

Allostery is a fundamental regulatory mechanism in the majority of biological processes of molecular machines. Allostery is well-known as a dynamic-driven process, and thus, the molecular mechanism of allosteric signal transmission needs to be established. Elastic network models (ENMs) provide efficient methods for investigating the intrinsic dynamics and allosteric communication pathways in proteins. In this chapter, two ENM methods including Gaussian network model (GNM) coupled with Markovian stochastic model, as well as the anisotropic network model (ANM), were introduced to identify allosteric effects in hemoglobins. Techniques on model parameters, scripting and calculation, analysis, and visualization are shown step by step.

Exploring Allosteric Signaling in the Exit Tunnel of the Bacterial Ribosome by Molecular Dynamics Simulations and Residue Network Model

The bacterial ribosomal tunnel is equipped with numerous sites highly sensitive to the course of the translation process. This study investigates allosteric pathways linking distant functional sites that collaboratively play a role either in translation regulation or recruitment of chaperones. We apply perturbation response scanning (PRS) analysis to 700 ns long and 500 ns long coarse-grained molecular dynamics simulations of E. coli and T. thermophilus large subunits, respectively, to reveal nucleotides/residues with the ability to transmit perturbations by dynamic rationale. We also use the residue network model with the k-shortest pathways method to calculate suboptimal pathways based on the contact topology of the ribosomal tunnel of E. coli crystal structure and 101 ClustENM generated conformers of T. thermophilus large subunit. In the upper part of the tunnel, results suggest that A2062 and A2451 can communicate in both directions for translation stalling, mostly through dynamically coupled C2063, C2064, and A2450. For a similar purpose, U2585 and U2586 are coupled with A2062, while they are also sensitive to uL4 and uL22 at the constriction region through two different pathways at the opposite sides of the tunnel wall. In addition, the constriction region communicates with the chaperone binding site on uL23 at the solvent side but through few nucleotides. Potential allosteric communication pathways between the lower part of the tunnel and chaperone binding site mostly use the flexible loop of uL23, while A1336–G1339 provide a suboptimal pathway. Both species seem to employ similar mechanisms in the long tunnel, where a non-conserved cavity at the bacterial uL23 and 23S rRNA interface is proposed as a novel drug target.

Intrinsic dynamics is evolutionarily optimized to enable allosteric behavior

Allosteric behavior is central to the function of many proteins, enabling molecular machinery, metabolism, signaling and regulation. Recent years have shown that the intrinsic dynamics of allosteric proteins defined by their 3-dimensional architecture or by the topology of inter-residue contacts favors cooperative motions that bear close similarity to structural changes they undergo during their allosteric actions. These conformational motions are usually driven by energetically favorable or soft modes at the low frequency end of the mode spectrum, and they are evolutionarily conserved among orthologs. These observations brought into light evolutionary adaptation mechanisms that help maintain, optimize or regulate allosteric behavior as the evolution from bacterial to higher organisms introduces sequential heterogeneities and structural complexities.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

Thermostability of Lipase A and Dynamic Communication Based on Residue Interaction Network

OBJECTIVE

Dynamic communication caused by mutation affects protein stability. The main objective of this study is to explore how mutations affect communication and to provide further insight into the relationship between heat resistance and signal propagation of Bacillus subtilis lipase (Lip A).

METHODS

The relationship between dynamic communication and Lip A thermostability is studied by long-time MD simulation and residue interaction network. The Dijkstra algorithm is used to get the shortest path of each residue pair. Subsequently, time-series frequent paths and spatio-temporal frequent paths are mined through an Apriori-like algorithm.

RESULTS

Time-series frequent paths show that the communication between residue pairs, both in wild-type lipase (WTL) and mutant 6B, becomes chaotic with an increase in temperature; however, more residues in 6B can maintain stable communication at high temperature, which may be associated with the structural rigidity. Furthermore, spatio-temporal frequent paths reflect the interactions among secondary structures. For WTL at 300K, β7, αC, αB, the longest loop, αA and αF contact frequently. The 310-helix between β3 and αA is penetrated by spatio-temporal frequent paths. At 400K, only αC can be frequently transmitted. For 6B, when at 300K, αA and αF are in more tight contact by spatio-temporal frequent paths though I157M and N166Y. Moreover, the rigidity of the active site His156 and the C-terminal of Lip A are increased, as reflected by the spatio-temporal frequent paths. At 400K, αA and αF, 310-helix between β3 and αA, the longest loop, and the loop where the active site Asp133 is located can still maintain stable communication.

CONCLUSION

From the perspective of residue dynamic communication, it is obviously found that mutations cause changes in interactions between secondary structures and enhance the rigidity of the structure, contributing to the thermal stability and functional activity of 6B.

Nervous-Like Circuits in the Ribosome Facts, Hypotheses and Perspectives

In the past few decades, studies on translation have converged towards the metaphor of a “ribosome nanomachine”; they also revealed intriguing ribosome properties challenging this view. Many studies have shown that to perform an accurate protein synthesis in a fluctuating cellular environment, ribosomes sense, transfer information and even make decisions. This complex “behaviour” that goes far beyond the skills of a simple mechanical machine has suggested that the ribosomal protein networks could play a role equivalent to nervous circuits at a molecular scale to enable information transfer and processing during translation. We analyse here the significance of this analogy and establish a preliminary link between two fields: ribosome structure-function studies and the analysis of information processing systems. This cross-disciplinary analysis opens new perspectives about the mechanisms of information transfer and processing in ribosomes and may provide new conceptual frameworks for the understanding of the behaviours of unicellular organisms.

Chokepoints in Mechanical Coupling Associated with Allosteric Proteins: The Pyruvate Kinase Example

Although the critical role of allostery in controlling enzymatic processes is well appreciated, there is a current dearth in our understanding of its underlying mechanisms, including communication between binding sites. One potential key aspect of intersite communication is the mechanical coupling between residues in a protein. Here, we introduce a graph-based computational approach to investigate the mechanical coupling between distant parts of a protein, highlighting effective pathways via which protein motion can transfer energy between sites. In this method, each residue is treated as a node on a weighted, undirected graph, in which the edges are defined by locally correlated motions of those residues and weighted by the strength of the correlation. The method was validated against experimental data on allosteric regulation in the human liver pyruvate kinase as obtained from full-protein alanine-scanning mutagenesis (systematic mutation) studies, as well as computational data on two G-protein-coupled receptors. The method provides semiquantitative information on the regulatory importance of specific structural elements. It is shown that these elements are key for the mechanical coupling between distant parts of the protein by providing effective pathways for energy transfer. It is also shown that, although there are a multitude of energy transfer pathways between distant parts of a protein, these pathways share a few common nodes that represent effective "chokepoints" for the communication.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

REDAN: Relative Entropy-Based Dynamical Allosteric Network Model

Protein allostery is ubiquitous phenomena that are important for cellular signaling processes. Despite extensive methodology development, a quantitative model is still needed to accurately measure protein allosteric response upon external perturbation. Here, we introduced the relative entropy concept from information theory as a quantitative metric to develop a method for measurement of the population shift with regard to protein structure during allosteric transition. This method is referred to as relative entropy-based dynamical allosteric network (REDAN) model. Using this method, protein allostery could be evaluated at three mutually dependent structural levels: allosteric residues, allosteric pathways, and allosteric communities. All three levels are carried out using rigorous searching algorithms based on relative entropy. Application of the REDAN model on the second PDZ domain (PDZ2) in the human PTP1E protein provided metric-based insight into its allostery upon peptide binding.

Exploring allosteric communication in multiple states of the bacterial ribosome using residue network analysis

Antibiotic resistance is one of the most important problems of our era and hence the discovery of new effective therapeutics is urgent. At this point, studying the allosteric communication pathways in the bacterial ribosome and revealing allosteric sites/residues is critical for designing new inhibitors or repurposing readily approved drugs for this enormous machine. To shed light onto molecular details of the allosteric mechanisms, here we construct residue networks of the bacterial ribosomal complex at four different states of translation by using an effective description of the intermolecular interactions. Centrality analysis of these networks highlights the functional roles of structural components and critical residues on the ribosomal complex. High betweenness scores reveal pathways of residues connecting numerous sites on the structure. Interestingly, these pathways assemble highly conserved residues, drug binding sites, and known allosterically linked regions on the same structure. This study proposes a new residue-level model to test how distant sites on the molecular machine may be linked through hub residues that are critically located on the contact topology to inherently form communication pathways. Findings also indicate intersubunit bridges B1b, B3, B5, B7, and B8 as critical targets to design novel antibiotics.

Origins of the Mechanochemical Coupling of Peptide Bond Formation to Protein Synthesis

Mechanical forces acting on the ribosome can alter the speed of protein synthesis, indicating that mechanochemistry can contribute to translation control of gene expression. The naturally occurring sources of these mechanical forces, the mechanism by which they are transmitted 10 nm to the ribosome's catalytic core, and how they influence peptide bond formation rates are largely unknown. Here, we identify a new source of mechanical force acting on the ribosome by using in situ experimental measurements of changes in nascent-chain extension in the exit tunnel in conjunction with all-atom and coarse-grained computer simulations. We demonstrate that when the number of residues composing a nascent chain increases, its unstructured segments outside the ribosome exit tunnel generate piconewtons of force that are fully transmitted to the ribosome's P-site. The route of force transmission is shown to be through the nascent polypetide's backbone, not through the wall of the ribosome's exit tunnel. Utilizing quantum mechanical calculations we find that a consequence of such a pulling force is to decrease the transition state free energy barrier to peptide bond formation, indicating that the elongation of a nascent chain can accelerate translation. Since nascent protein segments can start out as largely unfolded structural ensembles, these results suggest a pulling force is present during protein synthesis that can modulate translation speed. The mechanism of force transmission we have identified and its consequences for peptide bond formation should be relevant regardless of the source of the pulling force.